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'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=3 , A__=32 , A__=3 , A__=10 , A__=[10, 20, 30, 40] , A__=[1, 1, 2, 1] , A__=True , A__=True , A__="relu" , A__=3 , A__=None , ) -> List[str]:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = embeddings_size
_SCREAMING_SNAKE_CASE = hidden_sizes
_SCREAMING_SNAKE_CASE = depths
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = num_labels
_SCREAMING_SNAKE_CASE = scope
_SCREAMING_SNAKE_CASE = len(A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
_SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ) -> Optional[int]:
return ResNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> int:
_SCREAMING_SNAKE_CASE = TFResNetModel(config=A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFResNetForImageClassification(A__ )
_SCREAMING_SNAKE_CASE = model(A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE = (
{'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFResNetModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , has_text_modality=A__ )
def UpperCamelCase ( self ) -> int:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCamelCase ( self ) -> str:
return
@unittest.skip(reason="""ResNet does not use inputs_embeds""" )
def UpperCamelCase ( self ) -> Tuple:
pass
@unittest.skip(reason="""ResNet does not support input and output embeddings""" )
def UpperCamelCase ( self ) -> Union[str, Any]:
pass
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = model_class(A__ )
_SCREAMING_SNAKE_CASE = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
_SCREAMING_SNAKE_CASE = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , A__ )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
def check_hidden_states_output(A__ , A__ , A__ ):
_SCREAMING_SNAKE_CASE = model_class(A__ )
_SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(A__ , A__ ) )
_SCREAMING_SNAKE_CASE = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_SCREAMING_SNAKE_CASE = self.model_tester.num_stages
self.assertEqual(len(A__ ) , expected_num_stages + 1 )
# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
_SCREAMING_SNAKE_CASE = ["""basic""", """bottleneck"""]
for model_class in self.all_model_classes:
for layer_type in layers_type:
_SCREAMING_SNAKE_CASE = layer_type
_SCREAMING_SNAKE_CASE = True
check_hidden_states_output(A__ , A__ , A__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_SCREAMING_SNAKE_CASE = True
check_hidden_states_output(A__ , A__ , A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> List[Any]:
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE = TFResNetModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
def lowerCAmelCase_ ( ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class _a (unittest.TestCase):
"""simple docstring"""
@cached_property
def UpperCamelCase ( self ) -> Any:
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
_SCREAMING_SNAKE_CASE = self.default_image_processor
_SCREAMING_SNAKE_CASE = prepare_img()
_SCREAMING_SNAKE_CASE = image_processor(images=A__ , return_tensors="""tf""" )
# forward pass
_SCREAMING_SNAKE_CASE = model(**A__ )
# verify the logits
_SCREAMING_SNAKE_CASE = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , A__ )
_SCREAMING_SNAKE_CASE = tf.constant([-11.1069, -9.7877, -8.3777] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , A__ , atol=1E-4 ) )
| 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""encoder.embed_positions._float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = emb.weight.shape
_SCREAMING_SNAKE_CASE = nn.Linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , bias=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )
_SCREAMING_SNAKE_CASE = mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
_SCREAMING_SNAKE_CASE = mam_aaa["""model"""]
remove_ignore_keys_(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = state_dict["""encoder.embed_tokens.weight"""].shape[0]
_SCREAMING_SNAKE_CASE = MaMaaaConfig(
vocab_size=SCREAMING_SNAKE_CASE_ , max_position_embeddings=10_24 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="""relu""" , )
_SCREAMING_SNAKE_CASE = state_dict["""decoder.embed_tokens.weight"""]
_SCREAMING_SNAKE_CASE = MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
model.model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
UpperCamelCase__ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCamelCase__ : List[str] = parser.parse_args()
UpperCamelCase__ : Any = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 0 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEModel,
)
from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=10 , A__=3 , A__=2 , A__=2 , A__=2 , A__=True , A__=True , A__=32 , A__=5 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=10 , A__=0.02 , A__=0.9 , A__=None , ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = patch_size
_SCREAMING_SNAKE_CASE = tubelet_size
_SCREAMING_SNAKE_CASE = num_frames
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = type_sequence_label_size
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = mask_ratio
_SCREAMING_SNAKE_CASE = scope
# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
_SCREAMING_SNAKE_CASE = (image_size // patch_size) ** 2
_SCREAMING_SNAKE_CASE = (num_frames // tubelet_size) * self.num_patches_per_frame
# use this variable to define bool_masked_pos
_SCREAMING_SNAKE_CASE = int(mask_ratio * self.seq_length )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ) -> List[Any]:
return VideoMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , tubelet_size=self.tubelet_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=A__ , initializer_range=self.initializer_range , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = VideoMAEModel(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> int:
_SCREAMING_SNAKE_CASE = VideoMAEForPreTraining(A__ )
model.to(A__ )
model.eval()
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
_SCREAMING_SNAKE_CASE = torch.ones((self.num_masks,) )
_SCREAMING_SNAKE_CASE = torch.cat([mask, torch.zeros(self.seq_length - mask.size(0 ) )] )
_SCREAMING_SNAKE_CASE = mask.expand(self.batch_size , -1 ).bool()
_SCREAMING_SNAKE_CASE = model(A__ , A__ )
# model only returns predictions for masked patches
_SCREAMING_SNAKE_CASE = mask.sum().item()
_SCREAMING_SNAKE_CASE = 3 * self.tubelet_size * self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_masked_patches, decoder_num_labels) )
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
)
SCREAMING_SNAKE_CASE = (
{'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = VideoMAEModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , has_text_modality=A__ , hidden_size=37 )
def UpperCamelCase ( self , A__ , A__ , A__=False ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = copy.deepcopy(A__ )
if model_class == VideoMAEForPreTraining:
# important: each video needs to have the same number of masked patches
# hence we define a single mask, which we then repeat for each example in the batch
_SCREAMING_SNAKE_CASE = torch.ones((self.model_tester.num_masks,) )
_SCREAMING_SNAKE_CASE = torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0 ) )] )
_SCREAMING_SNAKE_CASE = mask.expand(self.model_tester.batch_size , -1 ).bool()
_SCREAMING_SNAKE_CASE = bool_masked_pos.to(A__ )
if return_labels:
if model_class in [
*get_values(A__ ),
]:
_SCREAMING_SNAKE_CASE = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A__ )
return inputs_dict
def UpperCamelCase ( self ) -> int:
self.config_tester.run_common_tests()
@unittest.skip(reason="""VideoMAE does not use inputs_embeds""" )
def UpperCamelCase ( self ) -> int:
pass
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = model_class(A__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_SCREAMING_SNAKE_CASE = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A__ , nn.Linear ) )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = model_class(A__ )
_SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
_SCREAMING_SNAKE_CASE = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , A__ )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*A__ )
@slow
def UpperCamelCase ( self ) -> Optional[int]:
for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE = VideoMAEModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
def UpperCamelCase ( self ) -> int:
if not self.has_attentions:
pass
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
_SCREAMING_SNAKE_CASE = True
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = self.model_tester.seq_length - self.model_tester.num_masks
_SCREAMING_SNAKE_CASE = (
num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
)
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = model_class(A__ )
model.to(A__ )
model.eval()
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(A__ , A__ ) )
_SCREAMING_SNAKE_CASE = outputs.attentions
self.assertEqual(len(A__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = model_class(A__ )
model.to(A__ )
model.eval()
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(A__ , A__ ) )
_SCREAMING_SNAKE_CASE = outputs.attentions
self.assertEqual(len(A__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
_SCREAMING_SNAKE_CASE = len(A__ )
# Check attention is always last and order is fine
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = model_class(A__ )
model.to(A__ )
model.eval()
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(A__ , A__ ) )
self.assertEqual(out_len + 1 , len(A__ ) )
_SCREAMING_SNAKE_CASE = outputs.attentions
self.assertEqual(len(A__ ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def UpperCamelCase ( self ) -> Dict:
def check_hidden_states_output(A__ , A__ , A__ ):
_SCREAMING_SNAKE_CASE = model_class(A__ )
model.to(A__ )
model.eval()
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(A__ , A__ ) )
_SCREAMING_SNAKE_CASE = outputs.hidden_states
_SCREAMING_SNAKE_CASE = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(A__ ) , A__ )
_SCREAMING_SNAKE_CASE = self.model_tester.seq_length - self.model_tester.num_masks
_SCREAMING_SNAKE_CASE = num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = True
check_hidden_states_output(A__ , A__ , A__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_SCREAMING_SNAKE_CASE = True
check_hidden_states_output(A__ , A__ , A__ )
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def UpperCamelCase ( self ) -> List[Any]:
pass
def lowerCAmelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
_SCREAMING_SNAKE_CASE = np.load(SCREAMING_SNAKE_CASE_ )
return list(SCREAMING_SNAKE_CASE_ )
@require_torch
@require_vision
class _a (unittest.TestCase):
"""simple docstring"""
@cached_property
def UpperCamelCase ( self ) -> str:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = VideoMAEForVideoClassification.from_pretrained("""MCG-NJU/videomae-base-finetuned-kinetics""" ).to(
A__ )
_SCREAMING_SNAKE_CASE = self.default_image_processor
_SCREAMING_SNAKE_CASE = prepare_video()
_SCREAMING_SNAKE_CASE = image_processor(A__ , return_tensors="""pt""" ).to(A__ )
# forward pass
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**A__ )
# verify the logits
_SCREAMING_SNAKE_CASE = torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , A__ )
_SCREAMING_SNAKE_CASE = torch.tensor([0.3669, -0.0688, -0.2421] ).to(A__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A__ , atol=1E-4 ) )
@slow
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short""" ).to(A__ )
_SCREAMING_SNAKE_CASE = self.default_image_processor
_SCREAMING_SNAKE_CASE = prepare_video()
_SCREAMING_SNAKE_CASE = image_processor(A__ , return_tensors="""pt""" ).to(A__ )
# add boolean mask, indicating which patches to mask
_SCREAMING_SNAKE_CASE = hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""" , filename="""bool_masked_pos.pt""" )
_SCREAMING_SNAKE_CASE = torch.load(A__ )
# forward pass
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**A__ )
# verify the logits
_SCREAMING_SNAKE_CASE = torch.Size([1, 14_08, 15_36] )
_SCREAMING_SNAKE_CASE = torch.tensor(
[[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]] , device=A__ )
self.assertEqual(outputs.logits.shape , A__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , A__ , atol=1E-4 ) )
# verify the loss (`config.norm_pix_loss` = `True`)
_SCREAMING_SNAKE_CASE = torch.tensor([0.5142] , device=A__ )
self.assertTrue(torch.allclose(outputs.loss , A__ , atol=1E-4 ) )
# verify the loss (`config.norm_pix_loss` = `False`)
_SCREAMING_SNAKE_CASE = VideoMAEForPreTraining.from_pretrained("""MCG-NJU/videomae-base-short""" , norm_pix_loss=A__ ).to(
A__ )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**A__ )
_SCREAMING_SNAKE_CASE = torch.tensor(torch.tensor([0.6469] ) , device=A__ )
self.assertTrue(torch.allclose(outputs.loss , A__ , atol=1E-4 ) )
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : str = {
"configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
"tokenization_canine": ["CanineTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : List[Any] = [
"CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
"CanineForMultipleChoice",
"CanineForQuestionAnswering",
"CanineForSequenceClassification",
"CanineForTokenClassification",
"CanineLayer",
"CanineModel",
"CaninePreTrainedModel",
"load_tf_weights_in_canine",
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
UpperCamelCase__ : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
import argparse
import datetime
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """Wednesday""",
"""4""": """Thursday""",
"""5""": """Friday""",
"""6""": """Saturday""",
}
_SCREAMING_SNAKE_CASE = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(SCREAMING_SNAKE_CASE_ ) < 11:
raise ValueError("""Must be 10 characters long""" )
# Get month
_SCREAMING_SNAKE_CASE = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError("""Month must be between 1 - 12""" )
_SCREAMING_SNAKE_CASE = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("""Date separator must be '-' or '/'""" )
# Get day
_SCREAMING_SNAKE_CASE = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError("""Date must be between 1 - 31""" )
# Get second separator
_SCREAMING_SNAKE_CASE = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("""Date separator must be '-' or '/'""" )
# Get year
_SCREAMING_SNAKE_CASE = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 85_00:
raise ValueError(
"""Year out of range. There has to be some sort of limit...right?""" )
# Get datetime obj for validation
_SCREAMING_SNAKE_CASE = datetime.date(int(SCREAMING_SNAKE_CASE_ ) , int(SCREAMING_SNAKE_CASE_ ) , int(SCREAMING_SNAKE_CASE_ ) )
# Start math
if m <= 2:
_SCREAMING_SNAKE_CASE = y - 1
_SCREAMING_SNAKE_CASE = m + 12
# maths var
_SCREAMING_SNAKE_CASE = int(str(SCREAMING_SNAKE_CASE_ )[:2] )
_SCREAMING_SNAKE_CASE = int(str(SCREAMING_SNAKE_CASE_ )[2:] )
_SCREAMING_SNAKE_CASE = int(2.6 * m - 5.39 )
_SCREAMING_SNAKE_CASE = int(c / 4 )
_SCREAMING_SNAKE_CASE = int(k / 4 )
_SCREAMING_SNAKE_CASE = int(d + k )
_SCREAMING_SNAKE_CASE = int(t + u + v + x )
_SCREAMING_SNAKE_CASE = int(z - (2 * c) )
_SCREAMING_SNAKE_CASE = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError("""The date was evaluated incorrectly. Contact developer.""" )
# Response
_SCREAMING_SNAKE_CASE = F"Your date {date_input}, is a {days[str(SCREAMING_SNAKE_CASE_ )]}!"
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ : Dict = argparse.ArgumentParser(
description=(
"Find out what day of the week nearly any date is or was. Enter "
"date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
)
)
parser.add_argument(
"date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
)
UpperCamelCase__ : int = parser.parse_args()
zeller(args.date_input)
| 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE = 'ChineseCLIPImageProcessor'
SCREAMING_SNAKE_CASE = ('BertTokenizer', 'BertTokenizerFast')
def __init__( self , A__=None , A__=None , **A__ ) -> int:
_SCREAMING_SNAKE_CASE = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , A__ , )
_SCREAMING_SNAKE_CASE = kwargs.pop("""feature_extractor""" )
_SCREAMING_SNAKE_CASE = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.image_processor
def __call__( self , A__=None , A__=None , A__=None , **A__ ) -> Optional[int]:
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
_SCREAMING_SNAKE_CASE = self.tokenizer(A__ , return_tensors=A__ , **A__ )
if images is not None:
_SCREAMING_SNAKE_CASE = self.image_processor(A__ , return_tensors=A__ , **A__ )
if text is not None and images is not None:
_SCREAMING_SNAKE_CASE = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**A__ ) , tensor_type=A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Dict:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Optional[Any]:
return self.tokenizer.decode(*A__ , **A__ )
@property
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.tokenizer.model_input_names
_SCREAMING_SNAKE_CASE = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCamelCase ( self ) -> Optional[int]:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , A__ , )
return self.image_processor_class
| 0 | 1 |
'''simple docstring'''
from typing import Dict
from .base import GenericTensor, Pipeline
class _a (_lowerCamelCase):
"""simple docstring"""
def UpperCamelCase ( self , A__=None , A__=None , A__=None , **A__ ) -> Optional[int]:
if tokenize_kwargs is None:
_SCREAMING_SNAKE_CASE = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
"""truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)""" )
_SCREAMING_SNAKE_CASE = truncation
_SCREAMING_SNAKE_CASE = tokenize_kwargs
_SCREAMING_SNAKE_CASE = {}
if return_tensors is not None:
_SCREAMING_SNAKE_CASE = return_tensors
return preprocess_params, {}, postprocess_params
def UpperCamelCase ( self , A__ , **A__ ) -> Dict[str, GenericTensor]:
_SCREAMING_SNAKE_CASE = self.framework
_SCREAMING_SNAKE_CASE = self.tokenizer(A__ , return_tensors=A__ , **A__ )
return model_inputs
def UpperCamelCase ( self , A__ ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.model(**A__ )
return model_outputs
def UpperCamelCase ( self , A__ , A__=False ) -> Optional[Any]:
# [0] is the first available tensor, logits or last_hidden_state.
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self , *A__ , **A__ ) -> List[str]:
return super().__call__(*A__ , **A__ )
| 0 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase__ : List[str] = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
UpperCamelCase__ : List[Any] = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
UpperCamelCase__ : Any = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a (datasets.Metric):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def UpperCamelCase ( self , A__ , A__ , A__=None ) -> List[str]:
return {
"matthews_correlation": float(matthews_corrcoef(A__ , A__ , sample_weight=A__ ) ),
}
| 0 | 1 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
UpperCamelCase__ : Tuple = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
from run_translation import main # noqa
set_seed(42)
UpperCamelCase__ : Union[str, Any] = "sshleifer/student_marian_en_ro_6_1"
UpperCamelCase__ : str = "sshleifer/tiny-mbart"
@require_torch
class _a (_lowerCamelCase):
"""simple docstring"""
def UpperCamelCase ( self , A__=False , A__=None , A__=True , A__=True , A__=True , A__=True , ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=A__ , num_train_epochs=1 , distributed=A__ , extra_args_str=A__ , predict_with_generate=A__ , do_train=A__ , do_eval=A__ , do_predict=A__ , )
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(os.path.join(A__ , """trainer_state.json""" ) ).log_history
if not do_eval:
return
_SCREAMING_SNAKE_CASE = [log for log in logs if """eval_loss""" in log.keys()]
_SCREAMING_SNAKE_CASE = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
_SCREAMING_SNAKE_CASE = eval_metrics[-1]
assert isinstance(last_step_stats["""eval_bleu"""] , A__ )
assert not math.isnan(float(last_step_stats["""eval_loss"""] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def UpperCamelCase ( self ) -> Optional[int]:
self.run_seqaseq_quick()
@require_torch_multi_gpu
def UpperCamelCase ( self ) -> Optional[Any]:
self.run_seqaseq_quick(distributed=A__ )
@require_torch_multi_gpu
def UpperCamelCase ( self ) -> Union[str, Any]:
self.run_seqaseq_quick(distributed=A__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> Any:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp simple""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> Tuple:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp simple --fp16""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> str:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp zero_dp_2""" , predict_with_generate=A__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> List[str]:
self.run_seqaseq_quick(
distributed=A__ , extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" , predict_with_generate=A__ )
@require_apex
@require_torch_gpu
def UpperCamelCase ( self ) -> Optional[Any]:
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
@parameterized.expand(["""base""", """low""", """high""", """mixed"""] )
@require_torch_multi_gpu
def UpperCamelCase ( self , A__ ) -> List[Any]:
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
_SCREAMING_SNAKE_CASE = {
# test with the default log_level - should be info and thus log info once
"""base""": {"""extra_args_str""": """""", """n_matches""": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"""low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"""high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"""mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
}
_SCREAMING_SNAKE_CASE = experiments[experiment_id]
_SCREAMING_SNAKE_CASE = {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
_SCREAMING_SNAKE_CASE = """Running training"""
with CaptureStderr() as cl:
self.run_seqaseq_quick(**A__ , extra_args_str=data["""extra_args_str"""] )
_SCREAMING_SNAKE_CASE = len(re.findall(A__ , cl.err ) )
self.assertEqual(A__ , data["""n_matches"""] )
@slow
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.run_trainer(
eval_steps=2 , max_len=1_28 , model_name=A__ , learning_rate=3E-4 , num_train_epochs=10 , distributed=A__ , )
# Check metrics
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(os.path.join(A__ , """trainer_state.json""" ) ).log_history
_SCREAMING_SNAKE_CASE = [log for log in logs if """eval_loss""" in log.keys()]
_SCREAMING_SNAKE_CASE = eval_metrics[0]
_SCREAMING_SNAKE_CASE = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["""eval_bleu"""] , A__ )
# test if do_predict saves generations and metrics
_SCREAMING_SNAKE_CASE = os.listdir(A__ )
_SCREAMING_SNAKE_CASE = {os.path.basename(A__ ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def UpperCamelCase ( self ) -> Dict:
from transformers.training_args import OptimizerNames
def train_and_return_metrics(A__ ) -> Tuple[int, float]:
_SCREAMING_SNAKE_CASE = """--skip_memory_metrics 0"""
_SCREAMING_SNAKE_CASE = self.run_trainer(
max_len=1_28 , model_name=A__ , learning_rate=3E-4 , num_train_epochs=1 , optim=A__ , distributed=A__ , extra_args_str=A__ , do_eval=A__ , do_predict=A__ , n_gpus_to_use=1 , )
# Check metrics
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(Path(A__ , """trainer_state.json""" ) ).log_history
_SCREAMING_SNAKE_CASE = int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20 )
_SCREAMING_SNAKE_CASE = int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20 )
_SCREAMING_SNAKE_CASE = logs[0]["""train_loss"""]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
_SCREAMING_SNAKE_CASE = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
_SCREAMING_SNAKE_CASE = gpu_peak_mem_orig + gpu_alloc_mem_orig
_SCREAMING_SNAKE_CASE = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
_SCREAMING_SNAKE_CASE = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
_SCREAMING_SNAKE_CASE = 1_20
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
A__ , A__ , """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
F" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
F" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB" , )
self.assertGreater(
A__ , A__ , """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
F" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
F" gpu_total_mem_bnb={gpu_total_mem_bnb}MB" , )
self.assertEqual(
A__ , A__ , F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}" )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ = 3E-3 , A__ = "adafactor" , A__ = False , A__ = None , A__ = 0 , A__ = True , A__ = True , A__ = True , A__ = True , A__ = None , ) -> Dict:
_SCREAMING_SNAKE_CASE = self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
_SCREAMING_SNAKE_CASE = self.get_auto_remove_tmp_dir()
_SCREAMING_SNAKE_CASE = F"\n --model_name_or_path {model_name}\n --train_file {data_dir}/train.json\n --validation_file {data_dir}/val.json\n --test_file {data_dir}/test.json\n --output_dir {output_dir}\n --overwrite_output_dir\n --max_train_samples 8\n --max_source_length {max_len}\n --max_target_length {max_len}\n --do_train\n --num_train_epochs {str(A__ )}\n --per_device_train_batch_size 4\n --learning_rate {learning_rate}\n --warmup_steps 8\n --logging_steps 0\n --logging_strategy no\n --save_steps {str(A__ )}\n --group_by_length\n --label_smoothing_factor 0.1\n --target_lang ro_RO\n --source_lang en_XX\n ".split()
_SCREAMING_SNAKE_CASE = F"\n --do_eval\n --per_device_eval_batch_size 4\n --max_eval_samples 8\n --val_max_target_length {max_len}\n --evaluation_strategy steps\n --eval_steps {str(A__ )}\n ".split()
_SCREAMING_SNAKE_CASE = """
--do_predict
""".split()
_SCREAMING_SNAKE_CASE = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F"--optim {optim}".split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
_SCREAMING_SNAKE_CASE = get_gpu_count()
_SCREAMING_SNAKE_CASE = get_torch_dist_unique_port()
_SCREAMING_SNAKE_CASE = F"\n -m torch.distributed.run\n --nproc_per_node={n_gpus_to_use}\n --master_port={master_port}\n {self.examples_dir_str}/pytorch/translation/run_translation.py\n ".split()
_SCREAMING_SNAKE_CASE = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(A__ , env=self.get_env() )
else:
_SCREAMING_SNAKE_CASE = ["""run_translation.py"""] + args
with patch.object(A__ , """argv""" , A__ ):
main()
return output_dir
| 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
print(F"Vertex\tShortest Distance from vertex {src}" )
for i, d in enumerate(SCREAMING_SNAKE_CASE_ ):
print(F"{i}\t\t{d}" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
for j in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
return True
return False
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list[float]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [float("""inf""" )] * vertex_count
_SCREAMING_SNAKE_CASE = 0.0
for _ in range(vertex_count - 1 ):
for j in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
_SCREAMING_SNAKE_CASE = distance[u] + w
_SCREAMING_SNAKE_CASE = check_negative_cycle(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if negative_cycle_exists:
raise Exception("""Negative cycle found""" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ : int = int(input("Enter number of vertices: ").strip())
UpperCamelCase__ : int = int(input("Enter number of edges: ").strip())
UpperCamelCase__ : list[dict[str, int]] = [{} for _ in range(E)]
for i in range(E):
print("Edge ", i + 1)
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Dict = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
UpperCamelCase__ : Optional[Any] = {"src": src, "dst": dest, "weight": weight}
UpperCamelCase__ : Optional[Any] = int(input("\nEnter shortest path source:").strip())
UpperCamelCase__ : Any = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 0 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
UpperCamelCase__ : Tuple = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"
" Distillation"
)
)
parser.add_argument("--model_type", default="roberta", choices=["roberta", "gpt2"])
parser.add_argument("--model_name", default="roberta-large", type=str)
parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_roberta_048131723.pth", type=str)
parser.add_argument("--vocab_transform", action="store_true")
UpperCamelCase__ : Optional[Any] = parser.parse_args()
if args.model_type == "roberta":
UpperCamelCase__ : Tuple = RobertaForMaskedLM.from_pretrained(args.model_name)
UpperCamelCase__ : List[str] = "roberta"
elif args.model_type == "gpt2":
UpperCamelCase__ : Any = GPTaLMHeadModel.from_pretrained(args.model_name)
UpperCamelCase__ : List[str] = "transformer"
UpperCamelCase__ : Tuple = model.state_dict()
UpperCamelCase__ : Tuple = {}
# Embeddings #
if args.model_type == "gpt2":
for param_name in ["wte.weight", "wpe.weight"]:
UpperCamelCase__ : Dict = state_dict[f"""{prefix}.{param_name}"""]
else:
for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
UpperCamelCase__ : List[str] = f"""{prefix}.embeddings.{w}.weight"""
UpperCamelCase__ : List[Any] = state_dict[param_name]
for w in ["weight", "bias"]:
UpperCamelCase__ : Optional[Any] = f"""{prefix}.embeddings.LayerNorm.{w}"""
UpperCamelCase__ : Union[str, Any] = state_dict[param_name]
# Transformer Blocks #
UpperCamelCase__ : List[Any] = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
if args.model_type == "gpt2":
for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
for w in ["weight", "bias"]:
UpperCamelCase__ : Tuple = state_dict[
f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
]
UpperCamelCase__ : List[str] = state_dict[f"""{prefix}.h.{teacher_idx}.attn.bias"""]
else:
for layer in [
"attention.self.query",
"attention.self.key",
"attention.self.value",
"attention.output.dense",
"attention.output.LayerNorm",
"intermediate.dense",
"output.dense",
"output.LayerNorm",
]:
for w in ["weight", "bias"]:
UpperCamelCase__ : Optional[Any] = state_dict[
f"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
]
std_idx += 1
# Language Modeling Head ###s
if args.model_type == "roberta":
for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
UpperCamelCase__ : Any = state_dict[f"""{layer}"""]
if args.vocab_transform:
for w in ["weight", "bias"]:
UpperCamelCase__ : Dict = state_dict[f"""lm_head.dense.{w}"""]
UpperCamelCase__ : int = state_dict[f"""lm_head.layer_norm.{w}"""]
elif args.model_type == "gpt2":
for w in ["weight", "bias"]:
UpperCamelCase__ : Dict = state_dict[f"""{prefix}.ln_f.{w}"""]
UpperCamelCase__ : Optional[Any] = state_dict["lm_head.weight"]
print(f"""N layers selected for distillation: {std_idx}""")
print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")
print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
torch.save(compressed_sd, args.dump_checkpoint)
| 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=True , A__=True , A__=99 , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=16 , A__=2 , A__=0.02 , A__=3 , A__=4 , A__=None , ) -> int:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = 13
_SCREAMING_SNAKE_CASE = 7
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = 99
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = 37
_SCREAMING_SNAKE_CASE = """gelu"""
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 5_12
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 0.02
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
_SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=A__ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModel(config=A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
_SCREAMING_SNAKE_CASE = [input_ids, input_mask]
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> str:
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = TFRoFormerForCausalLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )["""logits"""]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Dict:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForSequenceClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = TFRoFormerForMultipleChoice(config=A__ )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForTokenClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = TFRoFormerForQuestionAnswering(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': TFRoFormerModel,
'fill-mask': TFRoFormerForMaskedLM,
'question-answering': TFRoFormerForQuestionAnswering,
'text-classification': TFRoFormerForSequenceClassification,
'text-generation': TFRoFormerForCausalLM,
'token-classification': TFRoFormerForTokenClassification,
'zero-shot': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> str:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*A__ )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base""" )
self.assertIsNotNone(A__ )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
_SCREAMING_SNAKE_CASE = tf.constant([[0, 1, 2, 3, 4, 5]] )
_SCREAMING_SNAKE_CASE = model(A__ )[0]
# TODO Replace vocab size
_SCREAMING_SNAKE_CASE = 5_00_00
_SCREAMING_SNAKE_CASE = [1, 6, vocab_size]
self.assertEqual(output.shape , A__ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
_SCREAMING_SNAKE_CASE = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , A__ , atol=1E-4 )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant([[4, 10]] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
_SCREAMING_SNAKE_CASE = emba(input_ids.shape )
_SCREAMING_SNAKE_CASE = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 , embedding_dim=5_12 )
emba([2, 16, 5_12] )
_SCREAMING_SNAKE_CASE = emba.weight[:3, :5]
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> int:
# 2,12,16,64
_SCREAMING_SNAKE_CASE = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
_SCREAMING_SNAKE_CASE = embed_positions([2, 16, 7_68] )[None, None, :, :]
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
A__ , A__ , A__ )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
| 0 | 1 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase_ ( ) -> Iterator[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
while True:
if is_prime(SCREAMING_SNAKE_CASE_ ):
yield num
num += 1
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = 2_00_00_00 ) -> int:
"""simple docstring"""
return sum(takewhile(lambda SCREAMING_SNAKE_CASE_ : x < n , prime_generator() ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase__ : int = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
UpperCamelCase__ : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 | 1 |
'''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
UpperCamelCase__ : str = ["text", "image", "audio"]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
for input_type in input_types:
if input_type == "text":
inputs.append("""Text input""" )
elif input_type == "image":
inputs.append(
Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO""" ) ) / """000000039769.png""" ).resize((5_12, 5_12) ) )
elif input_type == "audio":
inputs.append(torch.ones(30_00 ) )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
inputs.append(create_inputs(SCREAMING_SNAKE_CASE_ ) )
else:
raise ValueError(F"Invalid type requested: {input_type}" )
return inputs
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
for output in outputs:
if isinstance(SCREAMING_SNAKE_CASE_ , (str, AgentText) ):
output_types.append("""text""" )
elif isinstance(SCREAMING_SNAKE_CASE_ , (Image.Image, AgentImage) ):
output_types.append("""image""" )
elif isinstance(SCREAMING_SNAKE_CASE_ , (torch.Tensor, AgentAudio) ):
output_types.append("""audio""" )
else:
raise ValueError(F"Invalid output: {output}" )
return output_types
@is_tool_test
class _a :
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[int]:
self.assertTrue(hasattr(self.tool , """inputs""" ) )
self.assertTrue(hasattr(self.tool , """outputs""" ) )
_SCREAMING_SNAKE_CASE = self.tool.inputs
for _input in inputs:
if isinstance(_input , A__ ):
for __input in _input:
self.assertTrue(__input in authorized_types )
else:
self.assertTrue(_input in authorized_types )
_SCREAMING_SNAKE_CASE = self.tool.outputs
for _output in outputs:
self.assertTrue(_output in authorized_types )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = create_inputs(self.tool.inputs )
_SCREAMING_SNAKE_CASE = self.tool(*A__ )
# There is a single output
if len(self.tool.outputs ) == 1:
_SCREAMING_SNAKE_CASE = [outputs]
self.assertListEqual(output_types(A__ ) , self.tool.outputs )
def UpperCamelCase ( self ) -> Optional[int]:
self.assertTrue(hasattr(self.tool , """description""" ) )
self.assertTrue(hasattr(self.tool , """default_checkpoint""" ) )
self.assertTrue(self.tool.description.startswith("""This is a tool that""" ) )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = create_inputs(self.tool.inputs )
_SCREAMING_SNAKE_CASE = self.tool(*A__ )
if not isinstance(A__ , A__ ):
_SCREAMING_SNAKE_CASE = [outputs]
self.assertEqual(len(A__ ) , len(self.tool.outputs ) )
for output, output_type in zip(A__ , self.tool.outputs ):
_SCREAMING_SNAKE_CASE = AGENT_TYPE_MAPPING[output_type]
self.assertTrue(isinstance(A__ , A__ ) )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = create_inputs(self.tool.inputs )
_SCREAMING_SNAKE_CASE = []
for _input, input_type in zip(A__ , self.tool.inputs ):
if isinstance(A__ , A__ ):
_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input ) for _input_type in input_type] )
else:
_inputs.append(AGENT_TYPE_MAPPING[input_type](_input ) )
# Should not raise an error
_SCREAMING_SNAKE_CASE = self.tool(*A__ )
if not isinstance(A__ , A__ ):
_SCREAMING_SNAKE_CASE = [outputs]
self.assertEqual(len(A__ ) , len(self.tool.outputs ) )
| 0 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = XCLIPTextConfig()
# derive patch size from model name
_SCREAMING_SNAKE_CASE = model_name.find("""patch""" )
_SCREAMING_SNAKE_CASE = int(model_name[start_idx + len("""patch""" ) : start_idx + len("""patch""" ) + 2] )
_SCREAMING_SNAKE_CASE = XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE_ , num_frames=SCREAMING_SNAKE_CASE_ )
if "large" in model_name:
_SCREAMING_SNAKE_CASE = 7_68
_SCREAMING_SNAKE_CASE = 30_72
_SCREAMING_SNAKE_CASE = 12
_SCREAMING_SNAKE_CASE = 10_24
_SCREAMING_SNAKE_CASE = 40_96
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 24
_SCREAMING_SNAKE_CASE = 7_68
_SCREAMING_SNAKE_CASE = 30_72
if model_name == "xclip-large-patch14-16-frames":
_SCREAMING_SNAKE_CASE = 3_36
_SCREAMING_SNAKE_CASE = XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if "large" in model_name:
_SCREAMING_SNAKE_CASE = 7_68
return config
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
# text encoder
if name == "token_embedding.weight":
_SCREAMING_SNAKE_CASE = name.replace("""token_embedding.weight""" , """text_model.embeddings.token_embedding.weight""" )
if name == "positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "ln_1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
_SCREAMING_SNAKE_CASE = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""c_proj""" , """fc2""" )
if name.startswith("""transformer.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""transformer.resblocks""" , """text_model.encoder.layers""" )
if "attn.out_proj" in name and "message" not in name:
_SCREAMING_SNAKE_CASE = name.replace("""attn.out_proj""" , """self_attn.out_proj""" )
if "ln_final" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_final""" , """text_model.final_layer_norm""" )
# visual encoder
if name == "visual.class_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""visual.class_embedding""" , """vision_model.embeddings.class_embedding""" )
if name == "visual.positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""visual.positional_embedding""" , """vision_model.embeddings.position_embedding.weight""" )
if name.startswith("""visual.transformer.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""visual.transformer.resblocks""" , """vision_model.encoder.layers""" )
if "visual.conv1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.conv1""" , """vision_model.embeddings.patch_embedding""" )
if "visual.ln_pre" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.ln_pre""" , """vision_model.pre_layernorm""" )
if "visual.ln_post" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.ln_post""" , """vision_model.post_layernorm""" )
if "visual.proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.proj""" , """visual_projection.weight""" )
if "text_projection" in name:
_SCREAMING_SNAKE_CASE = name.replace("""text_projection""" , """text_projection.weight""" )
# things on top
if "prompts_visual_proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""prompts_visual_proj""" , """prompts_visual_projection""" )
if "prompts_visual_ln" in name:
_SCREAMING_SNAKE_CASE = name.replace("""prompts_visual_ln""" , """prompts_visual_layernorm""" )
# mit
if name == "mit.positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""positional""" , """position""" )
if name.startswith("""mit.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""mit.resblocks""" , """mit.encoder.layers""" )
# prompts generator
if name.startswith("""prompts_generator.norm""" ):
_SCREAMING_SNAKE_CASE = name.replace("""prompts_generator.norm""" , """prompts_generator.layernorm""" )
return name
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_SCREAMING_SNAKE_CASE = orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )
if "attn.in_proj" in key:
_SCREAMING_SNAKE_CASE = key.split(""".""" )
if key.startswith("""visual""" ):
_SCREAMING_SNAKE_CASE = key_split[3]
_SCREAMING_SNAKE_CASE = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[
:dim, :
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[
-dim:, :
]
else:
_SCREAMING_SNAKE_CASE = val[
:dim
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[
:dim, :
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[
-dim:, :
]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[-dim:]
elif key.startswith("""mit""" ):
_SCREAMING_SNAKE_CASE = key_split[2]
_SCREAMING_SNAKE_CASE = config.vision_config.mit_hidden_size
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[:dim, :]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
_SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2]
_SCREAMING_SNAKE_CASE = val[-dim:]
else:
_SCREAMING_SNAKE_CASE = key_split[2]
_SCREAMING_SNAKE_CASE = config.text_config.hidden_size
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[:dim, :]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[-dim:]
else:
_SCREAMING_SNAKE_CASE = rename_key(SCREAMING_SNAKE_CASE_ )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
_SCREAMING_SNAKE_CASE = val.T
_SCREAMING_SNAKE_CASE = val
return orig_state_dict
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
if num_frames == 8:
_SCREAMING_SNAKE_CASE = """eating_spaghetti_8_frames.npy"""
elif num_frames == 16:
_SCREAMING_SNAKE_CASE = """eating_spaghetti.npy"""
elif num_frames == 32:
_SCREAMING_SNAKE_CASE = """eating_spaghetti_32_frames.npy"""
_SCREAMING_SNAKE_CASE = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename=SCREAMING_SNAKE_CASE_ , repo_type="""dataset""" , )
_SCREAMING_SNAKE_CASE = np.load(SCREAMING_SNAKE_CASE_ )
return list(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=False ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {
# fully supervised kinetics-400 checkpoints
"""xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
"""xclip-base-patch32-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
),
"""xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
"""xclip-base-patch16-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
),
"""xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
"""xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f""",
# fully supervised kinetics-600 checkpoints
"""xclip-base-patch16-kinetics-600""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
),
"""xclip-base-patch16-kinetics-600-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
),
"""xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
# few shot
"""xclip-base-patch16-hmdb-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
),
"""xclip-base-patch16-hmdb-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
),
"""xclip-base-patch16-hmdb-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
),
"""xclip-base-patch16-hmdb-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
),
"""xclip-base-patch16-ucf-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
),
"""xclip-base-patch16-ucf-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
),
"""xclip-base-patch16-ucf-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
),
"""xclip-base-patch16-ucf-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
),
# zero shot
"""xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
}
_SCREAMING_SNAKE_CASE = model_to_url[model_name]
_SCREAMING_SNAKE_CASE = 8
if "16-frames" in model_name:
_SCREAMING_SNAKE_CASE = 16
elif "shot" in model_name:
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = get_xclip_config(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = XCLIPModel(SCREAMING_SNAKE_CASE_ )
model.eval()
if "drive" in checkpoint_url:
_SCREAMING_SNAKE_CASE = """pytorch_model.bin"""
gdown.cached_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , quiet=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )["""model"""]
else:
_SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ )["""model"""]
_SCREAMING_SNAKE_CASE = convert_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = XCLIPModel(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
_SCREAMING_SNAKE_CASE = 3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
_SCREAMING_SNAKE_CASE = VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
_SCREAMING_SNAKE_CASE = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32""" )
_SCREAMING_SNAKE_CASE = XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = prepare_video(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = processor(
text=["""playing sports""", """eating spaghetti""", """go shopping"""] , videos=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , padding=SCREAMING_SNAKE_CASE_ )
print("""Shape of pixel values:""" , inputs.pixel_values.shape )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
# Verify outputs
_SCREAMING_SNAKE_CASE = outputs.logits_per_video
_SCREAMING_SNAKE_CASE = logits_per_video.softmax(dim=1 )
print("""Probs:""" , SCREAMING_SNAKE_CASE_ )
# kinetics-400
if model_name == "xclip-base-patch32":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0019, 0.9951, 0.0030]] )
elif model_name == "xclip-base-patch32-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]] )
elif model_name == "xclip-base-patch16":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0083, 0.9681, 0.0236]] )
elif model_name == "xclip-base-patch16-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]] )
elif model_name == "xclip-large-patch14":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0062, 0.9864, 0.0075]] )
elif model_name == "xclip-large-patch14-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0555, 0.8914, 0.0531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0036, 0.9920, 0.0045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0027, 0.9904, 0.0070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]] )
else:
raise ValueError(F"Model name {model_name} not supported" )
assert torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
print("""Pushing model, processor and slow tokenizer files to the hub...""" )
model.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
processor.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
slow_tokenizer.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="xclip-base-patch32",
type=str,
help="Name of the model.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
UpperCamelCase__ : str = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 0 | 1 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from transformers import AutoTokenizer, HfArgumentParser
UpperCamelCase__ : Optional[Any] = re.compile(R"\s+")
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
return {"hash": hashlib.mda(re.sub(SCREAMING_SNAKE_CASE_ , """""" , example["""content"""] ).encode("""utf-8""" ) ).hexdigest()}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [len(SCREAMING_SNAKE_CASE_ ) for line in example["""content"""].splitlines()]
return {"line_mean": np.mean(SCREAMING_SNAKE_CASE_ ), "line_max": max(SCREAMING_SNAKE_CASE_ )}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = np.mean([c.isalnum() for c in example["""content"""]] )
return {"alpha_frac": alpha_frac}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
if example["hash"] in uniques:
uniques.remove(example["""hash"""] )
return True
else:
return False
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=5 ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""auto-generated""", """autogenerated""", """automatically generated"""]
_SCREAMING_SNAKE_CASE = example["""content"""].splitlines()
for _, line in zip(range(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ ):
for keyword in keywords:
if keyword in line.lower():
return {"autogenerated": True}
else:
return {"autogenerated": False}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=5 , SCREAMING_SNAKE_CASE_=0.05 ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""unit tests""", """test file""", """configuration file"""]
_SCREAMING_SNAKE_CASE = example["""content"""].splitlines()
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
# first test
for _, line in zip(range(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ ):
for keyword in keywords:
if keyword in line.lower():
return {"config_or_test": True}
# second test
_SCREAMING_SNAKE_CASE = example["""content"""].count("""\n""" )
_SCREAMING_SNAKE_CASE = int(coeff * nlines )
for line in lines:
count_config += line.lower().count("""config""" )
count_test += line.lower().count("""test""" )
if count_config > threshold or count_test > threshold:
return {"config_or_test": True}
return {"config_or_test": False}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""def """, """class """, """for """, """while """]
_SCREAMING_SNAKE_CASE = example["""content"""].splitlines()
for line in lines:
for keyword in keywords:
if keyword in line.lower():
return {"has_no_keywords": False}
return {"has_no_keywords": True}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=4 ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = example["""content"""].splitlines()
_SCREAMING_SNAKE_CASE = 0
for line in lines:
counter += line.lower().count("""=""" )
if counter > minimum:
return {"has_few_assignments": False}
return {"has_few_assignments": True}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tokenizer(example["""content"""] , truncation=SCREAMING_SNAKE_CASE_ )["""input_ids"""]
_SCREAMING_SNAKE_CASE = len(example["""content"""] ) / len(SCREAMING_SNAKE_CASE_ )
return {"ratio": ratio}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {}
results.update(get_hash(SCREAMING_SNAKE_CASE_ ) )
results.update(line_stats(SCREAMING_SNAKE_CASE_ ) )
results.update(alpha_stats(SCREAMING_SNAKE_CASE_ ) )
results.update(char_token_ratio(SCREAMING_SNAKE_CASE_ ) )
results.update(is_autogenerated(SCREAMING_SNAKE_CASE_ ) )
results.update(is_config_or_test(SCREAMING_SNAKE_CASE_ ) )
results.update(has_no_keywords(SCREAMING_SNAKE_CASE_ ) )
results.update(has_few_assignments(SCREAMING_SNAKE_CASE_ ) )
return results
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if not check_uniques(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return False
elif example["autogenerated"]:
return False
elif example["line_max"] > args.line_max:
return False
elif example["line_mean"] > args.line_mean:
return False
elif example["alpha_frac"] < args.alpha_frac:
return False
elif example["ratio"] < args.min_token_ratio:
return False
elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
return False
elif example["has_few_assignments"]:
return False
else:
return True
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE_ , """rb""" ) as f_in:
with gzip.open(str(SCREAMING_SNAKE_CASE_ ) + """.gz""" , """wb""" , compresslevel=6 ) as f_out:
shutil.copyfileobj(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
os.unlink(SCREAMING_SNAKE_CASE_ )
# Settings
UpperCamelCase__ : List[str] = HfArgumentParser(PreprocessingArguments)
UpperCamelCase__ : List[Any] = parser.parse_args()
if args.num_workers is None:
UpperCamelCase__ : Optional[int] = multiprocessing.cpu_count()
UpperCamelCase__ : List[Any] = AutoTokenizer.from_pretrained(args.tokenizer_dir)
# Load dataset
UpperCamelCase__ : Union[str, Any] = time.time()
UpperCamelCase__ : Optional[Any] = load_dataset(args.dataset_name, split="train")
print(f"""Time to load dataset: {time.time()-t_start:.2f}""")
# Run preprocessing
UpperCamelCase__ : Any = time.time()
UpperCamelCase__ : Any = ds.map(preprocess, num_proc=args.num_workers)
print(f"""Time to preprocess dataset: {time.time()-t_start:.2f}""")
# Deduplicate hashes
UpperCamelCase__ : int = set(ds.unique("hash"))
UpperCamelCase__ : Optional[int] = len(uniques) / len(ds)
print(f"""Fraction of duplicates: {1-frac:.2%}""")
# Deduplicate data and apply heuristics
UpperCamelCase__ : Optional[Any] = time.time()
UpperCamelCase__ : Tuple = ds.filter(filter, fn_kwargs={"uniques": uniques, "args": args})
print(f"""Time to filter dataset: {time.time()-t_start:.2f}""")
print(f"""Size of filtered dataset: {len(ds_filter)}""")
# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
UpperCamelCase__ : Optional[int] = time.time()
UpperCamelCase__ , UpperCamelCase__ : List[Any] = deduplicate_dataset(ds_filter, args.jaccard_threshold)
print(f"""Time to deduplicate dataset: {time.time()-t_start:.2f}""")
print(f"""Size of deduplicate dataset: {len(ds_filter)}""")
# Save data in batches of samples_per_file
UpperCamelCase__ : str = Path(args.output_dir)
output_dir.mkdir(exist_ok=True)
# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
with open(output_dir / "duplicate_clusters.json", "w") as f:
json.dump(duplicate_clusters, f)
UpperCamelCase__ : Tuple = output_dir / "data"
data_dir.mkdir(exist_ok=True)
UpperCamelCase__ : Optional[Any] = time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
UpperCamelCase__ : List[str] = str(data_dir / f"""file-{file_number+1:012}.json""")
UpperCamelCase__ : Optional[int] = min(len(ds_filter), index + args.samples_per_file)
ds_filter.select(list(range(index, end_index))).to_json(file_path)
compress_file(file_path)
print(f"""Time to save dataset: {time.time()-t_start:.2f}""")
| 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = params
_SCREAMING_SNAKE_CASE = np.array(A__ )
_SCREAMING_SNAKE_CASE = np.array([len(A__ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self , A__ ) -> Dict:
return (self.token_ids[index], self.lengths[index])
def __len__( self ) -> Tuple:
return len(self.lengths )
def UpperCamelCase ( self ) -> Dict:
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.params.max_model_input_size
_SCREAMING_SNAKE_CASE = self.lengths > max_len
logger.info(F"Splitting {sum(A__ )} too long sequences." )
def divide_chunks(A__ , A__ ):
return [l[i : i + n] for i in range(0 , len(A__ ) , A__ )]
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
if self.params.mlm:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
_SCREAMING_SNAKE_CASE = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
_SCREAMING_SNAKE_CASE = np.insert(A__ , 0 , A__ )
if sub_s[-1] != sep_id:
_SCREAMING_SNAKE_CASE = np.insert(A__ , len(A__ ) , A__ )
assert len(A__ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(A__ )
new_tok_ids.extend(A__ )
new_lengths.extend([len(A__ ) for l in sub_seqs] )
_SCREAMING_SNAKE_CASE = np.array(A__ )
_SCREAMING_SNAKE_CASE = np.array(A__ )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = len(self )
_SCREAMING_SNAKE_CASE = self.lengths > 11
_SCREAMING_SNAKE_CASE = self.token_ids[indices]
_SCREAMING_SNAKE_CASE = self.lengths[indices]
_SCREAMING_SNAKE_CASE = len(self )
logger.info(F"Remove {init_size - new_size} too short (<=11 tokens) sequences." )
def UpperCamelCase ( self ) -> int:
if "unk_token" not in self.params.special_tok_ids:
return
else:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""unk_token"""]
_SCREAMING_SNAKE_CASE = len(self )
_SCREAMING_SNAKE_CASE = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
_SCREAMING_SNAKE_CASE = (unk_occs / self.lengths) < 0.5
_SCREAMING_SNAKE_CASE = self.token_ids[indices]
_SCREAMING_SNAKE_CASE = self.lengths[indices]
_SCREAMING_SNAKE_CASE = len(self )
logger.info(F"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)." )
def UpperCamelCase ( self ) -> Optional[Any]:
if not self.params.is_master:
return
logger.info(F"{len(self )} sequences" )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def UpperCamelCase ( self , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = [t[0] for t in batch]
_SCREAMING_SNAKE_CASE = [t[1] for t in batch]
assert len(A__ ) == len(A__ )
# Max for paddings
_SCREAMING_SNAKE_CASE = max(A__ )
# Pad token ids
if self.params.mlm:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""pad_token"""]
else:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""unk_token"""]
_SCREAMING_SNAKE_CASE = [list(t.astype(A__ ) ) + [pad_idx] * (max_seq_len_ - len(A__ )) for t in token_ids]
assert len(tk_ ) == len(A__ )
assert all(len(A__ ) == max_seq_len_ for t in tk_ )
_SCREAMING_SNAKE_CASE = torch.tensor(tk_ ) # (bs, max_seq_len_)
_SCREAMING_SNAKE_CASE = torch.tensor(A__ ) # (bs)
return tk_t, lg_t
| 0 | 1 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__ ) -> Union[str, Any]:
super().__init__()
self.register_modules(unet=A__ , scheduler=A__ )
@torch.no_grad()
def __call__( self , A__ = 1 , A__ = None , A__ = 50 , A__ = "pil" , A__ = True , **A__ , ) -> Union[ImagePipelineOutput, Tuple]:
_SCREAMING_SNAKE_CASE = torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=A__ , )
_SCREAMING_SNAKE_CASE = image.to(self.device )
# set step values
self.scheduler.set_timesteps(A__ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
_SCREAMING_SNAKE_CASE = self.unet(A__ , A__ ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
_SCREAMING_SNAKE_CASE = self.scheduler.step(A__ , A__ , A__ ).prev_sample
_SCREAMING_SNAKE_CASE = (image / 2 + 0.5).clamp(0 , 1 )
_SCREAMING_SNAKE_CASE = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_SCREAMING_SNAKE_CASE = self.numpy_to_pil(A__ )
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=A__ ), "This is a local test"
| 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : Any = "▁"
UpperCamelCase__ : Any = {"vocab_file": "spiece.model"}
UpperCamelCase__ : int = {
"vocab_file": {
"google/reformer-crime-and-punishment": (
"https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
)
}
}
UpperCamelCase__ : Optional[int] = {
"google/reformer-crime-and-punishment": 524_288,
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self , A__ , A__="</s>" , A__="<unk>" , A__=[] , A__ = None , **A__ , ) -> None:
_SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=A__ , unk_token=A__ , additional_special_tokens=A__ , sp_model_kwargs=self.sp_model_kwargs , **A__ , )
_SCREAMING_SNAKE_CASE = vocab_file
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A__ )
@property
def UpperCamelCase ( self ) -> Any:
return self.sp_model.get_piece_size()
def UpperCamelCase ( self ) -> Dict[str, int]:
_SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(A__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> int:
_SCREAMING_SNAKE_CASE = self.__dict__.copy()
_SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase ( self , A__ ) -> List[str]:
return self.sp_model.encode(A__ , out_type=A__ )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
return self.sp_model.piece_to_id(A__ )
def UpperCamelCase ( self , A__ ) -> List[Any]:
if index < self.sp_model.get_piece_size():
_SCREAMING_SNAKE_CASE = self.sp_model.IdToPiece(A__ )
return token
def UpperCamelCase ( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A__ ) + token
_SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(A__ )
out_string += self.sp_model.decode(A__ )
return out_string.strip()
def UpperCamelCase ( self , A__ , A__ = None ) -> Tuple[str]:
if not os.path.isdir(A__ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
_SCREAMING_SNAKE_CASE = os.path.join(
A__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A__ )
elif not os.path.isfile(self.vocab_file ):
with open(A__ , """wb""" ) as fi:
_SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(A__ )
return (out_vocab_file,)
| 0 | 1 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicative_persistence() does not accept negative values""" )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
while len(SCREAMING_SNAKE_CASE_ ) != 1:
_SCREAMING_SNAKE_CASE = [int(SCREAMING_SNAKE_CASE_ ) for i in num_string]
_SCREAMING_SNAKE_CASE = 1
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) ):
total *= numbers[i]
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
steps += 1
return steps
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("""additive_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""additive_persistence() does not accept negative values""" )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
while len(SCREAMING_SNAKE_CASE_ ) != 1:
_SCREAMING_SNAKE_CASE = [int(SCREAMING_SNAKE_CASE_ ) for i in num_string]
_SCREAMING_SNAKE_CASE = 0
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) ):
total += numbers[i]
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class _a (_lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = MobileBertTokenizer
SCREAMING_SNAKE_CASE = MobileBertTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = filter_non_english
SCREAMING_SNAKE_CASE = 'google/mobilebert-uncased'
def UpperCamelCase ( self ) -> Any:
super().setUp()
_SCREAMING_SNAKE_CASE = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
_SCREAMING_SNAKE_CASE = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def UpperCamelCase ( self , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = """unwanted, running"""
return input_text, output_text
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.tokenizer_class(self.vocab_file )
_SCREAMING_SNAKE_CASE = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(A__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , [9, 6, 7, 12, 10, 11] )
def UpperCamelCase ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
# With lower casing
_SCREAMING_SNAKE_CASE = self.get_tokenizer(do_lower_case=A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer(do_lower_case=A__ )
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
_SCREAMING_SNAKE_CASE = {}
for i, token in enumerate(A__ ):
_SCREAMING_SNAKE_CASE = i
_SCREAMING_SNAKE_CASE = WordpieceTokenizer(vocab=A__ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
def UpperCamelCase ( self ) -> str:
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def UpperCamelCase ( self ) -> Union[str, Any]:
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def UpperCamelCase ( self ) -> Dict:
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
self.assertListEqual(
[rust_tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
@slow
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained("""google/mobilebert-uncased""" )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""sequence builders""" , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""multi-sequence build""" , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A__ , A__ )
assert encoded_sentence == [1_01] + text + [1_02]
assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
def UpperCamelCase ( self ) -> List[str]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = F"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
_SCREAMING_SNAKE_CASE = tokenizer_r.encode_plus(
A__ , return_attention_mask=A__ , return_token_type_ids=A__ , return_offsets_mapping=A__ , add_special_tokens=A__ , )
_SCREAMING_SNAKE_CASE = tokenizer_r.do_lower_case if hasattr(A__ , """do_lower_case""" ) else False
_SCREAMING_SNAKE_CASE = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), """A"""),
((1, 2), ""","""),
((3, 5), """na"""),
((5, 6), """##ï"""),
((6, 8), """##ve"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """Allen"""),
((21, 23), """##NL"""),
((23, 24), """##P"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), """a"""),
((1, 2), ""","""),
((3, 8), """naive"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """allen"""),
((21, 23), """##nl"""),
((23, 24), """##p"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = ["""的""", """人""", """有"""]
_SCREAMING_SNAKE_CASE = """""".join(A__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(A__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(A__ , A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(A__ )
# it is expected that only the first Chinese character is not preceded by "##".
_SCREAMING_SNAKE_CASE = [
F"##{token}" if idx != 0 else token for idx, token in enumerate(A__ )
]
self.assertListEqual(A__ , A__ )
self.assertListEqual(A__ , A__ )
| 0 | 1 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCamelCase__ : int = "\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven Basart\n and Eric Tang\n and Dawn Song\n and Jacob Steinhardt},\n journal={arXiv preprint arXiv:2103.03874},\n year={2021}\n}\n"
UpperCamelCase__ : Optional[Any] = "\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting \"1/2\" to \"\\frac{1}{2}\") and then computes accuracy.\n"
UpperCamelCase__ : int = R"\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n predictions: list of predictions to score. Each prediction\n is a string that contains natural language and LaTex.\n references: list of reference for each prediction. Each\n reference is a string that contains natural language\n and LaTex.\nReturns:\n accuracy: accuracy after canonicalizing inputs\n (e.g., converting \"1/2\" to \"\\frac{1}{2}\")\n\nExamples:\n >>> metric = datasets.load_metric(\"competition_math\")\n >>> results = metric.compute(references=[\"\\frac{1}{2}\"], predictions=[\"1/2\"])\n >>> print(results)\n {'accuracy': 1.0}\n"
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a (datasets.Metric):
"""simple docstring"""
def UpperCamelCase ( self ) -> List[str]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" ),
"""references""": datasets.Value("""string""" ),
} ) , homepage="""https://github.com/hendrycks/math""" , codebase_urls=["""https://github.com/hendrycks/math"""] , )
def UpperCamelCase ( self , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = 0.0
for i, j in zip(A__ , A__ ):
n_correct += 1.0 if math_equivalence.is_equiv(A__ , A__ ) else 0.0
_SCREAMING_SNAKE_CASE = n_correct / len(A__ )
return {
"accuracy": accuracy,
}
| 0 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
UpperCamelCase__ : Tuple = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , *A__ , A__=None , A__=None , A__=None , **A__ ) -> Optional[int]:
super().__init__(*A__ , **A__ )
_SCREAMING_SNAKE_CASE = eval_examples
_SCREAMING_SNAKE_CASE = post_process_function
_SCREAMING_SNAKE_CASE = quant_trainer_args
_SCREAMING_SNAKE_CASE = 1_28 # default number of calibration samples
def UpperCamelCase ( self , A__=None ) -> Union[str, Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError("""Trainer: calibration requires an calib_dataset.""" )
_SCREAMING_SNAKE_CASE = calib_dataset if calib_dataset is not None else self.calib_dataset
_SCREAMING_SNAKE_CASE = self._remove_unused_columns(A__ , description="""Calibration""" )
return DataLoader(
A__ , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=A__ , )
def UpperCamelCase ( self , A__=None ) -> str:
_SCREAMING_SNAKE_CASE = self.train_dataset if calib_dataset is None else calib_dataset
_SCREAMING_SNAKE_CASE = self.get_calib_dataloader(A__ )
_SCREAMING_SNAKE_CASE = self.model
quant_trainer.configure_model(A__ , self.quant_trainer_args , calib=A__ )
model.eval()
quant_trainer.enable_calibration(A__ )
logger.info("""***** Running calibration *****""" )
logger.info(F" Num examples = {self.calib_num}" )
logger.info(F" Batch size = {calib_dataloader.batch_size}" )
for step, inputs in enumerate(A__ ):
# Prediction step
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.prediction_step(A__ , A__ , prediction_loss_only=A__ )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(A__ , self.quant_trainer_args )
_SCREAMING_SNAKE_CASE = model
def UpperCamelCase ( self , A__=None , A__=None , A__=None , A__ = "eval" ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.eval_dataset if eval_dataset is None else eval_dataset
_SCREAMING_SNAKE_CASE = self.get_eval_dataloader(A__ )
_SCREAMING_SNAKE_CASE = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_SCREAMING_SNAKE_CASE = self.compute_metrics
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_SCREAMING_SNAKE_CASE = self.post_process_function(A__ , A__ , output.predictions )
_SCREAMING_SNAKE_CASE = self.compute_metrics(A__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
_SCREAMING_SNAKE_CASE = metrics.pop(A__ )
self.log(A__ )
else:
_SCREAMING_SNAKE_CASE = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_SCREAMING_SNAKE_CASE = self.callback_handler.on_evaluate(self.args , self.state , self.control , A__ )
return metrics
def UpperCamelCase ( self , A__ , A__ , A__=None , A__ = "test" ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.get_test_dataloader(A__ )
# Temporarily disable metric computation, we will do it in the loop here.
_SCREAMING_SNAKE_CASE = self.compute_metrics
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_SCREAMING_SNAKE_CASE = self.post_process_function(A__ , A__ , output.predictions , """predict""" )
_SCREAMING_SNAKE_CASE = self.compute_metrics(A__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
_SCREAMING_SNAKE_CASE = metrics.pop(A__ )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=A__ )
def UpperCamelCase ( self , A__="./" ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.eval_dataset
_SCREAMING_SNAKE_CASE = self.get_eval_dataloader(A__ )
_SCREAMING_SNAKE_CASE = next(iter(A__ ) )
# saving device - to make it consistent
_SCREAMING_SNAKE_CASE = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
# convert to tuple
_SCREAMING_SNAKE_CASE = tuple(v.to(A__ ) for k, v in batch.items() )
logger.info("""Converting model to be onnx compatible""" )
from pytorch_quantization.nn import TensorQuantizer
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = self.model.to(A__ )
model.eval()
model.float()
_SCREAMING_SNAKE_CASE = model.module if hasattr(A__ , """module""" ) else model
quant_trainer.configure_model(A__ , self.quant_trainer_args )
_SCREAMING_SNAKE_CASE = os.path.join(A__ , """model.onnx""" )
logger.info(F"exporting model to {output_model_file}" )
_SCREAMING_SNAKE_CASE = {0: """batch_size""", 1: """seq_len"""}
torch.onnx.export(
A__ , A__ , A__ , export_params=A__ , opset_version=13 , do_constant_folding=A__ , input_names=["""input_ids""", """attention_mask""", """token_type_ids"""] , output_names=["""output_start_logits""", """output_end_logits"""] , dynamic_axes={
"""input_ids""": axes,
"""attention_mask""": axes,
"""token_type_ids""": axes,
"""output_start_logits""": axes,
"""output_end_logits""": axes,
} , verbose=A__ , )
logger.info("""onnx export finished""" )
| 0 | 1 |
'''simple docstring'''
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = args.log_outputs
_SCREAMING_SNAKE_CASE = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
_SCREAMING_SNAKE_CASE = load_metric("""wer""" )
_SCREAMING_SNAKE_CASE = load_metric("""cer""" )
# compute metrics
_SCREAMING_SNAKE_CASE = wer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
_SCREAMING_SNAKE_CASE = cer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
# print & log results
_SCREAMING_SNAKE_CASE = F"WER: {wer_result}\nCER: {cer_result}"
print(SCREAMING_SNAKE_CASE_ )
with open(F"{dataset_id}_eval_results.txt" , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
_SCREAMING_SNAKE_CASE = F"log_{dataset_id}_predictions.txt"
_SCREAMING_SNAKE_CASE = F"log_{dataset_id}_targets.txt"
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as p, open(SCREAMING_SNAKE_CASE_ , """w""" ) as t:
# mapping function to write output
def write_to_file(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
p.write(F"{i}" + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(F"{i}" + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(SCREAMING_SNAKE_CASE_ , with_indices=SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
_SCREAMING_SNAKE_CASE = re.sub(SCREAMING_SNAKE_CASE_ , """""" , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
_SCREAMING_SNAKE_CASE = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
_SCREAMING_SNAKE_CASE = """ """.join(text.split(SCREAMING_SNAKE_CASE_ ) )
return text
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
# load dataset
_SCREAMING_SNAKE_CASE = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=SCREAMING_SNAKE_CASE_ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
_SCREAMING_SNAKE_CASE = AutoFeatureExtractor.from_pretrained(args.model_id )
_SCREAMING_SNAKE_CASE = feature_extractor.sampling_rate
# resample audio
_SCREAMING_SNAKE_CASE = dataset.cast_column("""audio""" , Audio(sampling_rate=SCREAMING_SNAKE_CASE_ ) )
# load eval pipeline
if args.device is None:
_SCREAMING_SNAKE_CASE = 0 if torch.cuda.is_available() else -1
_SCREAMING_SNAKE_CASE = pipeline("""automatic-speech-recognition""" , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = asr(
batch["""audio"""]["""array"""] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
_SCREAMING_SNAKE_CASE = prediction["""text"""]
_SCREAMING_SNAKE_CASE = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
_SCREAMING_SNAKE_CASE = dataset.map(SCREAMING_SNAKE_CASE_ , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
UpperCamelCase__ : str = argparse.ArgumentParser()
parser.add_argument(
"--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
)
parser.add_argument(
"--dataset",
type=str,
required=True,
help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
)
parser.add_argument(
"--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'` for Common Voice"
)
parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
parser.add_argument(
"--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
)
parser.add_argument(
"--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
)
parser.add_argument(
"--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
)
parser.add_argument(
"--device",
type=int,
default=None,
help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
)
UpperCamelCase__ : List[str] = parser.parse_args()
main(args)
| 0 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
return "".join([hex(SCREAMING_SNAKE_CASE_ )[2:].zfill(2 ).upper() for byte in list(SCREAMING_SNAKE_CASE_ )] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bytes:
"""simple docstring"""
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(SCREAMING_SNAKE_CASE_ ) % 2) != 0:
raise ValueError(
"""Base16 encoded data is invalid:
Data does not have an even number of hex digits.""" )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(SCREAMING_SNAKE_CASE_ ) <= set("""0123456789ABCDEF""" ):
raise ValueError(
"""Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.""" )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : Optional[Any] = {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
"google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
"google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'big_bird'
def __init__( self , A__=5_03_58 , A__=7_68 , A__=12 , A__=12 , A__=30_72 , A__="gelu_new" , A__=0.1 , A__=0.1 , A__=40_96 , A__=2 , A__=0.02 , A__=1E-12 , A__=True , A__=0 , A__=1 , A__=2 , A__=66 , A__="block_sparse" , A__=True , A__=False , A__=64 , A__=3 , A__=None , **A__ , ) -> Dict:
super().__init__(
pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ , sep_token_id=A__ , **A__ , )
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = type_vocab_size
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = use_cache
_SCREAMING_SNAKE_CASE = rescale_embeddings
_SCREAMING_SNAKE_CASE = attention_type
_SCREAMING_SNAKE_CASE = use_bias
_SCREAMING_SNAKE_CASE = block_size
_SCREAMING_SNAKE_CASE = num_random_blocks
_SCREAMING_SNAKE_CASE = classifier_dropout
class _a (_lowerCamelCase):
"""simple docstring"""
@property
def UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_SCREAMING_SNAKE_CASE = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_SCREAMING_SNAKE_CASE = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 0 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
requests.request("""GET""" , """https://huggingface.co""" )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request("""GET""" , """https://huggingface.co""" , timeout=1.0 )
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request("""GET""" , """https://huggingface.co""" )
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_head("""https://huggingface.co""" )
| 0 | 1 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
UpperCamelCase__ : str = "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n"
UpperCamelCase__ : str = "\nArgs:\n predictions (`list` of `int`): Predicted class labels, as returned by a model.\n references (`list` of `int`): Ground truth labels.\n return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n Example 1-A simple example using only predictions and references.\n >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n >>> print(round(results['pearsonr'], 2))\n -0.74\n\n Example 2-The same as Example 1, but that also returns the `p-value`.\n >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n >>> print(sorted(list(results.keys())))\n ['p-value', 'pearsonr']\n >>> print(round(results['pearsonr'], 2))\n -0.74\n >>> print(round(results['p-value'], 2))\n 0.15\n"
UpperCamelCase__ : Dict = "\n@article{2020SciPy-NMeth,\nauthor = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Antonio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\njournal = {Nature Methods},\nyear = {2020},\nvolume = {17},\npages = {261--272},\nadsurl = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a (datasets.Metric):
"""simple docstring"""
def UpperCamelCase ( self ) -> Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""float""" ),
"""references""": datasets.Value("""float""" ),
} ) , reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""] , )
def UpperCamelCase ( self , A__ , A__ , A__=False ) -> Tuple:
if return_pvalue:
_SCREAMING_SNAKE_CASE = pearsonr(A__ , A__ )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(A__ , A__ )[0] )}
| 0 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase_ ( ) -> Iterator[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
while True:
if is_prime(SCREAMING_SNAKE_CASE_ ):
yield num
num += 1
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = 2_00_00_00 ) -> int:
"""simple docstring"""
return sum(takewhile(lambda SCREAMING_SNAKE_CASE_ : x < n , prime_generator() ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 0 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
UpperCamelCase__ : List[Any] = None
UpperCamelCase__ : List[str] = logging.get_logger(__name__)
UpperCamelCase__ : Optional[Any] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
UpperCamelCase__ : Tuple = {
"vocab_file": {
"facebook/mbart-large-en-ro": (
"https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
),
"facebook/mbart-large-cc25": (
"https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
"facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
},
}
UpperCamelCase__ : Optional[int] = {
"facebook/mbart-large-en-ro": 1_024,
"facebook/mbart-large-cc25": 1_024,
}
# fmt: off
UpperCamelCase__ : Optional[int] = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
SCREAMING_SNAKE_CASE = MBartTokenizer
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
def __init__( self , A__=None , A__=None , A__="<s>" , A__="</s>" , A__="</s>" , A__="<s>" , A__="<unk>" , A__="<pad>" , A__="<mask>" , A__=None , A__=None , A__=None , **A__ , ) -> str:
# Mask token behave like a normal word, i.e. include the space before it
_SCREAMING_SNAKE_CASE = AddedToken(A__ , lstrip=A__ , rstrip=A__ ) if isinstance(A__ , A__ ) else mask_token
super().__init__(
vocab_file=A__ , tokenizer_file=A__ , bos_token=A__ , eos_token=A__ , sep_token=A__ , cls_token=A__ , unk_token=A__ , pad_token=A__ , mask_token=A__ , src_lang=A__ , tgt_lang=A__ , additional_special_tokens=A__ , **A__ , )
_SCREAMING_SNAKE_CASE = vocab_file
_SCREAMING_SNAKE_CASE = False if not self.vocab_file else True
_SCREAMING_SNAKE_CASE = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens} )
_SCREAMING_SNAKE_CASE = {
lang_code: self.convert_tokens_to_ids(A__ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
_SCREAMING_SNAKE_CASE = src_lang if src_lang is not None else """en_XX"""
_SCREAMING_SNAKE_CASE = self.convert_tokens_to_ids(self._src_lang )
_SCREAMING_SNAKE_CASE = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def UpperCamelCase ( self ) -> str:
return self._src_lang
@src_lang.setter
def UpperCamelCase ( self , A__ ) -> None:
_SCREAMING_SNAKE_CASE = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def UpperCamelCase ( self , A__ , A__ = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def UpperCamelCase ( self , A__ , A__ = None ) -> List[int]:
_SCREAMING_SNAKE_CASE = [self.sep_token_id]
_SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , **A__ ) -> Tuple:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
_SCREAMING_SNAKE_CASE = src_lang
_SCREAMING_SNAKE_CASE = self(A__ , add_special_tokens=A__ , return_tensors=A__ , **A__ )
_SCREAMING_SNAKE_CASE = self.convert_tokens_to_ids(A__ )
_SCREAMING_SNAKE_CASE = tgt_lang_id
return inputs
def UpperCamelCase ( self , A__ , A__ = "en_XX" , A__ = None , A__ = "ro_RO" , **A__ , ) -> BatchEncoding:
_SCREAMING_SNAKE_CASE = src_lang
_SCREAMING_SNAKE_CASE = tgt_lang
return super().prepare_seqaseq_batch(A__ , A__ , **A__ )
def UpperCamelCase ( self ) -> Any:
return self.set_src_lang_special_tokens(self.src_lang )
def UpperCamelCase ( self ) -> Tuple:
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def UpperCamelCase ( self , A__ ) -> None:
_SCREAMING_SNAKE_CASE = self.convert_tokens_to_ids(A__ )
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = [self.eos_token_id, self.cur_lang_code]
_SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(self.prefix_tokens )
_SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(self.suffix_tokens )
_SCREAMING_SNAKE_CASE = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def UpperCamelCase ( self , A__ ) -> None:
_SCREAMING_SNAKE_CASE = self.convert_tokens_to_ids(A__ )
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = [self.eos_token_id, self.cur_lang_code]
_SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(self.prefix_tokens )
_SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(self.suffix_tokens )
_SCREAMING_SNAKE_CASE = processors.TemplateProcessing(
single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str , pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def UpperCamelCase ( self , A__ , A__ = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(A__ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory." )
return
_SCREAMING_SNAKE_CASE = os.path.join(
A__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A__ ):
copyfile(self.vocab_file , A__ )
return (out_vocab_file,)
| 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = {
"""task_specific_params""": {
"""summarization""": {"""length_penalty""": 1.0, """max_length""": 1_28, """min_length""": 12, """num_beams""": 4},
"""summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_42, """min_length""": 56, """num_beams""": 4},
"""summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
}
}
_SCREAMING_SNAKE_CASE = {
"""task_specific_params.summarization.length_penalty""": 1.0,
"""task_specific_params.summarization.max_length""": 1_28,
"""task_specific_params.summarization.min_length""": 12,
"""task_specific_params.summarization.num_beams""": 4,
"""task_specific_params.summarization_cnn.length_penalty""": 2.0,
"""task_specific_params.summarization_cnn.max_length""": 1_42,
"""task_specific_params.summarization_cnn.min_length""": 56,
"""task_specific_params.summarization_cnn.num_beams""": 4,
"""task_specific_params.summarization_xsum.length_penalty""": 1.0,
"""task_specific_params.summarization_xsum.max_length""": 62,
"""task_specific_params.summarization_xsum.min_length""": 11,
"""task_specific_params.summarization_xsum.num_beams""": 6,
}
self.assertEqual(flatten_dict(A__ ) , A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(A__ ) , x.transpose() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , transpose(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , transpose(A__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , transpose(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , transpose(A__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , np.asarray(transpose(A__ ) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , np.asarray(transpose(A__ , axes=(1, 2, 0) ) ) ) )
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , np.reshape(A__ , (4, 3) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , np.reshape(A__ , (12, 5) ) ) )
@require_torch
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , reshape(A__ , (4, 3) ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , reshape(A__ , (12, 5) ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , reshape(A__ , (4, 3) ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , reshape(A__ , (12, 5) ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , np.asarray(reshape(A__ , (4, 3) ) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , np.asarray(reshape(A__ , (12, 5) ) ) ) )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(A__ ) , np.squeeze(A__ ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , np.squeeze(A__ , axis=2 ) ) )
@require_torch
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , squeeze(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , squeeze(A__ , axis=2 ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , squeeze(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , squeeze(A__ , axis=2 ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , np.asarray(squeeze(A__ ) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , np.asarray(squeeze(A__ , axis=2 ) ) ) )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , np.expand_dims(A__ , axis=1 ) ) )
@require_torch
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , expand_dims(A__ , axis=1 ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , expand_dims(A__ , axis=1 ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , np.asarray(expand_dims(A__ , axis=1 ) ) ) )
| 0 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__ : List[str] = {
"Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'dpt'
def __init__( self , A__=7_68 , A__=12 , A__=12 , A__=30_72 , A__="gelu" , A__=0.0 , A__=0.0 , A__=0.02 , A__=1E-12 , A__=3_84 , A__=16 , A__=3 , A__=False , A__=True , A__=[2, 5, 8, 11] , A__="project" , A__=[4, 2, 1, 0.5] , A__=[96, 1_92, 3_84, 7_68] , A__=2_56 , A__=-1 , A__=False , A__=True , A__=0.4 , A__=2_55 , A__=0.1 , A__=[1, 10_24, 24, 24] , A__=[0, 1] , A__=None , **A__ , ) -> str:
super().__init__(**A__ )
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info("""Initializing the config with a `BiT` backbone.""" )
_SCREAMING_SNAKE_CASE = {
"""global_padding""": """same""",
"""layer_type""": """bottleneck""",
"""depths""": [3, 4, 9],
"""out_features""": ["""stage1""", """stage2""", """stage3"""],
"""embedding_dynamic_padding""": True,
}
_SCREAMING_SNAKE_CASE = BitConfig(**A__ )
elif isinstance(A__ , A__ ):
logger.info("""Initializing the config with a `BiT` backbone.""" )
_SCREAMING_SNAKE_CASE = BitConfig(**A__ )
elif isinstance(A__ , A__ ):
_SCREAMING_SNAKE_CASE = backbone_config
else:
raise ValueError(
F"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}." )
_SCREAMING_SNAKE_CASE = backbone_featmap_shape
_SCREAMING_SNAKE_CASE = neck_ignore_stages
if readout_type != "project":
raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode.""" )
else:
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = patch_size
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = qkv_bias
_SCREAMING_SNAKE_CASE = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']""" )
_SCREAMING_SNAKE_CASE = readout_type
_SCREAMING_SNAKE_CASE = reassemble_factors
_SCREAMING_SNAKE_CASE = neck_hidden_sizes
_SCREAMING_SNAKE_CASE = fusion_hidden_size
_SCREAMING_SNAKE_CASE = head_in_index
_SCREAMING_SNAKE_CASE = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
_SCREAMING_SNAKE_CASE = use_auxiliary_head
_SCREAMING_SNAKE_CASE = auxiliary_loss_weight
_SCREAMING_SNAKE_CASE = semantic_loss_ignore_index
_SCREAMING_SNAKE_CASE = semantic_classifier_dropout
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
_SCREAMING_SNAKE_CASE = self.backbone_config.to_dict()
_SCREAMING_SNAKE_CASE = self.__class__.model_type
return output
| 0 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ''
SCREAMING_SNAKE_CASE = 'hf-legacy' # "hf://"" is reserved for hffs
def __init__( self , A__ = None , A__ = None , **A__ , ) -> Optional[int]:
super().__init__(self , **A__ )
_SCREAMING_SNAKE_CASE = repo_info
_SCREAMING_SNAKE_CASE = token
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Tuple:
if self.dir_cache is None:
_SCREAMING_SNAKE_CASE = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
_SCREAMING_SNAKE_CASE = {
"""name""": hf_file.rfilename,
"""size""": None,
"""type""": """file""",
}
self.dir_cache.update(
{
str(A__ ): {"""name""": str(A__ ), """size""": None, """type""": """directory"""}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def UpperCamelCase ( self , A__ , A__ = "rb" , **A__ , ) -> Optional[int]:
if not isinstance(self.repo_info , A__ ):
raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}" )
_SCREAMING_SNAKE_CASE = hf_hub_url(self.repo_info.id , A__ , revision=self.repo_info.sha )
return fsspec.open(
A__ , mode=A__ , headers=get_authentication_headers_for_url(A__ , use_auth_token=self.token ) , client_kwargs={"""trust_env""": True} , ).open()
def UpperCamelCase ( self , A__ , **A__ ) -> str:
self._get_dirs()
_SCREAMING_SNAKE_CASE = self._strip_protocol(A__ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(A__ )
def UpperCamelCase ( self , A__ , A__=False , **A__ ) -> List[Any]:
self._get_dirs()
_SCREAMING_SNAKE_CASE = PurePosixPath(path.strip("""/""" ) )
_SCREAMING_SNAKE_CASE = {}
for p, f in self.dir_cache.items():
_SCREAMING_SNAKE_CASE = PurePosixPath(p.strip("""/""" ) )
_SCREAMING_SNAKE_CASE = p.parent
if root == path:
_SCREAMING_SNAKE_CASE = f
_SCREAMING_SNAKE_CASE = list(paths.values() )
if detail:
return out
else:
return sorted(f["""name"""] for f in out )
| 0 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
UpperCamelCase__ : str = logging.getLogger(__name__)
@dataclass
class _a :
"""simple docstring"""
SCREAMING_SNAKE_CASE = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
SCREAMING_SNAKE_CASE = field(
default=_lowerCamelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'})
SCREAMING_SNAKE_CASE = field(
default=_lowerCamelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'})
SCREAMING_SNAKE_CASE = field(
default=_lowerCamelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
SCREAMING_SNAKE_CASE = field(default=_lowerCamelCase , metadata={'help': 'Whether tp freeze the encoder.'})
SCREAMING_SNAKE_CASE = field(default=_lowerCamelCase , metadata={'help': 'Whether to freeze the embeddings.'})
@dataclass
class _a :
"""simple docstring"""
SCREAMING_SNAKE_CASE = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
SCREAMING_SNAKE_CASE = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
SCREAMING_SNAKE_CASE = field(
default=10_24 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=1_28 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=1_42 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=1_42 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
SCREAMING_SNAKE_CASE = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'})
SCREAMING_SNAKE_CASE = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'})
SCREAMING_SNAKE_CASE = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'})
SCREAMING_SNAKE_CASE = field(default=_lowerCamelCase , metadata={'help': 'Source language id for translation.'})
SCREAMING_SNAKE_CASE = field(default=_lowerCamelCase , metadata={'help': 'Target language id for translation.'})
SCREAMING_SNAKE_CASE = field(default=_lowerCamelCase , metadata={'help': '# num_beams to use for evaluation.'})
SCREAMING_SNAKE_CASE = field(
default=_lowerCamelCase , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
logger.info(F"***** {split} metrics *****" )
for key in sorted(metrics.keys() ):
logger.info(F" {key} = {metrics[key]}" )
save_json(SCREAMING_SNAKE_CASE_ , os.path.join(SCREAMING_SNAKE_CASE_ , F"{split}_results.json" ) )
def lowerCAmelCase_ ( ) -> Optional[int]:
"""simple docstring"""
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_SCREAMING_SNAKE_CASE = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = parser.parse_args_into_dataclasses()
check_output_dir(SCREAMING_SNAKE_CASE_ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("""Training/evaluation parameters %s""" , SCREAMING_SNAKE_CASE_ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_SCREAMING_SNAKE_CASE = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
assert hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ), F"({config.__class__.__name__}) doesn't have a `{p}` attribute"
setattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_SCREAMING_SNAKE_CASE = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=SCREAMING_SNAKE_CASE_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(SCREAMING_SNAKE_CASE_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_SCREAMING_SNAKE_CASE = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(SCREAMING_SNAKE_CASE_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(SCREAMING_SNAKE_CASE_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_SCREAMING_SNAKE_CASE = SeqaSeqDataset
# Get datasets
_SCREAMING_SNAKE_CASE = (
dataset_class(
SCREAMING_SNAKE_CASE_ , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_train
else None
)
_SCREAMING_SNAKE_CASE = (
dataset_class(
SCREAMING_SNAKE_CASE_ , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_SCREAMING_SNAKE_CASE = (
dataset_class(
SCREAMING_SNAKE_CASE_ , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_SCREAMING_SNAKE_CASE = (
build_compute_metrics_fn(data_args.task , SCREAMING_SNAKE_CASE_ ) if training_args.predict_with_generate else None
)
_SCREAMING_SNAKE_CASE = SeqaSeqTrainer(
model=SCREAMING_SNAKE_CASE_ , args=SCREAMING_SNAKE_CASE_ , data_args=SCREAMING_SNAKE_CASE_ , train_dataset=SCREAMING_SNAKE_CASE_ , eval_dataset=SCREAMING_SNAKE_CASE_ , data_collator=SeqaSeqDataCollator(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ , )
_SCREAMING_SNAKE_CASE = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
_SCREAMING_SNAKE_CASE = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_SCREAMING_SNAKE_CASE = train_result.metrics
_SCREAMING_SNAKE_CASE = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" , SCREAMING_SNAKE_CASE_ , training_args.output_dir )
all_metrics.update(SCREAMING_SNAKE_CASE_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_SCREAMING_SNAKE_CASE = trainer.evaluate(metric_key_prefix="""val""" )
_SCREAMING_SNAKE_CASE = data_args.n_val
_SCREAMING_SNAKE_CASE = round(metrics["""val_loss"""] , 4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" , SCREAMING_SNAKE_CASE_ , training_args.output_dir )
all_metrics.update(SCREAMING_SNAKE_CASE_ )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
_SCREAMING_SNAKE_CASE = trainer.predict(test_dataset=SCREAMING_SNAKE_CASE_ , metric_key_prefix="""test""" )
_SCREAMING_SNAKE_CASE = test_output.metrics
_SCREAMING_SNAKE_CASE = data_args.n_test
if trainer.is_world_process_zero():
_SCREAMING_SNAKE_CASE = round(metrics["""test_loss"""] , 4 )
handle_metrics("""test""" , SCREAMING_SNAKE_CASE_ , training_args.output_dir )
all_metrics.update(SCREAMING_SNAKE_CASE_ )
if training_args.predict_with_generate:
_SCREAMING_SNAKE_CASE = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = lmap(str.strip , SCREAMING_SNAKE_CASE_ )
write_txt_file(SCREAMING_SNAKE_CASE_ , os.path.join(training_args.output_dir , """test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(SCREAMING_SNAKE_CASE_ , os.path.join(training_args.output_dir , """all_results.json""" ) )
return all_metrics
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 0 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , keep_in_memory=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = features.copy() if features else default_expected_features
_SCREAMING_SNAKE_CASE = (
Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , split=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type""" , [str, list] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
if issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = parquet_path
elif issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = [parquet_path]
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=("train",) ) -> List[str]:
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for split in splits:
_SCREAMING_SNAKE_CASE = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(
{"""train""": parquet_path} , cache_dir=SCREAMING_SNAKE_CASE_ , keep_in_memory=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = features.copy() if features else default_expected_features
_SCREAMING_SNAKE_CASE = (
Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_SCREAMING_SNAKE_CASE = ParquetDatasetReader({"""train""": parquet_path} , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
if split:
_SCREAMING_SNAKE_CASE = {split: parquet_path}
else:
_SCREAMING_SNAKE_CASE = """train"""
_SCREAMING_SNAKE_CASE = {"""train""": parquet_path, """test""": parquet_path}
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ParquetDatasetWriter(SCREAMING_SNAKE_CASE_ , tmp_path / """foo.parquet""" )
assert writer.write() > 0
_SCREAMING_SNAKE_CASE = pq.ParquetFile(tmp_path / """foo.parquet""" )
_SCREAMING_SNAKE_CASE = pf.read()
assert dataset.data.table == output_table
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(shared_datadir / """test_image_rgb.jpg""" )
_SCREAMING_SNAKE_CASE = {"""image""": [image_path]}
_SCREAMING_SNAKE_CASE = Features({"""image""": Image()} )
_SCREAMING_SNAKE_CASE = Dataset.from_dict(SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = ParquetDatasetWriter(SCREAMING_SNAKE_CASE_ , tmp_path / """foo.parquet""" )
assert writer.write() > 0
_SCREAMING_SNAKE_CASE = Dataset.from_parquet(str(tmp_path / """foo.parquet""" ) )
assert dataset.features == reloaded_dataset.features
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(str(tmp_path / """foo.parquet""" ) , streaming=SCREAMING_SNAKE_CASE_ ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"""feature, expected""" , [
(Features({"""foo""": Value("""int32""" )} ), None),
(Features({"""image""": Image(), """foo""": Value("""int32""" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"""nested""": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
assert get_writer_batch_size(SCREAMING_SNAKE_CASE_ ) == expected
| 0 | 1 |
'''simple docstring'''
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=False , A__=True , A__=99 , A__=32 , A__=5 , A__=4 , A__=64 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=16 , A__=2 , A__=0.02 , A__=3 , A__=4 , A__=None , A__=2 , A__=2 , A__=2 , A__=2 , A__=4 , A__=1 , ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = seq_length
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_input_mask
_SCREAMING_SNAKE_CASE = use_token_type_ids
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = type_vocab_size
_SCREAMING_SNAKE_CASE = type_sequence_label_size
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = num_labels
_SCREAMING_SNAKE_CASE = num_choices
_SCREAMING_SNAKE_CASE = scope
_SCREAMING_SNAKE_CASE = q_groups
_SCREAMING_SNAKE_CASE = k_groups
_SCREAMING_SNAKE_CASE = v_groups
_SCREAMING_SNAKE_CASE = post_attention_groups
_SCREAMING_SNAKE_CASE = intermediate_groups
_SCREAMING_SNAKE_CASE = output_groups
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self ) -> Tuple:
return SqueezeBertConfig(
embedding_size=self.hidden_size , vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = SqueezeBertModel(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ , A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = SqueezeBertForMaskedLM(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ , attention_mask=A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ ) -> int:
_SCREAMING_SNAKE_CASE = SqueezeBertForQuestionAnswering(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(
A__ , attention_mask=A__ , start_positions=A__ , end_positions=A__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = SqueezeBertForSequenceClassification(A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ , attention_mask=A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = SqueezeBertForTokenClassification(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ , attention_mask=A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = SqueezeBertForMultipleChoice(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_SCREAMING_SNAKE_CASE = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_SCREAMING_SNAKE_CASE = model(
A__ , attention_mask=A__ , labels=A__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
((_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE)) = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = SqueezeBertModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , dim=37 )
def UpperCamelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*A__ )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*A__ )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*A__ )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*A__ )
@slow
def UpperCamelCase ( self ) -> List[Any]:
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE = SqueezeBertModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
@require_sentencepiece
@require_tokenizers
@require_torch
class _a (unittest.TestCase):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
_SCREAMING_SNAKE_CASE = torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]] )
_SCREAMING_SNAKE_CASE = model(A__ )[0]
_SCREAMING_SNAKE_CASE = torch.Size((1, 3) )
self.assertEqual(output.shape , A__ )
_SCREAMING_SNAKE_CASE = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(A__ , A__ , atol=1E-4 ) )
| 0 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicative_persistence() does not accept negative values""" )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
while len(SCREAMING_SNAKE_CASE_ ) != 1:
_SCREAMING_SNAKE_CASE = [int(SCREAMING_SNAKE_CASE_ ) for i in num_string]
_SCREAMING_SNAKE_CASE = 1
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) ):
total *= numbers[i]
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
steps += 1
return steps
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("""additive_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""additive_persistence() does not accept negative values""" )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
while len(SCREAMING_SNAKE_CASE_ ) != 1:
_SCREAMING_SNAKE_CASE = [int(SCREAMING_SNAKE_CASE_ ) for i in num_string]
_SCREAMING_SNAKE_CASE = 0
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) ):
total += numbers[i]
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 1 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [x.strip() for x in open(SCREAMING_SNAKE_CASE_ ).readlines()]
_SCREAMING_SNAKE_CASE = [x.strip() for x in open(SCREAMING_SNAKE_CASE_ ).readlines()][: len(SCREAMING_SNAKE_CASE_ )]
_SCREAMING_SNAKE_CASE = calculate_rouge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
if save_path is not None:
save_json(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , indent=SCREAMING_SNAKE_CASE_ )
return metrics # these print nicely
if __name__ == "__main__":
fire.Fire(calculate_rouge_path)
| 0 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
UpperCamelCase__ : Tuple = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
from run_translation import main # noqa
set_seed(42)
UpperCamelCase__ : Union[str, Any] = "sshleifer/student_marian_en_ro_6_1"
UpperCamelCase__ : str = "sshleifer/tiny-mbart"
@require_torch
class _a (_lowerCamelCase):
"""simple docstring"""
def UpperCamelCase ( self , A__=False , A__=None , A__=True , A__=True , A__=True , A__=True , ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=A__ , num_train_epochs=1 , distributed=A__ , extra_args_str=A__ , predict_with_generate=A__ , do_train=A__ , do_eval=A__ , do_predict=A__ , )
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(os.path.join(A__ , """trainer_state.json""" ) ).log_history
if not do_eval:
return
_SCREAMING_SNAKE_CASE = [log for log in logs if """eval_loss""" in log.keys()]
_SCREAMING_SNAKE_CASE = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
_SCREAMING_SNAKE_CASE = eval_metrics[-1]
assert isinstance(last_step_stats["""eval_bleu"""] , A__ )
assert not math.isnan(float(last_step_stats["""eval_loss"""] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def UpperCamelCase ( self ) -> Optional[int]:
self.run_seqaseq_quick()
@require_torch_multi_gpu
def UpperCamelCase ( self ) -> Optional[Any]:
self.run_seqaseq_quick(distributed=A__ )
@require_torch_multi_gpu
def UpperCamelCase ( self ) -> Union[str, Any]:
self.run_seqaseq_quick(distributed=A__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> Any:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp simple""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> Tuple:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp simple --fp16""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> str:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp zero_dp_2""" , predict_with_generate=A__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> List[str]:
self.run_seqaseq_quick(
distributed=A__ , extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" , predict_with_generate=A__ )
@require_apex
@require_torch_gpu
def UpperCamelCase ( self ) -> Optional[Any]:
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
@parameterized.expand(["""base""", """low""", """high""", """mixed"""] )
@require_torch_multi_gpu
def UpperCamelCase ( self , A__ ) -> List[Any]:
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
_SCREAMING_SNAKE_CASE = {
# test with the default log_level - should be info and thus log info once
"""base""": {"""extra_args_str""": """""", """n_matches""": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"""low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"""high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"""mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
}
_SCREAMING_SNAKE_CASE = experiments[experiment_id]
_SCREAMING_SNAKE_CASE = {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
_SCREAMING_SNAKE_CASE = """Running training"""
with CaptureStderr() as cl:
self.run_seqaseq_quick(**A__ , extra_args_str=data["""extra_args_str"""] )
_SCREAMING_SNAKE_CASE = len(re.findall(A__ , cl.err ) )
self.assertEqual(A__ , data["""n_matches"""] )
@slow
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.run_trainer(
eval_steps=2 , max_len=1_28 , model_name=A__ , learning_rate=3E-4 , num_train_epochs=10 , distributed=A__ , )
# Check metrics
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(os.path.join(A__ , """trainer_state.json""" ) ).log_history
_SCREAMING_SNAKE_CASE = [log for log in logs if """eval_loss""" in log.keys()]
_SCREAMING_SNAKE_CASE = eval_metrics[0]
_SCREAMING_SNAKE_CASE = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["""eval_bleu"""] , A__ )
# test if do_predict saves generations and metrics
_SCREAMING_SNAKE_CASE = os.listdir(A__ )
_SCREAMING_SNAKE_CASE = {os.path.basename(A__ ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def UpperCamelCase ( self ) -> Dict:
from transformers.training_args import OptimizerNames
def train_and_return_metrics(A__ ) -> Tuple[int, float]:
_SCREAMING_SNAKE_CASE = """--skip_memory_metrics 0"""
_SCREAMING_SNAKE_CASE = self.run_trainer(
max_len=1_28 , model_name=A__ , learning_rate=3E-4 , num_train_epochs=1 , optim=A__ , distributed=A__ , extra_args_str=A__ , do_eval=A__ , do_predict=A__ , n_gpus_to_use=1 , )
# Check metrics
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(Path(A__ , """trainer_state.json""" ) ).log_history
_SCREAMING_SNAKE_CASE = int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20 )
_SCREAMING_SNAKE_CASE = int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20 )
_SCREAMING_SNAKE_CASE = logs[0]["""train_loss"""]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
_SCREAMING_SNAKE_CASE = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
_SCREAMING_SNAKE_CASE = gpu_peak_mem_orig + gpu_alloc_mem_orig
_SCREAMING_SNAKE_CASE = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
_SCREAMING_SNAKE_CASE = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
_SCREAMING_SNAKE_CASE = 1_20
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
A__ , A__ , """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
F" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
F" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB" , )
self.assertGreater(
A__ , A__ , """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
F" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
F" gpu_total_mem_bnb={gpu_total_mem_bnb}MB" , )
self.assertEqual(
A__ , A__ , F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}" )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ = 3E-3 , A__ = "adafactor" , A__ = False , A__ = None , A__ = 0 , A__ = True , A__ = True , A__ = True , A__ = True , A__ = None , ) -> Dict:
_SCREAMING_SNAKE_CASE = self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
_SCREAMING_SNAKE_CASE = self.get_auto_remove_tmp_dir()
_SCREAMING_SNAKE_CASE = F"\n --model_name_or_path {model_name}\n --train_file {data_dir}/train.json\n --validation_file {data_dir}/val.json\n --test_file {data_dir}/test.json\n --output_dir {output_dir}\n --overwrite_output_dir\n --max_train_samples 8\n --max_source_length {max_len}\n --max_target_length {max_len}\n --do_train\n --num_train_epochs {str(A__ )}\n --per_device_train_batch_size 4\n --learning_rate {learning_rate}\n --warmup_steps 8\n --logging_steps 0\n --logging_strategy no\n --save_steps {str(A__ )}\n --group_by_length\n --label_smoothing_factor 0.1\n --target_lang ro_RO\n --source_lang en_XX\n ".split()
_SCREAMING_SNAKE_CASE = F"\n --do_eval\n --per_device_eval_batch_size 4\n --max_eval_samples 8\n --val_max_target_length {max_len}\n --evaluation_strategy steps\n --eval_steps {str(A__ )}\n ".split()
_SCREAMING_SNAKE_CASE = """
--do_predict
""".split()
_SCREAMING_SNAKE_CASE = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F"--optim {optim}".split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
_SCREAMING_SNAKE_CASE = get_gpu_count()
_SCREAMING_SNAKE_CASE = get_torch_dist_unique_port()
_SCREAMING_SNAKE_CASE = F"\n -m torch.distributed.run\n --nproc_per_node={n_gpus_to_use}\n --master_port={master_port}\n {self.examples_dir_str}/pytorch/translation/run_translation.py\n ".split()
_SCREAMING_SNAKE_CASE = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(A__ , env=self.get_env() )
else:
_SCREAMING_SNAKE_CASE = ["""run_translation.py"""] + args
with patch.object(A__ , """argv""" , A__ ):
main()
return output_dir
| 0 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
if "model" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""model.""" , """""" )
if "norm1" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""norm1""" , """attention.output.LayerNorm""" )
if "norm2" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""norm2""" , """output.LayerNorm""" )
if "norm" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""norm""" , """LayerNorm""" )
if "transformer" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.split(""".""" )[0].split("""_""" )[-1]
_SCREAMING_SNAKE_CASE = orig_key.replace(F"transformer_{layer_num}" , F"encoder.layer.{layer_num}" )
if "mha.attn" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""mha.attn""" , """attention.self""" )
if "mha" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""mha""" , """attention""" )
if "W_q" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""W_q""" , """self.query""" )
if "W_k" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""W_k""" , """self.key""" )
if "W_v" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""W_v""" , """self.value""" )
if "ff1" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""ff1""" , """intermediate.dense""" )
if "ff2" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""ff2""" , """output.dense""" )
if "ff" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""ff""" , """output.dense""" )
if "mlm_class" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""mlm.mlm_class""" , """cls.predictions.decoder""" )
if "mlm" in orig_key:
_SCREAMING_SNAKE_CASE = orig_key.replace("""mlm""" , """cls.predictions.transform""" )
if "cls" not in orig_key:
_SCREAMING_SNAKE_CASE = """yoso.""" + orig_key
return orig_key
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_SCREAMING_SNAKE_CASE = orig_state_dict.pop(__A )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
_SCREAMING_SNAKE_CASE = val
_SCREAMING_SNAKE_CASE = orig_state_dict["""cls.predictions.decoder.bias"""]
_SCREAMING_SNAKE_CASE = torch.arange(__A ).expand((1, -1) ) + 2
return orig_state_dict
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = torch.load(__A , map_location="""cpu""" )["""model_state_dict"""]
_SCREAMING_SNAKE_CASE = YosoConfig.from_json_file(__A )
_SCREAMING_SNAKE_CASE = YosoForMaskedLM(__A )
_SCREAMING_SNAKE_CASE = convert_checkpoint_helper(config.max_position_embeddings , __A )
print(model.load_state_dict(__A ) )
model.eval()
model.save_pretrained(__A )
print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}" )
if __name__ == "__main__":
UpperCamelCase__ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The json file for YOSO model config.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
UpperCamelCase__ : List[Any] = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 700 |
'''simple docstring'''
import sys
UpperCamelCase__ : int = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = N ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = -sys.maxsize - 1
for i in range(len(SCREAMING_SNAKE_CASE_ ) - 12 ):
_SCREAMING_SNAKE_CASE = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
_SCREAMING_SNAKE_CASE = product
return largest_product
if __name__ == "__main__":
print(f"""{solution() = }""")
| 0 | 0 |
'''simple docstring'''
import datasets
UpperCamelCase__ : Dict = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holger\n and Stoyanov, Veselin",\n title = "XNLI: Evaluating Cross-lingual Sentence Representations",\n booktitle = "Proceedings of the 2018 Conference on Empirical Methods\n in Natural Language Processing",\n year = "2018",\n publisher = "Association for Computational Linguistics",\n location = "Brussels, Belgium",\n}\n'
UpperCamelCase__ : str = '\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n'
UpperCamelCase__ : List[Any] = '\nComputes XNLI score which is just simple accuracy.\nArgs:\n predictions: Predicted labels.\n references: Ground truth labels.\nReturns:\n \'accuracy\': accuracy\nExamples:\n\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> xnli_metric = datasets.load_metric("xnli")\n >>> results = xnli_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n'
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
return (preds == labels).mean()
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a (datasets.Metric):
"""simple docstring"""
def UpperCamelCase ( self ) -> int:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32""" ),
"""references""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32""" ),
} ) , codebase_urls=[] , reference_urls=[] , format="""numpy""" , )
def UpperCamelCase ( self , A__ , A__ ) -> List[str]:
return {"accuracy": simple_accuracy(UpperCAmelCase_ , UpperCAmelCase_ )}
| 701 |
'''simple docstring'''
UpperCamelCase__ : Dict = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
"q": "ABBBB",
"r": "BAAAA",
"s": "BAAAB",
"t": "BAABA",
"u": "BAABB",
"v": "BBBAB",
"w": "BABAA",
"x": "BABAB",
"y": "BABBA",
"z": "BABBB",
" ": " ",
}
UpperCamelCase__ : str = {value: key for key, value in encode_dict.items()}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """"""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("""encode() accepts only letters of the alphabet and spaces""" )
return encoded
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
if set(SCREAMING_SNAKE_CASE_ ) - {"A", "B", " "} != set():
raise Exception("""decode() accepts only 'A', 'B' and spaces""" )
_SCREAMING_SNAKE_CASE = """"""
for word in coded.split():
while len(SCREAMING_SNAKE_CASE_ ) != 0:
decoded += decode_dict[word[:5]]
_SCREAMING_SNAKE_CASE = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 0 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ : Optional[Any] = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EfficientFormerConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Optional[Any] = ["EfficientFormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : List[str] = [
"EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"EfficientFormerForImageClassification",
"EfficientFormerForImageClassificationWithTeacher",
"EfficientFormerModel",
"EfficientFormerPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : int = [
"TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFEfficientFormerForImageClassification",
"TFEfficientFormerForImageClassificationWithTeacher",
"TFEfficientFormerModel",
"TFEfficientFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientformer import EfficientFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientformer import (
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
EfficientFormerPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
TFEfficientFormerPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 702 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""encoder.embed_positions._float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = emb.weight.shape
_SCREAMING_SNAKE_CASE = nn.Linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , bias=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )
_SCREAMING_SNAKE_CASE = mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
_SCREAMING_SNAKE_CASE = mam_aaa["""model"""]
remove_ignore_keys_(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = state_dict["""encoder.embed_tokens.weight"""].shape[0]
_SCREAMING_SNAKE_CASE = MaMaaaConfig(
vocab_size=SCREAMING_SNAKE_CASE_ , max_position_embeddings=10_24 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="""relu""" , )
_SCREAMING_SNAKE_CASE = state_dict["""decoder.embed_tokens.weight"""]
_SCREAMING_SNAKE_CASE = MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
model.model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
UpperCamelCase__ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCamelCase__ : List[str] = parser.parse_args()
UpperCamelCase__ : Any = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 0 | 0 |
'''simple docstring'''
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
UpperCamelCase__ : Any = re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class _a :
"""simple docstring"""
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = _str_to_version_tuple(self.version_str )
def __repr__( self ) -> int:
return F"{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"
@property
def UpperCamelCase ( self ) -> Union[str, Any]:
return self.major, self.minor, self.patch
def UpperCamelCase ( self , A__ ) -> List[Any]:
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
return Version(UpperCamelCase_ )
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ):
return other
raise TypeError(F"{other} (type {type(UpperCamelCase_ )}) cannot be compared to version." )
def __eq__( self , A__ ) -> Optional[int]:
try:
_SCREAMING_SNAKE_CASE = self._validate_operand(UpperCamelCase_ )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = self._validate_operand(UpperCamelCase_ )
return self.tuple < other.tuple
def __hash__( self ) -> List[str]:
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def UpperCamelCase ( cls , A__ ) -> List[Any]:
_SCREAMING_SNAKE_CASE = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def UpperCamelCase ( self ) -> str:
return self.version_str
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = _VERSION_REG.match(_lowercase )
if not res:
raise ValueError(F"Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits." )
return tuple(int(_lowercase ) for v in [res.group("""major""" ), res.group("""minor""" ), res.group("""patch""" )] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
return ".".join(str(_lowercase ) for v in version_tuple )
| 703 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : str = {
"configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
"tokenization_canine": ["CanineTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : List[Any] = [
"CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
"CanineForMultipleChoice",
"CanineForQuestionAnswering",
"CanineForSequenceClassification",
"CanineForTokenClassification",
"CanineLayer",
"CanineModel",
"CaninePreTrainedModel",
"load_tf_weights_in_canine",
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
UpperCamelCase__ : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 | 0 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self , A__ ) -> Tuple:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["""bs"""] , model_result["""ss"""] ):
_SCREAMING_SNAKE_CASE = model_result["result"][batch_size][sequence_length]
self.assertIsNotNone(__lowerCamelCase )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
_SCREAMING_SNAKE_CASE = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = PyTorchBenchmark(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = "sgugger/tiny-distilbert-classification"
_SCREAMING_SNAKE_CASE = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , only_pretrain_model=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = PyTorchBenchmark(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
_SCREAMING_SNAKE_CASE = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , torchscript=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = PyTorchBenchmark(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
_SCREAMING_SNAKE_CASE = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , fpaa=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = PyTorchBenchmark(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
_SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(__lowerCamelCase )
# set architectures equal to `None`
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = PyTorchBenchmark(__lowerCamelCase , configs=[config] )
_SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
_SCREAMING_SNAKE_CASE = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = PyTorchBenchmark(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == """cpu""" , """Can't do half precision""" )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
_SCREAMING_SNAKE_CASE = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , fpaa=__lowerCamelCase , multi_process=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = PyTorchBenchmark(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
_SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = PyTorchBenchmark(__lowerCamelCase , configs=[config] )
_SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = "sshleifer/tinier_bart"
_SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = PyTorchBenchmark(__lowerCamelCase , configs=[config] )
_SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
_SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = PyTorchBenchmark(__lowerCamelCase , configs=[config] )
_SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = "sshleifer/tinier_bart"
_SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = PyTorchBenchmark(__lowerCamelCase , configs=[config] )
_SCREAMING_SNAKE_CASE = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
with tempfile.TemporaryDirectory() as tmp_dir:
_SCREAMING_SNAKE_CASE = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , save_to_csv=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(__lowerCamelCase , """inf_time.csv""" ) , train_memory_csv_file=os.path.join(__lowerCamelCase , """train_mem.csv""" ) , inference_memory_csv_file=os.path.join(__lowerCamelCase , """inf_mem.csv""" ) , train_time_csv_file=os.path.join(__lowerCamelCase , """train_time.csv""" ) , env_info_csv_file=os.path.join(__lowerCamelCase , """env.csv""" ) , multi_process=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = PyTorchBenchmark(__lowerCamelCase )
benchmark.run()
self.assertTrue(Path(os.path.join(__lowerCamelCase , """inf_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(__lowerCamelCase , """train_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(__lowerCamelCase , """inf_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(__lowerCamelCase , """train_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(__lowerCamelCase , """env.csv""" ) ).exists() )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = "sshleifer/tiny-gpt2"
def _check_summary_is_not_empty(A__ ):
self.assertTrue(hasattr(__lowerCamelCase , """sequential""" ) )
self.assertTrue(hasattr(__lowerCamelCase , """cumulative""" ) )
self.assertTrue(hasattr(__lowerCamelCase , """current""" ) )
self.assertTrue(hasattr(__lowerCamelCase , """total""" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
_SCREAMING_SNAKE_CASE = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=__lowerCamelCase , inference=__lowerCamelCase , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(__lowerCamelCase , """log.txt""" ) , log_print=__lowerCamelCase , trace_memory_line_by_line=__lowerCamelCase , multi_process=__lowerCamelCase , )
_SCREAMING_SNAKE_CASE = PyTorchBenchmark(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(__lowerCamelCase , """log.txt""" ) ).exists() )
| 704 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE = 'ChineseCLIPImageProcessor'
SCREAMING_SNAKE_CASE = ('BertTokenizer', 'BertTokenizerFast')
def __init__( self , A__=None , A__=None , **A__ ) -> int:
_SCREAMING_SNAKE_CASE = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , A__ , )
_SCREAMING_SNAKE_CASE = kwargs.pop("""feature_extractor""" )
_SCREAMING_SNAKE_CASE = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.image_processor
def __call__( self , A__=None , A__=None , A__=None , **A__ ) -> Optional[int]:
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
_SCREAMING_SNAKE_CASE = self.tokenizer(A__ , return_tensors=A__ , **A__ )
if images is not None:
_SCREAMING_SNAKE_CASE = self.image_processor(A__ , return_tensors=A__ , **A__ )
if text is not None and images is not None:
_SCREAMING_SNAKE_CASE = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**A__ ) , tensor_type=A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Dict:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Optional[Any]:
return self.tokenizer.decode(*A__ , **A__ )
@property
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.tokenizer.model_input_names
_SCREAMING_SNAKE_CASE = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCamelCase ( self ) -> Optional[int]:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , A__ , )
return self.image_processor_class
| 0 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class _a (__lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 42
class _a (nn.Module):
"""simple docstring"""
def __init__( self , A__=3 , A__=3 , A__=("DownEncoderBlock2D",) , A__=(64,) , A__=2 , A__=32 , A__="silu" , A__=True , ) -> Any:
super().__init__()
_SCREAMING_SNAKE_CASE = layers_per_block
_SCREAMING_SNAKE_CASE = torch.nn.Convad(
UpperCAmelCase_ , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = nn.ModuleList([] )
# down
_SCREAMING_SNAKE_CASE = block_out_channels[0]
for i, down_block_type in enumerate(UpperCAmelCase_ ):
_SCREAMING_SNAKE_CASE = output_channel
_SCREAMING_SNAKE_CASE = block_out_channels[i]
_SCREAMING_SNAKE_CASE = i == len(UpperCAmelCase_ ) - 1
_SCREAMING_SNAKE_CASE = get_down_block(
UpperCAmelCase_ , num_layers=self.layers_per_block , in_channels=UpperCAmelCase_ , out_channels=UpperCAmelCase_ , add_downsample=not is_final_block , resnet_eps=1E-6 , downsample_padding=0 , resnet_act_fn=UpperCAmelCase_ , resnet_groups=UpperCAmelCase_ , attention_head_dim=UpperCAmelCase_ , temb_channels=UpperCAmelCase_ , )
self.down_blocks.append(UpperCAmelCase_ )
# mid
_SCREAMING_SNAKE_CASE = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1E-6 , resnet_act_fn=UpperCAmelCase_ , output_scale_factor=1 , resnet_time_scale_shift="""default""" , attention_head_dim=block_out_channels[-1] , resnet_groups=UpperCAmelCase_ , temb_channels=UpperCAmelCase_ , )
# out
_SCREAMING_SNAKE_CASE = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=UpperCAmelCase_ , eps=1E-6 )
_SCREAMING_SNAKE_CASE = nn.SiLU()
_SCREAMING_SNAKE_CASE = 2 * out_channels if double_z else out_channels
_SCREAMING_SNAKE_CASE = nn.Convad(block_out_channels[-1] , UpperCAmelCase_ , 3 , padding=1 )
_SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = x
_SCREAMING_SNAKE_CASE = self.conv_in(UpperCAmelCase_ )
if self.training and self.gradient_checkpointing:
def create_custom_forward(A__ ):
def custom_forward(*A__ ):
return module(*UpperCAmelCase_ )
return custom_forward
# down
if is_torch_version(""">=""" , """1.11.0""" ):
for down_block in self.down_blocks:
_SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(
create_custom_forward(UpperCAmelCase_ ) , UpperCAmelCase_ , use_reentrant=UpperCAmelCase_ )
# middle
_SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCAmelCase_ , use_reentrant=UpperCAmelCase_ )
else:
for down_block in self.down_blocks:
_SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase_ ) , UpperCAmelCase_ )
# middle
_SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , UpperCAmelCase_ )
else:
# down
for down_block in self.down_blocks:
_SCREAMING_SNAKE_CASE = down_block(UpperCAmelCase_ )
# middle
_SCREAMING_SNAKE_CASE = self.mid_block(UpperCAmelCase_ )
# post-process
_SCREAMING_SNAKE_CASE = self.conv_norm_out(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.conv_act(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.conv_out(UpperCAmelCase_ )
return sample
class _a (nn.Module):
"""simple docstring"""
def __init__( self , A__=3 , A__=3 , A__=("UpDecoderBlock2D",) , A__=(64,) , A__=2 , A__=32 , A__="silu" , A__="group" , ) -> int:
super().__init__()
_SCREAMING_SNAKE_CASE = layers_per_block
_SCREAMING_SNAKE_CASE = nn.Convad(
UpperCAmelCase_ , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = nn.ModuleList([] )
_SCREAMING_SNAKE_CASE = in_channels if norm_type == 'spatial' else None
# mid
_SCREAMING_SNAKE_CASE = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1E-6 , resnet_act_fn=UpperCAmelCase_ , output_scale_factor=1 , resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=UpperCAmelCase_ , temb_channels=UpperCAmelCase_ , )
# up
_SCREAMING_SNAKE_CASE = list(reversed(UpperCAmelCase_ ) )
_SCREAMING_SNAKE_CASE = reversed_block_out_channels[0]
for i, up_block_type in enumerate(UpperCAmelCase_ ):
_SCREAMING_SNAKE_CASE = output_channel
_SCREAMING_SNAKE_CASE = reversed_block_out_channels[i]
_SCREAMING_SNAKE_CASE = i == len(UpperCAmelCase_ ) - 1
_SCREAMING_SNAKE_CASE = get_up_block(
UpperCAmelCase_ , num_layers=self.layers_per_block + 1 , in_channels=UpperCAmelCase_ , out_channels=UpperCAmelCase_ , prev_output_channel=UpperCAmelCase_ , add_upsample=not is_final_block , resnet_eps=1E-6 , resnet_act_fn=UpperCAmelCase_ , resnet_groups=UpperCAmelCase_ , attention_head_dim=UpperCAmelCase_ , temb_channels=UpperCAmelCase_ , resnet_time_scale_shift=UpperCAmelCase_ , )
self.up_blocks.append(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = output_channel
# out
if norm_type == "spatial":
_SCREAMING_SNAKE_CASE = SpatialNorm(block_out_channels[0] , UpperCAmelCase_ )
else:
_SCREAMING_SNAKE_CASE = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=UpperCAmelCase_ , eps=1E-6 )
_SCREAMING_SNAKE_CASE = nn.SiLU()
_SCREAMING_SNAKE_CASE = nn.Convad(block_out_channels[0] , UpperCAmelCase_ , 3 , padding=1 )
_SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ , A__=None ) -> Dict:
_SCREAMING_SNAKE_CASE = z
_SCREAMING_SNAKE_CASE = self.conv_in(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(A__ ):
def custom_forward(*A__ ):
return module(*UpperCAmelCase_ )
return custom_forward
if is_torch_version(""">=""" , """1.11.0""" ):
# middle
_SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCAmelCase_ , UpperCAmelCase_ , use_reentrant=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = sample.to(UpperCAmelCase_ )
# up
for up_block in self.up_blocks:
_SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(
create_custom_forward(UpperCAmelCase_ ) , UpperCAmelCase_ , UpperCAmelCase_ , use_reentrant=UpperCAmelCase_ )
else:
# middle
_SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , UpperCAmelCase_ , UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = sample.to(UpperCAmelCase_ )
# up
for up_block in self.up_blocks:
_SCREAMING_SNAKE_CASE = torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCAmelCase_ ) , UpperCAmelCase_ , UpperCAmelCase_ )
else:
# middle
_SCREAMING_SNAKE_CASE = self.mid_block(UpperCAmelCase_ , UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = sample.to(UpperCAmelCase_ )
# up
for up_block in self.up_blocks:
_SCREAMING_SNAKE_CASE = up_block(UpperCAmelCase_ , UpperCAmelCase_ )
# post-process
if latent_embeds is None:
_SCREAMING_SNAKE_CASE = self.conv_norm_out(UpperCAmelCase_ )
else:
_SCREAMING_SNAKE_CASE = self.conv_norm_out(UpperCAmelCase_ , UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.conv_act(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = self.conv_out(UpperCAmelCase_ )
return sample
class _a (nn.Module):
"""simple docstring"""
def __init__( self , A__ , A__ , A__ , A__=None , A__="random" , A__=False , A__=True ) -> Optional[Any]:
super().__init__()
_SCREAMING_SNAKE_CASE = n_e
_SCREAMING_SNAKE_CASE = vq_embed_dim
_SCREAMING_SNAKE_CASE = beta
_SCREAMING_SNAKE_CASE = legacy
_SCREAMING_SNAKE_CASE = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
_SCREAMING_SNAKE_CASE = remap
if self.remap is not None:
self.register_buffer("""used""" , torch.tensor(np.load(self.remap ) ) )
_SCREAMING_SNAKE_CASE = self.used.shape[0]
_SCREAMING_SNAKE_CASE = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
_SCREAMING_SNAKE_CASE = self.re_embed
_SCREAMING_SNAKE_CASE = self.re_embed + 1
print(
F"Remapping {self.n_e} indices to {self.re_embed} indices. "
F"Using {self.unknown_index} for unknown indices." )
else:
_SCREAMING_SNAKE_CASE = n_e
_SCREAMING_SNAKE_CASE = sane_index_shape
def UpperCamelCase ( self , A__ ) -> List[Any]:
_SCREAMING_SNAKE_CASE = inds.shape
assert len(UpperCAmelCase_ ) > 1
_SCREAMING_SNAKE_CASE = inds.reshape(ishape[0] , -1 )
_SCREAMING_SNAKE_CASE = self.used.to(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = (inds[:, :, None] == used[None, None, ...]).long()
_SCREAMING_SNAKE_CASE = match.argmax(-1 )
_SCREAMING_SNAKE_CASE = match.sum(2 ) < 1
if self.unknown_index == "random":
_SCREAMING_SNAKE_CASE = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
_SCREAMING_SNAKE_CASE = self.unknown_index
return new.reshape(UpperCAmelCase_ )
def UpperCamelCase ( self , A__ ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = inds.shape
assert len(UpperCAmelCase_ ) > 1
_SCREAMING_SNAKE_CASE = inds.reshape(ishape[0] , -1 )
_SCREAMING_SNAKE_CASE = self.used.to(UpperCAmelCase_ )
if self.re_embed > self.used.shape[0]: # extra token
_SCREAMING_SNAKE_CASE = 0 # simply set to zero
_SCREAMING_SNAKE_CASE = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , UpperCAmelCase_ )
return back.reshape(UpperCAmelCase_ )
def UpperCamelCase ( self , A__ ) -> List[Any]:
# reshape z -> (batch, height, width, channel) and flatten
_SCREAMING_SNAKE_CASE = z.permute(0 , 2 , 3 , 1 ).contiguous()
_SCREAMING_SNAKE_CASE = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
_SCREAMING_SNAKE_CASE = torch.argmin(torch.cdist(UpperCAmelCase_ , self.embedding.weight ) , dim=1 )
_SCREAMING_SNAKE_CASE = self.embedding(UpperCAmelCase_ ).view(z.shape )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
# compute loss for embedding
if not self.legacy:
_SCREAMING_SNAKE_CASE = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
_SCREAMING_SNAKE_CASE = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
_SCREAMING_SNAKE_CASE = z + (z_q - z).detach()
# reshape back to match original input shape
_SCREAMING_SNAKE_CASE = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
_SCREAMING_SNAKE_CASE = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
_SCREAMING_SNAKE_CASE = self.remap_to_used(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
_SCREAMING_SNAKE_CASE = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def UpperCamelCase ( self , A__ , A__ ) -> Optional[Any]:
# shape specifying (batch, height, width, channel)
if self.remap is not None:
_SCREAMING_SNAKE_CASE = indices.reshape(shape[0] , -1 ) # add batch axis
_SCREAMING_SNAKE_CASE = self.unmap_to_all(UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
_SCREAMING_SNAKE_CASE = self.embedding(UpperCAmelCase_ )
if shape is not None:
_SCREAMING_SNAKE_CASE = z_q.view(UpperCAmelCase_ )
# reshape back to match original input shape
_SCREAMING_SNAKE_CASE = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class _a (__lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__=False ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = parameters
_SCREAMING_SNAKE_CASE = torch.chunk(UpperCAmelCase_ , 2 , dim=1 )
_SCREAMING_SNAKE_CASE = torch.clamp(self.logvar , -30.0 , 20.0 )
_SCREAMING_SNAKE_CASE = deterministic
_SCREAMING_SNAKE_CASE = torch.exp(0.5 * self.logvar )
_SCREAMING_SNAKE_CASE = torch.exp(self.logvar )
if self.deterministic:
_SCREAMING_SNAKE_CASE = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def UpperCamelCase ( self , A__ = None ) -> str:
# make sure sample is on the same device as the parameters and has same dtype
_SCREAMING_SNAKE_CASE = randn_tensor(
self.mean.shape , generator=UpperCAmelCase_ , device=self.parameters.device , dtype=self.parameters.dtype )
_SCREAMING_SNAKE_CASE = self.mean + self.std * sample
return x
def UpperCamelCase ( self , A__=None ) -> Optional[int]:
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def UpperCamelCase ( self , A__ , A__=[1, 2, 3] ) -> Union[str, Any]:
if self.deterministic:
return torch.Tensor([0.0] )
_SCREAMING_SNAKE_CASE = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=UpperCAmelCase_ )
def UpperCamelCase ( self ) -> Tuple:
return self.mean
| 705 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase__ : List[str] = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
UpperCamelCase__ : List[Any] = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
UpperCamelCase__ : Any = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a (datasets.Metric):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def UpperCamelCase ( self , A__ , A__ , A__=None ) -> List[str]:
return {
"matthews_correlation": float(matthews_corrcoef(A__ , A__ , sample_weight=A__ ) ),
}
| 0 | 0 |
import requests
UpperCamelCase__ : Tuple = "YOUR API KEY"
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = giphy_api_key ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = '+'.join(query.split() )
_SCREAMING_SNAKE_CASE = F"https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"
_SCREAMING_SNAKE_CASE = requests.get(SCREAMING_SNAKE_CASE_ ).json()['data']
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("\n".join(get_gifs("space ship")))
| 706 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
print(F"Vertex\tShortest Distance from vertex {src}" )
for i, d in enumerate(SCREAMING_SNAKE_CASE_ ):
print(F"{i}\t\t{d}" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
for j in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
return True
return False
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list[float]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [float("""inf""" )] * vertex_count
_SCREAMING_SNAKE_CASE = 0.0
for _ in range(vertex_count - 1 ):
for j in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
_SCREAMING_SNAKE_CASE = distance[u] + w
_SCREAMING_SNAKE_CASE = check_negative_cycle(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if negative_cycle_exists:
raise Exception("""Negative cycle found""" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ : int = int(input("Enter number of vertices: ").strip())
UpperCamelCase__ : int = int(input("Enter number of edges: ").strip())
UpperCamelCase__ : list[dict[str, int]] = [{} for _ in range(E)]
for i in range(E):
print("Edge ", i + 1)
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Dict = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
UpperCamelCase__ : Optional[Any] = {"src": src, "dst": dest, "weight": weight}
UpperCamelCase__ : Optional[Any] = int(input("\nEnter shortest path source:").strip())
UpperCamelCase__ : Any = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 0 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : List[Any] = {
"huggingface/informer-tourism-monthly": (
"https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"
),
# See all Informer models at https://huggingface.co/models?filter=informer
}
class _a (__A):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'informer'
SCREAMING_SNAKE_CASE = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
'num_hidden_layers': 'encoder_layers',
}
def __init__( self , A__ = None , A__ = None , A__ = "student_t" , A__ = "nll" , A__ = 1 , A__ = None , A__ = "mean" , A__ = 0 , A__ = 0 , A__ = 0 , A__ = 0 , A__ = None , A__ = None , A__ = 64 , A__ = 32 , A__ = 32 , A__ = 2 , A__ = 2 , A__ = 2 , A__ = 2 , A__ = True , A__ = "gelu" , A__ = 0.05 , A__ = 0.1 , A__ = 0.1 , A__ = 0.1 , A__ = 0.1 , A__ = 1_00 , A__ = 0.02 , A__=True , A__ = "prob" , A__ = 5 , A__ = True , **A__ , ) -> Dict:
_SCREAMING_SNAKE_CASE = prediction_length
_SCREAMING_SNAKE_CASE = context_length or prediction_length
_SCREAMING_SNAKE_CASE = distribution_output
_SCREAMING_SNAKE_CASE = loss
_SCREAMING_SNAKE_CASE = input_size
_SCREAMING_SNAKE_CASE = num_time_features
_SCREAMING_SNAKE_CASE = lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
_SCREAMING_SNAKE_CASE = scaling
_SCREAMING_SNAKE_CASE = num_dynamic_real_features
_SCREAMING_SNAKE_CASE = num_static_real_features
_SCREAMING_SNAKE_CASE = num_static_categorical_features
# set cardinality
if cardinality and num_static_categorical_features > 0:
if len(A__ ) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""" )
_SCREAMING_SNAKE_CASE = cardinality
else:
_SCREAMING_SNAKE_CASE = [0]
# set embedding_dimension
if embedding_dimension and num_static_categorical_features > 0:
if len(A__ ) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""" )
_SCREAMING_SNAKE_CASE = embedding_dimension
else:
_SCREAMING_SNAKE_CASE = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
_SCREAMING_SNAKE_CASE = num_parallel_samples
# Transformer architecture configuration
_SCREAMING_SNAKE_CASE = input_size * len(self.lags_sequence ) + self._number_of_features
_SCREAMING_SNAKE_CASE = d_model
_SCREAMING_SNAKE_CASE = encoder_attention_heads
_SCREAMING_SNAKE_CASE = decoder_attention_heads
_SCREAMING_SNAKE_CASE = encoder_ffn_dim
_SCREAMING_SNAKE_CASE = decoder_ffn_dim
_SCREAMING_SNAKE_CASE = encoder_layers
_SCREAMING_SNAKE_CASE = decoder_layers
_SCREAMING_SNAKE_CASE = dropout
_SCREAMING_SNAKE_CASE = attention_dropout
_SCREAMING_SNAKE_CASE = activation_dropout
_SCREAMING_SNAKE_CASE = encoder_layerdrop
_SCREAMING_SNAKE_CASE = decoder_layerdrop
_SCREAMING_SNAKE_CASE = activation_function
_SCREAMING_SNAKE_CASE = init_std
_SCREAMING_SNAKE_CASE = use_cache
# Informer
_SCREAMING_SNAKE_CASE = attention_type
_SCREAMING_SNAKE_CASE = sampling_factor
_SCREAMING_SNAKE_CASE = distil
super().__init__(is_encoder_decoder=A__ , **A__ )
@property
def UpperCamelCase ( self ) -> int:
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 707 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=True , A__=True , A__=99 , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=16 , A__=2 , A__=0.02 , A__=3 , A__=4 , A__=None , ) -> int:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = 13
_SCREAMING_SNAKE_CASE = 7
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = 99
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = 37
_SCREAMING_SNAKE_CASE = """gelu"""
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 5_12
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 0.02
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
_SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=A__ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModel(config=A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
_SCREAMING_SNAKE_CASE = [input_ids, input_mask]
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> str:
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = TFRoFormerForCausalLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )["""logits"""]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Dict:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForSequenceClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = TFRoFormerForMultipleChoice(config=A__ )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForTokenClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = TFRoFormerForQuestionAnswering(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': TFRoFormerModel,
'fill-mask': TFRoFormerForMaskedLM,
'question-answering': TFRoFormerForQuestionAnswering,
'text-classification': TFRoFormerForSequenceClassification,
'text-generation': TFRoFormerForCausalLM,
'token-classification': TFRoFormerForTokenClassification,
'zero-shot': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> str:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*A__ )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base""" )
self.assertIsNotNone(A__ )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
_SCREAMING_SNAKE_CASE = tf.constant([[0, 1, 2, 3, 4, 5]] )
_SCREAMING_SNAKE_CASE = model(A__ )[0]
# TODO Replace vocab size
_SCREAMING_SNAKE_CASE = 5_00_00
_SCREAMING_SNAKE_CASE = [1, 6, vocab_size]
self.assertEqual(output.shape , A__ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
_SCREAMING_SNAKE_CASE = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , A__ , atol=1E-4 )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant([[4, 10]] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
_SCREAMING_SNAKE_CASE = emba(input_ids.shape )
_SCREAMING_SNAKE_CASE = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 , embedding_dim=5_12 )
emba([2, 16, 5_12] )
_SCREAMING_SNAKE_CASE = emba.weight[:3, :5]
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> int:
# 2,12,16,64
_SCREAMING_SNAKE_CASE = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
_SCREAMING_SNAKE_CASE = embed_positions([2, 16, 7_68] )[None, None, :, :]
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
A__ , A__ , A__ )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
| 0 | 0 |
'''simple docstring'''
import cva
import numpy as np
class _a :
"""simple docstring"""
def __init__( self , A__ , A__ ) -> List[Any]:
if k in (0.04, 0.06):
_SCREAMING_SNAKE_CASE = k
_SCREAMING_SNAKE_CASE = window_size
else:
raise ValueError("""invalid k value""" )
def __str__( self ) -> str:
return str(self.k )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = cva.imread(_lowerCAmelCase , 0 )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = img.shape
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = img.copy()
_SCREAMING_SNAKE_CASE = cva.cvtColor(_lowerCAmelCase , cva.COLOR_GRAY2RGB )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = np.gradient(_lowerCAmelCase )
_SCREAMING_SNAKE_CASE = dx**2
_SCREAMING_SNAKE_CASE = dy**2
_SCREAMING_SNAKE_CASE = dx * dy
_SCREAMING_SNAKE_CASE = 0.04
_SCREAMING_SNAKE_CASE = self.window_size // 2
for y in range(_lowerCAmelCase , h - offset ):
for x in range(_lowerCAmelCase , w - offset ):
_SCREAMING_SNAKE_CASE = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_SCREAMING_SNAKE_CASE = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_SCREAMING_SNAKE_CASE = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_SCREAMING_SNAKE_CASE = (wxx * wyy) - (wxy**2)
_SCREAMING_SNAKE_CASE = wxx + wyy
_SCREAMING_SNAKE_CASE = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) , 0 )
color_img.itemset((y, x, 1) , 0 )
color_img.itemset((y, x, 2) , 2_55 )
return color_img, corner_list
if __name__ == "__main__":
UpperCamelCase__ : Optional[Any] = HarrisCorner(0.04, 3)
UpperCamelCase__ : Tuple = edge_detect.detect("path_to_image")
cva.imwrite("detect.png", color_img)
| 708 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase__ : int = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
UpperCamelCase__ : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class _a :
"""simple docstring"""
def __init__( self ) -> Any:
_SCREAMING_SNAKE_CASE = {}
def UpperCamelCase ( self , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = {}
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Any:
if nodea not in self.connections:
self.add_node(UpperCamelCase_ )
if nodea not in self.connections:
self.add_node(UpperCamelCase_ )
_SCREAMING_SNAKE_CASE = probability
def UpperCamelCase ( self ) -> Optional[int]:
return list(self.connections )
def UpperCamelCase ( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = random()
for dest in self.connections[node]:
current_probability += self.connections[node][dest]
if current_probability > random_value:
return dest
return ""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> dict[str, int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = MarkovChainGraphUndirectedUnweighted()
for nodea, nodea, probability in transitions:
graph.add_transition_probability(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
_SCREAMING_SNAKE_CASE = Counter(graph.get_nodes() )
_SCREAMING_SNAKE_CASE = start
for _ in range(lowerCamelCase__ ):
_SCREAMING_SNAKE_CASE = graph.transition(lowerCamelCase__ )
visited[node] += 1
return visited
if __name__ == "__main__":
import doctest
doctest.testmod()
| 709 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = XCLIPTextConfig()
# derive patch size from model name
_SCREAMING_SNAKE_CASE = model_name.find("""patch""" )
_SCREAMING_SNAKE_CASE = int(model_name[start_idx + len("""patch""" ) : start_idx + len("""patch""" ) + 2] )
_SCREAMING_SNAKE_CASE = XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE_ , num_frames=SCREAMING_SNAKE_CASE_ )
if "large" in model_name:
_SCREAMING_SNAKE_CASE = 7_68
_SCREAMING_SNAKE_CASE = 30_72
_SCREAMING_SNAKE_CASE = 12
_SCREAMING_SNAKE_CASE = 10_24
_SCREAMING_SNAKE_CASE = 40_96
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 24
_SCREAMING_SNAKE_CASE = 7_68
_SCREAMING_SNAKE_CASE = 30_72
if model_name == "xclip-large-patch14-16-frames":
_SCREAMING_SNAKE_CASE = 3_36
_SCREAMING_SNAKE_CASE = XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if "large" in model_name:
_SCREAMING_SNAKE_CASE = 7_68
return config
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
# text encoder
if name == "token_embedding.weight":
_SCREAMING_SNAKE_CASE = name.replace("""token_embedding.weight""" , """text_model.embeddings.token_embedding.weight""" )
if name == "positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "ln_1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
_SCREAMING_SNAKE_CASE = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""c_proj""" , """fc2""" )
if name.startswith("""transformer.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""transformer.resblocks""" , """text_model.encoder.layers""" )
if "attn.out_proj" in name and "message" not in name:
_SCREAMING_SNAKE_CASE = name.replace("""attn.out_proj""" , """self_attn.out_proj""" )
if "ln_final" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_final""" , """text_model.final_layer_norm""" )
# visual encoder
if name == "visual.class_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""visual.class_embedding""" , """vision_model.embeddings.class_embedding""" )
if name == "visual.positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""visual.positional_embedding""" , """vision_model.embeddings.position_embedding.weight""" )
if name.startswith("""visual.transformer.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""visual.transformer.resblocks""" , """vision_model.encoder.layers""" )
if "visual.conv1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.conv1""" , """vision_model.embeddings.patch_embedding""" )
if "visual.ln_pre" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.ln_pre""" , """vision_model.pre_layernorm""" )
if "visual.ln_post" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.ln_post""" , """vision_model.post_layernorm""" )
if "visual.proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.proj""" , """visual_projection.weight""" )
if "text_projection" in name:
_SCREAMING_SNAKE_CASE = name.replace("""text_projection""" , """text_projection.weight""" )
# things on top
if "prompts_visual_proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""prompts_visual_proj""" , """prompts_visual_projection""" )
if "prompts_visual_ln" in name:
_SCREAMING_SNAKE_CASE = name.replace("""prompts_visual_ln""" , """prompts_visual_layernorm""" )
# mit
if name == "mit.positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""positional""" , """position""" )
if name.startswith("""mit.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""mit.resblocks""" , """mit.encoder.layers""" )
# prompts generator
if name.startswith("""prompts_generator.norm""" ):
_SCREAMING_SNAKE_CASE = name.replace("""prompts_generator.norm""" , """prompts_generator.layernorm""" )
return name
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_SCREAMING_SNAKE_CASE = orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )
if "attn.in_proj" in key:
_SCREAMING_SNAKE_CASE = key.split(""".""" )
if key.startswith("""visual""" ):
_SCREAMING_SNAKE_CASE = key_split[3]
_SCREAMING_SNAKE_CASE = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[
:dim, :
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[
-dim:, :
]
else:
_SCREAMING_SNAKE_CASE = val[
:dim
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[
:dim, :
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[
-dim:, :
]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[-dim:]
elif key.startswith("""mit""" ):
_SCREAMING_SNAKE_CASE = key_split[2]
_SCREAMING_SNAKE_CASE = config.vision_config.mit_hidden_size
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[:dim, :]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
_SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2]
_SCREAMING_SNAKE_CASE = val[-dim:]
else:
_SCREAMING_SNAKE_CASE = key_split[2]
_SCREAMING_SNAKE_CASE = config.text_config.hidden_size
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[:dim, :]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[-dim:]
else:
_SCREAMING_SNAKE_CASE = rename_key(SCREAMING_SNAKE_CASE_ )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
_SCREAMING_SNAKE_CASE = val.T
_SCREAMING_SNAKE_CASE = val
return orig_state_dict
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
if num_frames == 8:
_SCREAMING_SNAKE_CASE = """eating_spaghetti_8_frames.npy"""
elif num_frames == 16:
_SCREAMING_SNAKE_CASE = """eating_spaghetti.npy"""
elif num_frames == 32:
_SCREAMING_SNAKE_CASE = """eating_spaghetti_32_frames.npy"""
_SCREAMING_SNAKE_CASE = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename=SCREAMING_SNAKE_CASE_ , repo_type="""dataset""" , )
_SCREAMING_SNAKE_CASE = np.load(SCREAMING_SNAKE_CASE_ )
return list(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=False ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {
# fully supervised kinetics-400 checkpoints
"""xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
"""xclip-base-patch32-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
),
"""xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
"""xclip-base-patch16-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
),
"""xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
"""xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f""",
# fully supervised kinetics-600 checkpoints
"""xclip-base-patch16-kinetics-600""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
),
"""xclip-base-patch16-kinetics-600-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
),
"""xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
# few shot
"""xclip-base-patch16-hmdb-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
),
"""xclip-base-patch16-hmdb-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
),
"""xclip-base-patch16-hmdb-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
),
"""xclip-base-patch16-hmdb-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
),
"""xclip-base-patch16-ucf-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
),
"""xclip-base-patch16-ucf-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
),
"""xclip-base-patch16-ucf-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
),
"""xclip-base-patch16-ucf-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
),
# zero shot
"""xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
}
_SCREAMING_SNAKE_CASE = model_to_url[model_name]
_SCREAMING_SNAKE_CASE = 8
if "16-frames" in model_name:
_SCREAMING_SNAKE_CASE = 16
elif "shot" in model_name:
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = get_xclip_config(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = XCLIPModel(SCREAMING_SNAKE_CASE_ )
model.eval()
if "drive" in checkpoint_url:
_SCREAMING_SNAKE_CASE = """pytorch_model.bin"""
gdown.cached_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , quiet=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )["""model"""]
else:
_SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ )["""model"""]
_SCREAMING_SNAKE_CASE = convert_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = XCLIPModel(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
_SCREAMING_SNAKE_CASE = 3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
_SCREAMING_SNAKE_CASE = VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
_SCREAMING_SNAKE_CASE = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32""" )
_SCREAMING_SNAKE_CASE = XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = prepare_video(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = processor(
text=["""playing sports""", """eating spaghetti""", """go shopping"""] , videos=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , padding=SCREAMING_SNAKE_CASE_ )
print("""Shape of pixel values:""" , inputs.pixel_values.shape )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
# Verify outputs
_SCREAMING_SNAKE_CASE = outputs.logits_per_video
_SCREAMING_SNAKE_CASE = logits_per_video.softmax(dim=1 )
print("""Probs:""" , SCREAMING_SNAKE_CASE_ )
# kinetics-400
if model_name == "xclip-base-patch32":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0019, 0.9951, 0.0030]] )
elif model_name == "xclip-base-patch32-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]] )
elif model_name == "xclip-base-patch16":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0083, 0.9681, 0.0236]] )
elif model_name == "xclip-base-patch16-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]] )
elif model_name == "xclip-large-patch14":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0062, 0.9864, 0.0075]] )
elif model_name == "xclip-large-patch14-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0555, 0.8914, 0.0531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0036, 0.9920, 0.0045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0027, 0.9904, 0.0070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]] )
else:
raise ValueError(F"Model name {model_name} not supported" )
assert torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
print("""Pushing model, processor and slow tokenizer files to the hub...""" )
model.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
processor.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
slow_tokenizer.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="xclip-base-patch32",
type=str,
help="Name of the model.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
UpperCamelCase__ : str = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 0 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
if any(not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(_lowerCAmelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(_lowerCAmelCase , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 710 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = params
_SCREAMING_SNAKE_CASE = np.array(A__ )
_SCREAMING_SNAKE_CASE = np.array([len(A__ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self , A__ ) -> Dict:
return (self.token_ids[index], self.lengths[index])
def __len__( self ) -> Tuple:
return len(self.lengths )
def UpperCamelCase ( self ) -> Dict:
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.params.max_model_input_size
_SCREAMING_SNAKE_CASE = self.lengths > max_len
logger.info(F"Splitting {sum(A__ )} too long sequences." )
def divide_chunks(A__ , A__ ):
return [l[i : i + n] for i in range(0 , len(A__ ) , A__ )]
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
if self.params.mlm:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
_SCREAMING_SNAKE_CASE = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
_SCREAMING_SNAKE_CASE = np.insert(A__ , 0 , A__ )
if sub_s[-1] != sep_id:
_SCREAMING_SNAKE_CASE = np.insert(A__ , len(A__ ) , A__ )
assert len(A__ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(A__ )
new_tok_ids.extend(A__ )
new_lengths.extend([len(A__ ) for l in sub_seqs] )
_SCREAMING_SNAKE_CASE = np.array(A__ )
_SCREAMING_SNAKE_CASE = np.array(A__ )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = len(self )
_SCREAMING_SNAKE_CASE = self.lengths > 11
_SCREAMING_SNAKE_CASE = self.token_ids[indices]
_SCREAMING_SNAKE_CASE = self.lengths[indices]
_SCREAMING_SNAKE_CASE = len(self )
logger.info(F"Remove {init_size - new_size} too short (<=11 tokens) sequences." )
def UpperCamelCase ( self ) -> int:
if "unk_token" not in self.params.special_tok_ids:
return
else:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""unk_token"""]
_SCREAMING_SNAKE_CASE = len(self )
_SCREAMING_SNAKE_CASE = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
_SCREAMING_SNAKE_CASE = (unk_occs / self.lengths) < 0.5
_SCREAMING_SNAKE_CASE = self.token_ids[indices]
_SCREAMING_SNAKE_CASE = self.lengths[indices]
_SCREAMING_SNAKE_CASE = len(self )
logger.info(F"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)." )
def UpperCamelCase ( self ) -> Optional[Any]:
if not self.params.is_master:
return
logger.info(F"{len(self )} sequences" )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def UpperCamelCase ( self , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = [t[0] for t in batch]
_SCREAMING_SNAKE_CASE = [t[1] for t in batch]
assert len(A__ ) == len(A__ )
# Max for paddings
_SCREAMING_SNAKE_CASE = max(A__ )
# Pad token ids
if self.params.mlm:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""pad_token"""]
else:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""unk_token"""]
_SCREAMING_SNAKE_CASE = [list(t.astype(A__ ) ) + [pad_idx] * (max_seq_len_ - len(A__ )) for t in token_ids]
assert len(tk_ ) == len(A__ )
assert all(len(A__ ) == max_seq_len_ for t in tk_ )
_SCREAMING_SNAKE_CASE = torch.tensor(tk_ ) # (bs, max_seq_len_)
_SCREAMING_SNAKE_CASE = torch.tensor(A__ ) # (bs)
return tk_t, lg_t
| 0 | 0 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ : List[str] = get_tests_dir("fixtures/test_sentencepiece.model")
@require_sentencepiece
@require_tokenizers
class _a (UpperCamelCase_ , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ReformerTokenizer
SCREAMING_SNAKE_CASE = ReformerTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = True
def UpperCamelCase ( self ) -> List[str]:
super().setUp()
_SCREAMING_SNAKE_CASE = ReformerTokenizer(_a , keep_accents=_a )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = """<s>"""
_SCREAMING_SNAKE_CASE = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(_a ) , 10_00 )
def UpperCamelCase ( self ) -> Any:
self.assertEqual(self.get_tokenizer().vocab_size , 10_00 )
def UpperCamelCase ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = """I was born in 92000, and this is falsé."""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(_a )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(_a )
self.assertListEqual(_a , _a )
_SCREAMING_SNAKE_CASE = tokenizer.encode(_a , add_special_tokens=_a )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(_a , add_special_tokens=_a )
self.assertListEqual(_a , _a )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(_a )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(_a )
self.assertListEqual(_a , _a )
def UpperCamelCase ( self , A__=15 ) -> Union[str, Any]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(_a , **_a )
# Simple input
_SCREAMING_SNAKE_CASE = """This is a simple input"""
_SCREAMING_SNAKE_CASE = ["""This is a simple input 1""", """This is a simple input 2"""]
_SCREAMING_SNAKE_CASE = ("""This is a simple input""", """This is a pair""")
_SCREAMING_SNAKE_CASE = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding="""max_length""" )
# Simple input
self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding="""max_length""" )
# Simple input
self.assertRaises(
_a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding="""max_length""" , )
# Pair input
self.assertRaises(_a , tokenizer_r.encode , _a , max_length=_a , padding="""max_length""" )
# Pair input
self.assertRaises(_a , tokenizer_r.encode_plus , _a , max_length=_a , padding="""max_length""" )
# Pair input
self.assertRaises(
_a , tokenizer_r.batch_encode_plus , _a , max_length=_a , padding="""max_length""" , )
def UpperCamelCase ( self ) -> Optional[Any]:
pass
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = ReformerTokenizer(_a , keep_accents=_a )
_SCREAMING_SNAKE_CASE = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(_a , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [2_85, 46, 10, 1_70, 3_82] , )
_SCREAMING_SNAKE_CASE = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
_SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_ids(_a )
self.assertListEqual(
_a , [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens(_a )
self.assertListEqual(
_a , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
@cached_property
def UpperCamelCase ( self ) -> Optional[Any]:
return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment""" )
@slow
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = """Hello World!"""
_SCREAMING_SNAKE_CASE = [1_26, 32, 2_62, 1_52, 38, 72, 2_87]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@slow
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = (
"""This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
""" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
)
_SCREAMING_SNAKE_CASE = [
1_08,
2_65,
24,
1_11,
4,
2_58,
1_56,
35,
28,
2_75,
3,
2_59,
2_97,
2_60,
84,
4,
35,
1_10,
44,
8,
2_59,
91,
2_68,
21,
11,
2_09,
2_74,
1_09,
2_66,
2_77,
1_17,
86,
93,
3_15,
2_58,
2_78,
2_58,
2_77,
2_58,
0,
2_58,
2_88,
2_58,
3_19,
2_58,
0,
2_58,
0,
2_58,
0,
2_58,
0,
2_58,
2_87,
2_58,
3_15,
2_58,
2_89,
2_58,
2_78,
99,
2_69,
2_66,
2_62,
8,
2_59,
2_41,
4,
2_17,
2_30,
2_68,
2_66,
55,
1_68,
1_06,
75,
1_93,
2_66,
2_23,
27,
49,
26,
2_82,
25,
2_64,
2_99,
19,
26,
0,
2_58,
2_77,
1_17,
86,
93,
1_76,
1_83,
2_70,
11,
2_62,
42,
61,
2_65,
]
self.assertListEqual(_a , self.big_tokenizer.encode(_a ) )
@require_torch
@slow
def UpperCamelCase ( self ) -> Tuple:
import torch
from transformers import ReformerConfig, ReformerModel
# Build sequence
_SCREAMING_SNAKE_CASE = list(self.big_tokenizer.get_vocab().keys() )[:10]
_SCREAMING_SNAKE_CASE = """ """.join(_a )
_SCREAMING_SNAKE_CASE = self.big_tokenizer.encode_plus(_a , return_tensors="""pt""" )
_SCREAMING_SNAKE_CASE = self.big_tokenizer.batch_encode_plus([sequence, sequence] , return_tensors="""pt""" )
_SCREAMING_SNAKE_CASE = ReformerConfig()
# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
_SCREAMING_SNAKE_CASE = encoded_sequence["""input_ids"""].shape
_SCREAMING_SNAKE_CASE = ReformerModel(_a )
# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**_a )
model(**_a )
@slow
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = {"""input_ids""": [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# This tokenizer does not know some characters like ")".
# That is the reason why we use very simple texts here.
# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
_SCREAMING_SNAKE_CASE = [
"""This is a very simple sentence.""",
"""The quick brown fox jumps over the lazy dog.""",
]
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name="""google/reformer-crime-and-punishment""" , revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a""" , padding=_a , sequences=_a , )
| 711 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : Any = "▁"
UpperCamelCase__ : Any = {"vocab_file": "spiece.model"}
UpperCamelCase__ : int = {
"vocab_file": {
"google/reformer-crime-and-punishment": (
"https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
)
}
}
UpperCamelCase__ : Optional[int] = {
"google/reformer-crime-and-punishment": 524_288,
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self , A__ , A__="</s>" , A__="<unk>" , A__=[] , A__ = None , **A__ , ) -> None:
_SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=A__ , unk_token=A__ , additional_special_tokens=A__ , sp_model_kwargs=self.sp_model_kwargs , **A__ , )
_SCREAMING_SNAKE_CASE = vocab_file
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A__ )
@property
def UpperCamelCase ( self ) -> Any:
return self.sp_model.get_piece_size()
def UpperCamelCase ( self ) -> Dict[str, int]:
_SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(A__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> int:
_SCREAMING_SNAKE_CASE = self.__dict__.copy()
_SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase ( self , A__ ) -> List[str]:
return self.sp_model.encode(A__ , out_type=A__ )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
return self.sp_model.piece_to_id(A__ )
def UpperCamelCase ( self , A__ ) -> List[Any]:
if index < self.sp_model.get_piece_size():
_SCREAMING_SNAKE_CASE = self.sp_model.IdToPiece(A__ )
return token
def UpperCamelCase ( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A__ ) + token
_SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(A__ )
out_string += self.sp_model.decode(A__ )
return out_string.strip()
def UpperCamelCase ( self , A__ , A__ = None ) -> Tuple[str]:
if not os.path.isdir(A__ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
_SCREAMING_SNAKE_CASE = os.path.join(
A__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A__ )
elif not os.path.isfile(self.vocab_file ):
with open(A__ , """wb""" ) as fi:
_SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(A__ )
return (out_vocab_file,)
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
UpperCamelCase__ : List[Any] = list[list[float | int]]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Matrix:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(__UpperCAmelCase )
_SCREAMING_SNAKE_CASE = [[0 for _ in range(size + 1 )] for _ in range(__UpperCAmelCase )]
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
for row in range(__UpperCAmelCase ):
for col in range(__UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = matrix[row][col]
_SCREAMING_SNAKE_CASE = vector[row][0]
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
while row < size and col < size:
# pivoting
_SCREAMING_SNAKE_CASE = max((abs(augmented[rowa][col] ), rowa) for rowa in range(__UpperCAmelCase , __UpperCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , __UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = augmented[rowa][col] / augmented[row][col]
_SCREAMING_SNAKE_CASE = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , __UpperCAmelCase ):
for row in range(__UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = augmented[row][col] / augmented[col][col]
for cola in range(__UpperCAmelCase , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(__UpperCAmelCase )
]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Callable[[int], int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(__UpperCAmelCase )
_SCREAMING_SNAKE_CASE = [[0 for _ in range(__UpperCAmelCase )] for _ in range(__UpperCAmelCase )]
_SCREAMING_SNAKE_CASE = [[0] for _ in range(__UpperCAmelCase )]
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
for x_val, y_val in enumerate(__UpperCAmelCase ):
for col in range(__UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = (x_val + 1) ** (size - col - 1)
_SCREAMING_SNAKE_CASE = y_val
_SCREAMING_SNAKE_CASE = solve(__UpperCAmelCase , __UpperCAmelCase )
def interpolated_func(SCREAMING_SNAKE_CASE_ ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(__UpperCAmelCase ) )
return interpolated_func
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = question_function , SCREAMING_SNAKE_CASE_ = 10 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [func(__UpperCAmelCase ) for x_val in range(1 , order + 1 )]
_SCREAMING_SNAKE_CASE = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = 42
for poly in polynomials:
_SCREAMING_SNAKE_CASE = 1
while func(__UpperCAmelCase ) == poly(__UpperCAmelCase ):
x_val += 1
ret += poly(__UpperCAmelCase )
return ret
if __name__ == "__main__":
print(f"""{solution() = }""")
| 712 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class _a (_lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = MobileBertTokenizer
SCREAMING_SNAKE_CASE = MobileBertTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = filter_non_english
SCREAMING_SNAKE_CASE = 'google/mobilebert-uncased'
def UpperCamelCase ( self ) -> Any:
super().setUp()
_SCREAMING_SNAKE_CASE = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
_SCREAMING_SNAKE_CASE = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def UpperCamelCase ( self , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = """unwanted, running"""
return input_text, output_text
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.tokenizer_class(self.vocab_file )
_SCREAMING_SNAKE_CASE = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(A__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , [9, 6, 7, 12, 10, 11] )
def UpperCamelCase ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
# With lower casing
_SCREAMING_SNAKE_CASE = self.get_tokenizer(do_lower_case=A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer(do_lower_case=A__ )
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
_SCREAMING_SNAKE_CASE = {}
for i, token in enumerate(A__ ):
_SCREAMING_SNAKE_CASE = i
_SCREAMING_SNAKE_CASE = WordpieceTokenizer(vocab=A__ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
def UpperCamelCase ( self ) -> str:
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def UpperCamelCase ( self ) -> Union[str, Any]:
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def UpperCamelCase ( self ) -> Dict:
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
self.assertListEqual(
[rust_tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
@slow
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained("""google/mobilebert-uncased""" )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""sequence builders""" , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""multi-sequence build""" , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A__ , A__ )
assert encoded_sentence == [1_01] + text + [1_02]
assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
def UpperCamelCase ( self ) -> List[str]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = F"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
_SCREAMING_SNAKE_CASE = tokenizer_r.encode_plus(
A__ , return_attention_mask=A__ , return_token_type_ids=A__ , return_offsets_mapping=A__ , add_special_tokens=A__ , )
_SCREAMING_SNAKE_CASE = tokenizer_r.do_lower_case if hasattr(A__ , """do_lower_case""" ) else False
_SCREAMING_SNAKE_CASE = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), """A"""),
((1, 2), ""","""),
((3, 5), """na"""),
((5, 6), """##ï"""),
((6, 8), """##ve"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """Allen"""),
((21, 23), """##NL"""),
((23, 24), """##P"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), """a"""),
((1, 2), ""","""),
((3, 8), """naive"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """allen"""),
((21, 23), """##nl"""),
((23, 24), """##p"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = ["""的""", """人""", """有"""]
_SCREAMING_SNAKE_CASE = """""".join(A__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(A__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(A__ , A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(A__ )
# it is expected that only the first Chinese character is not preceded by "##".
_SCREAMING_SNAKE_CASE = [
F"##{token}" if idx != 0 else token for idx, token in enumerate(A__ )
]
self.assertListEqual(A__ , A__ )
self.assertListEqual(A__ , A__ )
| 0 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
UpperCamelCase__ : Dict = 8
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=BITS ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = x.device
_SCREAMING_SNAKE_CASE = (x * 2_55).int().clamp(0 , 2_55 )
_SCREAMING_SNAKE_CASE = 2 ** torch.arange(bits - 1 , -1 , -1 , device=lowerCAmelCase__ )
_SCREAMING_SNAKE_CASE = rearrange(lowerCAmelCase__ , """d -> d 1 1""" )
_SCREAMING_SNAKE_CASE = rearrange(lowerCAmelCase__ , """b c h w -> b c 1 h w""" )
_SCREAMING_SNAKE_CASE = ((x & mask) != 0).float()
_SCREAMING_SNAKE_CASE = rearrange(lowerCAmelCase__ , """b c d h w -> b (c d) h w""" )
_SCREAMING_SNAKE_CASE = bits * 2 - 1
return bits
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=BITS ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = x.device
_SCREAMING_SNAKE_CASE = (x > 0).int()
_SCREAMING_SNAKE_CASE = 2 ** torch.arange(bits - 1 , -1 , -1 , device=lowerCAmelCase__ , dtype=torch.intaa )
_SCREAMING_SNAKE_CASE = rearrange(lowerCAmelCase__ , """d -> d 1 1""" )
_SCREAMING_SNAKE_CASE = rearrange(lowerCAmelCase__ , """b (c d) h w -> b c d h w""" , d=8 )
_SCREAMING_SNAKE_CASE = reduce(x * mask , """b c d h w -> b c h w""" , """sum""" )
return (dec / 2_55).clamp(0.0 , 1.0 )
def lowerCAmelCase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ) -> Union[str, Any]:
"""simple docstring"""
if self.num_inference_steps is None:
raise ValueError(
"""Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" )
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
_SCREAMING_SNAKE_CASE = timestep - self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
_SCREAMING_SNAKE_CASE = self.alphas_cumprod[timestep]
_SCREAMING_SNAKE_CASE = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
_SCREAMING_SNAKE_CASE = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_SCREAMING_SNAKE_CASE = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
# 4. Clip "predicted x_0"
_SCREAMING_SNAKE_CASE = self.bit_scale
if self.config.clip_sample:
_SCREAMING_SNAKE_CASE = torch.clamp(lowerCAmelCase__ , -scale , lowerCAmelCase__ )
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
_SCREAMING_SNAKE_CASE = self._get_variance(lowerCAmelCase__ , lowerCAmelCase__ )
_SCREAMING_SNAKE_CASE = eta * variance ** 0.5
if use_clipped_model_output:
# the model_output is always re-derived from the clipped x_0 in Glide
_SCREAMING_SNAKE_CASE = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_SCREAMING_SNAKE_CASE = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
_SCREAMING_SNAKE_CASE = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if eta > 0:
# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
_SCREAMING_SNAKE_CASE = model_output.device if torch.is_tensor(lowerCAmelCase__ ) else """cpu"""
_SCREAMING_SNAKE_CASE = torch.randn(model_output.shape , dtype=model_output.dtype , generator=lowerCAmelCase__ ).to(lowerCAmelCase__ )
_SCREAMING_SNAKE_CASE = self._get_variance(lowerCAmelCase__ , lowerCAmelCase__ ) ** 0.5 * eta * noise
_SCREAMING_SNAKE_CASE = prev_sample + variance
if not return_dict:
return (prev_sample,)
return DDIMSchedulerOutput(prev_sample=lowerCAmelCase__ , pred_original_sample=lowerCAmelCase__ )
def lowerCAmelCase_ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="epsilon" , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_ = True , ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = torch.split(lowerCAmelCase__ , sample.shape[1] , dim=1 )
else:
_SCREAMING_SNAKE_CASE = None
# 1. compute alphas, betas
_SCREAMING_SNAKE_CASE = self.alphas_cumprod[t]
_SCREAMING_SNAKE_CASE = self.alphas_cumprod[t - 1] if t > 0 else self.one
_SCREAMING_SNAKE_CASE = 1 - alpha_prod_t
_SCREAMING_SNAKE_CASE = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if prediction_type == "epsilon":
_SCREAMING_SNAKE_CASE = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif prediction_type == "sample":
_SCREAMING_SNAKE_CASE = model_output
else:
raise ValueError(F"Unsupported prediction_type {prediction_type}." )
# 3. Clip "predicted x_0"
_SCREAMING_SNAKE_CASE = self.bit_scale
if self.config.clip_sample:
_SCREAMING_SNAKE_CASE = torch.clamp(lowerCAmelCase__ , -scale , lowerCAmelCase__ )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_SCREAMING_SNAKE_CASE = (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
_SCREAMING_SNAKE_CASE = self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_SCREAMING_SNAKE_CASE = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
_SCREAMING_SNAKE_CASE = 0
if t > 0:
_SCREAMING_SNAKE_CASE = torch.randn(
model_output.size() , dtype=model_output.dtype , layout=model_output.layout , generator=lowerCAmelCase__ ).to(model_output.device )
_SCREAMING_SNAKE_CASE = (self._get_variance(lowerCAmelCase__ , predicted_variance=lowerCAmelCase__ ) ** 0.5) * noise
_SCREAMING_SNAKE_CASE = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return DDPMSchedulerOutput(prev_sample=lowerCAmelCase__ , pred_original_sample=lowerCAmelCase__ )
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__ , A__ = 1.0 , ) -> str:
super().__init__()
_SCREAMING_SNAKE_CASE = bit_scale
_SCREAMING_SNAKE_CASE = (
ddim_bit_scheduler_step if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else ddpm_bit_scheduler_step
)
self.register_modules(unet=_UpperCAmelCase , scheduler=_UpperCAmelCase )
@torch.no_grad()
def __call__( self , A__ = 2_56 , A__ = 2_56 , A__ = 50 , A__ = None , A__ = 1 , A__ = "pil" , A__ = True , **A__ , ) -> Union[Tuple, ImagePipelineOutput]:
_SCREAMING_SNAKE_CASE = torch.randn(
(batch_size, self.unet.config.in_channels, height, width) , generator=_UpperCAmelCase , )
_SCREAMING_SNAKE_CASE = decimal_to_bits(_UpperCAmelCase ) * self.bit_scale
_SCREAMING_SNAKE_CASE = latents.to(self.device )
self.scheduler.set_timesteps(_UpperCAmelCase )
for t in self.progress_bar(self.scheduler.timesteps ):
# predict the noise residual
_SCREAMING_SNAKE_CASE = self.unet(_UpperCAmelCase , _UpperCAmelCase ).sample
# compute the previous noisy sample x_t -> x_t-1
_SCREAMING_SNAKE_CASE = self.scheduler.step(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ).prev_sample
_SCREAMING_SNAKE_CASE = bits_to_decimal(_UpperCAmelCase )
if output_type == "pil":
_SCREAMING_SNAKE_CASE = self.numpy_to_pil(_UpperCAmelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_UpperCAmelCase )
| 713 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
UpperCamelCase__ : Tuple = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , *A__ , A__=None , A__=None , A__=None , **A__ ) -> Optional[int]:
super().__init__(*A__ , **A__ )
_SCREAMING_SNAKE_CASE = eval_examples
_SCREAMING_SNAKE_CASE = post_process_function
_SCREAMING_SNAKE_CASE = quant_trainer_args
_SCREAMING_SNAKE_CASE = 1_28 # default number of calibration samples
def UpperCamelCase ( self , A__=None ) -> Union[str, Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError("""Trainer: calibration requires an calib_dataset.""" )
_SCREAMING_SNAKE_CASE = calib_dataset if calib_dataset is not None else self.calib_dataset
_SCREAMING_SNAKE_CASE = self._remove_unused_columns(A__ , description="""Calibration""" )
return DataLoader(
A__ , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=A__ , )
def UpperCamelCase ( self , A__=None ) -> str:
_SCREAMING_SNAKE_CASE = self.train_dataset if calib_dataset is None else calib_dataset
_SCREAMING_SNAKE_CASE = self.get_calib_dataloader(A__ )
_SCREAMING_SNAKE_CASE = self.model
quant_trainer.configure_model(A__ , self.quant_trainer_args , calib=A__ )
model.eval()
quant_trainer.enable_calibration(A__ )
logger.info("""***** Running calibration *****""" )
logger.info(F" Num examples = {self.calib_num}" )
logger.info(F" Batch size = {calib_dataloader.batch_size}" )
for step, inputs in enumerate(A__ ):
# Prediction step
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.prediction_step(A__ , A__ , prediction_loss_only=A__ )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(A__ , self.quant_trainer_args )
_SCREAMING_SNAKE_CASE = model
def UpperCamelCase ( self , A__=None , A__=None , A__=None , A__ = "eval" ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.eval_dataset if eval_dataset is None else eval_dataset
_SCREAMING_SNAKE_CASE = self.get_eval_dataloader(A__ )
_SCREAMING_SNAKE_CASE = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_SCREAMING_SNAKE_CASE = self.compute_metrics
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_SCREAMING_SNAKE_CASE = self.post_process_function(A__ , A__ , output.predictions )
_SCREAMING_SNAKE_CASE = self.compute_metrics(A__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
_SCREAMING_SNAKE_CASE = metrics.pop(A__ )
self.log(A__ )
else:
_SCREAMING_SNAKE_CASE = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_SCREAMING_SNAKE_CASE = self.callback_handler.on_evaluate(self.args , self.state , self.control , A__ )
return metrics
def UpperCamelCase ( self , A__ , A__ , A__=None , A__ = "test" ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.get_test_dataloader(A__ )
# Temporarily disable metric computation, we will do it in the loop here.
_SCREAMING_SNAKE_CASE = self.compute_metrics
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_SCREAMING_SNAKE_CASE = self.post_process_function(A__ , A__ , output.predictions , """predict""" )
_SCREAMING_SNAKE_CASE = self.compute_metrics(A__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
_SCREAMING_SNAKE_CASE = metrics.pop(A__ )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=A__ )
def UpperCamelCase ( self , A__="./" ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.eval_dataset
_SCREAMING_SNAKE_CASE = self.get_eval_dataloader(A__ )
_SCREAMING_SNAKE_CASE = next(iter(A__ ) )
# saving device - to make it consistent
_SCREAMING_SNAKE_CASE = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
# convert to tuple
_SCREAMING_SNAKE_CASE = tuple(v.to(A__ ) for k, v in batch.items() )
logger.info("""Converting model to be onnx compatible""" )
from pytorch_quantization.nn import TensorQuantizer
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = self.model.to(A__ )
model.eval()
model.float()
_SCREAMING_SNAKE_CASE = model.module if hasattr(A__ , """module""" ) else model
quant_trainer.configure_model(A__ , self.quant_trainer_args )
_SCREAMING_SNAKE_CASE = os.path.join(A__ , """model.onnx""" )
logger.info(F"exporting model to {output_model_file}" )
_SCREAMING_SNAKE_CASE = {0: """batch_size""", 1: """seq_len"""}
torch.onnx.export(
A__ , A__ , A__ , export_params=A__ , opset_version=13 , do_constant_folding=A__ , input_names=["""input_ids""", """attention_mask""", """token_type_ids"""] , output_names=["""output_start_logits""", """output_end_logits"""] , dynamic_axes={
"""input_ids""": axes,
"""attention_mask""": axes,
"""token_type_ids""": axes,
"""output_start_logits""": axes,
"""output_end_logits""": axes,
} , verbose=A__ , )
logger.info("""onnx export finished""" )
| 0 | 0 |
import math
UpperCamelCase__ : List[str] = 10
UpperCamelCase__ : List[Any] = 7
UpperCamelCase__ : Dict = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = 20 ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = math.comb(__A , __A )
_SCREAMING_SNAKE_CASE = math.comb(NUM_BALLS - BALLS_PER_COLOUR , __A )
_SCREAMING_SNAKE_CASE = NUM_COLOURS * (1 - missing_colour / total)
return F"{result:.9f}"
if __name__ == "__main__":
print(solution(20))
| 714 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
return "".join([hex(SCREAMING_SNAKE_CASE_ )[2:].zfill(2 ).upper() for byte in list(SCREAMING_SNAKE_CASE_ )] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bytes:
"""simple docstring"""
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(SCREAMING_SNAKE_CASE_ ) % 2) != 0:
raise ValueError(
"""Base16 encoded data is invalid:
Data does not have an even number of hex digits.""" )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(SCREAMING_SNAKE_CASE_ ) <= set("""0123456789ABCDEF""" ):
raise ValueError(
"""Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.""" )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__ : str = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
# See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}
class _a (a__):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'mctct'
def __init__( self , A__=80_65 , A__=15_36 , A__=36 , A__=61_44 , A__=4 , A__=3_84 , A__=9_20 , A__=1E-5 , A__=0.3 , A__="relu" , A__=0.02 , A__=0.3 , A__=0.3 , A__=1 , A__=0 , A__=2 , A__=1 , A__=0.3 , A__=1 , A__=(7,) , A__=(3,) , A__=80 , A__=1 , A__=None , A__="sum" , A__=False , **A__ , ) -> str:
super().__init__(**_A , pad_token_id=_A , bos_token_id=_A , eos_token_id=_A )
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = attention_head_dim
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = layerdrop
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = pad_token_id
_SCREAMING_SNAKE_CASE = bos_token_id
_SCREAMING_SNAKE_CASE = eos_token_id
_SCREAMING_SNAKE_CASE = conv_glu_dim
_SCREAMING_SNAKE_CASE = conv_dropout
_SCREAMING_SNAKE_CASE = num_conv_layers
_SCREAMING_SNAKE_CASE = input_feat_per_channel
_SCREAMING_SNAKE_CASE = input_channels
_SCREAMING_SNAKE_CASE = conv_channels
_SCREAMING_SNAKE_CASE = ctc_loss_reduction
_SCREAMING_SNAKE_CASE = ctc_zero_infinity
# prevents config testing fail with exporting to json
_SCREAMING_SNAKE_CASE = list(_A )
_SCREAMING_SNAKE_CASE = list(_A )
if len(self.conv_kernel ) != self.num_conv_layers:
raise ValueError(
"""Configuration for convolutional module is incorrect. """
"""It is required that `len(config.conv_kernel)` == `config.num_conv_layers` """
F"but is `len(config.conv_kernel) = {len(self.conv_kernel )}`, "
F"`config.num_conv_layers = {self.num_conv_layers}`." )
| 715 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
requests.request("""GET""" , """https://huggingface.co""" )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request("""GET""" , """https://huggingface.co""" , timeout=1.0 )
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request("""GET""" , """https://huggingface.co""" )
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_head("""https://huggingface.co""" )
| 0 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class _a (_A):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'Wav2Vec2FeatureExtractor'
SCREAMING_SNAKE_CASE = 'AutoTokenizer'
def __init__( self , A__ , A__ ) -> Optional[int]:
super().__init__(__lowerCamelCase , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = self.feature_extractor
_SCREAMING_SNAKE_CASE = False
@classmethod
def UpperCamelCase ( cls , A__ , **A__ ) -> str:
try:
return super().from_pretrained(__lowerCamelCase , **__lowerCamelCase )
except OSError:
warnings.warn(
F"Loading a tokenizer inside {cls.__name__} from a config that does not"
""" include a `tokenizer_class` attribute is deprecated and will be """
"""removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
""" attribute to either your `config.json` or `tokenizer_config.json` """
"""file to suppress this warning: """ , __lowerCamelCase , )
_SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
_SCREAMING_SNAKE_CASE = WavaVecaCTCTokenizer.from_pretrained(__lowerCamelCase , **__lowerCamelCase )
return cls(feature_extractor=__lowerCamelCase , tokenizer=__lowerCamelCase )
def __call__( self , *A__ , **A__ ) -> List[Any]:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*__lowerCamelCase , **__lowerCamelCase )
if "raw_speech" in kwargs:
warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
_SCREAMING_SNAKE_CASE = kwargs.pop("""raw_speech""" )
else:
_SCREAMING_SNAKE_CASE = kwargs.pop("""audio""" , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = kwargs.pop("""sampling_rate""" , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = kwargs.pop("""text""" , __lowerCamelCase )
if len(__lowerCamelCase ) > 0:
_SCREAMING_SNAKE_CASE = args[0]
_SCREAMING_SNAKE_CASE = args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
_SCREAMING_SNAKE_CASE = self.feature_extractor(__lowerCamelCase , *__lowerCamelCase , sampling_rate=__lowerCamelCase , **__lowerCamelCase )
if text is not None:
_SCREAMING_SNAKE_CASE = self.tokenizer(__lowerCamelCase , **__lowerCamelCase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
_SCREAMING_SNAKE_CASE = encodings["input_ids"]
return inputs
def UpperCamelCase ( self , *A__ , **A__ ) -> str:
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*__lowerCamelCase , **__lowerCamelCase )
_SCREAMING_SNAKE_CASE = kwargs.pop("""input_features""" , __lowerCamelCase )
_SCREAMING_SNAKE_CASE = kwargs.pop("""labels""" , __lowerCamelCase )
if len(__lowerCamelCase ) > 0:
_SCREAMING_SNAKE_CASE = args[0]
_SCREAMING_SNAKE_CASE = args[1:]
if input_features is not None:
_SCREAMING_SNAKE_CASE = self.feature_extractor.pad(__lowerCamelCase , *__lowerCamelCase , **__lowerCamelCase )
if labels is not None:
_SCREAMING_SNAKE_CASE = self.tokenizer.pad(__lowerCamelCase , **__lowerCamelCase )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
_SCREAMING_SNAKE_CASE = labels["input_ids"]
return input_features
def UpperCamelCase ( self , *A__ , **A__ ) -> List[Any]:
return self.tokenizer.batch_decode(*__lowerCamelCase , **__lowerCamelCase )
def UpperCamelCase ( self , *A__ , **A__ ) -> List[Any]:
return self.tokenizer.decode(*__lowerCamelCase , **__lowerCamelCase )
@contextmanager
def UpperCamelCase ( self ) -> Union[str, Any]:
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your audio inputs, or in a separate call.""" )
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = self.tokenizer
yield
_SCREAMING_SNAKE_CASE = self.feature_extractor
_SCREAMING_SNAKE_CASE = False
| 716 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase_ ( ) -> Iterator[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
while True:
if is_prime(SCREAMING_SNAKE_CASE_ ):
yield num
num += 1
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = 2_00_00_00 ) -> int:
"""simple docstring"""
return sum(takewhile(lambda SCREAMING_SNAKE_CASE_ : x < n , prime_generator() ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 0 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Tuple = [
"word_embeddings_layernorm.weight",
"word_embeddings_layernorm.bias",
"input_layernorm.weight",
"input_layernorm.bias",
"post_attention_layernorm.weight",
"post_attention_layernorm.bias",
"self_attention.dense.bias",
"mlp.dense_4h_to_h.bias",
"ln_f.weight",
"ln_f.bias",
]
UpperCamelCase__ : Dict = [
"mlp.dense_4h_to_h.weight",
"self_attention.dense.weight",
]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {
'''word_embeddings.weight''': '''word_embeddings.weight''',
'''word_embeddings.norm.weight''': '''word_embeddings_layernorm.weight''',
'''word_embeddings.norm.bias''': '''word_embeddings_layernorm.bias''',
'''weight''': '''ln_f.weight''',
'''bias''': '''ln_f.bias''',
}
if key in layer_rename_map:
return layer_rename_map[key]
# Handle transformer blocks
_SCREAMING_SNAKE_CASE = int(re.match(r""".*layer_(\d*).*""" , a_ )[1] )
layer_number -= 3
return F"h.{layer_number}." + key
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if dtype == torch.bool:
return 1 / 8
_SCREAMING_SNAKE_CASE = re.search(r"""[^\d](\d+)$""" , str(a_ ) )
if bit_search is None:
raise ValueError(F"`dtype` is not a valid dtype: {dtype}." )
_SCREAMING_SNAKE_CASE = int(bit_search.groups()[0] )
return bit_size // 8
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
if bloom_config_file == "":
_SCREAMING_SNAKE_CASE = BloomConfig()
else:
_SCREAMING_SNAKE_CASE = BloomConfig.from_json_file(a_ )
if shard_model:
_SCREAMING_SNAKE_CASE = os.listdir(a_ )
_SCREAMING_SNAKE_CASE = sorted(filter(lambda SCREAMING_SNAKE_CASE_ : s.startswith("""layer""" ) and "model_00" in s , a_ ) )
_SCREAMING_SNAKE_CASE = {'''weight_map''': {}, '''metadata''': {}}
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = BloomConfig()
for j, file in enumerate(a_ ):
print("""Processing file: {}""".format(a_ ) )
_SCREAMING_SNAKE_CASE = None
for i in range(a_ ):
# load all TP files
_SCREAMING_SNAKE_CASE = file.replace("""model_00""" , F"model_0{i}" )
_SCREAMING_SNAKE_CASE = torch.load(os.path.join(a_ , a_ ) , map_location="""cpu""" )
# Rename keys in the transformers names
_SCREAMING_SNAKE_CASE = list(temp.keys() )
for key in keys:
_SCREAMING_SNAKE_CASE = temp.pop(a_ )
if tensors is None:
_SCREAMING_SNAKE_CASE = temp
else:
for key in tensors.keys():
if any(key.endswith(a_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
_SCREAMING_SNAKE_CASE = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
_SCREAMING_SNAKE_CASE = torch.cat([tensors[key], temp[key]] , dim=a_ )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(a_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
_SCREAMING_SNAKE_CASE = tensors[key] / pretraining_tp
torch.save(
a_ , os.path.join(
a_ , """pytorch_model_{}-of-{}.bin""".format(str(j + 1 ).zfill(5 ) , str(len(a_ ) ).zfill(5 ) ) , ) , )
for key in tensors.keys():
_SCREAMING_SNAKE_CASE = tensors[key]
total_size += value.numel() * get_dtype_size(value.dtype )
if key not in index_dict["weight_map"]:
_SCREAMING_SNAKE_CASE = '''pytorch_model_{}-of-{}.bin'''.format(
str(j + 1 ).zfill(5 ) , str(len(a_ ) ).zfill(5 ) )
_SCREAMING_SNAKE_CASE = BloomConfig()
_SCREAMING_SNAKE_CASE = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
_SCREAMING_SNAKE_CASE = total_size
with open(a_ , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
with open(os.path.join(a_ , WEIGHTS_NAME + """.index.json""" ) , """w""" , encoding="""utf-8""" ) as f:
_SCREAMING_SNAKE_CASE = json.dumps(a_ , indent=2 , sort_keys=a_ ) + '''\n'''
f.write(a_ )
else:
_SCREAMING_SNAKE_CASE = BloomModel(a_ )
_SCREAMING_SNAKE_CASE = os.listdir(a_ )
_SCREAMING_SNAKE_CASE = sorted(filter(lambda SCREAMING_SNAKE_CASE_ : s.startswith("""layer""" ) and "model_00" in s , a_ ) )
_SCREAMING_SNAKE_CASE = None
for i, file in enumerate(a_ ):
_SCREAMING_SNAKE_CASE = None
for i in range(a_ ):
# load all TP files
_SCREAMING_SNAKE_CASE = file.replace("""model_00""" , F"model_0{i}" )
_SCREAMING_SNAKE_CASE = torch.load(os.path.join(a_ , a_ ) , map_location="""cpu""" )
# Rename keys in the transformers names
_SCREAMING_SNAKE_CASE = list(temp.keys() )
for key in keys:
_SCREAMING_SNAKE_CASE = temp.pop(a_ )
if tensors is None:
_SCREAMING_SNAKE_CASE = temp
else:
for key in tensors.keys():
# We average (sum and then divide) some weights accross TP ranks (see https://github.com/bigscience-workshop/Megatron-DeepSpeed/blob/olruwase/sync_layer_norms/megatron/training.py#L425)
if any(key.endswith(a_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
tensors[key] += temp[key]
else:
# Some weights are RowParallelLinear in Megatron-Deepspeed, others are ColumnParallel
_SCREAMING_SNAKE_CASE = 1 if any(text in key for text in WEIGHTS_WITH_ROW_PARALLELISM_CONTAIN ) else 0
# We concatenate these weights accross TP ranks
_SCREAMING_SNAKE_CASE = torch.cat([tensors[key], temp[key]] , dim=a_ )
# Divide by the number of TP the weights we want to average
for key in tensors.keys():
if any(key.endswith(a_ ) for end in WEIGHTS_TO_AVERAGE_ENDSWITH ):
_SCREAMING_SNAKE_CASE = tensors[key] / pretraining_tp
_SCREAMING_SNAKE_CASE = model.load_state_dict(a_ , strict=a_ )
assert not other_keys.unexpected_keys, F"The keys {other_keys.unexpected_keys} are unexpected"
if missing_keys is None:
_SCREAMING_SNAKE_CASE = set(other_keys.missing_keys )
else:
_SCREAMING_SNAKE_CASE = missing_keys.intersection(set(other_keys.missing_keys ) )
assert not missing_keys, F"The keys {missing_keys} are missing"
# Save pytorch-model
os.makedirs(a_ , exist_ok=a_ )
_SCREAMING_SNAKE_CASE = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
_SCREAMING_SNAKE_CASE = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(F"Save PyTorch model to {pytorch_weights_dump_path} with dtype {config.torch_dtype}" )
if config.torch_dtype is not None:
_SCREAMING_SNAKE_CASE = model.to(config.torch_dtype )
torch.save(model.state_dict() , a_ )
print(F"Save configuration file to {pytorch_config_dump_path}" )
with open(a_ , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
UpperCamelCase__ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--bloom_checkpoint_path",
default=None,
type=str,
required=True,
help="Path to the Megatron-LM checkpoint path.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--bloom_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--shard_model",
action="store_true",
help="An optional setting to shard the output model \nThis enables sharding the converted checkpoint",
)
parser.add_argument(
"--pretraining_tp",
default=4,
type=int,
help="Pretraining TP rank that has been used when training the model in Megatron-LM \n",
)
UpperCamelCase__ : str = parser.parse_args()
convert_bloom_checkpoint_to_pytorch(
args.bloom_checkpoint_path,
args.bloom_config_file,
args.pytorch_dump_folder_path,
args.shard_model,
args.pretraining_tp,
)
| 717 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = {
"""task_specific_params""": {
"""summarization""": {"""length_penalty""": 1.0, """max_length""": 1_28, """min_length""": 12, """num_beams""": 4},
"""summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_42, """min_length""": 56, """num_beams""": 4},
"""summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
}
}
_SCREAMING_SNAKE_CASE = {
"""task_specific_params.summarization.length_penalty""": 1.0,
"""task_specific_params.summarization.max_length""": 1_28,
"""task_specific_params.summarization.min_length""": 12,
"""task_specific_params.summarization.num_beams""": 4,
"""task_specific_params.summarization_cnn.length_penalty""": 2.0,
"""task_specific_params.summarization_cnn.max_length""": 1_42,
"""task_specific_params.summarization_cnn.min_length""": 56,
"""task_specific_params.summarization_cnn.num_beams""": 4,
"""task_specific_params.summarization_xsum.length_penalty""": 1.0,
"""task_specific_params.summarization_xsum.max_length""": 62,
"""task_specific_params.summarization_xsum.min_length""": 11,
"""task_specific_params.summarization_xsum.num_beams""": 6,
}
self.assertEqual(flatten_dict(A__ ) , A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(A__ ) , x.transpose() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , transpose(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , transpose(A__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , transpose(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , transpose(A__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , np.asarray(transpose(A__ ) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , np.asarray(transpose(A__ , axes=(1, 2, 0) ) ) ) )
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , np.reshape(A__ , (4, 3) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , np.reshape(A__ , (12, 5) ) ) )
@require_torch
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , reshape(A__ , (4, 3) ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , reshape(A__ , (12, 5) ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , reshape(A__ , (4, 3) ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , reshape(A__ , (12, 5) ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , np.asarray(reshape(A__ , (4, 3) ) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , np.asarray(reshape(A__ , (12, 5) ) ) ) )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(A__ ) , np.squeeze(A__ ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , np.squeeze(A__ , axis=2 ) ) )
@require_torch
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , squeeze(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , squeeze(A__ , axis=2 ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , squeeze(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , squeeze(A__ , axis=2 ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , np.asarray(squeeze(A__ ) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , np.asarray(squeeze(A__ , axis=2 ) ) ) )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , np.expand_dims(A__ , axis=1 ) ) )
@require_torch
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , expand_dims(A__ , axis=1 ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , expand_dims(A__ , axis=1 ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , np.asarray(expand_dims(A__ , axis=1 ) ) ) )
| 0 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : str = logging.get_logger(__name__)
UpperCamelCase__ : Optional[int] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class _a (_UpperCAmelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'vivit'
def __init__( self , A__=2_24 , A__=32 , A__=[2, 16, 16] , A__=3 , A__=7_68 , A__=12 , A__=12 , A__=30_72 , A__="gelu_fast" , A__=0.0 , A__=0.0 , A__=0.02 , A__=1E-06 , A__=True , **A__ , ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = num_frames
_SCREAMING_SNAKE_CASE = tubelet_size
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = qkv_bias
super().__init__(**__UpperCamelCase )
| 718 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ''
SCREAMING_SNAKE_CASE = 'hf-legacy' # "hf://"" is reserved for hffs
def __init__( self , A__ = None , A__ = None , **A__ , ) -> Optional[int]:
super().__init__(self , **A__ )
_SCREAMING_SNAKE_CASE = repo_info
_SCREAMING_SNAKE_CASE = token
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Tuple:
if self.dir_cache is None:
_SCREAMING_SNAKE_CASE = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
_SCREAMING_SNAKE_CASE = {
"""name""": hf_file.rfilename,
"""size""": None,
"""type""": """file""",
}
self.dir_cache.update(
{
str(A__ ): {"""name""": str(A__ ), """size""": None, """type""": """directory"""}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def UpperCamelCase ( self , A__ , A__ = "rb" , **A__ , ) -> Optional[int]:
if not isinstance(self.repo_info , A__ ):
raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}" )
_SCREAMING_SNAKE_CASE = hf_hub_url(self.repo_info.id , A__ , revision=self.repo_info.sha )
return fsspec.open(
A__ , mode=A__ , headers=get_authentication_headers_for_url(A__ , use_auth_token=self.token ) , client_kwargs={"""trust_env""": True} , ).open()
def UpperCamelCase ( self , A__ , **A__ ) -> str:
self._get_dirs()
_SCREAMING_SNAKE_CASE = self._strip_protocol(A__ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(A__ )
def UpperCamelCase ( self , A__ , A__=False , **A__ ) -> List[Any]:
self._get_dirs()
_SCREAMING_SNAKE_CASE = PurePosixPath(path.strip("""/""" ) )
_SCREAMING_SNAKE_CASE = {}
for p, f in self.dir_cache.items():
_SCREAMING_SNAKE_CASE = PurePosixPath(p.strip("""/""" ) )
_SCREAMING_SNAKE_CASE = p.parent
if root == path:
_SCREAMING_SNAKE_CASE = f
_SCREAMING_SNAKE_CASE = list(paths.values() )
if detail:
return out
else:
return sorted(f["""name"""] for f in out )
| 0 | 0 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 10
_SCREAMING_SNAKE_CASE = datasets.Features(
{
"""tokens""": datasets.Sequence(datasets.Value("""string""" ) ),
"""labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""] ) ),
"""answers""": datasets.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
"""id""": datasets.Value("""int64""" ),
} )
_SCREAMING_SNAKE_CASE = datasets.Dataset.from_dict(
{
"""tokens""": [["""foo"""] * 5] * n,
"""labels""": [[1] * 5] * n,
"""answers""": [{"""answer_start""": [97], """text""": ["""1976"""]}] * 10,
"""id""": list(range(SCREAMING_SNAKE_CASE_ ) ),
} , features=SCREAMING_SNAKE_CASE_ , )
return dataset
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """file.arrow""" )
dataset.map(cache_file_name=SCREAMING_SNAKE_CASE_ )
return filename
# FILE_CONTENT + files
UpperCamelCase__ : Tuple = """\
Text data.
Second line of data."""
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """file.txt"""
_SCREAMING_SNAKE_CASE = FILE_CONTENT
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return filename
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
import bza
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """file.txt.bz2"""
_SCREAMING_SNAKE_CASE = bytes(SCREAMING_SNAKE_CASE_ , """utf-8""" )
with bza.open(SCREAMING_SNAKE_CASE_ , """wb""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
import gzip
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """file.txt.gz""" )
_SCREAMING_SNAKE_CASE = bytes(SCREAMING_SNAKE_CASE_ , """utf-8""" )
with gzip.open(SCREAMING_SNAKE_CASE_ , """wb""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
if datasets.config.LZ4_AVAILABLE:
import lza.frame
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """file.txt.lz4"""
_SCREAMING_SNAKE_CASE = bytes(SCREAMING_SNAKE_CASE_ , """utf-8""" )
with lza.frame.open(SCREAMING_SNAKE_CASE_ , """wb""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """file.txt.7z"""
with pyazr.SevenZipFile(SCREAMING_SNAKE_CASE_ , """w""" ) as archive:
archive.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
import tarfile
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """file.txt.tar"""
with tarfile.TarFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.add(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
import lzma
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """file.txt.xz"""
_SCREAMING_SNAKE_CASE = bytes(SCREAMING_SNAKE_CASE_ , """utf-8""" )
with lzma.open(SCREAMING_SNAKE_CASE_ , """wb""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
import zipfile
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """file.txt.zip"""
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """file.txt.zst"""
_SCREAMING_SNAKE_CASE = bytes(SCREAMING_SNAKE_CASE_ , """utf-8""" )
with zstd.open(SCREAMING_SNAKE_CASE_ , """wb""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """file.xml"""
_SCREAMING_SNAKE_CASE = textwrap.dedent(
"""\\n <?xml version=\"1.0\" encoding=\"UTF-8\" ?>\n <tmx version=\"1.4\">\n <header segtype=\"sentence\" srclang=\"ca\" />\n <body>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>\n </tu>\n <tu>\n <tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>\n <tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>\n </tu>\n </body>\n </tmx>""" )
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return filename
UpperCamelCase__ : Tuple = [
{"""col_1""": """0""", """col_2""": 0, """col_3""": 0.0},
{"""col_1""": """1""", """col_2""": 1, """col_3""": 1.0},
{"""col_1""": """2""", """col_2""": 2, """col_3""": 2.0},
{"""col_1""": """3""", """col_2""": 3, """col_3""": 3.0},
]
UpperCamelCase__ : Optional[Any] = [
{"""col_1""": """4""", """col_2""": 4, """col_3""": 4.0},
{"""col_1""": """5""", """col_2""": 5, """col_3""": 5.0},
]
UpperCamelCase__ : Any = {
"""col_1""": ["""0""", """1""", """2""", """3"""],
"""col_2""": [0, 1, 2, 3],
"""col_3""": [0.0, 1.0, 2.0, 3.0],
}
UpperCamelCase__ : List[Any] = [
{"""col_3""": 0.0, """col_1""": """0""", """col_2""": 0},
{"""col_3""": 1.0, """col_1""": """1""", """col_2""": 1},
]
UpperCamelCase__ : List[str] = [
{"""col_1""": """s0""", """col_2""": 0, """col_3""": 0.0},
{"""col_1""": """s1""", """col_2""": 1, """col_3""": 1.0},
{"""col_1""": """s2""", """col_2""": 2, """col_3""": 2.0},
{"""col_1""": """s3""", """col_2""": 3, """col_3""": 3.0},
]
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = datasets.Dataset.from_dict(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset.arrow""" )
dataset.map(cache_file_name=SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset.sqlite""" )
with contextlib.closing(sqlitea.connect(SCREAMING_SNAKE_CASE_ ) ) as con:
_SCREAMING_SNAKE_CASE = con.cursor()
cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""" )
for item in DATA:
cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)""" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset.csv""" )
with open(SCREAMING_SNAKE_CASE_ , """w""" , newline="""""" ) as f:
_SCREAMING_SNAKE_CASE = csv.DictWriter(SCREAMING_SNAKE_CASE_ , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.csv""" )
with open(SCREAMING_SNAKE_CASE_ , """w""" , newline="""""" ) as f:
_SCREAMING_SNAKE_CASE = csv.DictWriter(SCREAMING_SNAKE_CASE_ , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
import bza
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.bz2"""
with open(SCREAMING_SNAKE_CASE_ , """rb""" ) as f:
_SCREAMING_SNAKE_CASE = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(SCREAMING_SNAKE_CASE_ , """wb""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""" ) ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""" ) ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.csv.zip"""
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("""main_dir""" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("""main_dir""" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset.parquet""" )
_SCREAMING_SNAKE_CASE = pa.schema(
{
"""col_1""": pa.string(),
"""col_2""": pa.intaa(),
"""col_3""": pa.floataa(),
} )
with open(SCREAMING_SNAKE_CASE_ , """wb""" ) as f:
_SCREAMING_SNAKE_CASE = pq.ParquetWriter(SCREAMING_SNAKE_CASE_ , schema=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(SCREAMING_SNAKE_CASE_ ) )] for k in DATA[0]} , schema=SCREAMING_SNAKE_CASE_ )
writer.write_table(SCREAMING_SNAKE_CASE_ )
writer.close()
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
_SCREAMING_SNAKE_CASE = {"""data""": DATA}
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
_SCREAMING_SNAKE_CASE = {"""data""": DATA_DICT_OF_LISTS}
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl""" )
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
for item in DATA:
f.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.jsonl""" )
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
for item in DATA:
f.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset_312.jsonl""" )
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
for item in DATA_312:
f.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset-str.jsonl""" )
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
for item in DATA_STR:
f.write(json.dumps(SCREAMING_SNAKE_CASE_ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
import gzip
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt.gz""" )
with open(SCREAMING_SNAKE_CASE_ , """rb""" ) as orig_file:
with gzip.open(SCREAMING_SNAKE_CASE_ , """wb""" ) as zipped_file:
zipped_file.writelines(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
import gzip
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.gz""" )
with open(SCREAMING_SNAKE_CASE_ , """rb""" ) as orig_file:
with gzip.open(SCREAMING_SNAKE_CASE_ , """wb""" ) as zipped_file:
zipped_file.writelines(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.zip"""
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.zip"""
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("""nested""" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.jsonl.zip"""
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("""main_dir""" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("""main_dir""" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.tar"""
with tarfile.TarFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.add(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
f.add(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.tar"""
with tarfile.TarFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.add(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("""nested""" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""0""", """1""", """2""", """3"""]
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt""" )
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""0""", """1""", """2""", """3"""]
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.txt""" )
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""0""", """1""", """2""", """3"""]
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset.abc"""
with open(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset.text.zip"""
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.text.zip"""
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("""main_dir""" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.join("""main_dir""" , os.path.basename(SCREAMING_SNAKE_CASE_ ) ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset.ext.zip"""
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename("""unsupported.ext""" ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename("""unsupported_2.ext""" ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""] )
_SCREAMING_SNAKE_CASE = str(tmp_path_factory.mktemp("""data""" ) / """dataset_with_unicode_new_lines.txt""" )
with open(SCREAMING_SNAKE_CASE_ , """w""" , encoding="""utf-8""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( ) -> Tuple:
"""simple docstring"""
return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""" )
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( ) -> Any:
"""simple docstring"""
return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""" )
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data""" ) / """dataset.img.zip"""
with zipfile.ZipFile(SCREAMING_SNAKE_CASE_ , """w""" ) as f:
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ) )
f.write(SCREAMING_SNAKE_CASE_ , arcname=os.path.basename(SCREAMING_SNAKE_CASE_ ).replace(""".jpg""" , """2.jpg""" ) )
return path
@pytest.fixture(scope="""session""" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path_factory.mktemp("""data_dir""" )
(data_dir / "subdir").mkdir()
with open(data_dir / """subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 10 )
with open(data_dir / """subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 10 )
# hidden file
with open(data_dir / """subdir""" / """.test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 10 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / """.subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 10 )
with open(data_dir / """.subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 10 )
return data_dir
| 719 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , keep_in_memory=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = features.copy() if features else default_expected_features
_SCREAMING_SNAKE_CASE = (
Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , split=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type""" , [str, list] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
if issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = parquet_path
elif issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = [parquet_path]
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=("train",) ) -> List[str]:
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for split in splits:
_SCREAMING_SNAKE_CASE = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(
{"""train""": parquet_path} , cache_dir=SCREAMING_SNAKE_CASE_ , keep_in_memory=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = features.copy() if features else default_expected_features
_SCREAMING_SNAKE_CASE = (
Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_SCREAMING_SNAKE_CASE = ParquetDatasetReader({"""train""": parquet_path} , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
if split:
_SCREAMING_SNAKE_CASE = {split: parquet_path}
else:
_SCREAMING_SNAKE_CASE = """train"""
_SCREAMING_SNAKE_CASE = {"""train""": parquet_path, """test""": parquet_path}
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ParquetDatasetWriter(SCREAMING_SNAKE_CASE_ , tmp_path / """foo.parquet""" )
assert writer.write() > 0
_SCREAMING_SNAKE_CASE = pq.ParquetFile(tmp_path / """foo.parquet""" )
_SCREAMING_SNAKE_CASE = pf.read()
assert dataset.data.table == output_table
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(shared_datadir / """test_image_rgb.jpg""" )
_SCREAMING_SNAKE_CASE = {"""image""": [image_path]}
_SCREAMING_SNAKE_CASE = Features({"""image""": Image()} )
_SCREAMING_SNAKE_CASE = Dataset.from_dict(SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = ParquetDatasetWriter(SCREAMING_SNAKE_CASE_ , tmp_path / """foo.parquet""" )
assert writer.write() > 0
_SCREAMING_SNAKE_CASE = Dataset.from_parquet(str(tmp_path / """foo.parquet""" ) )
assert dataset.features == reloaded_dataset.features
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(str(tmp_path / """foo.parquet""" ) , streaming=SCREAMING_SNAKE_CASE_ ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"""feature, expected""" , [
(Features({"""foo""": Value("""int32""" )} ), None),
(Features({"""image""": Image(), """foo""": Value("""int32""" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"""nested""": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
assert get_writer_batch_size(SCREAMING_SNAKE_CASE_ ) == expected
| 0 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Dict = logging.get_logger(__name__)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
_SCREAMING_SNAKE_CASE = [1_44, 1_92, 2_40]
_SCREAMING_SNAKE_CASE = [16, 32, 64, 96, 1_28, 1_60, 6_40]
elif "mobilevit_xs" in mobilevit_name:
_SCREAMING_SNAKE_CASE = [96, 1_20, 1_44]
_SCREAMING_SNAKE_CASE = [16, 32, 48, 64, 80, 96, 3_84]
elif "mobilevit_xxs" in mobilevit_name:
_SCREAMING_SNAKE_CASE = [64, 80, 96]
_SCREAMING_SNAKE_CASE = [16, 16, 24, 48, 64, 80, 3_20]
_SCREAMING_SNAKE_CASE = 0.05
_SCREAMING_SNAKE_CASE = 2.0
if mobilevit_name.startswith("""deeplabv3_""" ):
_SCREAMING_SNAKE_CASE = 5_12
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 21
_SCREAMING_SNAKE_CASE = """pascal-voc-id2label.json"""
else:
_SCREAMING_SNAKE_CASE = 10_00
_SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
_SCREAMING_SNAKE_CASE = """huggingface/label-files"""
_SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type="""dataset""" ) , """r""" ) )
_SCREAMING_SNAKE_CASE = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE = idalabel
_SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ) -> Any:
"""simple docstring"""
for i in range(1 , 6 ):
if F"layer_{i}." in name:
_SCREAMING_SNAKE_CASE = name.replace(F"layer_{i}." , F"encoder.layer.{i - 1}." )
if "conv_1." in name:
_SCREAMING_SNAKE_CASE = name.replace("""conv_1.""" , """conv_stem.""" )
if ".block." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".block.""" , """.""" )
if "exp_1x1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""exp_1x1""" , """expand_1x1""" )
if "red_1x1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""red_1x1""" , """reduce_1x1""" )
if ".local_rep.conv_3x3." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" )
if ".local_rep.conv_1x1." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" )
if ".norm." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".norm.""" , """.normalization.""" )
if ".conv." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".conv.""" , """.convolution.""" )
if ".conv_proj." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".conv_proj.""" , """.conv_projection.""" )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F".{i}.{j}." in name:
_SCREAMING_SNAKE_CASE = name.replace(F".{i}.{j}." , F".{i}.layer.{j}." )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F".{i}.{j}." in name:
_SCREAMING_SNAKE_CASE = name.replace(F".{i}.{j}." , F".{i}." )
if "expand_1x1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" )
if "conv_3x3" in name:
_SCREAMING_SNAKE_CASE = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" )
if "reduce_1x1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" )
for i in range(2 , 5 ):
if F".global_rep.{i}.weight" in name:
_SCREAMING_SNAKE_CASE = name.replace(F".global_rep.{i}.weight" , """.layernorm.weight""" )
if F".global_rep.{i}.bias" in name:
_SCREAMING_SNAKE_CASE = name.replace(F".global_rep.{i}.bias" , """.layernorm.bias""" )
if ".global_rep." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".global_rep.""" , """.transformer.""" )
if ".pre_norm_mha.0." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" )
if ".pre_norm_mha.1.out_proj." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" )
if ".pre_norm_ffn.0." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" )
if ".pre_norm_ffn.1." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" )
if ".pre_norm_ffn.4." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" )
if ".transformer." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".transformer.""" , """.transformer.layer.""" )
if ".aspp_layer." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in name:
_SCREAMING_SNAKE_CASE = name.replace(""".aspp_pool.""" , """.""" )
if "seg_head." in name:
_SCREAMING_SNAKE_CASE = name.replace("""seg_head.""" , """segmentation_head.""" )
if "segmentation_head.classifier.classifier." in name:
_SCREAMING_SNAKE_CASE = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" )
if "classifier.fc." in name:
_SCREAMING_SNAKE_CASE = name.replace("""classifier.fc.""" , """classifier.""" )
elif (not base_model) and ("segmentation_head." not in name):
_SCREAMING_SNAKE_CASE = """mobilevit.""" + name
return name
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ) -> str:
"""simple docstring"""
if base_model:
_SCREAMING_SNAKE_CASE = """"""
else:
_SCREAMING_SNAKE_CASE = """mobilevit."""
for key in orig_state_dict.copy().keys():
_SCREAMING_SNAKE_CASE = orig_state_dict.pop(__UpperCamelCase )
if key[:8] == "encoder.":
_SCREAMING_SNAKE_CASE = key[8:]
if "qkv" in key:
_SCREAMING_SNAKE_CASE = key.split(""".""" )
_SCREAMING_SNAKE_CASE = int(key_split[0][6:] ) - 1
_SCREAMING_SNAKE_CASE = int(key_split[3] )
_SCREAMING_SNAKE_CASE = model.get_submodule(F"{model_prefix}encoder.layer.{layer_num}" )
_SCREAMING_SNAKE_CASE = layer.transformer.layer[transformer_num].attention.attention.all_head_size
_SCREAMING_SNAKE_CASE = (
F"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
)
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[:dim, :]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
_SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2]
_SCREAMING_SNAKE_CASE = val[-dim:]
else:
_SCREAMING_SNAKE_CASE = val
return orig_state_dict
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_SCREAMING_SNAKE_CASE = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = get_mobilevit_config(__UpperCamelCase )
# load original state_dict
_SCREAMING_SNAKE_CASE = torch.load(__UpperCamelCase , map_location="""cpu""" )
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_""" ):
_SCREAMING_SNAKE_CASE = MobileViTForSemanticSegmentation(__UpperCamelCase ).eval()
else:
_SCREAMING_SNAKE_CASE = MobileViTForImageClassification(__UpperCamelCase ).eval()
_SCREAMING_SNAKE_CASE = convert_state_dict(__UpperCamelCase , __UpperCamelCase )
model.load_state_dict(__UpperCamelCase )
# Check outputs on an image, prepared by MobileViTImageProcessor
_SCREAMING_SNAKE_CASE = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
_SCREAMING_SNAKE_CASE = image_processor(images=prepare_img() , return_tensors="""pt""" )
_SCREAMING_SNAKE_CASE = model(**__UpperCamelCase )
_SCREAMING_SNAKE_CASE = outputs.logits
if mobilevit_name.startswith("""deeplabv3_""" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
_SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
[[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
[[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
_SCREAMING_SNAKE_CASE = torch.tensor(
[
[[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
[[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
[[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
_SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
[[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
[[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
] )
else:
raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}" )
assert torch.allclose(logits[0, :3, :3, :3] , __UpperCamelCase , atol=1e-4 )
else:
assert logits.shape == (1, 10_00)
if mobilevit_name == "mobilevit_s":
_SCREAMING_SNAKE_CASE = torch.tensor([-0.9866, 0.2392, -1.1241] )
elif mobilevit_name == "mobilevit_xs":
_SCREAMING_SNAKE_CASE = torch.tensor([-2.4761, -0.9399, -1.9587] )
elif mobilevit_name == "mobilevit_xxs":
_SCREAMING_SNAKE_CASE = torch.tensor([-1.9364, -1.2327, -0.4653] )
else:
raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}" )
assert torch.allclose(logits[0, :3] , __UpperCamelCase , atol=1e-4 )
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
print(F"Saving model {mobilevit_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(__UpperCamelCase )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__UpperCamelCase )
if push_to_hub:
_SCREAMING_SNAKE_CASE = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""" )
_SCREAMING_SNAKE_CASE = model_mapping[mobilevit_name]
image_processor.push_to_hub(__UpperCamelCase , organization="""apple""" )
model.push_to_hub(__UpperCamelCase , organization="""apple""" )
if __name__ == "__main__":
UpperCamelCase__ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--mobilevit_name",
default="mobilevit_s",
type=str,
help=(
"Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\',"
" \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'."
),
)
parser.add_argument(
"--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
UpperCamelCase__ : str = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
) | 720 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicative_persistence() does not accept negative values""" )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
while len(SCREAMING_SNAKE_CASE_ ) != 1:
_SCREAMING_SNAKE_CASE = [int(SCREAMING_SNAKE_CASE_ ) for i in num_string]
_SCREAMING_SNAKE_CASE = 1
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) ):
total *= numbers[i]
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
steps += 1
return steps
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("""additive_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""additive_persistence() does not accept negative values""" )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
while len(SCREAMING_SNAKE_CASE_ ) != 1:
_SCREAMING_SNAKE_CASE = [int(SCREAMING_SNAKE_CASE_ ) for i in num_string]
_SCREAMING_SNAKE_CASE = 0
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) ):
total += numbers[i]
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
create_state_space_tree(snake_case__ , [] , 0 )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
if index == len(snake_case__ ):
print(snake_case__ )
return
create_state_space_tree(snake_case__ , snake_case__ , index + 1 )
current_subsequence.append(sequence[index] )
create_state_space_tree(snake_case__ , snake_case__ , index + 1 )
current_subsequence.pop()
if __name__ == "__main__":
UpperCamelCase__ : Any = [3, 1, 2, 4]
generate_all_subsequences(seq)
seq.clear()
seq.extend(["A", "B", "C"])
generate_all_subsequences(seq)
| 721 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
UpperCamelCase__ : Tuple = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
from run_translation import main # noqa
set_seed(42)
UpperCamelCase__ : Union[str, Any] = "sshleifer/student_marian_en_ro_6_1"
UpperCamelCase__ : str = "sshleifer/tiny-mbart"
@require_torch
class _a (_lowerCamelCase):
"""simple docstring"""
def UpperCamelCase ( self , A__=False , A__=None , A__=True , A__=True , A__=True , A__=True , ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=A__ , num_train_epochs=1 , distributed=A__ , extra_args_str=A__ , predict_with_generate=A__ , do_train=A__ , do_eval=A__ , do_predict=A__ , )
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(os.path.join(A__ , """trainer_state.json""" ) ).log_history
if not do_eval:
return
_SCREAMING_SNAKE_CASE = [log for log in logs if """eval_loss""" in log.keys()]
_SCREAMING_SNAKE_CASE = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
_SCREAMING_SNAKE_CASE = eval_metrics[-1]
assert isinstance(last_step_stats["""eval_bleu"""] , A__ )
assert not math.isnan(float(last_step_stats["""eval_loss"""] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def UpperCamelCase ( self ) -> Optional[int]:
self.run_seqaseq_quick()
@require_torch_multi_gpu
def UpperCamelCase ( self ) -> Optional[Any]:
self.run_seqaseq_quick(distributed=A__ )
@require_torch_multi_gpu
def UpperCamelCase ( self ) -> Union[str, Any]:
self.run_seqaseq_quick(distributed=A__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> Any:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp simple""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> Tuple:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp simple --fp16""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> str:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp zero_dp_2""" , predict_with_generate=A__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> List[str]:
self.run_seqaseq_quick(
distributed=A__ , extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" , predict_with_generate=A__ )
@require_apex
@require_torch_gpu
def UpperCamelCase ( self ) -> Optional[Any]:
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
@parameterized.expand(["""base""", """low""", """high""", """mixed"""] )
@require_torch_multi_gpu
def UpperCamelCase ( self , A__ ) -> List[Any]:
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
_SCREAMING_SNAKE_CASE = {
# test with the default log_level - should be info and thus log info once
"""base""": {"""extra_args_str""": """""", """n_matches""": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"""low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"""high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"""mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
}
_SCREAMING_SNAKE_CASE = experiments[experiment_id]
_SCREAMING_SNAKE_CASE = {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
_SCREAMING_SNAKE_CASE = """Running training"""
with CaptureStderr() as cl:
self.run_seqaseq_quick(**A__ , extra_args_str=data["""extra_args_str"""] )
_SCREAMING_SNAKE_CASE = len(re.findall(A__ , cl.err ) )
self.assertEqual(A__ , data["""n_matches"""] )
@slow
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.run_trainer(
eval_steps=2 , max_len=1_28 , model_name=A__ , learning_rate=3E-4 , num_train_epochs=10 , distributed=A__ , )
# Check metrics
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(os.path.join(A__ , """trainer_state.json""" ) ).log_history
_SCREAMING_SNAKE_CASE = [log for log in logs if """eval_loss""" in log.keys()]
_SCREAMING_SNAKE_CASE = eval_metrics[0]
_SCREAMING_SNAKE_CASE = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["""eval_bleu"""] , A__ )
# test if do_predict saves generations and metrics
_SCREAMING_SNAKE_CASE = os.listdir(A__ )
_SCREAMING_SNAKE_CASE = {os.path.basename(A__ ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def UpperCamelCase ( self ) -> Dict:
from transformers.training_args import OptimizerNames
def train_and_return_metrics(A__ ) -> Tuple[int, float]:
_SCREAMING_SNAKE_CASE = """--skip_memory_metrics 0"""
_SCREAMING_SNAKE_CASE = self.run_trainer(
max_len=1_28 , model_name=A__ , learning_rate=3E-4 , num_train_epochs=1 , optim=A__ , distributed=A__ , extra_args_str=A__ , do_eval=A__ , do_predict=A__ , n_gpus_to_use=1 , )
# Check metrics
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(Path(A__ , """trainer_state.json""" ) ).log_history
_SCREAMING_SNAKE_CASE = int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20 )
_SCREAMING_SNAKE_CASE = int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20 )
_SCREAMING_SNAKE_CASE = logs[0]["""train_loss"""]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
_SCREAMING_SNAKE_CASE = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
_SCREAMING_SNAKE_CASE = gpu_peak_mem_orig + gpu_alloc_mem_orig
_SCREAMING_SNAKE_CASE = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
_SCREAMING_SNAKE_CASE = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
_SCREAMING_SNAKE_CASE = 1_20
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
A__ , A__ , """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
F" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
F" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB" , )
self.assertGreater(
A__ , A__ , """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
F" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
F" gpu_total_mem_bnb={gpu_total_mem_bnb}MB" , )
self.assertEqual(
A__ , A__ , F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}" )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ = 3E-3 , A__ = "adafactor" , A__ = False , A__ = None , A__ = 0 , A__ = True , A__ = True , A__ = True , A__ = True , A__ = None , ) -> Dict:
_SCREAMING_SNAKE_CASE = self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
_SCREAMING_SNAKE_CASE = self.get_auto_remove_tmp_dir()
_SCREAMING_SNAKE_CASE = F"\n --model_name_or_path {model_name}\n --train_file {data_dir}/train.json\n --validation_file {data_dir}/val.json\n --test_file {data_dir}/test.json\n --output_dir {output_dir}\n --overwrite_output_dir\n --max_train_samples 8\n --max_source_length {max_len}\n --max_target_length {max_len}\n --do_train\n --num_train_epochs {str(A__ )}\n --per_device_train_batch_size 4\n --learning_rate {learning_rate}\n --warmup_steps 8\n --logging_steps 0\n --logging_strategy no\n --save_steps {str(A__ )}\n --group_by_length\n --label_smoothing_factor 0.1\n --target_lang ro_RO\n --source_lang en_XX\n ".split()
_SCREAMING_SNAKE_CASE = F"\n --do_eval\n --per_device_eval_batch_size 4\n --max_eval_samples 8\n --val_max_target_length {max_len}\n --evaluation_strategy steps\n --eval_steps {str(A__ )}\n ".split()
_SCREAMING_SNAKE_CASE = """
--do_predict
""".split()
_SCREAMING_SNAKE_CASE = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F"--optim {optim}".split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
_SCREAMING_SNAKE_CASE = get_gpu_count()
_SCREAMING_SNAKE_CASE = get_torch_dist_unique_port()
_SCREAMING_SNAKE_CASE = F"\n -m torch.distributed.run\n --nproc_per_node={n_gpus_to_use}\n --master_port={master_port}\n {self.examples_dir_str}/pytorch/translation/run_translation.py\n ".split()
_SCREAMING_SNAKE_CASE = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(A__ , env=self.get_env() )
else:
_SCREAMING_SNAKE_CASE = ["""run_translation.py"""] + args
with patch.object(A__ , """argv""" , A__ ):
main()
return output_dir
| 0 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase__ : int = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Union[str, Any] = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
UpperCamelCase__ : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 700 |
'''simple docstring'''
import sys
UpperCamelCase__ : int = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = N ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = -sys.maxsize - 1
for i in range(len(SCREAMING_SNAKE_CASE_ ) - 12 ):
_SCREAMING_SNAKE_CASE = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
_SCREAMING_SNAKE_CASE = product
return largest_product
if __name__ == "__main__":
print(f"""{solution() = }""")
| 0 | 0 |
'''simple docstring'''
import os
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE_ ) )
_SCREAMING_SNAKE_CASE = os.path.join(SCREAMING_SNAKE_CASE_ , """triangle.txt""" )
with open(SCREAMING_SNAKE_CASE_ ) as f:
_SCREAMING_SNAKE_CASE = f.readlines()
_SCREAMING_SNAKE_CASE = []
for line in triangle:
_SCREAMING_SNAKE_CASE = []
for number in line.strip().split(""" """ ):
numbers_from_line.append(int(SCREAMING_SNAKE_CASE_ ) )
a.append(SCREAMING_SNAKE_CASE_ )
for i in range(1 , len(SCREAMING_SNAKE_CASE_ ) ):
for j in range(len(a[i] ) ):
_SCREAMING_SNAKE_CASE = a[i - 1][j] if j != len(a[i - 1] ) else 0
_SCREAMING_SNAKE_CASE = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 701 |
'''simple docstring'''
UpperCamelCase__ : Dict = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
"q": "ABBBB",
"r": "BAAAA",
"s": "BAAAB",
"t": "BAABA",
"u": "BAABB",
"v": "BBBAB",
"w": "BABAA",
"x": "BABAB",
"y": "BABBA",
"z": "BABBB",
" ": " ",
}
UpperCamelCase__ : str = {value: key for key, value in encode_dict.items()}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """"""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("""encode() accepts only letters of the alphabet and spaces""" )
return encoded
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
if set(SCREAMING_SNAKE_CASE_ ) - {"A", "B", " "} != set():
raise Exception("""decode() accepts only 'A', 'B' and spaces""" )
_SCREAMING_SNAKE_CASE = """"""
for word in coded.split():
while len(SCREAMING_SNAKE_CASE_ ) != 0:
decoded += decode_dict[word[:5]]
_SCREAMING_SNAKE_CASE = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 0 | 0 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
UpperCamelCase__ : List[str] = """base_with_context"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=snake_case_ )
for lyr_num, lyr in enumerate(model.encoders ):
_SCREAMING_SNAKE_CASE = weights[F"layers_{lyr_num}"]
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = ly_weight["attention"]
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""] ) )
return model
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=snake_case_ )
for lyr_num, lyr in enumerate(model.encoders ):
_SCREAMING_SNAKE_CASE = weights[F"layers_{lyr_num}"]
_SCREAMING_SNAKE_CASE = ly_weight["attention"]
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""] ) )
return model
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=snake_case_ )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
_SCREAMING_SNAKE_CASE = weights[F"layers_{lyr_num}"]
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = ly_weight["self_attention"]
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = ly_weight["MultiHeadDotProductAttention_0"]
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(
torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""] ) )
_SCREAMING_SNAKE_CASE = nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T ) )
return model
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = checkpoints.load_tax_checkpoint(args.checkpoint_path )
_SCREAMING_SNAKE_CASE = jnp.tree_util.tree_map(onp.array , snake_case_ )
_SCREAMING_SNAKE_CASE = [
"from __gin__ import dynamic_registration",
"from music_spectrogram_diffusion.models.diffusion import diffusion_utils",
"diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0",
"diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()",
]
_SCREAMING_SNAKE_CASE = os.path.join(args.checkpoint_path , """..""" , """config.gin""" )
_SCREAMING_SNAKE_CASE = inference.parse_training_gin_file(snake_case_ , snake_case_ )
_SCREAMING_SNAKE_CASE = inference.InferenceModel(args.checkpoint_path , snake_case_ )
_SCREAMING_SNAKE_CASE = DDPMScheduler(beta_schedule="""squaredcos_cap_v2""" , variance_type="""fixed_large""" )
_SCREAMING_SNAKE_CASE = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length["""inputs"""] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="""gated-gelu""" , )
_SCREAMING_SNAKE_CASE = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length["""targets_context"""] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="""gated-gelu""" , )
_SCREAMING_SNAKE_CASE = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length["""targets_context"""] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
_SCREAMING_SNAKE_CASE = load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""] , snake_case_ )
_SCREAMING_SNAKE_CASE = load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""] , snake_case_ )
_SCREAMING_SNAKE_CASE = load_decoder(ta_checkpoint["""target"""]["""decoder"""] , snake_case_ )
_SCREAMING_SNAKE_CASE = OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder""" )
_SCREAMING_SNAKE_CASE = SpectrogramDiffusionPipeline(
notes_encoder=snake_case_ , continuous_encoder=snake_case_ , decoder=snake_case_ , scheduler=snake_case_ , melgan=snake_case_ , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
UpperCamelCase__ : List[str] = argparse.ArgumentParser()
parser.add_argument("--output_path", default=None, type=str, required=True, help="Path to the converted model.")
parser.add_argument(
"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
)
parser.add_argument(
"--checkpoint_path",
default=f"""{MODEL}/checkpoint_500000""",
type=str,
required=False,
help="Path to the original jax model checkpoint.",
)
UpperCamelCase__ : List[str] = parser.parse_args()
main(args)
| 702 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""encoder.embed_positions._float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = emb.weight.shape
_SCREAMING_SNAKE_CASE = nn.Linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , bias=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )
_SCREAMING_SNAKE_CASE = mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
_SCREAMING_SNAKE_CASE = mam_aaa["""model"""]
remove_ignore_keys_(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = state_dict["""encoder.embed_tokens.weight"""].shape[0]
_SCREAMING_SNAKE_CASE = MaMaaaConfig(
vocab_size=SCREAMING_SNAKE_CASE_ , max_position_embeddings=10_24 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="""relu""" , )
_SCREAMING_SNAKE_CASE = state_dict["""decoder.embed_tokens.weight"""]
_SCREAMING_SNAKE_CASE = MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
model.model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
UpperCamelCase__ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCamelCase__ : List[str] = parser.parse_args()
UpperCamelCase__ : Any = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 0 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = int(_lowerCamelCase )
# Initialize Result
_SCREAMING_SNAKE_CASE = []
# Traverse through all denomination
for denomination in reversed(_lowerCamelCase ):
# Find denominations
while int(_lowerCamelCase ) >= int(_lowerCamelCase ):
total_value -= int(_lowerCamelCase )
answer.append(_lowerCamelCase ) # Append the "answers" array
return answer
# Driver Code
if __name__ == "__main__":
UpperCamelCase__ : Dict = []
UpperCamelCase__ : Tuple = "0"
if (
input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
== "y"
):
UpperCamelCase__ : str = int(input("Enter the number of denominations you want to add: ").strip())
for i in range(0, n):
denominations.append(int(input(f"""Denomination {i}: """).strip()))
UpperCamelCase__ : str = input("Enter the change you want to make in Indian Currency: ").strip()
else:
# All denominations of Indian Currency if user does not enter
UpperCamelCase__ : Dict = [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
UpperCamelCase__ : Tuple = input("Enter the change you want to make: ").strip()
if int(value) == 0 or int(value) < 0:
print("The total value cannot be zero or negative.")
else:
print(f"""Following is minimal change for {value}: """)
UpperCamelCase__ : Dict = find_minimum_change(denominations, value)
# Print result
for i in range(len(answer)):
print(answer[i], end=" ")
| 703 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : str = {
"configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
"tokenization_canine": ["CanineTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : List[Any] = [
"CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
"CanineForMultipleChoice",
"CanineForQuestionAnswering",
"CanineForSequenceClassification",
"CanineForTokenClassification",
"CanineLayer",
"CanineModel",
"CaninePreTrainedModel",
"load_tf_weights_in_canine",
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
UpperCamelCase__ : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Dict = logging.get_logger(__name__)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((F"blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append((F"blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((F"blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((F"blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((F"blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((F"blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((F"blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((F"blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((F"blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias") )
# projection layer + position embeddings
rename_keys.extend(
[
("""cls_token""", """vit.embeddings.cls_token"""),
("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
_SCREAMING_SNAKE_CASE = [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""" ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
("""norm.weight""", """vit.layernorm.weight"""),
("""norm.bias""", """vit.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ) -> List[str]:
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
_SCREAMING_SNAKE_CASE = """"""
else:
_SCREAMING_SNAKE_CASE = """vit."""
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_SCREAMING_SNAKE_CASE = state_dict.pop(F"blocks.{i}.attn.qkv.weight" )
_SCREAMING_SNAKE_CASE = state_dict.pop(F"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
_SCREAMING_SNAKE_CASE = in_proj_weight[
: config.hidden_size, :
]
_SCREAMING_SNAKE_CASE = in_proj_bias[: config.hidden_size]
_SCREAMING_SNAKE_CASE = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_SCREAMING_SNAKE_CASE = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_SCREAMING_SNAKE_CASE = in_proj_weight[
-config.hidden_size :, :
]
_SCREAMING_SNAKE_CASE = in_proj_bias[-config.hidden_size :]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""head.weight""", """head.bias"""]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = dct.pop(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = val
def lowerCAmelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_SCREAMING_SNAKE_CASE = Image.open(requests.get(SCREAMING_SNAKE_CASE_ , stream=SCREAMING_SNAKE_CASE_ ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=True ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ViTConfig()
# patch_size
if model_name[-1] == "8":
_SCREAMING_SNAKE_CASE = 8
# set labels if required
if not base_model:
_SCREAMING_SNAKE_CASE = 10_00
_SCREAMING_SNAKE_CASE = """huggingface/label-files"""
_SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
_SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type="""dataset""" ) , """r""" ) )
_SCREAMING_SNAKE_CASE = {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE = idalabel
_SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
_SCREAMING_SNAKE_CASE = 3_84
_SCREAMING_SNAKE_CASE = 15_36
_SCREAMING_SNAKE_CASE = 12
_SCREAMING_SNAKE_CASE = 6
# load original model from torch hub
_SCREAMING_SNAKE_CASE = torch.hub.load("""facebookresearch/dino:main""" , SCREAMING_SNAKE_CASE_ )
original_model.eval()
# load state_dict of original model, remove and rename some keys
_SCREAMING_SNAKE_CASE = original_model.state_dict()
if base_model:
remove_classification_head_(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = create_rename_keys(SCREAMING_SNAKE_CASE_ , base_model=SCREAMING_SNAKE_CASE_ )
for src, dest in rename_keys:
rename_key(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
read_in_q_k_v(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# load HuggingFace model
if base_model:
_SCREAMING_SNAKE_CASE = ViTModel(SCREAMING_SNAKE_CASE_ , add_pooling_layer=SCREAMING_SNAKE_CASE_ ).eval()
else:
_SCREAMING_SNAKE_CASE = ViTForImageClassification(SCREAMING_SNAKE_CASE_ ).eval()
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
# Check outputs on an image, prepared by ViTImageProcessor
_SCREAMING_SNAKE_CASE = ViTImageProcessor()
_SCREAMING_SNAKE_CASE = image_processor(images=prepare_img() , return_tensors="""pt""" )
_SCREAMING_SNAKE_CASE = encoding["""pixel_values"""]
_SCREAMING_SNAKE_CASE = model(SCREAMING_SNAKE_CASE_ )
if base_model:
_SCREAMING_SNAKE_CASE = original_model(SCREAMING_SNAKE_CASE_ )
assert torch.allclose(SCREAMING_SNAKE_CASE_ , outputs.last_hidden_state[:, 0, :] , atol=1e-1 )
else:
_SCREAMING_SNAKE_CASE = original_model(SCREAMING_SNAKE_CASE_ )
assert logits.shape == outputs.logits.shape
assert torch.allclose(SCREAMING_SNAKE_CASE_ , outputs.logits , atol=1e-3 )
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
UpperCamelCase__ : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="dino_vitb16",
type=str,
help="Name of the model trained with DINO you\'d like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--base_model",
action="store_true",
help="Whether to only convert the base model (no projection head weights).",
)
parser.set_defaults(base_model=True)
UpperCamelCase__ : Tuple = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)
| 704 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE = 'ChineseCLIPImageProcessor'
SCREAMING_SNAKE_CASE = ('BertTokenizer', 'BertTokenizerFast')
def __init__( self , A__=None , A__=None , **A__ ) -> int:
_SCREAMING_SNAKE_CASE = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , A__ , )
_SCREAMING_SNAKE_CASE = kwargs.pop("""feature_extractor""" )
_SCREAMING_SNAKE_CASE = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.image_processor
def __call__( self , A__=None , A__=None , A__=None , **A__ ) -> Optional[int]:
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
_SCREAMING_SNAKE_CASE = self.tokenizer(A__ , return_tensors=A__ , **A__ )
if images is not None:
_SCREAMING_SNAKE_CASE = self.image_processor(A__ , return_tensors=A__ , **A__ )
if text is not None and images is not None:
_SCREAMING_SNAKE_CASE = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**A__ ) , tensor_type=A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Dict:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Optional[Any]:
return self.tokenizer.decode(*A__ , **A__ )
@property
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.tokenizer.model_input_names
_SCREAMING_SNAKE_CASE = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCamelCase ( self ) -> Optional[int]:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , A__ , )
return self.image_processor_class
| 0 | 0 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : str = TypeVar("DatasetType", Dataset, IterableDataset)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = "first_exhausted" , ) -> List[str]:
"""simple docstring"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError("""Unable to interleave an empty list of datasets.""" )
for i, dataset in enumerate(snake_case__ ):
if not isinstance(snake_case__ , (Dataset, IterableDataset) ):
if isinstance(snake_case__ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
"""is an empty dataset dictionary.""" )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(snake_case__ )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(snake_case__ ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(snake_case__ ).__name__}." )
if i == 0:
_SCREAMING_SNAKE_CASE = (
(Dataset, IterableDataset) if isinstance(snake_case__ , snake_case__ ) else (IterableDataset, Dataset)
)
elif not isinstance(snake_case__ , snake_case__ ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy." )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
snake_case__ , snake_case__ , snake_case__ , info=snake_case__ , split=snake_case__ , stopping_strategy=snake_case__ )
else:
return _interleave_iterable_datasets(
snake_case__ , snake_case__ , snake_case__ , info=snake_case__ , split=snake_case__ , stopping_strategy=snake_case__ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = 0 , ) -> int:
"""simple docstring"""
if not dsets:
raise ValueError("""Unable to concatenate an empty list of datasets.""" )
for i, dataset in enumerate(snake_case__ ):
if not isinstance(snake_case__ , (Dataset, IterableDataset) ):
if isinstance(snake_case__ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
"""is an empty dataset dictionary.""" )
raise ValueError(
F"Dataset at position {i} has at least one split: {list(snake_case__ )}\n"
F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(snake_case__ ) )}']" )
raise ValueError(
F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(snake_case__ ).__name__}." )
if i == 0:
_SCREAMING_SNAKE_CASE = (
(Dataset, IterableDataset) if isinstance(snake_case__ , snake_case__ ) else (IterableDataset, Dataset)
)
elif not isinstance(snake_case__ , snake_case__ ):
raise ValueError(
F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects." )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(snake_case__ , info=snake_case__ , split=snake_case__ , axis=snake_case__ )
else:
return _concatenate_iterable_datasets(snake_case__ , info=snake_case__ , split=snake_case__ , axis=snake_case__ )
| 705 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase__ : List[str] = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
UpperCamelCase__ : List[Any] = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
UpperCamelCase__ : Any = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a (datasets.Metric):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def UpperCamelCase ( self , A__ , A__ , A__=None ) -> List[str]:
return {
"matthews_correlation": float(matthews_corrcoef(A__ , A__ , sample_weight=A__ ) ),
}
| 0 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """backbone.""" if is_semantic else """"""
_SCREAMING_SNAKE_CASE = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append(
(F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append(
(F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias") )
# projection layer + position embeddings
rename_keys.extend(
[
(F"{prefix}cls_token", """beit.embeddings.cls_token"""),
(F"{prefix}patch_embed.proj.weight", """beit.embeddings.patch_embeddings.projection.weight"""),
(F"{prefix}patch_embed.proj.bias", """beit.embeddings.patch_embeddings.projection.bias"""),
(F"{prefix}pos_embed", """beit.embeddings.position_embeddings"""),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
("""mask_token""", """beit.embeddings.mask_token"""),
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False ) -> Optional[int]:
"""simple docstring"""
for i in range(config.num_hidden_layers ):
_SCREAMING_SNAKE_CASE = """backbone.""" if is_semantic else """"""
# queries, keys and values
_SCREAMING_SNAKE_CASE = state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight" )
_SCREAMING_SNAKE_CASE = state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias" )
_SCREAMING_SNAKE_CASE = state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias" )
_SCREAMING_SNAKE_CASE = in_proj_weight[
: config.hidden_size, :
]
_SCREAMING_SNAKE_CASE = q_bias
_SCREAMING_SNAKE_CASE = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_SCREAMING_SNAKE_CASE = in_proj_weight[
-config.hidden_size :, :
]
_SCREAMING_SNAKE_CASE = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
_SCREAMING_SNAKE_CASE = state_dict.pop(F"{prefix}blocks.{i}.gamma_1" )
_SCREAMING_SNAKE_CASE = state_dict.pop(F"{prefix}blocks.{i}.gamma_2" )
_SCREAMING_SNAKE_CASE = gamma_a
_SCREAMING_SNAKE_CASE = gamma_a
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = dct.pop(__snake_case )
_SCREAMING_SNAKE_CASE = val
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_SCREAMING_SNAKE_CASE = Image.open(requests.get(__snake_case , stream=__snake_case ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = False if """rvlcdip""" in checkpoint_url else True
_SCREAMING_SNAKE_CASE = BeitConfig(use_absolute_position_embeddings=__snake_case , use_mask_token=__snake_case )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
_SCREAMING_SNAKE_CASE = 10_24
_SCREAMING_SNAKE_CASE = 40_96
_SCREAMING_SNAKE_CASE = 24
_SCREAMING_SNAKE_CASE = 16
# labels
if "rvlcdip" in checkpoint_url:
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = """huggingface/label-files"""
_SCREAMING_SNAKE_CASE = """rvlcdip-id2label.json"""
_SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(__snake_case , __snake_case , repo_type="""dataset""" ) , """r""" ) )
_SCREAMING_SNAKE_CASE = {int(__snake_case ): v for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE = idalabel
_SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
_SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(__snake_case , map_location="""cpu""" )["""model"""]
_SCREAMING_SNAKE_CASE = create_rename_keys(__snake_case , has_lm_head=__snake_case )
for src, dest in rename_keys:
rename_key(__snake_case , __snake_case , __snake_case )
read_in_q_k_v(__snake_case , __snake_case , has_lm_head=__snake_case )
# load HuggingFace model
_SCREAMING_SNAKE_CASE = BeitForMaskedImageModeling(__snake_case ) if has_lm_head else BeitForImageClassification(__snake_case )
model.eval()
model.load_state_dict(__snake_case )
# Check outputs on an image
_SCREAMING_SNAKE_CASE = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=__snake_case )
_SCREAMING_SNAKE_CASE = prepare_img()
_SCREAMING_SNAKE_CASE = image_processor(images=__snake_case , return_tensors="""pt""" )
_SCREAMING_SNAKE_CASE = encoding["""pixel_values"""]
_SCREAMING_SNAKE_CASE = model(__snake_case )
_SCREAMING_SNAKE_CASE = outputs.logits
# verify logits
_SCREAMING_SNAKE_CASE = [1, 16] if """rvlcdip""" in checkpoint_url else [1, 1_96, 81_92]
assert logits.shape == torch.Size(__snake_case ), "Shape of logits not as expected"
Path(__snake_case ).mkdir(exist_ok=__snake_case )
print(F"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(__snake_case )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__snake_case )
if push_to_hub:
if has_lm_head:
_SCREAMING_SNAKE_CASE = """dit-base""" if """base""" in checkpoint_url else """dit-large"""
else:
_SCREAMING_SNAKE_CASE = """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
image_processor.push_to_hub(
repo_path_or_name=Path(__snake_case , __snake_case ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=__snake_case , )
model.push_to_hub(
repo_path_or_name=Path(__snake_case , __snake_case ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=__snake_case , )
if __name__ == "__main__":
UpperCamelCase__ : str = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_url",
default="https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth",
type=str,
help="URL to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
UpperCamelCase__ : List[str] = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 706 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
print(F"Vertex\tShortest Distance from vertex {src}" )
for i, d in enumerate(SCREAMING_SNAKE_CASE_ ):
print(F"{i}\t\t{d}" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
for j in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
return True
return False
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list[float]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [float("""inf""" )] * vertex_count
_SCREAMING_SNAKE_CASE = 0.0
for _ in range(vertex_count - 1 ):
for j in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
_SCREAMING_SNAKE_CASE = distance[u] + w
_SCREAMING_SNAKE_CASE = check_negative_cycle(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if negative_cycle_exists:
raise Exception("""Negative cycle found""" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ : int = int(input("Enter number of vertices: ").strip())
UpperCamelCase__ : int = int(input("Enter number of edges: ").strip())
UpperCamelCase__ : list[dict[str, int]] = [{} for _ in range(E)]
for i in range(E):
print("Edge ", i + 1)
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Dict = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
UpperCamelCase__ : Optional[Any] = {"src": src, "dst": dest, "weight": weight}
UpperCamelCase__ : Optional[Any] = int(input("\nEnter shortest path source:").strip())
UpperCamelCase__ : Any = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase__ : str = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
UpperCamelCase__ : List[Any] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list[float]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
for i in range(UpperCAmelCase__ ):
_SCREAMING_SNAKE_CASE = -1
for j in range(i + 1 , UpperCAmelCase__ ):
if arr[i] < arr[j]:
_SCREAMING_SNAKE_CASE = arr[j]
break
result.append(UpperCAmelCase__ )
return result
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list[float]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
for i, outer in enumerate(UpperCAmelCase__ ):
_SCREAMING_SNAKE_CASE = -1
for inner in arr[i + 1 :]:
if outer < inner:
_SCREAMING_SNAKE_CASE = inner
break
result.append(UpperCAmelCase__ )
return result
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list[float]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = [-1] * arr_size
for index in reversed(range(UpperCAmelCase__ ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
_SCREAMING_SNAKE_CASE = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
UpperCamelCase__ : List[Any] = (
"from __main__ import arr, next_greatest_element_slow, "
"next_greatest_element_fast, next_greatest_element"
)
print(
"next_greatest_element_slow():",
timeit("next_greatest_element_slow(arr)", setup=setup),
)
print(
"next_greatest_element_fast():",
timeit("next_greatest_element_fast(arr)", setup=setup),
)
print(
" next_greatest_element():",
timeit("next_greatest_element(arr)", setup=setup),
)
| 707 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=True , A__=True , A__=99 , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=16 , A__=2 , A__=0.02 , A__=3 , A__=4 , A__=None , ) -> int:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = 13
_SCREAMING_SNAKE_CASE = 7
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = 99
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = 37
_SCREAMING_SNAKE_CASE = """gelu"""
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 5_12
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 0.02
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
_SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=A__ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModel(config=A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
_SCREAMING_SNAKE_CASE = [input_ids, input_mask]
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> str:
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = TFRoFormerForCausalLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )["""logits"""]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Dict:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForSequenceClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = TFRoFormerForMultipleChoice(config=A__ )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForTokenClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = TFRoFormerForQuestionAnswering(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': TFRoFormerModel,
'fill-mask': TFRoFormerForMaskedLM,
'question-answering': TFRoFormerForQuestionAnswering,
'text-classification': TFRoFormerForSequenceClassification,
'text-generation': TFRoFormerForCausalLM,
'token-classification': TFRoFormerForTokenClassification,
'zero-shot': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> str:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*A__ )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base""" )
self.assertIsNotNone(A__ )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
_SCREAMING_SNAKE_CASE = tf.constant([[0, 1, 2, 3, 4, 5]] )
_SCREAMING_SNAKE_CASE = model(A__ )[0]
# TODO Replace vocab size
_SCREAMING_SNAKE_CASE = 5_00_00
_SCREAMING_SNAKE_CASE = [1, 6, vocab_size]
self.assertEqual(output.shape , A__ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
_SCREAMING_SNAKE_CASE = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , A__ , atol=1E-4 )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant([[4, 10]] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
_SCREAMING_SNAKE_CASE = emba(input_ids.shape )
_SCREAMING_SNAKE_CASE = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 , embedding_dim=5_12 )
emba([2, 16, 5_12] )
_SCREAMING_SNAKE_CASE = emba.weight[:3, :5]
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> int:
# 2,12,16,64
_SCREAMING_SNAKE_CASE = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
_SCREAMING_SNAKE_CASE = embed_positions([2, 16, 7_68] )[None, None, :, :]
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
A__ , A__ , A__ )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
| 0 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( ) -> Any:
"""simple docstring"""
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 2
while i * i <= n:
_SCREAMING_SNAKE_CASE = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def lowerCAmelCase_ ( ) -> str:
"""simple docstring"""
return next(i for i in triangle_number_generator() if count_divisors(_lowercase ) > 5_00 )
if __name__ == "__main__":
print(solution())
| 708 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase__ : int = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
UpperCamelCase__ : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 | 0 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
temp_seed,
temporary_assignment,
zip_dict,
)
from .utils import require_tf, require_torch
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Tuple: # picklable for multiprocessing
"""simple docstring"""
return x.sum()
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]: # picklable for multiprocessing
"""simple docstring"""
return i + 1
@dataclass
class _a :
"""simple docstring"""
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
class _a (UpperCAmelCase__):
"""simple docstring"""
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = [1, 2]
_SCREAMING_SNAKE_CASE = {"""a""": 1, """b""": 2}
_SCREAMING_SNAKE_CASE = {"""a""": [1, 2], """b""": [3, 4]}
_SCREAMING_SNAKE_CASE = {"""a""": {"""1""": 1}, """b""": 2}
_SCREAMING_SNAKE_CASE = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = [2, 3]
_SCREAMING_SNAKE_CASE = {"""a""": 2, """b""": 3}
_SCREAMING_SNAKE_CASE = {"""a""": [2, 3], """b""": [4, 5]}
_SCREAMING_SNAKE_CASE = {"""a""": {"""1""": 2}, """b""": 3}
_SCREAMING_SNAKE_CASE = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase ) , __lowerCAmelCase )
_SCREAMING_SNAKE_CASE = 2
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
_SCREAMING_SNAKE_CASE = {"""a""": np.eye(2 ), """b""": np.zeros(3 ), """c""": np.ones(2 )}
_SCREAMING_SNAKE_CASE = {"""a""": 2, """b""": 0, """c""": 2}
_SCREAMING_SNAKE_CASE = {
"""a""": np.eye(2 ).astype(__lowerCAmelCase ),
"""b""": np.zeros(3 ).astype(__lowerCAmelCase ),
"""c""": np.ones(2 ).astype(__lowerCAmelCase ),
}
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , map_numpy=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(__lowerCAmelCase , __lowerCAmelCase , map_numpy=__lowerCAmelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
self.assertEqual(map_nested(__lowerCAmelCase , __lowerCAmelCase , map_numpy=__lowerCAmelCase , num_proc=__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(
{k: v.tolist() for k, v in map_nested(__lowerCAmelCase , __lowerCAmelCase , map_numpy=__lowerCAmelCase , num_proc=__lowerCAmelCase ).items()} , {k: v.tolist() for k, v in expected_map_nested_sna_int.items()} , )
with self.assertRaises(__lowerCAmelCase ): # can't pickle a local lambda
map_nested(lambda A__ : x + 1 , __lowerCAmelCase , num_proc=__lowerCAmelCase )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = {"""a""": 1, """b""": 2}
_SCREAMING_SNAKE_CASE = {"""a""": 3, """b""": 4}
_SCREAMING_SNAKE_CASE = {"""a""": 5, """b""": 6}
_SCREAMING_SNAKE_CASE = sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))] )
self.assertEqual(sorted(zip_dict(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) ) , __lowerCAmelCase )
def UpperCamelCase ( self ) -> Dict:
class _a :
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'bar'
_SCREAMING_SNAKE_CASE = Foo()
self.assertEqual(foo.my_attr , """bar""" )
with temporary_assignment(__lowerCAmelCase , """my_attr""" , """BAR""" ):
self.assertEqual(foo.my_attr , """BAR""" )
self.assertEqual(foo.my_attr , """bar""" )
@pytest.mark.parametrize(
"""iterable_length, num_proc, expected_num_proc""" , [
(1, None, 1),
(1, 1, 1),
(2, None, 1),
(2, 1, 1),
(2, 2, 1),
(2, 3, 1),
(3, 2, 1),
(16, 16, 16),
(16, 17, 16),
(17, 16, 16),
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
with patch("""datasets.utils.py_utils._single_map_nested""" ) as mock_single_map_nested, patch(
"""datasets.parallel.parallel.Pool""" ) as mock_multiprocessing_pool:
_SCREAMING_SNAKE_CASE = {F"{i}": i for i in range(UpperCAmelCase__ )}
_SCREAMING_SNAKE_CASE = map_nested(lambda SCREAMING_SNAKE_CASE_ : x + 10 , UpperCAmelCase__ , num_proc=UpperCAmelCase__ , parallel_min_length=16 )
if expected_num_proc == 1:
assert mock_single_map_nested.called
assert not mock_multiprocessing_pool.called
else:
assert not mock_single_map_nested.called
assert mock_multiprocessing_pool.called
assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc
class _a (UpperCAmelCase__):
"""simple docstring"""
@require_tf
def UpperCamelCase ( self ) -> Optional[Any]:
import tensorflow as tf
from tensorflow.keras import layers
_SCREAMING_SNAKE_CASE = layers.Dense(2 )
def gen_random_output():
_SCREAMING_SNAKE_CASE = tf.random.uniform((1, 3) )
return model(__lowerCAmelCase ).numpy()
with temp_seed(42 , set_tensorflow=__lowerCAmelCase ):
_SCREAMING_SNAKE_CASE = gen_random_output()
with temp_seed(42 , set_tensorflow=__lowerCAmelCase ):
_SCREAMING_SNAKE_CASE = gen_random_output()
_SCREAMING_SNAKE_CASE = gen_random_output()
np.testing.assert_equal(__lowerCAmelCase , __lowerCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@require_torch
def UpperCamelCase ( self ) -> Tuple:
import torch
def gen_random_output():
_SCREAMING_SNAKE_CASE = torch.nn.Linear(3 , 2 )
_SCREAMING_SNAKE_CASE = torch.rand(1 , 3 )
return model(__lowerCAmelCase ).detach().numpy()
with temp_seed(42 , set_pytorch=__lowerCAmelCase ):
_SCREAMING_SNAKE_CASE = gen_random_output()
with temp_seed(42 , set_pytorch=__lowerCAmelCase ):
_SCREAMING_SNAKE_CASE = gen_random_output()
_SCREAMING_SNAKE_CASE = gen_random_output()
np.testing.assert_equal(__lowerCAmelCase , __lowerCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
def UpperCamelCase ( self ) -> List[Any]:
def gen_random_output():
return np.random.rand(1 , 3 )
with temp_seed(42 ):
_SCREAMING_SNAKE_CASE = gen_random_output()
with temp_seed(42 ):
_SCREAMING_SNAKE_CASE = gen_random_output()
_SCREAMING_SNAKE_CASE = gen_random_output()
np.testing.assert_equal(__lowerCAmelCase , __lowerCAmelCase )
self.assertGreater(np.abs(outa - outa ).sum() , 0 )
@pytest.mark.parametrize("""input_data""" , [{}] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = NestedDataStructure(UpperCAmelCase__ ).data
assert output_data == input_data
@pytest.mark.parametrize(
"""data, expected_output""" , [
({}, []),
([], []),
("""foo""", ["""foo"""]),
(["""foo""", """bar"""], ["""foo""", """bar"""]),
([["""foo""", """bar"""]], ["""foo""", """bar"""]),
([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
({"""a""": 1, """b""": 2}, [1, 2]),
({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = NestedDataStructure(UpperCAmelCase__ ).flatten()
assert output == expected_output
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = A(x=1 , y="""foobar""" )
_SCREAMING_SNAKE_CASE = {"""x""": 1, """y""": """foobar"""}
assert asdict(UpperCAmelCase__ ) == expected_output
_SCREAMING_SNAKE_CASE = {"""a""": {"""b""": A(x=10 , y="""foo""" )}, """c""": [A(x=20 , y="""bar""" )]}
_SCREAMING_SNAKE_CASE = {"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
assert asdict(UpperCAmelCase__ ) == expected_output
with pytest.raises(UpperCAmelCase__ ):
asdict([1, A(x=10 , y="""foo""" )] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
return text.split()
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
yield (time.time(), content)
time.sleep(2 )
yield (time.time(), content)
def lowerCAmelCase_ ( ) -> List[str]:
"""simple docstring"""
with Pool(2 ) as pool:
_SCREAMING_SNAKE_CASE = list(iflatmap_unordered(UpperCAmelCase__ , _split_text , kwargs_iterable=[{"""text""": """hello there"""}] * 10 ) )
assert out.count("""hello""" ) == 10
assert out.count("""there""" ) == 10
assert len(UpperCAmelCase__ ) == 20
# check multiprocess from pathos (uses dill for pickling)
with multiprocess.Pool(2 ) as pool:
_SCREAMING_SNAKE_CASE = list(iflatmap_unordered(UpperCAmelCase__ , _split_text , kwargs_iterable=[{"""text""": """hello there"""}] * 10 ) )
assert out.count("""hello""" ) == 10
assert out.count("""there""" ) == 10
assert len(UpperCAmelCase__ ) == 20
# check that we get items as fast as possible
with Pool(2 ) as pool:
_SCREAMING_SNAKE_CASE = []
for yield_time, content in iflatmap_unordered(
UpperCAmelCase__ , _aseconds_generator_of_aitems_with_timing , kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}] ):
assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
out.append(UpperCAmelCase__ )
assert out.count("""a""" ) == 2
assert out.count("""b""" ) == 2
assert len(UpperCAmelCase__ ) == 4
| 709 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = XCLIPTextConfig()
# derive patch size from model name
_SCREAMING_SNAKE_CASE = model_name.find("""patch""" )
_SCREAMING_SNAKE_CASE = int(model_name[start_idx + len("""patch""" ) : start_idx + len("""patch""" ) + 2] )
_SCREAMING_SNAKE_CASE = XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE_ , num_frames=SCREAMING_SNAKE_CASE_ )
if "large" in model_name:
_SCREAMING_SNAKE_CASE = 7_68
_SCREAMING_SNAKE_CASE = 30_72
_SCREAMING_SNAKE_CASE = 12
_SCREAMING_SNAKE_CASE = 10_24
_SCREAMING_SNAKE_CASE = 40_96
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 24
_SCREAMING_SNAKE_CASE = 7_68
_SCREAMING_SNAKE_CASE = 30_72
if model_name == "xclip-large-patch14-16-frames":
_SCREAMING_SNAKE_CASE = 3_36
_SCREAMING_SNAKE_CASE = XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if "large" in model_name:
_SCREAMING_SNAKE_CASE = 7_68
return config
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
# text encoder
if name == "token_embedding.weight":
_SCREAMING_SNAKE_CASE = name.replace("""token_embedding.weight""" , """text_model.embeddings.token_embedding.weight""" )
if name == "positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "ln_1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
_SCREAMING_SNAKE_CASE = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""c_proj""" , """fc2""" )
if name.startswith("""transformer.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""transformer.resblocks""" , """text_model.encoder.layers""" )
if "attn.out_proj" in name and "message" not in name:
_SCREAMING_SNAKE_CASE = name.replace("""attn.out_proj""" , """self_attn.out_proj""" )
if "ln_final" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_final""" , """text_model.final_layer_norm""" )
# visual encoder
if name == "visual.class_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""visual.class_embedding""" , """vision_model.embeddings.class_embedding""" )
if name == "visual.positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""visual.positional_embedding""" , """vision_model.embeddings.position_embedding.weight""" )
if name.startswith("""visual.transformer.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""visual.transformer.resblocks""" , """vision_model.encoder.layers""" )
if "visual.conv1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.conv1""" , """vision_model.embeddings.patch_embedding""" )
if "visual.ln_pre" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.ln_pre""" , """vision_model.pre_layernorm""" )
if "visual.ln_post" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.ln_post""" , """vision_model.post_layernorm""" )
if "visual.proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.proj""" , """visual_projection.weight""" )
if "text_projection" in name:
_SCREAMING_SNAKE_CASE = name.replace("""text_projection""" , """text_projection.weight""" )
# things on top
if "prompts_visual_proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""prompts_visual_proj""" , """prompts_visual_projection""" )
if "prompts_visual_ln" in name:
_SCREAMING_SNAKE_CASE = name.replace("""prompts_visual_ln""" , """prompts_visual_layernorm""" )
# mit
if name == "mit.positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""positional""" , """position""" )
if name.startswith("""mit.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""mit.resblocks""" , """mit.encoder.layers""" )
# prompts generator
if name.startswith("""prompts_generator.norm""" ):
_SCREAMING_SNAKE_CASE = name.replace("""prompts_generator.norm""" , """prompts_generator.layernorm""" )
return name
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_SCREAMING_SNAKE_CASE = orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )
if "attn.in_proj" in key:
_SCREAMING_SNAKE_CASE = key.split(""".""" )
if key.startswith("""visual""" ):
_SCREAMING_SNAKE_CASE = key_split[3]
_SCREAMING_SNAKE_CASE = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[
:dim, :
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[
-dim:, :
]
else:
_SCREAMING_SNAKE_CASE = val[
:dim
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[
:dim, :
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[
-dim:, :
]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[-dim:]
elif key.startswith("""mit""" ):
_SCREAMING_SNAKE_CASE = key_split[2]
_SCREAMING_SNAKE_CASE = config.vision_config.mit_hidden_size
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[:dim, :]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
_SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2]
_SCREAMING_SNAKE_CASE = val[-dim:]
else:
_SCREAMING_SNAKE_CASE = key_split[2]
_SCREAMING_SNAKE_CASE = config.text_config.hidden_size
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[:dim, :]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[-dim:]
else:
_SCREAMING_SNAKE_CASE = rename_key(SCREAMING_SNAKE_CASE_ )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
_SCREAMING_SNAKE_CASE = val.T
_SCREAMING_SNAKE_CASE = val
return orig_state_dict
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
if num_frames == 8:
_SCREAMING_SNAKE_CASE = """eating_spaghetti_8_frames.npy"""
elif num_frames == 16:
_SCREAMING_SNAKE_CASE = """eating_spaghetti.npy"""
elif num_frames == 32:
_SCREAMING_SNAKE_CASE = """eating_spaghetti_32_frames.npy"""
_SCREAMING_SNAKE_CASE = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename=SCREAMING_SNAKE_CASE_ , repo_type="""dataset""" , )
_SCREAMING_SNAKE_CASE = np.load(SCREAMING_SNAKE_CASE_ )
return list(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=False ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {
# fully supervised kinetics-400 checkpoints
"""xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
"""xclip-base-patch32-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
),
"""xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
"""xclip-base-patch16-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
),
"""xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
"""xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f""",
# fully supervised kinetics-600 checkpoints
"""xclip-base-patch16-kinetics-600""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
),
"""xclip-base-patch16-kinetics-600-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
),
"""xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
# few shot
"""xclip-base-patch16-hmdb-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
),
"""xclip-base-patch16-hmdb-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
),
"""xclip-base-patch16-hmdb-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
),
"""xclip-base-patch16-hmdb-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
),
"""xclip-base-patch16-ucf-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
),
"""xclip-base-patch16-ucf-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
),
"""xclip-base-patch16-ucf-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
),
"""xclip-base-patch16-ucf-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
),
# zero shot
"""xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
}
_SCREAMING_SNAKE_CASE = model_to_url[model_name]
_SCREAMING_SNAKE_CASE = 8
if "16-frames" in model_name:
_SCREAMING_SNAKE_CASE = 16
elif "shot" in model_name:
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = get_xclip_config(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = XCLIPModel(SCREAMING_SNAKE_CASE_ )
model.eval()
if "drive" in checkpoint_url:
_SCREAMING_SNAKE_CASE = """pytorch_model.bin"""
gdown.cached_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , quiet=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )["""model"""]
else:
_SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ )["""model"""]
_SCREAMING_SNAKE_CASE = convert_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = XCLIPModel(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
_SCREAMING_SNAKE_CASE = 3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
_SCREAMING_SNAKE_CASE = VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
_SCREAMING_SNAKE_CASE = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32""" )
_SCREAMING_SNAKE_CASE = XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = prepare_video(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = processor(
text=["""playing sports""", """eating spaghetti""", """go shopping"""] , videos=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , padding=SCREAMING_SNAKE_CASE_ )
print("""Shape of pixel values:""" , inputs.pixel_values.shape )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
# Verify outputs
_SCREAMING_SNAKE_CASE = outputs.logits_per_video
_SCREAMING_SNAKE_CASE = logits_per_video.softmax(dim=1 )
print("""Probs:""" , SCREAMING_SNAKE_CASE_ )
# kinetics-400
if model_name == "xclip-base-patch32":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0019, 0.9951, 0.0030]] )
elif model_name == "xclip-base-patch32-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]] )
elif model_name == "xclip-base-patch16":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0083, 0.9681, 0.0236]] )
elif model_name == "xclip-base-patch16-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]] )
elif model_name == "xclip-large-patch14":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0062, 0.9864, 0.0075]] )
elif model_name == "xclip-large-patch14-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0555, 0.8914, 0.0531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0036, 0.9920, 0.0045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0027, 0.9904, 0.0070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]] )
else:
raise ValueError(F"Model name {model_name} not supported" )
assert torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
print("""Pushing model, processor and slow tokenizer files to the hub...""" )
model.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
processor.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
slow_tokenizer.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="xclip-base-patch32",
type=str,
help="Name of the model.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
UpperCamelCase__ : str = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 0 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
if height >= 1:
move_tower(height - 1 , _snake_case , _snake_case , _snake_case )
move_disk(_snake_case , _snake_case )
move_tower(height - 1 , _snake_case , _snake_case , _snake_case )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
print("""moving disk from""" , _snake_case , """to""" , _snake_case )
def lowerCAmelCase_ ( ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = int(input("""Height of hanoi: """ ).strip() )
move_tower(_snake_case , """A""" , """B""" , """C""" )
if __name__ == "__main__":
main()
| 710 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = params
_SCREAMING_SNAKE_CASE = np.array(A__ )
_SCREAMING_SNAKE_CASE = np.array([len(A__ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self , A__ ) -> Dict:
return (self.token_ids[index], self.lengths[index])
def __len__( self ) -> Tuple:
return len(self.lengths )
def UpperCamelCase ( self ) -> Dict:
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.params.max_model_input_size
_SCREAMING_SNAKE_CASE = self.lengths > max_len
logger.info(F"Splitting {sum(A__ )} too long sequences." )
def divide_chunks(A__ , A__ ):
return [l[i : i + n] for i in range(0 , len(A__ ) , A__ )]
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
if self.params.mlm:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
_SCREAMING_SNAKE_CASE = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
_SCREAMING_SNAKE_CASE = np.insert(A__ , 0 , A__ )
if sub_s[-1] != sep_id:
_SCREAMING_SNAKE_CASE = np.insert(A__ , len(A__ ) , A__ )
assert len(A__ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(A__ )
new_tok_ids.extend(A__ )
new_lengths.extend([len(A__ ) for l in sub_seqs] )
_SCREAMING_SNAKE_CASE = np.array(A__ )
_SCREAMING_SNAKE_CASE = np.array(A__ )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = len(self )
_SCREAMING_SNAKE_CASE = self.lengths > 11
_SCREAMING_SNAKE_CASE = self.token_ids[indices]
_SCREAMING_SNAKE_CASE = self.lengths[indices]
_SCREAMING_SNAKE_CASE = len(self )
logger.info(F"Remove {init_size - new_size} too short (<=11 tokens) sequences." )
def UpperCamelCase ( self ) -> int:
if "unk_token" not in self.params.special_tok_ids:
return
else:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""unk_token"""]
_SCREAMING_SNAKE_CASE = len(self )
_SCREAMING_SNAKE_CASE = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
_SCREAMING_SNAKE_CASE = (unk_occs / self.lengths) < 0.5
_SCREAMING_SNAKE_CASE = self.token_ids[indices]
_SCREAMING_SNAKE_CASE = self.lengths[indices]
_SCREAMING_SNAKE_CASE = len(self )
logger.info(F"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)." )
def UpperCamelCase ( self ) -> Optional[Any]:
if not self.params.is_master:
return
logger.info(F"{len(self )} sequences" )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def UpperCamelCase ( self , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = [t[0] for t in batch]
_SCREAMING_SNAKE_CASE = [t[1] for t in batch]
assert len(A__ ) == len(A__ )
# Max for paddings
_SCREAMING_SNAKE_CASE = max(A__ )
# Pad token ids
if self.params.mlm:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""pad_token"""]
else:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""unk_token"""]
_SCREAMING_SNAKE_CASE = [list(t.astype(A__ ) ) + [pad_idx] * (max_seq_len_ - len(A__ )) for t in token_ids]
assert len(tk_ ) == len(A__ )
assert all(len(A__ ) == max_seq_len_ for t in tk_ )
_SCREAMING_SNAKE_CASE = torch.tensor(tk_ ) # (bs, max_seq_len_)
_SCREAMING_SNAKE_CASE = torch.tensor(A__ ) # (bs)
return tk_t, lg_t
| 0 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [0] * len(_lowercase )
for i in range(1 , len(_lowercase ) ):
# use last results for better performance - dynamic programming
_SCREAMING_SNAKE_CASE = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
_SCREAMING_SNAKE_CASE = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
_SCREAMING_SNAKE_CASE = j
return prefix_result
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
return max(prefix_function(_lowercase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 711 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : Any = "▁"
UpperCamelCase__ : Any = {"vocab_file": "spiece.model"}
UpperCamelCase__ : int = {
"vocab_file": {
"google/reformer-crime-and-punishment": (
"https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
)
}
}
UpperCamelCase__ : Optional[int] = {
"google/reformer-crime-and-punishment": 524_288,
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self , A__ , A__="</s>" , A__="<unk>" , A__=[] , A__ = None , **A__ , ) -> None:
_SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=A__ , unk_token=A__ , additional_special_tokens=A__ , sp_model_kwargs=self.sp_model_kwargs , **A__ , )
_SCREAMING_SNAKE_CASE = vocab_file
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A__ )
@property
def UpperCamelCase ( self ) -> Any:
return self.sp_model.get_piece_size()
def UpperCamelCase ( self ) -> Dict[str, int]:
_SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(A__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> int:
_SCREAMING_SNAKE_CASE = self.__dict__.copy()
_SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase ( self , A__ ) -> List[str]:
return self.sp_model.encode(A__ , out_type=A__ )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
return self.sp_model.piece_to_id(A__ )
def UpperCamelCase ( self , A__ ) -> List[Any]:
if index < self.sp_model.get_piece_size():
_SCREAMING_SNAKE_CASE = self.sp_model.IdToPiece(A__ )
return token
def UpperCamelCase ( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A__ ) + token
_SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(A__ )
out_string += self.sp_model.decode(A__ )
return out_string.strip()
def UpperCamelCase ( self , A__ , A__ = None ) -> Tuple[str]:
if not os.path.isdir(A__ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
_SCREAMING_SNAKE_CASE = os.path.join(
A__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A__ )
elif not os.path.isfile(self.vocab_file ):
with open(A__ , """wb""" ) as fi:
_SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(A__ )
return (out_vocab_file,)
| 0 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 712 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class _a (_lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = MobileBertTokenizer
SCREAMING_SNAKE_CASE = MobileBertTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = filter_non_english
SCREAMING_SNAKE_CASE = 'google/mobilebert-uncased'
def UpperCamelCase ( self ) -> Any:
super().setUp()
_SCREAMING_SNAKE_CASE = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
_SCREAMING_SNAKE_CASE = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def UpperCamelCase ( self , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = """unwanted, running"""
return input_text, output_text
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.tokenizer_class(self.vocab_file )
_SCREAMING_SNAKE_CASE = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(A__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , [9, 6, 7, 12, 10, 11] )
def UpperCamelCase ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
# With lower casing
_SCREAMING_SNAKE_CASE = self.get_tokenizer(do_lower_case=A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer(do_lower_case=A__ )
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
_SCREAMING_SNAKE_CASE = {}
for i, token in enumerate(A__ ):
_SCREAMING_SNAKE_CASE = i
_SCREAMING_SNAKE_CASE = WordpieceTokenizer(vocab=A__ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
def UpperCamelCase ( self ) -> str:
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def UpperCamelCase ( self ) -> Union[str, Any]:
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def UpperCamelCase ( self ) -> Dict:
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
self.assertListEqual(
[rust_tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
@slow
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained("""google/mobilebert-uncased""" )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""sequence builders""" , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""multi-sequence build""" , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A__ , A__ )
assert encoded_sentence == [1_01] + text + [1_02]
assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
def UpperCamelCase ( self ) -> List[str]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = F"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
_SCREAMING_SNAKE_CASE = tokenizer_r.encode_plus(
A__ , return_attention_mask=A__ , return_token_type_ids=A__ , return_offsets_mapping=A__ , add_special_tokens=A__ , )
_SCREAMING_SNAKE_CASE = tokenizer_r.do_lower_case if hasattr(A__ , """do_lower_case""" ) else False
_SCREAMING_SNAKE_CASE = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), """A"""),
((1, 2), ""","""),
((3, 5), """na"""),
((5, 6), """##ï"""),
((6, 8), """##ve"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """Allen"""),
((21, 23), """##NL"""),
((23, 24), """##P"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), """a"""),
((1, 2), ""","""),
((3, 8), """naive"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """allen"""),
((21, 23), """##nl"""),
((23, 24), """##p"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = ["""的""", """人""", """有"""]
_SCREAMING_SNAKE_CASE = """""".join(A__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(A__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(A__ , A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(A__ )
# it is expected that only the first Chinese character is not preceded by "##".
_SCREAMING_SNAKE_CASE = [
F"##{token}" if idx != 0 else token for idx, token in enumerate(A__ )
]
self.assertListEqual(A__ , A__ )
self.assertListEqual(A__ , A__ )
| 0 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) <= 1:
return lst
_SCREAMING_SNAKE_CASE = 1
while i < len(SCREAMING_SNAKE_CASE_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_SCREAMING_SNAKE_CASE = lst[i], lst[i - 1]
i -= 1
if i == 0:
_SCREAMING_SNAKE_CASE = 1
return lst
if __name__ == "__main__":
UpperCamelCase__ : Tuple = input("Enter numbers separated by a comma:\n").strip()
UpperCamelCase__ : Union[str, Any] = [int(item) for item in user_input.split(",")]
print(gnome_sort(unsorted))
| 713 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
UpperCamelCase__ : Tuple = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , *A__ , A__=None , A__=None , A__=None , **A__ ) -> Optional[int]:
super().__init__(*A__ , **A__ )
_SCREAMING_SNAKE_CASE = eval_examples
_SCREAMING_SNAKE_CASE = post_process_function
_SCREAMING_SNAKE_CASE = quant_trainer_args
_SCREAMING_SNAKE_CASE = 1_28 # default number of calibration samples
def UpperCamelCase ( self , A__=None ) -> Union[str, Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError("""Trainer: calibration requires an calib_dataset.""" )
_SCREAMING_SNAKE_CASE = calib_dataset if calib_dataset is not None else self.calib_dataset
_SCREAMING_SNAKE_CASE = self._remove_unused_columns(A__ , description="""Calibration""" )
return DataLoader(
A__ , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=A__ , )
def UpperCamelCase ( self , A__=None ) -> str:
_SCREAMING_SNAKE_CASE = self.train_dataset if calib_dataset is None else calib_dataset
_SCREAMING_SNAKE_CASE = self.get_calib_dataloader(A__ )
_SCREAMING_SNAKE_CASE = self.model
quant_trainer.configure_model(A__ , self.quant_trainer_args , calib=A__ )
model.eval()
quant_trainer.enable_calibration(A__ )
logger.info("""***** Running calibration *****""" )
logger.info(F" Num examples = {self.calib_num}" )
logger.info(F" Batch size = {calib_dataloader.batch_size}" )
for step, inputs in enumerate(A__ ):
# Prediction step
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.prediction_step(A__ , A__ , prediction_loss_only=A__ )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(A__ , self.quant_trainer_args )
_SCREAMING_SNAKE_CASE = model
def UpperCamelCase ( self , A__=None , A__=None , A__=None , A__ = "eval" ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.eval_dataset if eval_dataset is None else eval_dataset
_SCREAMING_SNAKE_CASE = self.get_eval_dataloader(A__ )
_SCREAMING_SNAKE_CASE = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_SCREAMING_SNAKE_CASE = self.compute_metrics
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_SCREAMING_SNAKE_CASE = self.post_process_function(A__ , A__ , output.predictions )
_SCREAMING_SNAKE_CASE = self.compute_metrics(A__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
_SCREAMING_SNAKE_CASE = metrics.pop(A__ )
self.log(A__ )
else:
_SCREAMING_SNAKE_CASE = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_SCREAMING_SNAKE_CASE = self.callback_handler.on_evaluate(self.args , self.state , self.control , A__ )
return metrics
def UpperCamelCase ( self , A__ , A__ , A__=None , A__ = "test" ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.get_test_dataloader(A__ )
# Temporarily disable metric computation, we will do it in the loop here.
_SCREAMING_SNAKE_CASE = self.compute_metrics
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_SCREAMING_SNAKE_CASE = self.post_process_function(A__ , A__ , output.predictions , """predict""" )
_SCREAMING_SNAKE_CASE = self.compute_metrics(A__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
_SCREAMING_SNAKE_CASE = metrics.pop(A__ )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=A__ )
def UpperCamelCase ( self , A__="./" ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.eval_dataset
_SCREAMING_SNAKE_CASE = self.get_eval_dataloader(A__ )
_SCREAMING_SNAKE_CASE = next(iter(A__ ) )
# saving device - to make it consistent
_SCREAMING_SNAKE_CASE = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
# convert to tuple
_SCREAMING_SNAKE_CASE = tuple(v.to(A__ ) for k, v in batch.items() )
logger.info("""Converting model to be onnx compatible""" )
from pytorch_quantization.nn import TensorQuantizer
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = self.model.to(A__ )
model.eval()
model.float()
_SCREAMING_SNAKE_CASE = model.module if hasattr(A__ , """module""" ) else model
quant_trainer.configure_model(A__ , self.quant_trainer_args )
_SCREAMING_SNAKE_CASE = os.path.join(A__ , """model.onnx""" )
logger.info(F"exporting model to {output_model_file}" )
_SCREAMING_SNAKE_CASE = {0: """batch_size""", 1: """seq_len"""}
torch.onnx.export(
A__ , A__ , A__ , export_params=A__ , opset_version=13 , do_constant_folding=A__ , input_names=["""input_ids""", """attention_mask""", """token_type_ids"""] , output_names=["""output_start_logits""", """output_end_logits"""] , dynamic_axes={
"""input_ids""": axes,
"""attention_mask""": axes,
"""token_type_ids""": axes,
"""output_start_logits""": axes,
"""output_end_logits""": axes,
} , verbose=A__ , )
logger.info("""onnx export finished""" )
| 0 | 0 |
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
stooge(SCREAMING_SNAKE_CASE_ , 0 , len(SCREAMING_SNAKE_CASE_ ) - 1 )
return arr
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
_SCREAMING_SNAKE_CASE = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , (h - t) )
# Recursively sort last 2/3 elements
stooge(SCREAMING_SNAKE_CASE_ , i + t , (SCREAMING_SNAKE_CASE_) )
# Recursively sort first 2/3 elements
stooge(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , (h - t) )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = input("Enter numbers separated by a comma:\n").strip()
UpperCamelCase__ : List[Any] = [int(item) for item in user_input.split(",")]
print(stooge_sort(unsorted))
| 714 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
return "".join([hex(SCREAMING_SNAKE_CASE_ )[2:].zfill(2 ).upper() for byte in list(SCREAMING_SNAKE_CASE_ )] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bytes:
"""simple docstring"""
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(SCREAMING_SNAKE_CASE_ ) % 2) != 0:
raise ValueError(
"""Base16 encoded data is invalid:
Data does not have an even number of hex digits.""" )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(SCREAMING_SNAKE_CASE_ ) <= set("""0123456789ABCDEF""" ):
raise ValueError(
"""Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.""" )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Any = {"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : int = ["""XGLMTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = ["""XGLMTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : str = [
"""XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XGLMForCausalLM""",
"""XGLMModel""",
"""XGLMPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Union[str, Any] = [
"""FlaxXGLMForCausalLM""",
"""FlaxXGLMModel""",
"""FlaxXGLMPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : int = [
"""TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFXGLMForCausalLM""",
"""TFXGLMModel""",
"""TFXGLMPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : Dict = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 715 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
requests.request("""GET""" , """https://huggingface.co""" )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request("""GET""" , """https://huggingface.co""" , timeout=1.0 )
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request("""GET""" , """https://huggingface.co""" )
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_head("""https://huggingface.co""" )
| 0 | 0 |
from __future__ import annotations
import math
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
if num <= 0:
_SCREAMING_SNAKE_CASE = F"{num}: Invalid input, please enter a positive integer."
raise ValueError(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = [True] * (num + 1)
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = int(math.sqrt(SCREAMING_SNAKE_CASE_ ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(SCREAMING_SNAKE_CASE_ )
# Set multiples of start be False
for i in range(start * start , num + 1 , SCREAMING_SNAKE_CASE_ ):
if sieve[i] is True:
_SCREAMING_SNAKE_CASE = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(SCREAMING_SNAKE_CASE_ )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("Enter a positive integer: ").strip())))
| 716 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase_ ( ) -> Iterator[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
while True:
if is_prime(SCREAMING_SNAKE_CASE_ ):
yield num
num += 1
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = 2_00_00_00 ) -> int:
"""simple docstring"""
return sum(takewhile(lambda SCREAMING_SNAKE_CASE_ : x < n , prime_generator() ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 0 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : int = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
"tokenization_deberta": ["DebertaTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : List[Any] = ["DebertaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : List[Any] = [
"DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"DebertaForMaskedLM",
"DebertaForQuestionAnswering",
"DebertaForSequenceClassification",
"DebertaForTokenClassification",
"DebertaModel",
"DebertaPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Optional[int] = [
"TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDebertaForMaskedLM",
"TFDebertaForQuestionAnswering",
"TFDebertaForSequenceClassification",
"TFDebertaForTokenClassification",
"TFDebertaModel",
"TFDebertaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
from .tokenization_deberta import DebertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_deberta_fast import DebertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deberta import (
DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
DebertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deberta import (
TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDebertaForMaskedLM,
TFDebertaForQuestionAnswering,
TFDebertaForSequenceClassification,
TFDebertaForTokenClassification,
TFDebertaModel,
TFDebertaPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 717 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = {
"""task_specific_params""": {
"""summarization""": {"""length_penalty""": 1.0, """max_length""": 1_28, """min_length""": 12, """num_beams""": 4},
"""summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_42, """min_length""": 56, """num_beams""": 4},
"""summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
}
}
_SCREAMING_SNAKE_CASE = {
"""task_specific_params.summarization.length_penalty""": 1.0,
"""task_specific_params.summarization.max_length""": 1_28,
"""task_specific_params.summarization.min_length""": 12,
"""task_specific_params.summarization.num_beams""": 4,
"""task_specific_params.summarization_cnn.length_penalty""": 2.0,
"""task_specific_params.summarization_cnn.max_length""": 1_42,
"""task_specific_params.summarization_cnn.min_length""": 56,
"""task_specific_params.summarization_cnn.num_beams""": 4,
"""task_specific_params.summarization_xsum.length_penalty""": 1.0,
"""task_specific_params.summarization_xsum.max_length""": 62,
"""task_specific_params.summarization_xsum.min_length""": 11,
"""task_specific_params.summarization_xsum.num_beams""": 6,
}
self.assertEqual(flatten_dict(A__ ) , A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(A__ ) , x.transpose() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , transpose(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , transpose(A__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , transpose(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , transpose(A__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , np.asarray(transpose(A__ ) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , np.asarray(transpose(A__ , axes=(1, 2, 0) ) ) ) )
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , np.reshape(A__ , (4, 3) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , np.reshape(A__ , (12, 5) ) ) )
@require_torch
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , reshape(A__ , (4, 3) ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , reshape(A__ , (12, 5) ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , reshape(A__ , (4, 3) ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , reshape(A__ , (12, 5) ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , np.asarray(reshape(A__ , (4, 3) ) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , np.asarray(reshape(A__ , (12, 5) ) ) ) )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(A__ ) , np.squeeze(A__ ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , np.squeeze(A__ , axis=2 ) ) )
@require_torch
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , squeeze(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , squeeze(A__ , axis=2 ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , squeeze(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , squeeze(A__ , axis=2 ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , np.asarray(squeeze(A__ ) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , np.asarray(squeeze(A__ , axis=2 ) ) ) )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , np.expand_dims(A__ , axis=1 ) ) )
@require_torch
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , expand_dims(A__ , axis=1 ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , expand_dims(A__ , axis=1 ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , np.asarray(expand_dims(A__ , axis=1 ) ) ) )
| 0 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : List[Any] = {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
),
"distilbert-base-uncased-finetuned-sst-2-english": (
"https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
),
}
class _a (__a):
"""simple docstring"""
SCREAMING_SNAKE_CASE = "distilbert"
SCREAMING_SNAKE_CASE = {
"hidden_size": "dim",
"num_attention_heads": "n_heads",
"num_hidden_layers": "n_layers",
}
def __init__( self , A__=3_05_22 , A__=5_12 , A__=False , A__=6 , A__=12 , A__=7_68 , A__=4 * 7_68 , A__=0.1 , A__=0.1 , A__="gelu" , A__=0.02 , A__=0.1 , A__=0.2 , A__=0 , **A__ , ) -> List[Any]:
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = sinusoidal_pos_embds
_SCREAMING_SNAKE_CASE = n_layers
_SCREAMING_SNAKE_CASE = n_heads
_SCREAMING_SNAKE_CASE = dim
_SCREAMING_SNAKE_CASE = hidden_dim
_SCREAMING_SNAKE_CASE = dropout
_SCREAMING_SNAKE_CASE = attention_dropout
_SCREAMING_SNAKE_CASE = activation
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = qa_dropout
_SCREAMING_SNAKE_CASE = seq_classif_dropout
super().__init__(**lowerCAmelCase_ , pad_token_id=lowerCAmelCase_ )
class _a (__a):
"""simple docstring"""
@property
def UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_SCREAMING_SNAKE_CASE = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_SCREAMING_SNAKE_CASE = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 718 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ''
SCREAMING_SNAKE_CASE = 'hf-legacy' # "hf://"" is reserved for hffs
def __init__( self , A__ = None , A__ = None , **A__ , ) -> Optional[int]:
super().__init__(self , **A__ )
_SCREAMING_SNAKE_CASE = repo_info
_SCREAMING_SNAKE_CASE = token
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Tuple:
if self.dir_cache is None:
_SCREAMING_SNAKE_CASE = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
_SCREAMING_SNAKE_CASE = {
"""name""": hf_file.rfilename,
"""size""": None,
"""type""": """file""",
}
self.dir_cache.update(
{
str(A__ ): {"""name""": str(A__ ), """size""": None, """type""": """directory"""}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def UpperCamelCase ( self , A__ , A__ = "rb" , **A__ , ) -> Optional[int]:
if not isinstance(self.repo_info , A__ ):
raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}" )
_SCREAMING_SNAKE_CASE = hf_hub_url(self.repo_info.id , A__ , revision=self.repo_info.sha )
return fsspec.open(
A__ , mode=A__ , headers=get_authentication_headers_for_url(A__ , use_auth_token=self.token ) , client_kwargs={"""trust_env""": True} , ).open()
def UpperCamelCase ( self , A__ , **A__ ) -> str:
self._get_dirs()
_SCREAMING_SNAKE_CASE = self._strip_protocol(A__ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(A__ )
def UpperCamelCase ( self , A__ , A__=False , **A__ ) -> List[Any]:
self._get_dirs()
_SCREAMING_SNAKE_CASE = PurePosixPath(path.strip("""/""" ) )
_SCREAMING_SNAKE_CASE = {}
for p, f in self.dir_cache.items():
_SCREAMING_SNAKE_CASE = PurePosixPath(p.strip("""/""" ) )
_SCREAMING_SNAKE_CASE = p.parent
if root == path:
_SCREAMING_SNAKE_CASE = f
_SCREAMING_SNAKE_CASE = list(paths.values() )
if detail:
return out
else:
return sorted(f["""name"""] for f in out )
| 0 | 0 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
print("""Loading config file...""" )
def flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="" , SCREAMING_SNAKE_CASE_="." ):
_SCREAMING_SNAKE_CASE = []
for k, v in d.items():
_SCREAMING_SNAKE_CASE = parent_key + sep + k if parent_key else k
if isinstance(UpperCamelCase__ , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(UpperCamelCase__ , UpperCamelCase__ , sep=UpperCamelCase__ ).items() )
else:
items.append((new_key, v) )
return dict(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = argparse.Namespace()
with open(UpperCamelCase__ , """r""" ) as yaml_file:
try:
_SCREAMING_SNAKE_CASE = yaml.load(UpperCamelCase__ , Loader=yaml.FullLoader )
_SCREAMING_SNAKE_CASE = flatten_yaml_as_dict(UpperCamelCase__ )
for k, v in flat_cfg.items():
setattr(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
except yaml.YAMLError as exc:
logger.error("""Error while loading config file: {}. Error message: {}""".format(UpperCamelCase__ , str(UpperCamelCase__ ) ) )
return config
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = MobileViTVaConfig()
_SCREAMING_SNAKE_CASE = False
# dataset
if task_name.startswith("""imagenet1k_""" ):
_SCREAMING_SNAKE_CASE = 10_00
if int(task_name.strip().split("""_""" )[-1] ) == 3_84:
_SCREAMING_SNAKE_CASE = 3_84
else:
_SCREAMING_SNAKE_CASE = 2_56
_SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
elif task_name.startswith("""imagenet21k_to_1k_""" ):
_SCREAMING_SNAKE_CASE = 2_10_00
if int(task_name.strip().split("""_""" )[-1] ) == 3_84:
_SCREAMING_SNAKE_CASE = 3_84
else:
_SCREAMING_SNAKE_CASE = 2_56
_SCREAMING_SNAKE_CASE = """imagenet-22k-id2label.json"""
elif task_name.startswith("""ade20k_""" ):
_SCREAMING_SNAKE_CASE = 1_51
_SCREAMING_SNAKE_CASE = 5_12
_SCREAMING_SNAKE_CASE = """ade20k-id2label.json"""
_SCREAMING_SNAKE_CASE = True
elif task_name.startswith("""voc_""" ):
_SCREAMING_SNAKE_CASE = 21
_SCREAMING_SNAKE_CASE = 5_12
_SCREAMING_SNAKE_CASE = """pascal-voc-id2label.json"""
_SCREAMING_SNAKE_CASE = True
# orig_config
_SCREAMING_SNAKE_CASE = load_orig_config_file(UpperCamelCase__ )
assert getattr(UpperCamelCase__ , """model.classification.name""" , -1 ) == "mobilevit_v2", "Invalid model"
_SCREAMING_SNAKE_CASE = getattr(UpperCamelCase__ , """model.classification.mitv2.width_multiplier""" , 1.0 )
assert (
getattr(UpperCamelCase__ , """model.classification.mitv2.attn_norm_layer""" , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
_SCREAMING_SNAKE_CASE = getattr(UpperCamelCase__ , """model.classification.activation.name""" , """swish""" )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
_SCREAMING_SNAKE_CASE = getattr(UpperCamelCase__ , """model.segmentation.output_stride""" , 16 )
if "_deeplabv3" in task_name:
_SCREAMING_SNAKE_CASE = getattr(UpperCamelCase__ , """model.segmentation.deeplabv3.aspp_rates""" , [12, 24, 36] )
_SCREAMING_SNAKE_CASE = getattr(UpperCamelCase__ , """model.segmentation.deeplabv3.aspp_out_channels""" , 5_12 )
_SCREAMING_SNAKE_CASE = getattr(UpperCamelCase__ , """model.segmentation.deeplabv3.aspp_dropout""" , 0.1 )
# id2label
_SCREAMING_SNAKE_CASE = """huggingface/label-files"""
_SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type="""dataset""" ) , """r""" ) )
_SCREAMING_SNAKE_CASE = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE = idalabel
_SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = dct.pop(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = val
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ) -> int:
"""simple docstring"""
if base_model:
_SCREAMING_SNAKE_CASE = """"""
else:
_SCREAMING_SNAKE_CASE = """mobilevitv2."""
_SCREAMING_SNAKE_CASE = []
for k in state_dict.keys():
if k[:8] == "encoder.":
_SCREAMING_SNAKE_CASE = k[8:]
else:
_SCREAMING_SNAKE_CASE = k
if ".block." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(""".block.""" , """.""" )
if ".conv." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(""".conv.""" , """.convolution.""" )
if ".norm." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(""".norm.""" , """.normalization.""" )
if "conv_1." in k:
_SCREAMING_SNAKE_CASE = k_new.replace("""conv_1.""" , F"{model_prefix}conv_stem." )
for i in [1, 2]:
if F"layer_{i}." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(F"layer_{i}." , F"{model_prefix}encoder.layer.{i-1}.layer." )
if ".exp_1x1." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(""".exp_1x1.""" , """.expand_1x1.""" )
if ".red_1x1." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(""".red_1x1.""" , """.reduce_1x1.""" )
for i in [3, 4, 5]:
if F"layer_{i}.0." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(F"layer_{i}.0." , F"{model_prefix}encoder.layer.{i-1}.downsampling_layer." )
if F"layer_{i}.1.local_rep.0." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(F"layer_{i}.1.local_rep.0." , F"{model_prefix}encoder.layer.{i-1}.conv_kxk." )
if F"layer_{i}.1.local_rep.1." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(F"layer_{i}.1.local_rep.1." , F"{model_prefix}encoder.layer.{i-1}.conv_1x1." )
for i in [3, 4, 5]:
if i == 3:
_SCREAMING_SNAKE_CASE = [0, 1]
elif i == 4:
_SCREAMING_SNAKE_CASE = [0, 1, 2, 3]
elif i == 5:
_SCREAMING_SNAKE_CASE = [0, 1, 2]
for j in j_in:
if F"layer_{i}.1.global_rep.{j}." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(
F"layer_{i}.1.global_rep.{j}." , F"{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}." )
if F"layer_{i}.1.global_rep.{j+1}." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(
F"layer_{i}.1.global_rep.{j+1}." , F"{model_prefix}encoder.layer.{i-1}.layernorm." )
if F"layer_{i}.1.conv_proj." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(F"layer_{i}.1.conv_proj." , F"{model_prefix}encoder.layer.{i-1}.conv_projection." )
if "pre_norm_attn.0." in k:
_SCREAMING_SNAKE_CASE = k_new.replace("""pre_norm_attn.0.""" , """layernorm_before.""" )
if "pre_norm_attn.1." in k:
_SCREAMING_SNAKE_CASE = k_new.replace("""pre_norm_attn.1.""" , """attention.""" )
if "pre_norm_ffn.0." in k:
_SCREAMING_SNAKE_CASE = k_new.replace("""pre_norm_ffn.0.""" , """layernorm_after.""" )
if "pre_norm_ffn.1." in k:
_SCREAMING_SNAKE_CASE = k_new.replace("""pre_norm_ffn.1.""" , """ffn.conv1.""" )
if "pre_norm_ffn.3." in k:
_SCREAMING_SNAKE_CASE = k_new.replace("""pre_norm_ffn.3.""" , """ffn.conv2.""" )
if "classifier.1." in k:
_SCREAMING_SNAKE_CASE = k_new.replace("""classifier.1.""" , """classifier.""" )
if "seg_head." in k:
_SCREAMING_SNAKE_CASE = k_new.replace("""seg_head.""" , """segmentation_head.""" )
if ".aspp_layer." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in k:
_SCREAMING_SNAKE_CASE = k_new.replace(""".aspp_pool.""" , """.""" )
rename_keys.append((k, k_new) )
return rename_keys
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
for k in state_dict.keys():
if k.startswith("""seg_head.aux_head.""" ):
keys_to_ignore.append(UpperCamelCase__ )
for k in keys_to_ignore:
state_dict.pop(UpperCamelCase__ , UpperCamelCase__ )
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
_SCREAMING_SNAKE_CASE = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw )
return im
@torch.no_grad()
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = get_mobilevitva_config(UpperCamelCase__ , UpperCamelCase__ )
# load original state_dict
_SCREAMING_SNAKE_CASE = torch.load(UpperCamelCase__ , map_location="""cpu""" )
# load huggingface model
if task_name.startswith("""ade20k_""" ) or task_name.startswith("""voc_""" ):
_SCREAMING_SNAKE_CASE = MobileViTVaForSemanticSegmentation(UpperCamelCase__ ).eval()
_SCREAMING_SNAKE_CASE = False
else:
_SCREAMING_SNAKE_CASE = MobileViTVaForImageClassification(UpperCamelCase__ ).eval()
_SCREAMING_SNAKE_CASE = False
# remove and rename some keys of load the original model
_SCREAMING_SNAKE_CASE = checkpoint
remove_unused_keys(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = create_rename_keys(UpperCamelCase__ , base_model=UpperCamelCase__ )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# load modified state_dict
model.load_state_dict(UpperCamelCase__ )
# Check outputs on an image, prepared by MobileViTImageProcessor
_SCREAMING_SNAKE_CASE = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
_SCREAMING_SNAKE_CASE = image_processor(images=prepare_img() , return_tensors="""pt""" )
_SCREAMING_SNAKE_CASE = model(**UpperCamelCase__ )
# verify classification model
if task_name.startswith("""imagenet""" ):
_SCREAMING_SNAKE_CASE = outputs.logits
_SCREAMING_SNAKE_CASE = logits.argmax(-1 ).item()
print("""Predicted class:""" , model.config.idalabel[predicted_class_idx] )
if task_name.startswith("""imagenet1k_256""" ) and config.width_multiplier == 1.0:
# expected_logits for base variant
_SCREAMING_SNAKE_CASE = torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01] )
assert torch.allclose(logits[0, :3] , UpperCamelCase__ , atol=1e-4 )
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
print(F"Saving model {task_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(UpperCamelCase__ )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
UpperCamelCase__ : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
UpperCamelCase__ : Any = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 719 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , keep_in_memory=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = features.copy() if features else default_expected_features
_SCREAMING_SNAKE_CASE = (
Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , split=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type""" , [str, list] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
if issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = parquet_path
elif issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = [parquet_path]
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=("train",) ) -> List[str]:
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for split in splits:
_SCREAMING_SNAKE_CASE = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(
{"""train""": parquet_path} , cache_dir=SCREAMING_SNAKE_CASE_ , keep_in_memory=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = features.copy() if features else default_expected_features
_SCREAMING_SNAKE_CASE = (
Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_SCREAMING_SNAKE_CASE = ParquetDatasetReader({"""train""": parquet_path} , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
if split:
_SCREAMING_SNAKE_CASE = {split: parquet_path}
else:
_SCREAMING_SNAKE_CASE = """train"""
_SCREAMING_SNAKE_CASE = {"""train""": parquet_path, """test""": parquet_path}
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ParquetDatasetWriter(SCREAMING_SNAKE_CASE_ , tmp_path / """foo.parquet""" )
assert writer.write() > 0
_SCREAMING_SNAKE_CASE = pq.ParquetFile(tmp_path / """foo.parquet""" )
_SCREAMING_SNAKE_CASE = pf.read()
assert dataset.data.table == output_table
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(shared_datadir / """test_image_rgb.jpg""" )
_SCREAMING_SNAKE_CASE = {"""image""": [image_path]}
_SCREAMING_SNAKE_CASE = Features({"""image""": Image()} )
_SCREAMING_SNAKE_CASE = Dataset.from_dict(SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = ParquetDatasetWriter(SCREAMING_SNAKE_CASE_ , tmp_path / """foo.parquet""" )
assert writer.write() > 0
_SCREAMING_SNAKE_CASE = Dataset.from_parquet(str(tmp_path / """foo.parquet""" ) )
assert dataset.features == reloaded_dataset.features
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(str(tmp_path / """foo.parquet""" ) , streaming=SCREAMING_SNAKE_CASE_ ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"""feature, expected""" , [
(Features({"""foo""": Value("""int32""" )} ), None),
(Features({"""image""": Image(), """foo""": Value("""int32""" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"""nested""": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
assert get_writer_batch_size(SCREAMING_SNAKE_CASE_ ) == expected
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTForImageClassification, TFViTModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=30 , A__=2 , A__=3 , A__=True , A__=True , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=10 , A__=0.02 , A__=3 , A__=None , ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = patch_size
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = type_sequence_label_size
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = scope
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
_SCREAMING_SNAKE_CASE = (image_size // patch_size) ** 2
_SCREAMING_SNAKE_CASE = num_patches + 1
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ) -> Any:
return ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=UpperCAmelCase__ , initializer_range=self.initializer_range , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = TFViTModel(config=UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ , training=UpperCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# Test with an image with different size than the one specified in config.
_SCREAMING_SNAKE_CASE = self.image_size // 2
_SCREAMING_SNAKE_CASE = pixel_values[:, :, :image_size, :image_size]
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ , interpolate_pos_encoding=UpperCAmelCase__ , training=UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = (image_size // self.patch_size) ** 2 + 1
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.type_sequence_label_size
_SCREAMING_SNAKE_CASE = TFViTForImageClassification(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ , labels=UpperCAmelCase__ , training=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# Test with an image with different size than the one specified in config.
_SCREAMING_SNAKE_CASE = self.image_size // 2
_SCREAMING_SNAKE_CASE = pixel_values[:, :, :image_size, :image_size]
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ , interpolate_pos_encoding=UpperCAmelCase__ , training=UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = TFViTForImageClassification(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE = (
{'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = TFViTModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=UpperCAmelCase__ , has_text_modality=UpperCAmelCase__ , hidden_size=37 )
def UpperCamelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def UpperCamelCase ( self ) -> Tuple:
pass
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def UpperCamelCase ( self ) -> List[str]:
pass
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = model_class(UpperCAmelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
_SCREAMING_SNAKE_CASE = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCAmelCase__ , tf.keras.layers.Layer ) )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = model_class(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
_SCREAMING_SNAKE_CASE = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCAmelCase__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__ )
@slow
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = TFViTModel.from_pretrained("""google/vit-base-patch16-224""" )
self.assertIsNotNone(UpperCAmelCase__ )
def lowerCAmelCase_ ( ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class _a (unittest.TestCase):
"""simple docstring"""
@cached_property
def UpperCamelCase ( self ) -> str:
return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None
@slow
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" )
_SCREAMING_SNAKE_CASE = self.default_image_processor
_SCREAMING_SNAKE_CASE = prepare_img()
_SCREAMING_SNAKE_CASE = image_processor(images=UpperCAmelCase__ , return_tensors="""tf""" )
# forward pass
_SCREAMING_SNAKE_CASE = model(**UpperCAmelCase__ )
# verify the logits
_SCREAMING_SNAKE_CASE = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = tf.constant([-0.2744, 0.8215, -0.0836] )
tf.debugging.assert_near(outputs.logits[0, :3] , UpperCAmelCase__ , atol=1E-4 ) | 720 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicative_persistence() does not accept negative values""" )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
while len(SCREAMING_SNAKE_CASE_ ) != 1:
_SCREAMING_SNAKE_CASE = [int(SCREAMING_SNAKE_CASE_ ) for i in num_string]
_SCREAMING_SNAKE_CASE = 1
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) ):
total *= numbers[i]
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
steps += 1
return steps
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("""additive_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""additive_persistence() does not accept negative values""" )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
while len(SCREAMING_SNAKE_CASE_ ) != 1:
_SCREAMING_SNAKE_CASE = [int(SCREAMING_SNAKE_CASE_ ) for i in num_string]
_SCREAMING_SNAKE_CASE = 0
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) ):
total += numbers[i]
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
UpperCamelCase__ : Dict = logging.get_logger(__name__)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[List[ImageInput]]:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(SCREAMING_SNAKE_CASE_ , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(SCREAMING_SNAKE_CASE_ ):
return [[videos]]
raise ValueError(F"Could not make batched video from {videos}" )
class _a (__a):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['pixel_values']
def __init__( self , A__ = True , A__ = None , A__ = PILImageResampling.BILINEAR , A__ = True , A__ = None , A__ = True , A__ = 1 / 2_55 , A__ = True , A__ = True , A__ = None , A__ = None , **A__ , ) -> None:
super().__init__(**A__ )
_SCREAMING_SNAKE_CASE = size if size is not None else {"""shortest_edge""": 2_56}
_SCREAMING_SNAKE_CASE = get_size_dict(A__ , default_to_square=A__ )
_SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
_SCREAMING_SNAKE_CASE = get_size_dict(A__ , param_name="""crop_size""" )
_SCREAMING_SNAKE_CASE = do_resize
_SCREAMING_SNAKE_CASE = size
_SCREAMING_SNAKE_CASE = do_center_crop
_SCREAMING_SNAKE_CASE = crop_size
_SCREAMING_SNAKE_CASE = resample
_SCREAMING_SNAKE_CASE = do_rescale
_SCREAMING_SNAKE_CASE = rescale_factor
_SCREAMING_SNAKE_CASE = offset
_SCREAMING_SNAKE_CASE = do_normalize
_SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_SCREAMING_SNAKE_CASE = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCamelCase ( self , A__ , A__ , A__ = PILImageResampling.BILINEAR , A__ = None , **A__ , ) -> np.ndarray:
_SCREAMING_SNAKE_CASE = get_size_dict(A__ , default_to_square=A__ )
if "shortest_edge" in size:
_SCREAMING_SNAKE_CASE = get_resize_output_image_size(A__ , size["""shortest_edge"""] , default_to_square=A__ )
elif "height" in size and "width" in size:
_SCREAMING_SNAKE_CASE = (size["""height"""], size["""width"""])
else:
raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(A__ , size=A__ , resample=A__ , data_format=A__ , **A__ )
def UpperCamelCase ( self , A__ , A__ , A__ = None , **A__ , ) -> np.ndarray:
_SCREAMING_SNAKE_CASE = get_size_dict(A__ )
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(A__ , size=(size["""height"""], size["""width"""]) , data_format=A__ , **A__ )
def UpperCamelCase ( self , A__ , A__ , A__ = True , A__ = None , **A__ , ) -> List[Any]:
_SCREAMING_SNAKE_CASE = image.astype(np.floataa )
if offset:
_SCREAMING_SNAKE_CASE = image - (scale / 2)
return rescale(A__ , scale=A__ , data_format=A__ , **A__ )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ = None , **A__ , ) -> np.ndarray:
return normalize(A__ , mean=A__ , std=A__ , data_format=A__ , **A__ )
def UpperCamelCase ( self , A__ , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = ChannelDimension.FIRST , ) -> np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
if offset and not do_rescale:
raise ValueError("""For offset, do_rescale must also be set to True.""" )
# All transformations expect numpy arrays.
_SCREAMING_SNAKE_CASE = to_numpy_array(A__ )
if do_resize:
_SCREAMING_SNAKE_CASE = self.resize(image=A__ , size=A__ , resample=A__ )
if do_center_crop:
_SCREAMING_SNAKE_CASE = self.center_crop(A__ , size=A__ )
if do_rescale:
_SCREAMING_SNAKE_CASE = self.rescale(image=A__ , scale=A__ , offset=A__ )
if do_normalize:
_SCREAMING_SNAKE_CASE = self.normalize(image=A__ , mean=A__ , std=A__ )
_SCREAMING_SNAKE_CASE = to_channel_dimension_format(A__ , A__ )
return image
def UpperCamelCase ( self , A__ , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = ChannelDimension.FIRST , **A__ , ) -> PIL.Image.Image:
_SCREAMING_SNAKE_CASE = do_resize if do_resize is not None else self.do_resize
_SCREAMING_SNAKE_CASE = resample if resample is not None else self.resample
_SCREAMING_SNAKE_CASE = do_center_crop if do_center_crop is not None else self.do_center_crop
_SCREAMING_SNAKE_CASE = do_rescale if do_rescale is not None else self.do_rescale
_SCREAMING_SNAKE_CASE = rescale_factor if rescale_factor is not None else self.rescale_factor
_SCREAMING_SNAKE_CASE = offset if offset is not None else self.offset
_SCREAMING_SNAKE_CASE = do_normalize if do_normalize is not None else self.do_normalize
_SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else self.image_mean
_SCREAMING_SNAKE_CASE = image_std if image_std is not None else self.image_std
_SCREAMING_SNAKE_CASE = size if size is not None else self.size
_SCREAMING_SNAKE_CASE = get_size_dict(A__ , default_to_square=A__ )
_SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else self.crop_size
_SCREAMING_SNAKE_CASE = get_size_dict(A__ , param_name="""crop_size""" )
if not valid_images(A__ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
_SCREAMING_SNAKE_CASE = make_batched(A__ )
_SCREAMING_SNAKE_CASE = [
[
self._preprocess_image(
image=A__ , do_resize=A__ , size=A__ , resample=A__ , do_center_crop=A__ , crop_size=A__ , do_rescale=A__ , rescale_factor=A__ , offset=A__ , do_normalize=A__ , image_mean=A__ , image_std=A__ , data_format=A__ , )
for img in video
]
for video in videos
]
_SCREAMING_SNAKE_CASE = {"""pixel_values""": videos}
return BatchFeature(data=A__ , tensor_type=A__ )
| 721 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
UpperCamelCase__ : Tuple = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
from run_translation import main # noqa
set_seed(42)
UpperCamelCase__ : Union[str, Any] = "sshleifer/student_marian_en_ro_6_1"
UpperCamelCase__ : str = "sshleifer/tiny-mbart"
@require_torch
class _a (_lowerCamelCase):
"""simple docstring"""
def UpperCamelCase ( self , A__=False , A__=None , A__=True , A__=True , A__=True , A__=True , ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=A__ , num_train_epochs=1 , distributed=A__ , extra_args_str=A__ , predict_with_generate=A__ , do_train=A__ , do_eval=A__ , do_predict=A__ , )
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(os.path.join(A__ , """trainer_state.json""" ) ).log_history
if not do_eval:
return
_SCREAMING_SNAKE_CASE = [log for log in logs if """eval_loss""" in log.keys()]
_SCREAMING_SNAKE_CASE = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
_SCREAMING_SNAKE_CASE = eval_metrics[-1]
assert isinstance(last_step_stats["""eval_bleu"""] , A__ )
assert not math.isnan(float(last_step_stats["""eval_loss"""] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def UpperCamelCase ( self ) -> Optional[int]:
self.run_seqaseq_quick()
@require_torch_multi_gpu
def UpperCamelCase ( self ) -> Optional[Any]:
self.run_seqaseq_quick(distributed=A__ )
@require_torch_multi_gpu
def UpperCamelCase ( self ) -> Union[str, Any]:
self.run_seqaseq_quick(distributed=A__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> Any:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp simple""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> Tuple:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp simple --fp16""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> str:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp zero_dp_2""" , predict_with_generate=A__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> List[str]:
self.run_seqaseq_quick(
distributed=A__ , extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" , predict_with_generate=A__ )
@require_apex
@require_torch_gpu
def UpperCamelCase ( self ) -> Optional[Any]:
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
@parameterized.expand(["""base""", """low""", """high""", """mixed"""] )
@require_torch_multi_gpu
def UpperCamelCase ( self , A__ ) -> List[Any]:
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
_SCREAMING_SNAKE_CASE = {
# test with the default log_level - should be info and thus log info once
"""base""": {"""extra_args_str""": """""", """n_matches""": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"""low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"""high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"""mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
}
_SCREAMING_SNAKE_CASE = experiments[experiment_id]
_SCREAMING_SNAKE_CASE = {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
_SCREAMING_SNAKE_CASE = """Running training"""
with CaptureStderr() as cl:
self.run_seqaseq_quick(**A__ , extra_args_str=data["""extra_args_str"""] )
_SCREAMING_SNAKE_CASE = len(re.findall(A__ , cl.err ) )
self.assertEqual(A__ , data["""n_matches"""] )
@slow
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.run_trainer(
eval_steps=2 , max_len=1_28 , model_name=A__ , learning_rate=3E-4 , num_train_epochs=10 , distributed=A__ , )
# Check metrics
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(os.path.join(A__ , """trainer_state.json""" ) ).log_history
_SCREAMING_SNAKE_CASE = [log for log in logs if """eval_loss""" in log.keys()]
_SCREAMING_SNAKE_CASE = eval_metrics[0]
_SCREAMING_SNAKE_CASE = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["""eval_bleu"""] , A__ )
# test if do_predict saves generations and metrics
_SCREAMING_SNAKE_CASE = os.listdir(A__ )
_SCREAMING_SNAKE_CASE = {os.path.basename(A__ ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def UpperCamelCase ( self ) -> Dict:
from transformers.training_args import OptimizerNames
def train_and_return_metrics(A__ ) -> Tuple[int, float]:
_SCREAMING_SNAKE_CASE = """--skip_memory_metrics 0"""
_SCREAMING_SNAKE_CASE = self.run_trainer(
max_len=1_28 , model_name=A__ , learning_rate=3E-4 , num_train_epochs=1 , optim=A__ , distributed=A__ , extra_args_str=A__ , do_eval=A__ , do_predict=A__ , n_gpus_to_use=1 , )
# Check metrics
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(Path(A__ , """trainer_state.json""" ) ).log_history
_SCREAMING_SNAKE_CASE = int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20 )
_SCREAMING_SNAKE_CASE = int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20 )
_SCREAMING_SNAKE_CASE = logs[0]["""train_loss"""]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
_SCREAMING_SNAKE_CASE = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
_SCREAMING_SNAKE_CASE = gpu_peak_mem_orig + gpu_alloc_mem_orig
_SCREAMING_SNAKE_CASE = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
_SCREAMING_SNAKE_CASE = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
_SCREAMING_SNAKE_CASE = 1_20
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
A__ , A__ , """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
F" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
F" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB" , )
self.assertGreater(
A__ , A__ , """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
F" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
F" gpu_total_mem_bnb={gpu_total_mem_bnb}MB" , )
self.assertEqual(
A__ , A__ , F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}" )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ = 3E-3 , A__ = "adafactor" , A__ = False , A__ = None , A__ = 0 , A__ = True , A__ = True , A__ = True , A__ = True , A__ = None , ) -> Dict:
_SCREAMING_SNAKE_CASE = self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
_SCREAMING_SNAKE_CASE = self.get_auto_remove_tmp_dir()
_SCREAMING_SNAKE_CASE = F"\n --model_name_or_path {model_name}\n --train_file {data_dir}/train.json\n --validation_file {data_dir}/val.json\n --test_file {data_dir}/test.json\n --output_dir {output_dir}\n --overwrite_output_dir\n --max_train_samples 8\n --max_source_length {max_len}\n --max_target_length {max_len}\n --do_train\n --num_train_epochs {str(A__ )}\n --per_device_train_batch_size 4\n --learning_rate {learning_rate}\n --warmup_steps 8\n --logging_steps 0\n --logging_strategy no\n --save_steps {str(A__ )}\n --group_by_length\n --label_smoothing_factor 0.1\n --target_lang ro_RO\n --source_lang en_XX\n ".split()
_SCREAMING_SNAKE_CASE = F"\n --do_eval\n --per_device_eval_batch_size 4\n --max_eval_samples 8\n --val_max_target_length {max_len}\n --evaluation_strategy steps\n --eval_steps {str(A__ )}\n ".split()
_SCREAMING_SNAKE_CASE = """
--do_predict
""".split()
_SCREAMING_SNAKE_CASE = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F"--optim {optim}".split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
_SCREAMING_SNAKE_CASE = get_gpu_count()
_SCREAMING_SNAKE_CASE = get_torch_dist_unique_port()
_SCREAMING_SNAKE_CASE = F"\n -m torch.distributed.run\n --nproc_per_node={n_gpus_to_use}\n --master_port={master_port}\n {self.examples_dir_str}/pytorch/translation/run_translation.py\n ".split()
_SCREAMING_SNAKE_CASE = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(A__ , env=self.get_env() )
else:
_SCREAMING_SNAKE_CASE = ["""run_translation.py"""] + args
with patch.object(A__ , """argv""" , A__ ):
main()
return output_dir
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _a :
def __init__( self , A__ , A__=2 , A__=3 , A__=4 , A__=2 , A__=7 , A__=True , A__=True , A__=True , A__=True , A__=99 , A__=36 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=16 , A__=2 , A__=0.02 , A__=6 , A__=6 , A__=3 , A__=4 , A__=None , A__=10_00 , ) -> List[str]:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = patch_size
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_input_mask
_SCREAMING_SNAKE_CASE = use_token_type_ids
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = type_vocab_size
_SCREAMING_SNAKE_CASE = type_sequence_label_size
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = coordinate_size
_SCREAMING_SNAKE_CASE = shape_size
_SCREAMING_SNAKE_CASE = num_labels
_SCREAMING_SNAKE_CASE = num_choices
_SCREAMING_SNAKE_CASE = scope
_SCREAMING_SNAKE_CASE = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
_SCREAMING_SNAKE_CASE = text_seq_length
_SCREAMING_SNAKE_CASE = (image_size // patch_size) ** 2 + 1
_SCREAMING_SNAKE_CASE = self.text_seq_length + self.image_seq_length
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
_SCREAMING_SNAKE_CASE = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
_SCREAMING_SNAKE_CASE = bbox[i, j, 3]
_SCREAMING_SNAKE_CASE = bbox[i, j, 1]
_SCREAMING_SNAKE_CASE = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
_SCREAMING_SNAKE_CASE = bbox[i, j, 2]
_SCREAMING_SNAKE_CASE = bbox[i, j, 0]
_SCREAMING_SNAKE_CASE = tmp_coordinate
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
_SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.text_seq_length] )
_SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = TFLayoutLMvaModel(config=A__ )
# text + image
_SCREAMING_SNAKE_CASE = model(A__ , pixel_values=A__ , training=A__ )
_SCREAMING_SNAKE_CASE = model(
A__ , bbox=A__ , pixel_values=A__ , attention_mask=A__ , token_type_ids=A__ , training=A__ , )
_SCREAMING_SNAKE_CASE = model(A__ , bbox=A__ , pixel_values=A__ , training=A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
_SCREAMING_SNAKE_CASE = model(A__ , training=A__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
_SCREAMING_SNAKE_CASE = model({"""pixel_values""": pixel_values} , training=A__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFLayoutLMvaForSequenceClassification(config=A__ )
_SCREAMING_SNAKE_CASE = model(
A__ , bbox=A__ , pixel_values=A__ , attention_mask=A__ , token_type_ids=A__ , labels=A__ , training=A__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFLayoutLMvaForTokenClassification(config=A__ )
_SCREAMING_SNAKE_CASE = model(
A__ , bbox=A__ , pixel_values=A__ , attention_mask=A__ , token_type_ids=A__ , labels=A__ , training=A__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = TFLayoutLMvaForQuestionAnswering(config=A__ )
_SCREAMING_SNAKE_CASE = model(
A__ , bbox=A__ , pixel_values=A__ , attention_mask=A__ , token_type_ids=A__ , start_positions=A__ , end_positions=A__ , training=A__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
((_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE) , (_SCREAMING_SNAKE_CASE)) = config_and_inputs
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""bbox""": bbox,
"""pixel_values""": pixel_values,
"""token_type_ids""": token_type_ids,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_tf
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
SCREAMING_SNAKE_CASE = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{'document-question-answering': TFLayoutLMvaForQuestionAnswering, 'feature-extraction': TFLayoutLMvaModel}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> List[Any]:
return True
def UpperCamelCase ( self , A__ , A__ , A__=False ) -> dict:
_SCREAMING_SNAKE_CASE = copy.deepcopy(A__ )
if model_class in get_values(A__ ):
_SCREAMING_SNAKE_CASE = {
k: tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(A__ , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(A__ ):
_SCREAMING_SNAKE_CASE = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(A__ ):
_SCREAMING_SNAKE_CASE = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
_SCREAMING_SNAKE_CASE = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(A__ ):
_SCREAMING_SNAKE_CASE = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(A__ ):
_SCREAMING_SNAKE_CASE = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = TFLayoutLMvaModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = model_class(A__ )
if getattr(A__ , """hf_compute_loss""" , A__ ):
# The number of elements in the loss should be the same as the number of elements in the label
_SCREAMING_SNAKE_CASE = self._prepare_for_class(inputs_dict.copy() , A__ , return_labels=A__ )
_SCREAMING_SNAKE_CASE = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=A__ )[0]
]
_SCREAMING_SNAKE_CASE = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
_SCREAMING_SNAKE_CASE = self._prepare_for_class(inputs_dict.copy() , A__ , return_labels=A__ )
_SCREAMING_SNAKE_CASE = prepared_for_class.pop("""input_ids""" )
_SCREAMING_SNAKE_CASE = model(A__ , **A__ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
_SCREAMING_SNAKE_CASE = self._prepare_for_class(inputs_dict.copy() , A__ , return_labels=A__ )
_SCREAMING_SNAKE_CASE = prepared_for_class.pop("""input_ids""" )
if "labels" in prepared_for_class:
_SCREAMING_SNAKE_CASE = prepared_for_class["""labels"""].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
_SCREAMING_SNAKE_CASE = -1_00
_SCREAMING_SNAKE_CASE = tf.convert_to_tensor(A__ )
_SCREAMING_SNAKE_CASE = model(A__ , **A__ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
_SCREAMING_SNAKE_CASE = self._prepare_for_class(inputs_dict.copy() , A__ , return_labels=A__ )
_SCREAMING_SNAKE_CASE = model(A__ )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
_SCREAMING_SNAKE_CASE = self._prepare_for_class(inputs_dict.copy() , A__ , return_labels=A__ )
# Get keys that were added with the _prepare_for_class function
_SCREAMING_SNAKE_CASE = prepared_for_class.keys() - inputs_dict.keys()
_SCREAMING_SNAKE_CASE = inspect.signature(model.call ).parameters
_SCREAMING_SNAKE_CASE = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
_SCREAMING_SNAKE_CASE = {0: """input_ids"""}
for label_key in label_keys:
_SCREAMING_SNAKE_CASE = signature_names.index(A__ )
_SCREAMING_SNAKE_CASE = label_key
_SCREAMING_SNAKE_CASE = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
_SCREAMING_SNAKE_CASE = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
_SCREAMING_SNAKE_CASE = prepared_for_class[value]
_SCREAMING_SNAKE_CASE = tuple(A__ )
# Send to model
_SCREAMING_SNAKE_CASE = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def UpperCamelCase ( self ) -> Tuple:
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(A__ , A__ , A__ , A__ , A__ , A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_SCREAMING_SNAKE_CASE = type
self.model_tester.create_and_check_model(A__ , A__ , A__ , A__ , A__ , A__ )
def UpperCamelCase ( self ) -> Any:
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
A__ , A__ , A__ , A__ , A__ , A__ , A__ )
def UpperCamelCase ( self ) -> List[str]:
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
A__ , A__ , A__ , A__ , A__ , A__ , A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
A__ , A__ , A__ , A__ , A__ , A__ , A__ )
@slow
def UpperCamelCase ( self ) -> int:
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE = TFLayoutLMvaModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
class _a (unittest.TestCase):
@cached_property
def UpperCamelCase ( self ) -> List[Any]:
return LayoutLMvaImageProcessor(apply_ocr=A__ ) if is_vision_available() else None
@slow
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = TFLayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" )
_SCREAMING_SNAKE_CASE = self.default_image_processor
_SCREAMING_SNAKE_CASE = prepare_img()
_SCREAMING_SNAKE_CASE = image_processor(images=A__ , return_tensors="""tf""" ).pixel_values
_SCREAMING_SNAKE_CASE = tf.constant([[1, 2]] )
_SCREAMING_SNAKE_CASE = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
_SCREAMING_SNAKE_CASE = model(input_ids=A__ , bbox=A__ , pixel_values=A__ , training=A__ )
# verify the logits
_SCREAMING_SNAKE_CASE = (1, 1_99, 7_68)
self.assertEqual(outputs.last_hidden_state.shape , A__ )
_SCREAMING_SNAKE_CASE = tf.constant(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , A__ , atol=1E-4 ) )
| 700 |
'''simple docstring'''
import sys
UpperCamelCase__ : int = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = N ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = -sys.maxsize - 1
for i in range(len(SCREAMING_SNAKE_CASE_ ) - 12 ):
_SCREAMING_SNAKE_CASE = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
_SCREAMING_SNAKE_CASE = product
return largest_product
if __name__ == "__main__":
print(f"""{solution() = }""")
| 0 | 0 |
'''simple docstring'''
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_pt_objects import * # noqa F403
else:
from .scheduling_consistency_models import CMStochasticIterativeScheduler
from .scheduling_ddim import DDIMScheduler
from .scheduling_ddim_inverse import DDIMInverseScheduler
from .scheduling_ddim_parallel import DDIMParallelScheduler
from .scheduling_ddpm import DDPMScheduler
from .scheduling_ddpm_parallel import DDPMParallelScheduler
from .scheduling_deis_multistep import DEISMultistepScheduler
from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
from .scheduling_euler_discrete import EulerDiscreteScheduler
from .scheduling_heun_discrete import HeunDiscreteScheduler
from .scheduling_ipndm import IPNDMScheduler
from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
from .scheduling_karras_ve import KarrasVeScheduler
from .scheduling_pndm import PNDMScheduler
from .scheduling_repaint import RePaintScheduler
from .scheduling_sde_ve import ScoreSdeVeScheduler
from .scheduling_sde_vp import ScoreSdeVpScheduler
from .scheduling_unclip import UnCLIPScheduler
from .scheduling_unipc_multistep import UniPCMultistepScheduler
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
from .scheduling_vq_diffusion import VQDiffusionScheduler
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_flax_objects import * # noqa F403
else:
from .scheduling_ddim_flax import FlaxDDIMScheduler
from .scheduling_ddpm_flax import FlaxDDPMScheduler
from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
from .scheduling_pndm_flax import FlaxPNDMScheduler
from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
from .scheduling_utils_flax import (
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
broadcast_to_shape_from_left,
)
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .scheduling_lms_discrete import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler
| 701 |
'''simple docstring'''
UpperCamelCase__ : Dict = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
"q": "ABBBB",
"r": "BAAAA",
"s": "BAAAB",
"t": "BAABA",
"u": "BAABB",
"v": "BBBAB",
"w": "BABAA",
"x": "BABAB",
"y": "BABBA",
"z": "BABBB",
" ": " ",
}
UpperCamelCase__ : str = {value: key for key, value in encode_dict.items()}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """"""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("""encode() accepts only letters of the alphabet and spaces""" )
return encoded
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
if set(SCREAMING_SNAKE_CASE_ ) - {"A", "B", " "} != set():
raise Exception("""decode() accepts only 'A', 'B' and spaces""" )
_SCREAMING_SNAKE_CASE = """"""
for word in coded.split():
while len(SCREAMING_SNAKE_CASE_ ) != 0:
decoded += decode_dict[word[:5]]
_SCREAMING_SNAKE_CASE = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 0 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : Union[str, Any] = {
'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class _a (a__):
"""simple docstring"""
SCREAMING_SNAKE_CASE = """xlm-roberta-xl"""
def __init__( self , A__=25_08_80 , A__=25_60 , A__=36 , A__=32 , A__=1_02_40 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_14 , A__=1 , A__=0.02 , A__=1E-05 , A__=1 , A__=0 , A__=2 , A__="absolute" , A__=True , A__=None , **A__ , ) -> List[str]:
super().__init__(pad_token_id=lowercase__ , bos_token_id=lowercase__ , eos_token_id=lowercase__ , **lowercase__ )
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = type_vocab_size
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = position_embedding_type
_SCREAMING_SNAKE_CASE = use_cache
_SCREAMING_SNAKE_CASE = classifier_dropout
class _a (a__):
"""simple docstring"""
@property
def UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_SCREAMING_SNAKE_CASE = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_SCREAMING_SNAKE_CASE = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 702 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""encoder.embed_positions._float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = emb.weight.shape
_SCREAMING_SNAKE_CASE = nn.Linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , bias=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )
_SCREAMING_SNAKE_CASE = mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
_SCREAMING_SNAKE_CASE = mam_aaa["""model"""]
remove_ignore_keys_(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = state_dict["""encoder.embed_tokens.weight"""].shape[0]
_SCREAMING_SNAKE_CASE = MaMaaaConfig(
vocab_size=SCREAMING_SNAKE_CASE_ , max_position_embeddings=10_24 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="""relu""" , )
_SCREAMING_SNAKE_CASE = state_dict["""decoder.embed_tokens.weight"""]
_SCREAMING_SNAKE_CASE = MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
model.model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
UpperCamelCase__ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCamelCase__ : List[str] = parser.parse_args()
UpperCamelCase__ : Any = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 0 | 0 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _a :
"""simple docstring"""
def __init__( self , A__ ) -> List[str]:
if isinstance(lowercase__ , lowercase__ ):
# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
# modified it, it will not be accepted here again, since `auto` values would have been overridden
_SCREAMING_SNAKE_CASE = deepcopy(lowercase__ )
elif os.path.exists(lowercase__ ):
with io.open(lowercase__ , """r""" , encoding="""utf-8""" ) as f:
_SCREAMING_SNAKE_CASE = json.load(lowercase__ )
else:
try:
_SCREAMING_SNAKE_CASE = baseaa.urlsafe_baadecode(lowercase__ ).decode("""utf-8""" )
_SCREAMING_SNAKE_CASE = json.loads(lowercase__ )
except (UnicodeDecodeError, AttributeError, ValueError):
raise ValueError(
F"Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}" )
_SCREAMING_SNAKE_CASE = config
self.set_stage_and_offload()
def UpperCamelCase ( self ) -> Optional[int]:
# zero stage - this is done as early as possible, before model is created, to allow
# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
_SCREAMING_SNAKE_CASE = self.get_value("""zero_optimization.stage""" , -1 )
# offload
_SCREAMING_SNAKE_CASE = False
if self.is_zeroa() or self.is_zeroa():
_SCREAMING_SNAKE_CASE = set(["""cpu""", """nvme"""] )
_SCREAMING_SNAKE_CASE = set(
[
self.get_value("""zero_optimization.offload_optimizer.device""" ),
self.get_value("""zero_optimization.offload_param.device""" ),
] )
if len(offload_devices & offload_devices_valid ) > 0:
_SCREAMING_SNAKE_CASE = True
def UpperCamelCase ( self , A__ ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.config
# find the config node of interest if it exists
_SCREAMING_SNAKE_CASE = ds_key_long.split(""".""" )
_SCREAMING_SNAKE_CASE = nodes.pop()
for node in nodes:
_SCREAMING_SNAKE_CASE = config.get(lowercase__ )
if config is None:
return None, ds_key
return config, ds_key
def UpperCamelCase ( self , A__ , A__=None ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.find_config_node(lowercase__ )
if config is None:
return default
return config.get(lowercase__ , lowercase__ )
def UpperCamelCase ( self , A__ , A__=False ) -> Any:
_SCREAMING_SNAKE_CASE = self.config
# find the config node of interest if it exists
_SCREAMING_SNAKE_CASE = ds_key_long.split(""".""" )
for node in nodes:
_SCREAMING_SNAKE_CASE = config
_SCREAMING_SNAKE_CASE = config.get(lowercase__ )
if config is None:
if must_exist:
raise ValueError(F"Can\'t find {ds_key_long} entry in the config: {self.config}" )
else:
return
# if found remove it
if parent_config is not None:
parent_config.pop(lowercase__ )
def UpperCamelCase ( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = self.get_value(lowercase__ )
return False if value is None else bool(lowercase__ )
def UpperCamelCase ( self , A__ ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.get_value(lowercase__ )
return False if value is None else not bool(lowercase__ )
def UpperCamelCase ( self ) -> int:
return self._stage == 2
def UpperCamelCase ( self ) -> Dict:
return self._stage == 3
def UpperCamelCase ( self ) -> Any:
return self._offload
class _a :
"""simple docstring"""
def __init__( self , A__ ) -> List[Any]:
_SCREAMING_SNAKE_CASE = engine
def UpperCamelCase ( self , A__ , **A__ ) -> List[str]:
# runs backpropagation and handles mixed precision
self.engine.backward(lowercase__ , **lowercase__ )
# Deepspeed's `engine.step` performs the following operations:
# - gradient accumulation check
# - gradient clipping
# - optimizer step
# - zero grad
# - checking overflow
# - lr_scheduler step (only if engine.lr_scheduler is not None)
self.engine.step()
# and this plugin overrides the above calls with no-ops when Accelerate runs under
# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
# training loop that works transparently under many training regimes.
class _a (_UpperCAmelCase):
"""simple docstring"""
def __init__( self , A__ ) -> Union[str, Any]:
super().__init__(lowercase__ , device_placement=lowercase__ , scaler=lowercase__ )
_SCREAMING_SNAKE_CASE = hasattr(self.optimizer , """overflow""" )
def UpperCamelCase ( self , A__=None ) -> Dict:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
def UpperCamelCase ( self ) -> Any:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
@property
def UpperCamelCase ( self ) -> Optional[Any]:
if self.__has_overflow__:
return self.optimizer.overflow
return False
class _a (_UpperCAmelCase):
"""simple docstring"""
def __init__( self , A__ , A__ ) -> Dict:
super().__init__(lowercase__ , lowercase__ )
def UpperCamelCase ( self ) -> Optional[Any]:
pass # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=0.001 , A__=0 , **A__ ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = params
_SCREAMING_SNAKE_CASE = lr
_SCREAMING_SNAKE_CASE = weight_decay
_SCREAMING_SNAKE_CASE = kwargs
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=None , A__=0 , **A__ ) -> Any:
_SCREAMING_SNAKE_CASE = optimizer
_SCREAMING_SNAKE_CASE = total_num_steps
_SCREAMING_SNAKE_CASE = warmup_num_steps
_SCREAMING_SNAKE_CASE = kwargs
| 703 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : str = {
"configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
"tokenization_canine": ["CanineTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : List[Any] = [
"CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
"CanineForMultipleChoice",
"CanineForQuestionAnswering",
"CanineForSequenceClassification",
"CanineForTokenClassification",
"CanineLayer",
"CanineModel",
"CaninePreTrainedModel",
"load_tf_weights_in_canine",
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
UpperCamelCase__ : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _a (__SCREAMING_SNAKE_CASE , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = KandinskyImgaImgPipeline
SCREAMING_SNAKE_CASE = ['prompt', 'image_embeds', 'negative_image_embeds', 'image']
SCREAMING_SNAKE_CASE = [
'prompt',
'negative_prompt',
'image_embeds',
'negative_image_embeds',
'image',
]
SCREAMING_SNAKE_CASE = [
'generator',
'height',
'width',
'strength',
'guidance_scale',
'negative_prompt',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
SCREAMING_SNAKE_CASE = False
@property
def UpperCamelCase ( self ) -> Dict:
return 32
@property
def UpperCamelCase ( self ) -> Optional[Any]:
return 32
@property
def UpperCamelCase ( self ) -> Any:
return self.time_input_dim
@property
def UpperCamelCase ( self ) -> Tuple:
return self.time_input_dim * 4
@property
def UpperCamelCase ( self ) -> Dict:
return 1_00
@property
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def UpperCamelCase ( self ) -> Any:
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=10_05 , )
_SCREAMING_SNAKE_CASE = MultilingualCLIP(__snake_case )
_SCREAMING_SNAKE_CASE = text_encoder.eval()
return text_encoder
@property
def UpperCamelCase ( self ) -> Dict:
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''text_image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''text_image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
_SCREAMING_SNAKE_CASE = UNetaDConditionModel(**__snake_case )
return model
@property
def UpperCamelCase ( self ) -> Dict:
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def UpperCamelCase ( self ) -> Union[str, Any]:
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = VQModel(**self.dummy_movq_kwargs )
return model
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.dummy_text_encoder
_SCREAMING_SNAKE_CASE = self.dummy_tokenizer
_SCREAMING_SNAKE_CASE = self.dummy_unet
_SCREAMING_SNAKE_CASE = self.dummy_movq
_SCREAMING_SNAKE_CASE = {
'''num_train_timesteps''': 10_00,
'''beta_schedule''': '''linear''',
'''beta_start''': 0.0_0085,
'''beta_end''': 0.012,
'''clip_sample''': False,
'''set_alpha_to_one''': False,
'''steps_offset''': 0,
'''prediction_type''': '''epsilon''',
'''thresholding''': False,
}
_SCREAMING_SNAKE_CASE = DDIMScheduler(**__snake_case )
_SCREAMING_SNAKE_CASE = {
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def UpperCamelCase ( self , A__ , A__=0 ) -> Tuple:
_SCREAMING_SNAKE_CASE = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__snake_case ) ).to(__snake_case )
_SCREAMING_SNAKE_CASE = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__snake_case )
# create init_image
_SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 64, 64) , rng=random.Random(__snake_case ) ).to(__snake_case )
_SCREAMING_SNAKE_CASE = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_SCREAMING_SNAKE_CASE = Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((2_56, 2_56) )
if str(__snake_case ).startswith("""mps""" ):
_SCREAMING_SNAKE_CASE = torch.manual_seed(__snake_case )
else:
_SCREAMING_SNAKE_CASE = torch.Generator(device=__snake_case ).manual_seed(__snake_case )
_SCREAMING_SNAKE_CASE = {
'''prompt''': '''horse''',
'''image''': init_image,
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''num_inference_steps''': 10,
'''guidance_scale''': 7.0,
'''strength''': 0.2,
'''output_type''': '''np''',
}
return inputs
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = '''cpu'''
_SCREAMING_SNAKE_CASE = self.get_dummy_components()
_SCREAMING_SNAKE_CASE = self.pipeline_class(**__snake_case )
_SCREAMING_SNAKE_CASE = pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
_SCREAMING_SNAKE_CASE = pipe(**self.get_dummy_inputs(__snake_case ) )
_SCREAMING_SNAKE_CASE = output.images
_SCREAMING_SNAKE_CASE = pipe(
**self.get_dummy_inputs(__snake_case ) , return_dict=__snake_case , )[0]
_SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
_SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_SCREAMING_SNAKE_CASE = np.array(
[0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_img2img_frog.npy""" )
_SCREAMING_SNAKE_CASE = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
_SCREAMING_SNAKE_CASE = '''A red cartoon frog, 4k'''
_SCREAMING_SNAKE_CASE = KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(__snake_case )
_SCREAMING_SNAKE_CASE = KandinskyImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1""" , torch_dtype=torch.floataa )
_SCREAMING_SNAKE_CASE = pipeline.to(__snake_case )
pipeline.set_progress_bar_config(disable=__snake_case )
_SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
_SCREAMING_SNAKE_CASE = pipe_prior(
__snake_case , generator=__snake_case , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
_SCREAMING_SNAKE_CASE = pipeline(
__snake_case , image=__snake_case , image_embeds=__snake_case , negative_image_embeds=__snake_case , generator=__snake_case , num_inference_steps=1_00 , height=7_68 , width=7_68 , strength=0.2 , output_type="""np""" , )
_SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__snake_case , __snake_case )
| 704 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE = 'ChineseCLIPImageProcessor'
SCREAMING_SNAKE_CASE = ('BertTokenizer', 'BertTokenizerFast')
def __init__( self , A__=None , A__=None , **A__ ) -> int:
_SCREAMING_SNAKE_CASE = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , A__ , )
_SCREAMING_SNAKE_CASE = kwargs.pop("""feature_extractor""" )
_SCREAMING_SNAKE_CASE = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.image_processor
def __call__( self , A__=None , A__=None , A__=None , **A__ ) -> Optional[int]:
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
_SCREAMING_SNAKE_CASE = self.tokenizer(A__ , return_tensors=A__ , **A__ )
if images is not None:
_SCREAMING_SNAKE_CASE = self.image_processor(A__ , return_tensors=A__ , **A__ )
if text is not None and images is not None:
_SCREAMING_SNAKE_CASE = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**A__ ) , tensor_type=A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Dict:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Optional[Any]:
return self.tokenizer.decode(*A__ , **A__ )
@property
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.tokenizer.model_input_names
_SCREAMING_SNAKE_CASE = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCamelCase ( self ) -> Optional[int]:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , A__ , )
return self.image_processor_class
| 0 | 0 |
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = int(SCREAMING_SNAKE_CASE_ )
if decimal in (0, 1): # Exit cases for the recursion
return str(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = divmod(SCREAMING_SNAKE_CASE_ , 2 )
return binary_recursive(SCREAMING_SNAKE_CASE_ ) + str(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ ).strip()
if not number:
raise ValueError("""No input value was provided""" )
_SCREAMING_SNAKE_CASE = """-""" if number.startswith("""-""" ) else """"""
_SCREAMING_SNAKE_CASE = number.lstrip("""-""" )
if not number.isnumeric():
raise ValueError("""Input value is not an integer""" )
return F"{negative}0b{binary_recursive(int(SCREAMING_SNAKE_CASE_ ) )}"
if __name__ == "__main__":
from doctest import testmod
testmod()
| 705 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase__ : List[str] = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
UpperCamelCase__ : List[Any] = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
UpperCamelCase__ : Any = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a (datasets.Metric):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def UpperCamelCase ( self , A__ , A__ , A__=None ) -> List[str]:
return {
"matthews_correlation": float(matthews_corrcoef(A__ , A__ , sample_weight=A__ ) ),
}
| 0 | 0 |
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_=None ) -> Any:
"""simple docstring"""
if subparsers is not None:
_SCREAMING_SNAKE_CASE = subparsers.add_parser("""test""" )
else:
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser("""Accelerate test command""" )
parser.add_argument(
"""--config_file""" , default=lowercase_ , help=(
"""The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
"""location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
"""such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
"""with 'huggingface'."""
) , )
if subparsers is not None:
parser.set_defaults(func=lowercase_ )
return parser
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["""test_utils""", """scripts""", """test_script.py"""] )
if args.config_file is None:
_SCREAMING_SNAKE_CASE = script_name
else:
_SCREAMING_SNAKE_CASE = F"--config_file={args.config_file} {script_name}"
_SCREAMING_SNAKE_CASE = ["accelerate-launch"] + test_args.split()
_SCREAMING_SNAKE_CASE = execute_subprocess_async(lowercase_ , env=os.environ.copy() )
if result.returncode == 0:
print("""Test is a success! You are ready for your distributed training!""" )
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = test_command_parser()
_SCREAMING_SNAKE_CASE = parser.parse_args()
test_command(lowercase_ )
if __name__ == "__main__":
main()
| 706 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
print(F"Vertex\tShortest Distance from vertex {src}" )
for i, d in enumerate(SCREAMING_SNAKE_CASE_ ):
print(F"{i}\t\t{d}" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
for j in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
return True
return False
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list[float]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [float("""inf""" )] * vertex_count
_SCREAMING_SNAKE_CASE = 0.0
for _ in range(vertex_count - 1 ):
for j in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
_SCREAMING_SNAKE_CASE = distance[u] + w
_SCREAMING_SNAKE_CASE = check_negative_cycle(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if negative_cycle_exists:
raise Exception("""Negative cycle found""" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ : int = int(input("Enter number of vertices: ").strip())
UpperCamelCase__ : int = int(input("Enter number of edges: ").strip())
UpperCamelCase__ : list[dict[str, int]] = [{} for _ in range(E)]
for i in range(E):
print("Edge ", i + 1)
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Dict = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
UpperCamelCase__ : Optional[Any] = {"src": src, "dst": dest, "weight": weight}
UpperCamelCase__ : Optional[Any] = int(input("\nEnter shortest path source:").strip())
UpperCamelCase__ : Any = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 0 | 0 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class _a (UpperCAmelCase_):
"""simple docstring"""
def __init__( self , A__ , A__ , A__ = None , A__ = None , A__ = False , **A__ , ) -> str:
super().__init__(features=_snake_case , cache_dir=_snake_case , keep_in_memory=_snake_case , **_snake_case )
_SCREAMING_SNAKE_CASE = Sql(
cache_dir=_snake_case , features=_snake_case , sql=_snake_case , con=_snake_case , **_snake_case , )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
self.builder.download_and_prepare(
download_config=_snake_case , download_mode=_snake_case , verification_mode=_snake_case , base_path=_snake_case , )
# Build dataset for splits
_SCREAMING_SNAKE_CASE = self.builder.as_dataset(
split="""train""" , verification_mode=_snake_case , in_memory=self.keep_in_memory )
return dataset
class _a :
"""simple docstring"""
def __init__( self , A__ , A__ , A__ , A__ = None , A__ = None , **A__ , ) -> Any:
if num_proc is not None and num_proc <= 0:
raise ValueError(F"num_proc {num_proc} must be an integer > 0." )
_SCREAMING_SNAKE_CASE = dataset
_SCREAMING_SNAKE_CASE = name
_SCREAMING_SNAKE_CASE = con
_SCREAMING_SNAKE_CASE = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
_SCREAMING_SNAKE_CASE = num_proc
_SCREAMING_SNAKE_CASE = to_sql_kwargs
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.to_sql_kwargs.pop("""sql""" , _snake_case )
_SCREAMING_SNAKE_CASE = self.to_sql_kwargs.pop("""con""" , _snake_case )
_SCREAMING_SNAKE_CASE = self.to_sql_kwargs.pop("""index""" , _snake_case )
_SCREAMING_SNAKE_CASE = self._write(index=_snake_case , **self.to_sql_kwargs )
return written
def UpperCamelCase ( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = args
_SCREAMING_SNAKE_CASE = {**to_sql_kwargs, """if_exists""": """append"""} if offset > 0 else to_sql_kwargs
_SCREAMING_SNAKE_CASE = query_table(
table=self.dataset.data , key=slice(_snake_case , offset + self.batch_size ) , indices=self.dataset._indices , )
_SCREAMING_SNAKE_CASE = batch.to_pandas()
_SCREAMING_SNAKE_CASE = df.to_sql(self.name , self.con , index=_snake_case , **_snake_case )
return num_rows or len(_snake_case )
def UpperCamelCase ( self , A__ , **A__ ) -> int:
_SCREAMING_SNAKE_CASE = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 , len(self.dataset ) , self.batch_size ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += self._batch_sql((offset, index, to_sql_kwargs) )
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql , [(offset, index, to_sql_kwargs) for offset in range(0 , _snake_case , _snake_case )] , ) , total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating SQL from Arrow format""" , ):
written += num_rows
return written
| 707 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=True , A__=True , A__=99 , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=16 , A__=2 , A__=0.02 , A__=3 , A__=4 , A__=None , ) -> int:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = 13
_SCREAMING_SNAKE_CASE = 7
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = 99
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = 37
_SCREAMING_SNAKE_CASE = """gelu"""
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 5_12
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 0.02
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
_SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=A__ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModel(config=A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
_SCREAMING_SNAKE_CASE = [input_ids, input_mask]
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> str:
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = TFRoFormerForCausalLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )["""logits"""]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Dict:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForSequenceClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = TFRoFormerForMultipleChoice(config=A__ )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForTokenClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = TFRoFormerForQuestionAnswering(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': TFRoFormerModel,
'fill-mask': TFRoFormerForMaskedLM,
'question-answering': TFRoFormerForQuestionAnswering,
'text-classification': TFRoFormerForSequenceClassification,
'text-generation': TFRoFormerForCausalLM,
'token-classification': TFRoFormerForTokenClassification,
'zero-shot': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> str:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*A__ )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base""" )
self.assertIsNotNone(A__ )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
_SCREAMING_SNAKE_CASE = tf.constant([[0, 1, 2, 3, 4, 5]] )
_SCREAMING_SNAKE_CASE = model(A__ )[0]
# TODO Replace vocab size
_SCREAMING_SNAKE_CASE = 5_00_00
_SCREAMING_SNAKE_CASE = [1, 6, vocab_size]
self.assertEqual(output.shape , A__ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
_SCREAMING_SNAKE_CASE = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , A__ , atol=1E-4 )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant([[4, 10]] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
_SCREAMING_SNAKE_CASE = emba(input_ids.shape )
_SCREAMING_SNAKE_CASE = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 , embedding_dim=5_12 )
emba([2, 16, 5_12] )
_SCREAMING_SNAKE_CASE = emba.weight[:3, :5]
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> int:
# 2,12,16,64
_SCREAMING_SNAKE_CASE = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
_SCREAMING_SNAKE_CASE = embed_positions([2, 16, 7_68] )[None, None, :, :]
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
A__ , A__ , A__ )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
| 0 | 0 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
UpperCamelCase__ : Any = input("Enter image url: ").strip()
print(f"""Downloading image from {url} ...""")
UpperCamelCase__ : List[str] = BeautifulSoup(requests.get(url).content, "html.parser")
# The image URL is in the content field of the first meta tag with property og:image
UpperCamelCase__ : Union[str, Any] = soup.find("meta", {"property": "og:image"})["""content"""]
UpperCamelCase__ : int = requests.get(image_url).content
UpperCamelCase__ : Dict = f"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
with open(file_name, "wb") as fp:
fp.write(image_data)
print(f"""Done. Image saved to disk as {file_name}.""")
| 708 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase__ : int = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
UpperCamelCase__ : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
class _a :
"""simple docstring"""
def __init__( self , A__ = 0 ) -> str:
_SCREAMING_SNAKE_CASE = key
def UpperCamelCase ( self , A__ , A__ ) -> list[str]:
assert isinstance(_a , _a ) and isinstance(_a , _a )
_SCREAMING_SNAKE_CASE = key or self.__key or 1
# make sure key is an appropriate size
key %= 2_55
return [chr(ord(_a ) ^ key ) for ch in content]
def UpperCamelCase ( self , A__ , A__ ) -> list[str]:
assert isinstance(_a , _a ) and isinstance(_a , _a )
_SCREAMING_SNAKE_CASE = key or self.__key or 1
# make sure key is an appropriate size
key %= 2_55
return [chr(ord(_a ) ^ key ) for ch in content]
def UpperCamelCase ( self , A__ , A__ = 0 ) -> str:
assert isinstance(_a , _a ) and isinstance(_a , _a )
_SCREAMING_SNAKE_CASE = key or self.__key or 1
# make sure key can be any size
while key > 2_55:
key -= 2_55
# This will be returned
_SCREAMING_SNAKE_CASE = """"""
for ch in content:
ans += chr(ord(_a ) ^ key )
return ans
def UpperCamelCase ( self , A__ , A__ = 0 ) -> str:
assert isinstance(_a , _a ) and isinstance(_a , _a )
_SCREAMING_SNAKE_CASE = key or self.__key or 1
# make sure key can be any size
while key > 2_55:
key -= 2_55
# This will be returned
_SCREAMING_SNAKE_CASE = """"""
for ch in content:
ans += chr(ord(_a ) ^ key )
return ans
def UpperCamelCase ( self , A__ , A__ = 0 ) -> bool:
assert isinstance(_a , _a ) and isinstance(_a , _a )
try:
with open(_a ) as fin, open("""encrypt.out""" , """w+""" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(_a , _a ) )
except OSError:
return False
return True
def UpperCamelCase ( self , A__ , A__ ) -> bool:
assert isinstance(_a , _a ) and isinstance(_a , _a )
try:
with open(_a ) as fin, open("""decrypt.out""" , """w+""" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(_a , _a ) )
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful")
| 709 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = XCLIPTextConfig()
# derive patch size from model name
_SCREAMING_SNAKE_CASE = model_name.find("""patch""" )
_SCREAMING_SNAKE_CASE = int(model_name[start_idx + len("""patch""" ) : start_idx + len("""patch""" ) + 2] )
_SCREAMING_SNAKE_CASE = XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE_ , num_frames=SCREAMING_SNAKE_CASE_ )
if "large" in model_name:
_SCREAMING_SNAKE_CASE = 7_68
_SCREAMING_SNAKE_CASE = 30_72
_SCREAMING_SNAKE_CASE = 12
_SCREAMING_SNAKE_CASE = 10_24
_SCREAMING_SNAKE_CASE = 40_96
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 24
_SCREAMING_SNAKE_CASE = 7_68
_SCREAMING_SNAKE_CASE = 30_72
if model_name == "xclip-large-patch14-16-frames":
_SCREAMING_SNAKE_CASE = 3_36
_SCREAMING_SNAKE_CASE = XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if "large" in model_name:
_SCREAMING_SNAKE_CASE = 7_68
return config
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
# text encoder
if name == "token_embedding.weight":
_SCREAMING_SNAKE_CASE = name.replace("""token_embedding.weight""" , """text_model.embeddings.token_embedding.weight""" )
if name == "positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "ln_1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
_SCREAMING_SNAKE_CASE = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""c_proj""" , """fc2""" )
if name.startswith("""transformer.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""transformer.resblocks""" , """text_model.encoder.layers""" )
if "attn.out_proj" in name and "message" not in name:
_SCREAMING_SNAKE_CASE = name.replace("""attn.out_proj""" , """self_attn.out_proj""" )
if "ln_final" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_final""" , """text_model.final_layer_norm""" )
# visual encoder
if name == "visual.class_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""visual.class_embedding""" , """vision_model.embeddings.class_embedding""" )
if name == "visual.positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""visual.positional_embedding""" , """vision_model.embeddings.position_embedding.weight""" )
if name.startswith("""visual.transformer.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""visual.transformer.resblocks""" , """vision_model.encoder.layers""" )
if "visual.conv1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.conv1""" , """vision_model.embeddings.patch_embedding""" )
if "visual.ln_pre" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.ln_pre""" , """vision_model.pre_layernorm""" )
if "visual.ln_post" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.ln_post""" , """vision_model.post_layernorm""" )
if "visual.proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.proj""" , """visual_projection.weight""" )
if "text_projection" in name:
_SCREAMING_SNAKE_CASE = name.replace("""text_projection""" , """text_projection.weight""" )
# things on top
if "prompts_visual_proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""prompts_visual_proj""" , """prompts_visual_projection""" )
if "prompts_visual_ln" in name:
_SCREAMING_SNAKE_CASE = name.replace("""prompts_visual_ln""" , """prompts_visual_layernorm""" )
# mit
if name == "mit.positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""positional""" , """position""" )
if name.startswith("""mit.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""mit.resblocks""" , """mit.encoder.layers""" )
# prompts generator
if name.startswith("""prompts_generator.norm""" ):
_SCREAMING_SNAKE_CASE = name.replace("""prompts_generator.norm""" , """prompts_generator.layernorm""" )
return name
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_SCREAMING_SNAKE_CASE = orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )
if "attn.in_proj" in key:
_SCREAMING_SNAKE_CASE = key.split(""".""" )
if key.startswith("""visual""" ):
_SCREAMING_SNAKE_CASE = key_split[3]
_SCREAMING_SNAKE_CASE = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[
:dim, :
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[
-dim:, :
]
else:
_SCREAMING_SNAKE_CASE = val[
:dim
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[
:dim, :
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[
-dim:, :
]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[-dim:]
elif key.startswith("""mit""" ):
_SCREAMING_SNAKE_CASE = key_split[2]
_SCREAMING_SNAKE_CASE = config.vision_config.mit_hidden_size
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[:dim, :]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
_SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2]
_SCREAMING_SNAKE_CASE = val[-dim:]
else:
_SCREAMING_SNAKE_CASE = key_split[2]
_SCREAMING_SNAKE_CASE = config.text_config.hidden_size
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[:dim, :]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[-dim:]
else:
_SCREAMING_SNAKE_CASE = rename_key(SCREAMING_SNAKE_CASE_ )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
_SCREAMING_SNAKE_CASE = val.T
_SCREAMING_SNAKE_CASE = val
return orig_state_dict
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
if num_frames == 8:
_SCREAMING_SNAKE_CASE = """eating_spaghetti_8_frames.npy"""
elif num_frames == 16:
_SCREAMING_SNAKE_CASE = """eating_spaghetti.npy"""
elif num_frames == 32:
_SCREAMING_SNAKE_CASE = """eating_spaghetti_32_frames.npy"""
_SCREAMING_SNAKE_CASE = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename=SCREAMING_SNAKE_CASE_ , repo_type="""dataset""" , )
_SCREAMING_SNAKE_CASE = np.load(SCREAMING_SNAKE_CASE_ )
return list(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=False ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {
# fully supervised kinetics-400 checkpoints
"""xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
"""xclip-base-patch32-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
),
"""xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
"""xclip-base-patch16-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
),
"""xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
"""xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f""",
# fully supervised kinetics-600 checkpoints
"""xclip-base-patch16-kinetics-600""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
),
"""xclip-base-patch16-kinetics-600-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
),
"""xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
# few shot
"""xclip-base-patch16-hmdb-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
),
"""xclip-base-patch16-hmdb-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
),
"""xclip-base-patch16-hmdb-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
),
"""xclip-base-patch16-hmdb-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
),
"""xclip-base-patch16-ucf-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
),
"""xclip-base-patch16-ucf-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
),
"""xclip-base-patch16-ucf-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
),
"""xclip-base-patch16-ucf-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
),
# zero shot
"""xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
}
_SCREAMING_SNAKE_CASE = model_to_url[model_name]
_SCREAMING_SNAKE_CASE = 8
if "16-frames" in model_name:
_SCREAMING_SNAKE_CASE = 16
elif "shot" in model_name:
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = get_xclip_config(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = XCLIPModel(SCREAMING_SNAKE_CASE_ )
model.eval()
if "drive" in checkpoint_url:
_SCREAMING_SNAKE_CASE = """pytorch_model.bin"""
gdown.cached_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , quiet=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )["""model"""]
else:
_SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ )["""model"""]
_SCREAMING_SNAKE_CASE = convert_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = XCLIPModel(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
_SCREAMING_SNAKE_CASE = 3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
_SCREAMING_SNAKE_CASE = VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
_SCREAMING_SNAKE_CASE = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32""" )
_SCREAMING_SNAKE_CASE = XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = prepare_video(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = processor(
text=["""playing sports""", """eating spaghetti""", """go shopping"""] , videos=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , padding=SCREAMING_SNAKE_CASE_ )
print("""Shape of pixel values:""" , inputs.pixel_values.shape )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
# Verify outputs
_SCREAMING_SNAKE_CASE = outputs.logits_per_video
_SCREAMING_SNAKE_CASE = logits_per_video.softmax(dim=1 )
print("""Probs:""" , SCREAMING_SNAKE_CASE_ )
# kinetics-400
if model_name == "xclip-base-patch32":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0019, 0.9951, 0.0030]] )
elif model_name == "xclip-base-patch32-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]] )
elif model_name == "xclip-base-patch16":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0083, 0.9681, 0.0236]] )
elif model_name == "xclip-base-patch16-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]] )
elif model_name == "xclip-large-patch14":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0062, 0.9864, 0.0075]] )
elif model_name == "xclip-large-patch14-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0555, 0.8914, 0.0531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0036, 0.9920, 0.0045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0027, 0.9904, 0.0070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]] )
else:
raise ValueError(F"Model name {model_name} not supported" )
assert torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
print("""Pushing model, processor and slow tokenizer files to the hub...""" )
model.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
processor.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
slow_tokenizer.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="xclip-base-patch32",
type=str,
help="Name of the model.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
UpperCamelCase__ : str = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 0 | 0 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torch_available():
import torch
UpperCamelCase__ = logging.get_logger(__name__)
@add_end_docstrings(
SCREAMING_SNAKE_CASE_ , r'\n top_k (`int`, defaults to 5):\n The number of predictions to return.\n targets (`str` or `List[str]`, *optional*):\n When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n token will be used (with a warning, and that might be slower).\n\n ' , )
class _a (SCREAMING_SNAKE_CASE_):
"""simple docstring"""
def UpperCamelCase ( self , A__ ) -> Dict:
if self.framework == "tf":
_SCREAMING_SNAKE_CASE = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()
elif self.framework == "pt":
_SCREAMING_SNAKE_CASE = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=UpperCamelCase__ )
else:
raise ValueError("""Unsupported framework""" )
return masked_index
def UpperCamelCase ( self , A__ ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.get_masked_index(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = np.prod(masked_index.shape )
if numel < 1:
raise PipelineException(
"""fill-mask""" , self.model.base_model_prefix , F"No mask_token ({self.tokenizer.mask_token}) found on the input" , )
def UpperCamelCase ( self , A__ ) -> Optional[int]:
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
for model_input in model_inputs:
self._ensure_exactly_one_mask_token(model_input["""input_ids"""][0] )
else:
for input_ids in model_inputs["input_ids"]:
self._ensure_exactly_one_mask_token(UpperCamelCase__ )
def UpperCamelCase ( self , A__ , A__=None , **A__ ) -> int:
if return_tensors is None:
_SCREAMING_SNAKE_CASE = self.framework
_SCREAMING_SNAKE_CASE = self.tokenizer(UpperCamelCase__ , return_tensors=UpperCamelCase__ )
self.ensure_exactly_one_mask_token(UpperCamelCase__ )
return model_inputs
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.model(**UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = model_inputs['''input_ids''']
return model_outputs
def UpperCamelCase ( self , A__ , A__=5 , A__=None ) -> Dict:
# Cap top_k if there are targets
if target_ids is not None and target_ids.shape[0] < top_k:
_SCREAMING_SNAKE_CASE = target_ids.shape[0]
_SCREAMING_SNAKE_CASE = model_outputs['''input_ids'''][0]
_SCREAMING_SNAKE_CASE = model_outputs['''logits''']
if self.framework == "tf":
_SCREAMING_SNAKE_CASE = tf.where(input_ids == self.tokenizer.mask_token_id ).numpy()[:, 0]
_SCREAMING_SNAKE_CASE = outputs.numpy()
_SCREAMING_SNAKE_CASE = outputs[0, masked_index, :]
_SCREAMING_SNAKE_CASE = stable_softmax(UpperCamelCase__ , axis=-1 )
if target_ids is not None:
_SCREAMING_SNAKE_CASE = tf.gather_nd(tf.squeeze(UpperCamelCase__ , 0 ) , target_ids.reshape(-1 , 1 ) )
_SCREAMING_SNAKE_CASE = tf.expand_dims(UpperCamelCase__ , 0 )
_SCREAMING_SNAKE_CASE = tf.math.top_k(UpperCamelCase__ , k=UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = topk.values.numpy(), topk.indices.numpy()
else:
_SCREAMING_SNAKE_CASE = torch.nonzero(input_ids == self.tokenizer.mask_token_id , as_tuple=UpperCamelCase__ ).squeeze(-1 )
# Fill mask pipeline supports only one ${mask_token} per sample
_SCREAMING_SNAKE_CASE = outputs[0, masked_index, :]
_SCREAMING_SNAKE_CASE = logits.softmax(dim=-1 )
if target_ids is not None:
_SCREAMING_SNAKE_CASE = probs[..., target_ids]
_SCREAMING_SNAKE_CASE = probs.topk(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = values.shape[0] == 1
for i, (_values, _predictions) in enumerate(zip(values.tolist() , predictions.tolist() ) ):
_SCREAMING_SNAKE_CASE = []
for v, p in zip(_values , _predictions ):
# Copy is important since we're going to modify this array in place
_SCREAMING_SNAKE_CASE = input_ids.numpy().copy()
if target_ids is not None:
_SCREAMING_SNAKE_CASE = target_ids[p].tolist()
_SCREAMING_SNAKE_CASE = p
# Filter padding out:
_SCREAMING_SNAKE_CASE = tokens[np.where(tokens != self.tokenizer.pad_token_id )]
# Originally we skip special tokens to give readable output.
# For multi masks though, the other [MASK] would be removed otherwise
# making the output look odd, so we add them back
_SCREAMING_SNAKE_CASE = self.tokenizer.decode(UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p] ), '''sequence''': sequence}
row.append(UpperCamelCase__ )
result.append(UpperCamelCase__ )
if single_mask:
return result[0]
return result
def UpperCamelCase ( self , A__ , A__=None ) -> int:
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
_SCREAMING_SNAKE_CASE = [targets]
try:
_SCREAMING_SNAKE_CASE = self.tokenizer.get_vocab()
except Exception:
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = []
for target in targets:
_SCREAMING_SNAKE_CASE = vocab.get(UpperCamelCase__ , UpperCamelCase__ )
if id_ is None:
_SCREAMING_SNAKE_CASE = self.tokenizer(
UpperCamelCase__ , add_special_tokens=UpperCamelCase__ , return_attention_mask=UpperCamelCase__ , return_token_type_ids=UpperCamelCase__ , max_length=1 , truncation=UpperCamelCase__ , )['''input_ids''']
if len(UpperCamelCase__ ) == 0:
logger.warning(
F"The specified target token `{target}` does not exist in the model vocabulary. "
"""We cannot replace it with anything meaningful, ignoring it""" )
continue
_SCREAMING_SNAKE_CASE = input_ids[0]
# XXX: If users encounter this pass
# it becomes pretty slow, so let's make sure
# The warning enables them to fix the input to
# get faster performance.
logger.warning(
F"The specified target token `{target}` does not exist in the model vocabulary. "
F"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_ )}`." )
target_ids.append(id_ )
_SCREAMING_SNAKE_CASE = list(set(UpperCamelCase__ ) )
if len(UpperCamelCase__ ) == 0:
raise ValueError("""At least one target must be provided when passed.""" )
_SCREAMING_SNAKE_CASE = np.array(UpperCamelCase__ )
return target_ids
def UpperCamelCase ( self , A__=None , A__=None ) -> Tuple:
_SCREAMING_SNAKE_CASE = {}
if targets is not None:
_SCREAMING_SNAKE_CASE = self.get_target_ids(UpperCamelCase__ , UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = target_ids
if top_k is not None:
_SCREAMING_SNAKE_CASE = top_k
if self.tokenizer.mask_token_id is None:
raise PipelineException(
"""fill-mask""" , self.model.base_model_prefix , """The tokenizer does not define a `mask_token`.""" )
return {}, {}, postprocess_params
def __call__( self , A__ , *A__ , **A__ ) -> List[Any]:
_SCREAMING_SNAKE_CASE = super().__call__(UpperCamelCase__ , **UpperCamelCase__ )
if isinstance(UpperCamelCase__ , UpperCamelCase__ ) and len(UpperCamelCase__ ) == 1:
return outputs[0]
return outputs
| 710 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = params
_SCREAMING_SNAKE_CASE = np.array(A__ )
_SCREAMING_SNAKE_CASE = np.array([len(A__ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self , A__ ) -> Dict:
return (self.token_ids[index], self.lengths[index])
def __len__( self ) -> Tuple:
return len(self.lengths )
def UpperCamelCase ( self ) -> Dict:
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.params.max_model_input_size
_SCREAMING_SNAKE_CASE = self.lengths > max_len
logger.info(F"Splitting {sum(A__ )} too long sequences." )
def divide_chunks(A__ , A__ ):
return [l[i : i + n] for i in range(0 , len(A__ ) , A__ )]
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
if self.params.mlm:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
_SCREAMING_SNAKE_CASE = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
_SCREAMING_SNAKE_CASE = np.insert(A__ , 0 , A__ )
if sub_s[-1] != sep_id:
_SCREAMING_SNAKE_CASE = np.insert(A__ , len(A__ ) , A__ )
assert len(A__ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(A__ )
new_tok_ids.extend(A__ )
new_lengths.extend([len(A__ ) for l in sub_seqs] )
_SCREAMING_SNAKE_CASE = np.array(A__ )
_SCREAMING_SNAKE_CASE = np.array(A__ )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = len(self )
_SCREAMING_SNAKE_CASE = self.lengths > 11
_SCREAMING_SNAKE_CASE = self.token_ids[indices]
_SCREAMING_SNAKE_CASE = self.lengths[indices]
_SCREAMING_SNAKE_CASE = len(self )
logger.info(F"Remove {init_size - new_size} too short (<=11 tokens) sequences." )
def UpperCamelCase ( self ) -> int:
if "unk_token" not in self.params.special_tok_ids:
return
else:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""unk_token"""]
_SCREAMING_SNAKE_CASE = len(self )
_SCREAMING_SNAKE_CASE = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
_SCREAMING_SNAKE_CASE = (unk_occs / self.lengths) < 0.5
_SCREAMING_SNAKE_CASE = self.token_ids[indices]
_SCREAMING_SNAKE_CASE = self.lengths[indices]
_SCREAMING_SNAKE_CASE = len(self )
logger.info(F"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)." )
def UpperCamelCase ( self ) -> Optional[Any]:
if not self.params.is_master:
return
logger.info(F"{len(self )} sequences" )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def UpperCamelCase ( self , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = [t[0] for t in batch]
_SCREAMING_SNAKE_CASE = [t[1] for t in batch]
assert len(A__ ) == len(A__ )
# Max for paddings
_SCREAMING_SNAKE_CASE = max(A__ )
# Pad token ids
if self.params.mlm:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""pad_token"""]
else:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""unk_token"""]
_SCREAMING_SNAKE_CASE = [list(t.astype(A__ ) ) + [pad_idx] * (max_seq_len_ - len(A__ )) for t in token_ids]
assert len(tk_ ) == len(A__ )
assert all(len(A__ ) == max_seq_len_ for t in tk_ )
_SCREAMING_SNAKE_CASE = torch.tensor(tk_ ) # (bs, max_seq_len_)
_SCREAMING_SNAKE_CASE = torch.tensor(A__ ) # (bs)
return tk_t, lg_t
| 0 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : Optional[int] = logging.get_logger(__name__)
UpperCamelCase__ : Dict = "▁"
UpperCamelCase__ : str = {"vocab_file": "spiece.model"}
UpperCamelCase__ : Tuple = {
"vocab_file": {
"google/reformer-crime-and-punishment": (
"https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
)
}
}
UpperCamelCase__ : Any = {
"google/reformer-crime-and-punishment": 524_288,
}
class _a (lowerCAmelCase__):
"""simple docstring"""
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = ["input_ids", "attention_mask"]
def __init__( self , A__ , A__="</s>" , A__="<unk>" , A__=[] , A__ = None , **A__ , ) -> None:
_SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , additional_special_tokens=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCamelCase , )
_SCREAMING_SNAKE_CASE = vocab_file
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowerCamelCase )
@property
def UpperCamelCase ( self ) -> List[str]:
return self.sp_model.get_piece_size()
def UpperCamelCase ( self ) -> Dict[str, int]:
_SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(_lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.__dict__.copy()
_SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase ( self , A__ ) -> List[str]:
return self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
def UpperCamelCase ( self , A__ ) -> List[Any]:
return self.sp_model.piece_to_id(_lowerCamelCase )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
if index < self.sp_model.get_piece_size():
_SCREAMING_SNAKE_CASE = self.sp_model.IdToPiece(_lowerCamelCase )
return token
def UpperCamelCase ( self , A__ ) -> Dict:
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_lowerCamelCase ) + token
_SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(_lowerCamelCase )
out_string += self.sp_model.decode(_lowerCamelCase )
return out_string.strip()
def UpperCamelCase ( self , A__ , A__ = None ) -> Tuple[str]:
if not os.path.isdir(_lowerCamelCase ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
_SCREAMING_SNAKE_CASE = os.path.join(
_lowerCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_lowerCamelCase , """wb""" ) as fi:
_SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (out_vocab_file,)
| 711 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : Any = "▁"
UpperCamelCase__ : Any = {"vocab_file": "spiece.model"}
UpperCamelCase__ : int = {
"vocab_file": {
"google/reformer-crime-and-punishment": (
"https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
)
}
}
UpperCamelCase__ : Optional[int] = {
"google/reformer-crime-and-punishment": 524_288,
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self , A__ , A__="</s>" , A__="<unk>" , A__=[] , A__ = None , **A__ , ) -> None:
_SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=A__ , unk_token=A__ , additional_special_tokens=A__ , sp_model_kwargs=self.sp_model_kwargs , **A__ , )
_SCREAMING_SNAKE_CASE = vocab_file
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A__ )
@property
def UpperCamelCase ( self ) -> Any:
return self.sp_model.get_piece_size()
def UpperCamelCase ( self ) -> Dict[str, int]:
_SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(A__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> int:
_SCREAMING_SNAKE_CASE = self.__dict__.copy()
_SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase ( self , A__ ) -> List[str]:
return self.sp_model.encode(A__ , out_type=A__ )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
return self.sp_model.piece_to_id(A__ )
def UpperCamelCase ( self , A__ ) -> List[Any]:
if index < self.sp_model.get_piece_size():
_SCREAMING_SNAKE_CASE = self.sp_model.IdToPiece(A__ )
return token
def UpperCamelCase ( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A__ ) + token
_SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(A__ )
out_string += self.sp_model.decode(A__ )
return out_string.strip()
def UpperCamelCase ( self , A__ , A__ = None ) -> Tuple[str]:
if not os.path.isdir(A__ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
_SCREAMING_SNAKE_CASE = os.path.join(
A__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A__ )
elif not os.path.isfile(self.vocab_file ):
with open(A__ , """wb""" ) as fi:
_SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(A__ )
return (out_vocab_file,)
| 0 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list:
"""simple docstring"""
if len(__snake_case ) < 2:
return collection
def circle_sort_util(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> bool:
_SCREAMING_SNAKE_CASE = False
if low == high:
return swapped
_SCREAMING_SNAKE_CASE = low
_SCREAMING_SNAKE_CASE = high
while left < right:
if collection[left] > collection[right]:
_SCREAMING_SNAKE_CASE = (
collection[right],
collection[left],
)
_SCREAMING_SNAKE_CASE = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
_SCREAMING_SNAKE_CASE = (
collection[right + 1],
collection[left],
)
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = low + int((high - low) / 2 )
_SCREAMING_SNAKE_CASE = circle_sort_util(__snake_case , __snake_case , __snake_case )
_SCREAMING_SNAKE_CASE = circle_sort_util(__snake_case , mid + 1 , __snake_case )
return swapped or left_swap or right_swap
_SCREAMING_SNAKE_CASE = True
while is_not_sorted is True:
_SCREAMING_SNAKE_CASE = circle_sort_util(__snake_case , 0 , len(__snake_case ) - 1 )
return collection
if __name__ == "__main__":
UpperCamelCase__ : Dict = input("Enter numbers separated by a comma:\n").strip()
UpperCamelCase__ : Dict = [int(item) for item in user_input.split(",")]
print(circle_sort(unsorted))
| 712 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class _a (_lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = MobileBertTokenizer
SCREAMING_SNAKE_CASE = MobileBertTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = filter_non_english
SCREAMING_SNAKE_CASE = 'google/mobilebert-uncased'
def UpperCamelCase ( self ) -> Any:
super().setUp()
_SCREAMING_SNAKE_CASE = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
_SCREAMING_SNAKE_CASE = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def UpperCamelCase ( self , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = """unwanted, running"""
return input_text, output_text
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.tokenizer_class(self.vocab_file )
_SCREAMING_SNAKE_CASE = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(A__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , [9, 6, 7, 12, 10, 11] )
def UpperCamelCase ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
# With lower casing
_SCREAMING_SNAKE_CASE = self.get_tokenizer(do_lower_case=A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer(do_lower_case=A__ )
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
_SCREAMING_SNAKE_CASE = {}
for i, token in enumerate(A__ ):
_SCREAMING_SNAKE_CASE = i
_SCREAMING_SNAKE_CASE = WordpieceTokenizer(vocab=A__ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
def UpperCamelCase ( self ) -> str:
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def UpperCamelCase ( self ) -> Union[str, Any]:
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def UpperCamelCase ( self ) -> Dict:
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
self.assertListEqual(
[rust_tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
@slow
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained("""google/mobilebert-uncased""" )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""sequence builders""" , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""multi-sequence build""" , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A__ , A__ )
assert encoded_sentence == [1_01] + text + [1_02]
assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
def UpperCamelCase ( self ) -> List[str]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = F"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
_SCREAMING_SNAKE_CASE = tokenizer_r.encode_plus(
A__ , return_attention_mask=A__ , return_token_type_ids=A__ , return_offsets_mapping=A__ , add_special_tokens=A__ , )
_SCREAMING_SNAKE_CASE = tokenizer_r.do_lower_case if hasattr(A__ , """do_lower_case""" ) else False
_SCREAMING_SNAKE_CASE = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), """A"""),
((1, 2), ""","""),
((3, 5), """na"""),
((5, 6), """##ï"""),
((6, 8), """##ve"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """Allen"""),
((21, 23), """##NL"""),
((23, 24), """##P"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), """a"""),
((1, 2), ""","""),
((3, 8), """naive"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """allen"""),
((21, 23), """##nl"""),
((23, 24), """##p"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = ["""的""", """人""", """有"""]
_SCREAMING_SNAKE_CASE = """""".join(A__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(A__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(A__ , A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(A__ )
# it is expected that only the first Chinese character is not preceded by "##".
_SCREAMING_SNAKE_CASE = [
F"##{token}" if idx != 0 else token for idx, token in enumerate(A__ )
]
self.assertListEqual(A__ , A__ )
self.assertListEqual(A__ , A__ )
| 0 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase__ : List[str] = logging.get_logger(__name__)
UpperCamelCase__ : Any = {
"kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
"kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
"kssteven/ibert-roberta-large-mnli": (
"https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
),
}
class _a (__lowercase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'ibert'
def __init__( self , A__=3_05_22 , A__=7_68 , A__=12 , A__=12 , A__=30_72 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=2 , A__=0.02 , A__=1E-12 , A__=1 , A__=0 , A__=2 , A__="absolute" , A__=False , A__="none" , **A__ , ) -> Any:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = type_vocab_size
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = position_embedding_type
_SCREAMING_SNAKE_CASE = quant_mode
_SCREAMING_SNAKE_CASE = force_dequant
class _a (__lowercase):
"""simple docstring"""
@property
def UpperCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_SCREAMING_SNAKE_CASE = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_SCREAMING_SNAKE_CASE = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 713 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
UpperCamelCase__ : Tuple = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , *A__ , A__=None , A__=None , A__=None , **A__ ) -> Optional[int]:
super().__init__(*A__ , **A__ )
_SCREAMING_SNAKE_CASE = eval_examples
_SCREAMING_SNAKE_CASE = post_process_function
_SCREAMING_SNAKE_CASE = quant_trainer_args
_SCREAMING_SNAKE_CASE = 1_28 # default number of calibration samples
def UpperCamelCase ( self , A__=None ) -> Union[str, Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError("""Trainer: calibration requires an calib_dataset.""" )
_SCREAMING_SNAKE_CASE = calib_dataset if calib_dataset is not None else self.calib_dataset
_SCREAMING_SNAKE_CASE = self._remove_unused_columns(A__ , description="""Calibration""" )
return DataLoader(
A__ , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=A__ , )
def UpperCamelCase ( self , A__=None ) -> str:
_SCREAMING_SNAKE_CASE = self.train_dataset if calib_dataset is None else calib_dataset
_SCREAMING_SNAKE_CASE = self.get_calib_dataloader(A__ )
_SCREAMING_SNAKE_CASE = self.model
quant_trainer.configure_model(A__ , self.quant_trainer_args , calib=A__ )
model.eval()
quant_trainer.enable_calibration(A__ )
logger.info("""***** Running calibration *****""" )
logger.info(F" Num examples = {self.calib_num}" )
logger.info(F" Batch size = {calib_dataloader.batch_size}" )
for step, inputs in enumerate(A__ ):
# Prediction step
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.prediction_step(A__ , A__ , prediction_loss_only=A__ )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(A__ , self.quant_trainer_args )
_SCREAMING_SNAKE_CASE = model
def UpperCamelCase ( self , A__=None , A__=None , A__=None , A__ = "eval" ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.eval_dataset if eval_dataset is None else eval_dataset
_SCREAMING_SNAKE_CASE = self.get_eval_dataloader(A__ )
_SCREAMING_SNAKE_CASE = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_SCREAMING_SNAKE_CASE = self.compute_metrics
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_SCREAMING_SNAKE_CASE = self.post_process_function(A__ , A__ , output.predictions )
_SCREAMING_SNAKE_CASE = self.compute_metrics(A__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
_SCREAMING_SNAKE_CASE = metrics.pop(A__ )
self.log(A__ )
else:
_SCREAMING_SNAKE_CASE = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_SCREAMING_SNAKE_CASE = self.callback_handler.on_evaluate(self.args , self.state , self.control , A__ )
return metrics
def UpperCamelCase ( self , A__ , A__ , A__=None , A__ = "test" ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.get_test_dataloader(A__ )
# Temporarily disable metric computation, we will do it in the loop here.
_SCREAMING_SNAKE_CASE = self.compute_metrics
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_SCREAMING_SNAKE_CASE = self.post_process_function(A__ , A__ , output.predictions , """predict""" )
_SCREAMING_SNAKE_CASE = self.compute_metrics(A__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
_SCREAMING_SNAKE_CASE = metrics.pop(A__ )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=A__ )
def UpperCamelCase ( self , A__="./" ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.eval_dataset
_SCREAMING_SNAKE_CASE = self.get_eval_dataloader(A__ )
_SCREAMING_SNAKE_CASE = next(iter(A__ ) )
# saving device - to make it consistent
_SCREAMING_SNAKE_CASE = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
# convert to tuple
_SCREAMING_SNAKE_CASE = tuple(v.to(A__ ) for k, v in batch.items() )
logger.info("""Converting model to be onnx compatible""" )
from pytorch_quantization.nn import TensorQuantizer
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = self.model.to(A__ )
model.eval()
model.float()
_SCREAMING_SNAKE_CASE = model.module if hasattr(A__ , """module""" ) else model
quant_trainer.configure_model(A__ , self.quant_trainer_args )
_SCREAMING_SNAKE_CASE = os.path.join(A__ , """model.onnx""" )
logger.info(F"exporting model to {output_model_file}" )
_SCREAMING_SNAKE_CASE = {0: """batch_size""", 1: """seq_len"""}
torch.onnx.export(
A__ , A__ , A__ , export_params=A__ , opset_version=13 , do_constant_folding=A__ , input_names=["""input_ids""", """attention_mask""", """token_type_ids"""] , output_names=["""output_start_logits""", """output_end_logits"""] , dynamic_axes={
"""input_ids""": axes,
"""attention_mask""": axes,
"""token_type_ids""": axes,
"""output_start_logits""": axes,
"""output_end_logits""": axes,
} , verbose=A__ , )
logger.info("""onnx export finished""" )
| 0 | 0 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
UpperCamelCase__ : List[Any] = data_utils.TransfoXLTokenizer
UpperCamelCase__ : List[Any] = data_utils.TransfoXLCorpus
UpperCamelCase__ : str = data_utils
UpperCamelCase__ : Optional[int] = data_utils
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(UpperCamelCase__ , """rb""" ) as fp:
_SCREAMING_SNAKE_CASE = pickle.load(UpperCamelCase__ , encoding="""latin1""" )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
_SCREAMING_SNAKE_CASE = pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
print(F"Save vocabulary to {pytorch_vocab_dump_path}" )
_SCREAMING_SNAKE_CASE = corpus.vocab.__dict__
torch.save(UpperCamelCase__ , UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = corpus.__dict__
corpus_dict_no_vocab.pop("""vocab""" , UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = pytorch_dump_folder_path + """/""" + CORPUS_NAME
print(F"Save dataset to {pytorch_dataset_dump_path}" )
torch.save(UpperCamelCase__ , UpperCamelCase__ )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
_SCREAMING_SNAKE_CASE = os.path.abspath(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = os.path.abspath(UpperCamelCase__ )
print(F"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}." )
# Initialise PyTorch model
if transfo_xl_config_file == "":
_SCREAMING_SNAKE_CASE = TransfoXLConfig()
else:
_SCREAMING_SNAKE_CASE = TransfoXLConfig.from_json_file(UpperCamelCase__ )
print(F"Building PyTorch model from configuration: {config}" )
_SCREAMING_SNAKE_CASE = TransfoXLLMHeadModel(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = load_tf_weights_in_transfo_xl(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Save pytorch-model
_SCREAMING_SNAKE_CASE = os.path.join(UpperCamelCase__ , UpperCamelCase__ )
_SCREAMING_SNAKE_CASE = os.path.join(UpperCamelCase__ , UpperCamelCase__ )
print(F"Save PyTorch model to {os.path.abspath(UpperCamelCase__ )}" )
torch.save(model.state_dict() , UpperCamelCase__ )
print(F"Save configuration file to {os.path.abspath(UpperCamelCase__ )}" )
with open(UpperCamelCase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
UpperCamelCase__ : Dict = argparse.ArgumentParser()
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the folder to store the PyTorch model or dataset/vocab.",
)
parser.add_argument(
"--tf_checkpoint_path",
default="",
type=str,
help="An optional path to a TensorFlow checkpoint path to be converted.",
)
parser.add_argument(
"--transfo_xl_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--transfo_xl_dataset_file",
default="",
type=str,
help="An optional dataset file to be converted in a vocabulary.",
)
UpperCamelCase__ : int = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 714 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
return "".join([hex(SCREAMING_SNAKE_CASE_ )[2:].zfill(2 ).upper() for byte in list(SCREAMING_SNAKE_CASE_ )] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bytes:
"""simple docstring"""
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(SCREAMING_SNAKE_CASE_ ) % 2) != 0:
raise ValueError(
"""Base16 encoded data is invalid:
Data does not have an even number of hex digits.""" )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(SCREAMING_SNAKE_CASE_ ) <= set("""0123456789ABCDEF""" ):
raise ValueError(
"""Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.""" )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError("""The grid does not contain the appropriate information""" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_SCREAMING_SNAKE_CASE = grid[0]
for row_n in range(1 , len(_UpperCAmelCase ) ):
_SCREAMING_SNAKE_CASE = grid[row_n]
_SCREAMING_SNAKE_CASE = fill_row(_UpperCAmelCase , _UpperCAmelCase )
_SCREAMING_SNAKE_CASE = grid[row_n]
return grid[-1][-1]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
current_row[0] += row_above[0]
for cell_n in range(1 , len(_UpperCAmelCase ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 715 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
requests.request("""GET""" , """https://huggingface.co""" )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request("""GET""" , """https://huggingface.co""" , timeout=1.0 )
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request("""GET""" , """https://huggingface.co""" )
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
http_head("""https://huggingface.co""" )
| 0 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _a (UpperCamelCase_):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (DEISMultistepScheduler,)
SCREAMING_SNAKE_CASE = (('num_inference_steps', 25),)
def UpperCamelCase ( self , **A__ ) -> List[Any]:
_SCREAMING_SNAKE_CASE = {
'num_train_timesteps': 10_00,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
}
config.update(**__a )
return config
def UpperCamelCase ( self , A__=0 , **A__ ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = dict(self.forward_default_kwargs )
_SCREAMING_SNAKE_CASE = kwargs.pop("""num_inference_steps""" , __a )
_SCREAMING_SNAKE_CASE = self.dummy_sample
_SCREAMING_SNAKE_CASE = 0.1 * sample
_SCREAMING_SNAKE_CASE = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_SCREAMING_SNAKE_CASE = self.get_scheduler_config(**__a )
_SCREAMING_SNAKE_CASE = scheduler_class(**__a )
scheduler.set_timesteps(__a )
# copy over dummy past residuals
_SCREAMING_SNAKE_CASE = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__a )
_SCREAMING_SNAKE_CASE = scheduler_class.from_pretrained(__a )
new_scheduler.set_timesteps(__a )
# copy over dummy past residuals
_SCREAMING_SNAKE_CASE = dummy_past_residuals[: new_scheduler.config.solver_order]
_SCREAMING_SNAKE_CASE = sample, sample
for t in range(__a , time_step + scheduler.config.solver_order + 1 ):
_SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a , **__a ).prev_sample
_SCREAMING_SNAKE_CASE = new_scheduler.step(__a , __a , __a , **__a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCamelCase ( self ) -> Tuple:
pass
def UpperCamelCase ( self , A__=0 , **A__ ) -> Any:
_SCREAMING_SNAKE_CASE = dict(self.forward_default_kwargs )
_SCREAMING_SNAKE_CASE = kwargs.pop("""num_inference_steps""" , __a )
_SCREAMING_SNAKE_CASE = self.dummy_sample
_SCREAMING_SNAKE_CASE = 0.1 * sample
_SCREAMING_SNAKE_CASE = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_SCREAMING_SNAKE_CASE = self.get_scheduler_config()
_SCREAMING_SNAKE_CASE = scheduler_class(**__a )
scheduler.set_timesteps(__a )
# copy over dummy past residuals (must be after setting timesteps)
_SCREAMING_SNAKE_CASE = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__a )
_SCREAMING_SNAKE_CASE = scheduler_class.from_pretrained(__a )
# copy over dummy past residuals
new_scheduler.set_timesteps(__a )
# copy over dummy past residual (must be after setting timesteps)
_SCREAMING_SNAKE_CASE = dummy_past_residuals[: new_scheduler.config.solver_order]
_SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a , **__a ).prev_sample
_SCREAMING_SNAKE_CASE = new_scheduler.step(__a , __a , __a , **__a ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCamelCase ( self , A__=None , **A__ ) -> Optional[Any]:
if scheduler is None:
_SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
_SCREAMING_SNAKE_CASE = self.get_scheduler_config(**__a )
_SCREAMING_SNAKE_CASE = scheduler_class(**__a )
_SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
_SCREAMING_SNAKE_CASE = self.get_scheduler_config(**__a )
_SCREAMING_SNAKE_CASE = scheduler_class(**__a )
_SCREAMING_SNAKE_CASE = 10
_SCREAMING_SNAKE_CASE = self.dummy_model()
_SCREAMING_SNAKE_CASE = self.dummy_sample_deter
scheduler.set_timesteps(__a )
for i, t in enumerate(scheduler.timesteps ):
_SCREAMING_SNAKE_CASE = model(__a , __a )
_SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a ).prev_sample
return sample
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = dict(self.forward_default_kwargs )
_SCREAMING_SNAKE_CASE = kwargs.pop("""num_inference_steps""" , __a )
for scheduler_class in self.scheduler_classes:
_SCREAMING_SNAKE_CASE = self.get_scheduler_config()
_SCREAMING_SNAKE_CASE = scheduler_class(**__a )
_SCREAMING_SNAKE_CASE = self.dummy_sample
_SCREAMING_SNAKE_CASE = 0.1 * sample
if num_inference_steps is not None and hasattr(__a , """set_timesteps""" ):
scheduler.set_timesteps(__a )
elif num_inference_steps is not None and not hasattr(__a , """set_timesteps""" ):
_SCREAMING_SNAKE_CASE = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_SCREAMING_SNAKE_CASE = [residual + 0.2, residual + 0.15, residual + 0.10]
_SCREAMING_SNAKE_CASE = dummy_past_residuals[: scheduler.config.solver_order]
_SCREAMING_SNAKE_CASE = scheduler.timesteps[5]
_SCREAMING_SNAKE_CASE = scheduler.timesteps[6]
_SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a , **__a ).prev_sample
_SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a , **__a ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = DEISMultistepScheduler(**self.get_scheduler_config() )
_SCREAMING_SNAKE_CASE = self.full_loop(scheduler=__a )
_SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__a ) )
assert abs(result_mean.item() - 0.2_3916 ) < 1E-3
_SCREAMING_SNAKE_CASE = DPMSolverSinglestepScheduler.from_config(scheduler.config )
_SCREAMING_SNAKE_CASE = DPMSolverMultistepScheduler.from_config(scheduler.config )
_SCREAMING_SNAKE_CASE = UniPCMultistepScheduler.from_config(scheduler.config )
_SCREAMING_SNAKE_CASE = DEISMultistepScheduler.from_config(scheduler.config )
_SCREAMING_SNAKE_CASE = self.full_loop(scheduler=__a )
_SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__a ) )
assert abs(result_mean.item() - 0.2_3916 ) < 1E-3
def UpperCamelCase ( self ) -> int:
for timesteps in [25, 50, 1_00, 9_99, 10_00]:
self.check_over_configs(num_train_timesteps=__a )
def UpperCamelCase ( self ) -> Tuple:
self.check_over_configs(thresholding=__a )
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__a , prediction_type=__a , sample_max_value=__a , algorithm_type="""deis""" , solver_order=__a , solver_type=__a , )
def UpperCamelCase ( self ) -> int:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a )
def UpperCamelCase ( self ) -> Optional[Any]:
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__a , solver_type=__a , prediction_type=__a , algorithm_type=__a , )
_SCREAMING_SNAKE_CASE = self.full_loop(
solver_order=__a , solver_type=__a , prediction_type=__a , algorithm_type=__a , )
assert not torch.isnan(__a ).any(), "Samples have nan numbers"
def UpperCamelCase ( self ) -> Any:
self.check_over_configs(lower_order_final=__a )
self.check_over_configs(lower_order_final=__a )
def UpperCamelCase ( self ) -> Optional[int]:
for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
self.check_over_forward(num_inference_steps=__a , time_step=0 )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.full_loop()
_SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__a ) )
assert abs(result_mean.item() - 0.2_3916 ) < 1E-3
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.full_loop(prediction_type="""v_prediction""" )
_SCREAMING_SNAKE_CASE = torch.mean(torch.abs(__a ) )
assert abs(result_mean.item() - 0.091 ) < 1E-3
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.scheduler_classes[0]
_SCREAMING_SNAKE_CASE = self.get_scheduler_config(thresholding=__a , dynamic_thresholding_ratio=0 )
_SCREAMING_SNAKE_CASE = scheduler_class(**__a )
_SCREAMING_SNAKE_CASE = 10
_SCREAMING_SNAKE_CASE = self.dummy_model()
_SCREAMING_SNAKE_CASE = self.dummy_sample_deter.half()
scheduler.set_timesteps(__a )
for i, t in enumerate(scheduler.timesteps ):
_SCREAMING_SNAKE_CASE = model(__a , __a )
_SCREAMING_SNAKE_CASE = scheduler.step(__a , __a , __a ).prev_sample
assert sample.dtype == torch.floataa
| 716 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowerCAmelCase_ ( ) -> Iterator[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 2
while True:
if is_prime(SCREAMING_SNAKE_CASE_ ):
yield num
num += 1
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = 2_00_00_00 ) -> int:
"""simple docstring"""
return sum(takewhile(lambda SCREAMING_SNAKE_CASE_ : x < n , prime_generator() ) )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 0 | 0 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
UpperCamelCase__ : List[str] = datasets.load_iris()
UpperCamelCase__ : List[Any] = np.array(data["data"])
UpperCamelCase__ : str = np.array(data["target"])
UpperCamelCase__ : Optional[int] = data["target_names"]
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : str = train_test_split(X, y)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
return np.linalg.norm(np.array(_lowercase ) - np.array(_lowercase ) )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=5 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = zip(_lowercase , _lowercase )
# List of distances of all points from the point to be classified
_SCREAMING_SNAKE_CASE = []
for data_point in data:
_SCREAMING_SNAKE_CASE = euclidean_distance(data_point[0] , _lowercase )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
_SCREAMING_SNAKE_CASE = [i[1] for i in sorted(_lowercase )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
_SCREAMING_SNAKE_CASE = Counter(_lowercase ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 717 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = {
"""task_specific_params""": {
"""summarization""": {"""length_penalty""": 1.0, """max_length""": 1_28, """min_length""": 12, """num_beams""": 4},
"""summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_42, """min_length""": 56, """num_beams""": 4},
"""summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
}
}
_SCREAMING_SNAKE_CASE = {
"""task_specific_params.summarization.length_penalty""": 1.0,
"""task_specific_params.summarization.max_length""": 1_28,
"""task_specific_params.summarization.min_length""": 12,
"""task_specific_params.summarization.num_beams""": 4,
"""task_specific_params.summarization_cnn.length_penalty""": 2.0,
"""task_specific_params.summarization_cnn.max_length""": 1_42,
"""task_specific_params.summarization_cnn.min_length""": 56,
"""task_specific_params.summarization_cnn.num_beams""": 4,
"""task_specific_params.summarization_xsum.length_penalty""": 1.0,
"""task_specific_params.summarization_xsum.max_length""": 62,
"""task_specific_params.summarization_xsum.min_length""": 11,
"""task_specific_params.summarization_xsum.num_beams""": 6,
}
self.assertEqual(flatten_dict(A__ ) , A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(A__ ) , x.transpose() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , transpose(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , transpose(A__ , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , transpose(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , transpose(A__ , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(transpose(A__ ) , np.asarray(transpose(A__ ) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(transpose(A__ , axes=(1, 2, 0) ) , np.asarray(transpose(A__ , axes=(1, 2, 0) ) ) ) )
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , np.reshape(A__ , (4, 3) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , np.reshape(A__ , (12, 5) ) ) )
@require_torch
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , reshape(A__ , (4, 3) ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , reshape(A__ , (12, 5) ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , reshape(A__ , (4, 3) ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , reshape(A__ , (12, 5) ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(reshape(A__ , (4, 3) ) , np.asarray(reshape(A__ , (4, 3) ) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 , 5 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(reshape(A__ , (12, 5) ) , np.asarray(reshape(A__ , (12, 5) ) ) ) )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(A__ ) , np.squeeze(A__ ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , np.squeeze(A__ , axis=2 ) ) )
@require_torch
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , squeeze(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , squeeze(A__ , axis=2 ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , squeeze(A__ ).numpy() ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , squeeze(A__ , axis=2 ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(squeeze(A__ ) , np.asarray(squeeze(A__ ) ) ) )
_SCREAMING_SNAKE_CASE = np.random.randn(1 , 4 , 1 , 5 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(squeeze(A__ , axis=2 ) , np.asarray(squeeze(A__ , axis=2 ) ) ) )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , np.expand_dims(A__ , axis=1 ) ) )
@require_torch
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = torch.tensor(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , expand_dims(A__ , axis=1 ).numpy() ) )
@require_tf
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = tf.constant(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , expand_dims(A__ , axis=1 ).numpy() ) )
@require_flax
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = np.random.randn(3 , 4 )
_SCREAMING_SNAKE_CASE = jnp.array(A__ )
self.assertTrue(np.allclose(expand_dims(A__ , axis=1 ) , np.asarray(expand_dims(A__ , axis=1 ) ) ) )
| 0 | 0 |
'''simple docstring'''
UpperCamelCase__ : List[Any] = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
"q": "ABBBB",
"r": "BAAAA",
"s": "BAAAB",
"t": "BAABA",
"u": "BAABB",
"v": "BBBAB",
"w": "BABAA",
"x": "BABAB",
"y": "BABBA",
"z": "BABBB",
" ": " ",
}
UpperCamelCase__ : Union[str, Any] = {value: key for key, value in encode_dict.items()}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """"""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("""encode() accepts only letters of the alphabet and spaces""" )
return encoded
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
if set(_lowerCamelCase ) - {"A", "B", " "} != set():
raise Exception("""decode() accepts only 'A', 'B' and spaces""" )
_SCREAMING_SNAKE_CASE = """"""
for word in coded.split():
while len(_lowerCamelCase ) != 0:
decoded += decode_dict[word[:5]]
_SCREAMING_SNAKE_CASE = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 718 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ''
SCREAMING_SNAKE_CASE = 'hf-legacy' # "hf://"" is reserved for hffs
def __init__( self , A__ = None , A__ = None , **A__ , ) -> Optional[int]:
super().__init__(self , **A__ )
_SCREAMING_SNAKE_CASE = repo_info
_SCREAMING_SNAKE_CASE = token
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Tuple:
if self.dir_cache is None:
_SCREAMING_SNAKE_CASE = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
_SCREAMING_SNAKE_CASE = {
"""name""": hf_file.rfilename,
"""size""": None,
"""type""": """file""",
}
self.dir_cache.update(
{
str(A__ ): {"""name""": str(A__ ), """size""": None, """type""": """directory"""}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def UpperCamelCase ( self , A__ , A__ = "rb" , **A__ , ) -> Optional[int]:
if not isinstance(self.repo_info , A__ ):
raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}" )
_SCREAMING_SNAKE_CASE = hf_hub_url(self.repo_info.id , A__ , revision=self.repo_info.sha )
return fsspec.open(
A__ , mode=A__ , headers=get_authentication_headers_for_url(A__ , use_auth_token=self.token ) , client_kwargs={"""trust_env""": True} , ).open()
def UpperCamelCase ( self , A__ , **A__ ) -> str:
self._get_dirs()
_SCREAMING_SNAKE_CASE = self._strip_protocol(A__ )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(A__ )
def UpperCamelCase ( self , A__ , A__=False , **A__ ) -> List[Any]:
self._get_dirs()
_SCREAMING_SNAKE_CASE = PurePosixPath(path.strip("""/""" ) )
_SCREAMING_SNAKE_CASE = {}
for p, f in self.dir_cache.items():
_SCREAMING_SNAKE_CASE = PurePosixPath(p.strip("""/""" ) )
_SCREAMING_SNAKE_CASE = p.parent
if root == path:
_SCREAMING_SNAKE_CASE = f
_SCREAMING_SNAKE_CASE = list(paths.values() )
if detail:
return out
else:
return sorted(f["""name"""] for f in out )
| 0 | 0 |
import numpy as np
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = int(np.ceil((x_end - xa) / h ) )
_SCREAMING_SNAKE_CASE = np.zeros((n + 1,) )
_SCREAMING_SNAKE_CASE = ya
_SCREAMING_SNAKE_CASE = xa
for k in range(lowercase_ ):
_SCREAMING_SNAKE_CASE = f(lowercase_ , y[k] )
_SCREAMING_SNAKE_CASE = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
_SCREAMING_SNAKE_CASE = f(x + 0.5 * h , y[k] + 0.5 * h * ka )
_SCREAMING_SNAKE_CASE = f(x + h , y[k] + h * ka )
_SCREAMING_SNAKE_CASE = y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
x += h
return y
if __name__ == "__main__":
import doctest
doctest.testmod()
| 719 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , keep_in_memory=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = features.copy() if features else default_expected_features
_SCREAMING_SNAKE_CASE = (
Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ , split=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type""" , [str, list] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
if issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = parquet_path
elif issubclass(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = [parquet_path]
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=("train",) ) -> List[str]:
"""simple docstring"""
assert isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for split in splits:
_SCREAMING_SNAKE_CASE = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(
{"""train""": parquet_path} , cache_dir=SCREAMING_SNAKE_CASE_ , keep_in_memory=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = features.copy() if features else default_expected_features
_SCREAMING_SNAKE_CASE = (
Features({feature: Value(SCREAMING_SNAKE_CASE_ ) for feature, dtype in features.items()} ) if features is not None else None
)
_SCREAMING_SNAKE_CASE = ParquetDatasetReader({"""train""": parquet_path} , features=SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
if split:
_SCREAMING_SNAKE_CASE = {split: parquet_path}
else:
_SCREAMING_SNAKE_CASE = """train"""
_SCREAMING_SNAKE_CASE = {"""train""": parquet_path, """test""": parquet_path}
_SCREAMING_SNAKE_CASE = tmp_path / """cache"""
_SCREAMING_SNAKE_CASE = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(SCREAMING_SNAKE_CASE_ , cache_dir=SCREAMING_SNAKE_CASE_ ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ParquetDatasetWriter(SCREAMING_SNAKE_CASE_ , tmp_path / """foo.parquet""" )
assert writer.write() > 0
_SCREAMING_SNAKE_CASE = pq.ParquetFile(tmp_path / """foo.parquet""" )
_SCREAMING_SNAKE_CASE = pf.read()
assert dataset.data.table == output_table
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = str(shared_datadir / """test_image_rgb.jpg""" )
_SCREAMING_SNAKE_CASE = {"""image""": [image_path]}
_SCREAMING_SNAKE_CASE = Features({"""image""": Image()} )
_SCREAMING_SNAKE_CASE = Dataset.from_dict(SCREAMING_SNAKE_CASE_ , features=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = ParquetDatasetWriter(SCREAMING_SNAKE_CASE_ , tmp_path / """foo.parquet""" )
assert writer.write() > 0
_SCREAMING_SNAKE_CASE = Dataset.from_parquet(str(tmp_path / """foo.parquet""" ) )
assert dataset.features == reloaded_dataset.features
_SCREAMING_SNAKE_CASE = ParquetDatasetReader(str(tmp_path / """foo.parquet""" ) , streaming=SCREAMING_SNAKE_CASE_ ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"""feature, expected""" , [
(Features({"""foo""": Value("""int32""" )} ), None),
(Features({"""image""": Image(), """foo""": Value("""int32""" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"""nested""": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
assert get_writer_batch_size(SCREAMING_SNAKE_CASE_ ) == expected
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _a (__SCREAMING_SNAKE_CASE):
"""simple docstring"""
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_a , """embed_dim""" ) )
self.parent.assertTrue(hasattr(_a , """num_heads""" ) )
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=64 , A__=3 , A__=[16, 48, 96] , A__=[1, 3, 6] , A__=[1, 2, 10] , A__=[7, 3, 3] , A__=[4, 2, 2] , A__=[2, 1, 1] , A__=[2, 2, 2] , A__=[False, False, True] , A__=[0.0, 0.0, 0.0] , A__=0.02 , A__=1E-12 , A__=True , A__=True , A__=2 , ) -> int:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = patch_sizes
_SCREAMING_SNAKE_CASE = patch_stride
_SCREAMING_SNAKE_CASE = patch_padding
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = num_labels
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = embed_dim
_SCREAMING_SNAKE_CASE = num_heads
_SCREAMING_SNAKE_CASE = stride_kv
_SCREAMING_SNAKE_CASE = depth
_SCREAMING_SNAKE_CASE = cls_token
_SCREAMING_SNAKE_CASE = attention_drop_rate
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = layer_norm_eps
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
# create a random int32 tensor of given shape
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_labels )
_SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ) -> Union[str, Any]:
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFCvtModel(config=_a )
_SCREAMING_SNAKE_CASE = model(_a , training=_a )
_SCREAMING_SNAKE_CASE = (self.image_size, self.image_size)
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
_SCREAMING_SNAKE_CASE = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
_SCREAMING_SNAKE_CASE = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> str:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFCvtForImageClassification(_a )
_SCREAMING_SNAKE_CASE = model(_a , labels=_a , training=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class _a (__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
SCREAMING_SNAKE_CASE = (
{'feature-extraction': TFCvtModel, 'image-classification': TFCvtForImageClassification}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = TFCvtModelTester(self )
_SCREAMING_SNAKE_CASE = TFCvtConfigTester(self , config_class=_a , has_text_modality=_a , hidden_size=37 )
def UpperCamelCase ( self ) -> List[str]:
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason="""Cvt does not output attentions""" )
def UpperCamelCase ( self ) -> List[Any]:
pass
@unittest.skip(reason="""Cvt does not use inputs_embeds""" )
def UpperCamelCase ( self ) -> Union[str, Any]:
pass
@unittest.skip(reason="""Cvt does not support input and output embeddings""" )
def UpperCamelCase ( self ) -> List[str]:
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
def UpperCamelCase ( self ) -> List[str]:
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 , reason="""TF does not support backprop for grouped convolutions on CPU.""" , )
@slow
def UpperCamelCase ( self ) -> List[str]:
super().test_keras_fit()
@unittest.skip(reason="""Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8""" )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = tf.keras.mixed_precision.Policy("""mixed_float16""" )
tf.keras.mixed_precision.set_global_policy(_a )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy("""float32""" )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = model_class(_a )
_SCREAMING_SNAKE_CASE = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
_SCREAMING_SNAKE_CASE = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _a )
def UpperCamelCase ( self ) -> Any:
def check_hidden_states_output(A__ , A__ , A__ ):
_SCREAMING_SNAKE_CASE = model_class(_a )
_SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(_a , _a ) )
_SCREAMING_SNAKE_CASE = outputs.hidden_states
_SCREAMING_SNAKE_CASE = len(self.model_tester.depth )
self.assertEqual(len(_a ) , _a )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = True
check_hidden_states_output(_a , _a , _a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_SCREAMING_SNAKE_CASE = True
check_hidden_states_output(_a , _a , _a )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
@slow
def UpperCamelCase ( self ) -> List[str]:
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE = TFCvtModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def lowerCAmelCase_ ( ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class _a (unittest.TestCase):
"""simple docstring"""
@cached_property
def UpperCamelCase ( self ) -> Optional[int]:
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
_SCREAMING_SNAKE_CASE = self.default_image_processor
_SCREAMING_SNAKE_CASE = prepare_img()
_SCREAMING_SNAKE_CASE = image_processor(images=_a , return_tensors="""tf""" )
# forward pass
_SCREAMING_SNAKE_CASE = model(**_a )
# verify the logits
_SCREAMING_SNAKE_CASE = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , _a )
_SCREAMING_SNAKE_CASE = tf.constant([0.9285, 0.9015, -0.3150] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , _a , atol=1E-4 ) ) | 720 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicative_persistence() does not accept negative values""" )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
while len(SCREAMING_SNAKE_CASE_ ) != 1:
_SCREAMING_SNAKE_CASE = [int(SCREAMING_SNAKE_CASE_ ) for i in num_string]
_SCREAMING_SNAKE_CASE = 1
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) ):
total *= numbers[i]
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
steps += 1
return steps
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("""additive_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""additive_persistence() does not accept negative values""" )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
while len(SCREAMING_SNAKE_CASE_ ) != 1:
_SCREAMING_SNAKE_CASE = [int(SCREAMING_SNAKE_CASE_ ) for i in num_string]
_SCREAMING_SNAKE_CASE = 0
for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) ):
total += numbers[i]
_SCREAMING_SNAKE_CASE = str(SCREAMING_SNAKE_CASE_ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"""{price_plus_tax(100, 0.25) = }""")
print(f"""{price_plus_tax(125.50, 0.05) = }""")
| 721 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
UpperCamelCase__ : Tuple = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
from run_translation import main # noqa
set_seed(42)
UpperCamelCase__ : Union[str, Any] = "sshleifer/student_marian_en_ro_6_1"
UpperCamelCase__ : str = "sshleifer/tiny-mbart"
@require_torch
class _a (_lowerCamelCase):
"""simple docstring"""
def UpperCamelCase ( self , A__=False , A__=None , A__=True , A__=True , A__=True , A__=True , ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=A__ , num_train_epochs=1 , distributed=A__ , extra_args_str=A__ , predict_with_generate=A__ , do_train=A__ , do_eval=A__ , do_predict=A__ , )
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(os.path.join(A__ , """trainer_state.json""" ) ).log_history
if not do_eval:
return
_SCREAMING_SNAKE_CASE = [log for log in logs if """eval_loss""" in log.keys()]
_SCREAMING_SNAKE_CASE = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
_SCREAMING_SNAKE_CASE = eval_metrics[-1]
assert isinstance(last_step_stats["""eval_bleu"""] , A__ )
assert not math.isnan(float(last_step_stats["""eval_loss"""] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def UpperCamelCase ( self ) -> Optional[int]:
self.run_seqaseq_quick()
@require_torch_multi_gpu
def UpperCamelCase ( self ) -> Optional[Any]:
self.run_seqaseq_quick(distributed=A__ )
@require_torch_multi_gpu
def UpperCamelCase ( self ) -> Union[str, Any]:
self.run_seqaseq_quick(distributed=A__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> Any:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp simple""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> Tuple:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp simple --fp16""" )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> str:
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--sharded_ddp zero_dp_2""" , predict_with_generate=A__ )
@unittest.skip("""Requires an update of the env running those tests""" )
@require_torch_multi_gpu
@require_fairscale
def UpperCamelCase ( self ) -> List[str]:
self.run_seqaseq_quick(
distributed=A__ , extra_args_str="""--sharded_ddp zero_dp_2 --fp16""" , predict_with_generate=A__ )
@require_apex
@require_torch_gpu
def UpperCamelCase ( self ) -> Optional[Any]:
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=A__ , extra_args_str="""--fp16 --fp16_backend=apex""" )
@parameterized.expand(["""base""", """low""", """high""", """mixed"""] )
@require_torch_multi_gpu
def UpperCamelCase ( self , A__ ) -> List[Any]:
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
_SCREAMING_SNAKE_CASE = {
# test with the default log_level - should be info and thus log info once
"""base""": {"""extra_args_str""": """""", """n_matches""": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"""low""": {"""extra_args_str""": """--log_level debug --log_level_replica debug""", """n_matches""": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"""high""": {"""extra_args_str""": """--log_level error --log_level_replica debug""", """n_matches""": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"""mixed""": {"""extra_args_str""": """--log_level error --log_level_replica error""", """n_matches""": 0},
}
_SCREAMING_SNAKE_CASE = experiments[experiment_id]
_SCREAMING_SNAKE_CASE = {"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
_SCREAMING_SNAKE_CASE = """Running training"""
with CaptureStderr() as cl:
self.run_seqaseq_quick(**A__ , extra_args_str=data["""extra_args_str"""] )
_SCREAMING_SNAKE_CASE = len(re.findall(A__ , cl.err ) )
self.assertEqual(A__ , data["""n_matches"""] )
@slow
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.run_trainer(
eval_steps=2 , max_len=1_28 , model_name=A__ , learning_rate=3E-4 , num_train_epochs=10 , distributed=A__ , )
# Check metrics
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(os.path.join(A__ , """trainer_state.json""" ) ).log_history
_SCREAMING_SNAKE_CASE = [log for log in logs if """eval_loss""" in log.keys()]
_SCREAMING_SNAKE_CASE = eval_metrics[0]
_SCREAMING_SNAKE_CASE = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["""eval_bleu"""] , A__ )
# test if do_predict saves generations and metrics
_SCREAMING_SNAKE_CASE = os.listdir(A__ )
_SCREAMING_SNAKE_CASE = {os.path.basename(A__ ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def UpperCamelCase ( self ) -> Dict:
from transformers.training_args import OptimizerNames
def train_and_return_metrics(A__ ) -> Tuple[int, float]:
_SCREAMING_SNAKE_CASE = """--skip_memory_metrics 0"""
_SCREAMING_SNAKE_CASE = self.run_trainer(
max_len=1_28 , model_name=A__ , learning_rate=3E-4 , num_train_epochs=1 , optim=A__ , distributed=A__ , extra_args_str=A__ , do_eval=A__ , do_predict=A__ , n_gpus_to_use=1 , )
# Check metrics
_SCREAMING_SNAKE_CASE = TrainerState.load_from_json(Path(A__ , """trainer_state.json""" ) ).log_history
_SCREAMING_SNAKE_CASE = int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20 )
_SCREAMING_SNAKE_CASE = int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20 )
_SCREAMING_SNAKE_CASE = logs[0]["""train_loss"""]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
_SCREAMING_SNAKE_CASE = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
_SCREAMING_SNAKE_CASE = gpu_peak_mem_orig + gpu_alloc_mem_orig
_SCREAMING_SNAKE_CASE = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
_SCREAMING_SNAKE_CASE = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
_SCREAMING_SNAKE_CASE = 1_20
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
A__ , A__ , """should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"""
F" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
F" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB" , )
self.assertGreater(
A__ , A__ , """should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"""
F" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
F" gpu_total_mem_bnb={gpu_total_mem_bnb}MB" , )
self.assertEqual(
A__ , A__ , F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}" )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ = 3E-3 , A__ = "adafactor" , A__ = False , A__ = None , A__ = 0 , A__ = True , A__ = True , A__ = True , A__ = True , A__ = None , ) -> Dict:
_SCREAMING_SNAKE_CASE = self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
_SCREAMING_SNAKE_CASE = self.get_auto_remove_tmp_dir()
_SCREAMING_SNAKE_CASE = F"\n --model_name_or_path {model_name}\n --train_file {data_dir}/train.json\n --validation_file {data_dir}/val.json\n --test_file {data_dir}/test.json\n --output_dir {output_dir}\n --overwrite_output_dir\n --max_train_samples 8\n --max_source_length {max_len}\n --max_target_length {max_len}\n --do_train\n --num_train_epochs {str(A__ )}\n --per_device_train_batch_size 4\n --learning_rate {learning_rate}\n --warmup_steps 8\n --logging_steps 0\n --logging_strategy no\n --save_steps {str(A__ )}\n --group_by_length\n --label_smoothing_factor 0.1\n --target_lang ro_RO\n --source_lang en_XX\n ".split()
_SCREAMING_SNAKE_CASE = F"\n --do_eval\n --per_device_eval_batch_size 4\n --max_eval_samples 8\n --val_max_target_length {max_len}\n --evaluation_strategy steps\n --eval_steps {str(A__ )}\n ".split()
_SCREAMING_SNAKE_CASE = """
--do_predict
""".split()
_SCREAMING_SNAKE_CASE = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F"--optim {optim}".split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
_SCREAMING_SNAKE_CASE = get_gpu_count()
_SCREAMING_SNAKE_CASE = get_torch_dist_unique_port()
_SCREAMING_SNAKE_CASE = F"\n -m torch.distributed.run\n --nproc_per_node={n_gpus_to_use}\n --master_port={master_port}\n {self.examples_dir_str}/pytorch/translation/run_translation.py\n ".split()
_SCREAMING_SNAKE_CASE = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(A__ , env=self.get_env() )
else:
_SCREAMING_SNAKE_CASE = ["""run_translation.py"""] + args
with patch.object(A__ , """argv""" , A__ ):
main()
return output_dir
| 0 | 0 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch """
"""helper utility that will spawn up """
"""multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=SCREAMING_SNAKE_CASE_ , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=SCREAMING_SNAKE_CASE_ , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=SCREAMING_SNAKE_CASE_ )
return parser.parse_args()
def lowerCAmelCase_ ( ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = parse_args()
# Import training_script as a module.
_SCREAMING_SNAKE_CASE = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
_SCREAMING_SNAKE_CASE = script_fpath.stem
_SCREAMING_SNAKE_CASE = importlib.import_module(SCREAMING_SNAKE_CASE_ )
# Patch sys.argv
_SCREAMING_SNAKE_CASE = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 700 |
'''simple docstring'''
import sys
UpperCamelCase__ : int = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"66896648950445244523161731856403098711121722383113"
"62229893423380308135336276614282806444486645238749"
"30358907296290491560440772390713810515859307960866"
"70172427121883998797908792274921901699720888093776"
"65727333001053367881220235421809751254540594752243"
"52584907711670556013604839586446706324415722155397"
"53697817977846174064955149290862569321978468622482"
"83972241375657056057490261407972968652414535100474"
"82166370484403199890008895243450658541227588666881"
"16427171479924442928230863465674813919123162824586"
"17866458359124566529476545682848912883142607690042"
"24219022671055626321111109370544217506941658960408"
"07198403850962455444362981230987879927244284909188"
"84580156166097919133875499200524063689912560717606"
"05886116467109405077541002256983155200055935729725"
"71636269561882670428252483600823257530420752963450"
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = N ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = -sys.maxsize - 1
for i in range(len(SCREAMING_SNAKE_CASE_ ) - 12 ):
_SCREAMING_SNAKE_CASE = 1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
_SCREAMING_SNAKE_CASE = product
return largest_product
if __name__ == "__main__":
print(f"""{solution() = }""")
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> float:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = u
for i in range(1 , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = temp * (u - i)
return temp
def lowerCAmelCase_ ( ) -> None:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = int(input("""enter the numbers of values: """ ) )
_SCREAMING_SNAKE_CASE = []
for _ in range(SCREAMING_SNAKE_CASE_ ):
y.append([] )
for i in range(SCREAMING_SNAKE_CASE_ ):
for j in range(SCREAMING_SNAKE_CASE_ ):
y[i].append(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = 0
print("""enter the values of parameters in a list: """ )
_SCREAMING_SNAKE_CASE = list(map(SCREAMING_SNAKE_CASE_ , input().split() ) )
print("""enter the values of corresponding parameters: """ )
for i in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = float(input() )
_SCREAMING_SNAKE_CASE = int(input("""enter the value to interpolate: """ ) )
_SCREAMING_SNAKE_CASE = (value - x[0]) / (x[1] - x[0])
# for calculating forward difference table
for i in range(1 , SCREAMING_SNAKE_CASE_ ):
for j in range(n - i ):
_SCREAMING_SNAKE_CASE = y[j + 1][i - 1] - y[j][i - 1]
_SCREAMING_SNAKE_CASE = y[0][0]
for i in range(1 , SCREAMING_SNAKE_CASE_ ):
summ += (ucal(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) * y[0][i]) / math.factorial(SCREAMING_SNAKE_CASE_ )
print(F"the value at {value} is {summ}" )
if __name__ == "__main__":
main()
| 701 |
'''simple docstring'''
UpperCamelCase__ : Dict = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p": "ABBBA",
"q": "ABBBB",
"r": "BAAAA",
"s": "BAAAB",
"t": "BAABA",
"u": "BAABB",
"v": "BBBAB",
"w": "BABAA",
"x": "BABAB",
"y": "BABBA",
"z": "BABBB",
" ": " ",
}
UpperCamelCase__ : str = {value: key for key, value in encode_dict.items()}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """"""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("""encode() accepts only letters of the alphabet and spaces""" )
return encoded
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
if set(SCREAMING_SNAKE_CASE_ ) - {"A", "B", " "} != set():
raise Exception("""decode() accepts only 'A', 'B' and spaces""" )
_SCREAMING_SNAKE_CASE = """"""
for word in coded.split():
while len(SCREAMING_SNAKE_CASE_ ) != 0:
decoded += decode_dict[word[:5]]
_SCREAMING_SNAKE_CASE = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 0 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Any = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"tokenization_roc_bert": ["RoCBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Dict = [
"ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"RoCBertForCausalLM",
"RoCBertForMaskedLM",
"RoCBertForMultipleChoice",
"RoCBertForPreTraining",
"RoCBertForQuestionAnswering",
"RoCBertForSequenceClassification",
"RoCBertForTokenClassification",
"RoCBertLayer",
"RoCBertModel",
"RoCBertPreTrainedModel",
"load_tf_weights_in_roc_bert",
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
UpperCamelCase__ : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 702 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""encoder.embed_positions._float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = emb.weight.shape
_SCREAMING_SNAKE_CASE = nn.Linear(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , bias=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )
_SCREAMING_SNAKE_CASE = mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
_SCREAMING_SNAKE_CASE = mam_aaa["""model"""]
remove_ignore_keys_(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = state_dict["""encoder.embed_tokens.weight"""].shape[0]
_SCREAMING_SNAKE_CASE = MaMaaaConfig(
vocab_size=SCREAMING_SNAKE_CASE_ , max_position_embeddings=10_24 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="""relu""" , )
_SCREAMING_SNAKE_CASE = state_dict["""decoder.embed_tokens.weight"""]
_SCREAMING_SNAKE_CASE = MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
model.model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
UpperCamelCase__ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
UpperCamelCase__ : List[str] = parser.parse_args()
UpperCamelCase__ : Any = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
return np.maximum(0 , __lowercase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 703 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : str = {
"configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
"tokenization_canine": ["CanineTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : List[Any] = [
"CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
"CanineForMultipleChoice",
"CanineForQuestionAnswering",
"CanineForSequenceClassification",
"CanineForTokenClassification",
"CanineLayer",
"CanineModel",
"CaninePreTrainedModel",
"load_tf_weights_in_canine",
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
UpperCamelCase__ : str = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 | 0 |
import argparse
import os
import re
UpperCamelCase__ : Tuple = "src/diffusers"
# Pattern that looks at the indentation in a line.
UpperCamelCase__ : Optional[int] = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
UpperCamelCase__ : Union[str, Any] = re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
UpperCamelCase__ : str = re.compile(R"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
UpperCamelCase__ : List[Any] = re.compile(R"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
UpperCamelCase__ : Dict = re.compile(R"\[([^\]]+)\]")
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = _re_indent.search(__snake_case )
return "" if search is None else search.groups()[0]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="" , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=None ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = code.split("""\n""" )
if start_prompt is not None:
while not lines[index].startswith(__snake_case ):
index += 1
_SCREAMING_SNAKE_CASE = ["""\n""".join(lines[:index] )]
else:
_SCREAMING_SNAKE_CASE = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
_SCREAMING_SNAKE_CASE = [lines[index]]
index += 1
while index < len(__snake_case ) and (end_prompt is None or not lines[index].startswith(__snake_case )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(__snake_case ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + """ """ ):
current_block.append(lines[index] )
blocks.append("""\n""".join(__snake_case ) )
if index < len(__snake_case ) - 1:
_SCREAMING_SNAKE_CASE = [lines[index + 1]]
index += 1
else:
_SCREAMING_SNAKE_CASE = []
else:
blocks.append("""\n""".join(__snake_case ) )
_SCREAMING_SNAKE_CASE = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(__snake_case ) > 0:
blocks.append("""\n""".join(__snake_case ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(__snake_case ):
blocks.append("""\n""".join(lines[index:] ) )
return blocks
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
def _inner(SCREAMING_SNAKE_CASE_ ):
return key(__snake_case ).lower().replace("""_""" , """""" )
return _inner
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> Tuple:
"""simple docstring"""
# If no key is provided, we use a noop.
def noop(SCREAMING_SNAKE_CASE_ ):
return x
if key is None:
_SCREAMING_SNAKE_CASE = noop
# Constants are all uppercase, they go first.
_SCREAMING_SNAKE_CASE = [obj for obj in objects if key(__snake_case ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
_SCREAMING_SNAKE_CASE = [obj for obj in objects if key(__snake_case )[0].isupper() and not key(__snake_case ).isupper()]
# Functions begin with a lowercase, they go last.
_SCREAMING_SNAKE_CASE = [obj for obj in objects if not key(__snake_case )[0].isupper()]
_SCREAMING_SNAKE_CASE = ignore_underscore(__snake_case )
return sorted(__snake_case , key=__snake_case ) + sorted(__snake_case , key=__snake_case ) + sorted(__snake_case , key=__snake_case )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
# This inner function sort imports between [ ].
def _replace(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = match.groups()[0]
if "," not in imports:
return F"[{imports}]"
_SCREAMING_SNAKE_CASE = [part.strip().replace("""\"""" , """""" ) for part in imports.split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
_SCREAMING_SNAKE_CASE = keys[:-1]
return "[" + ", ".join([F"\"{k}\"" for k in sort_objects(__snake_case )] ) + "]"
_SCREAMING_SNAKE_CASE = import_statement.split("""\n""" )
if len(__snake_case ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
_SCREAMING_SNAKE_CASE = 2 if lines[1].strip() == """[""" else 1
_SCREAMING_SNAKE_CASE = [(i, _re_strip_line.search(__snake_case ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
_SCREAMING_SNAKE_CASE = sort_objects(__snake_case , key=lambda SCREAMING_SNAKE_CASE_ : x[1] )
_SCREAMING_SNAKE_CASE = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(__snake_case ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
_SCREAMING_SNAKE_CASE = _re_bracket_content.sub(_replace , lines[1] )
else:
_SCREAMING_SNAKE_CASE = [part.strip().replace("""\"""" , """""" ) for part in lines[1].split(""",""" )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
_SCREAMING_SNAKE_CASE = keys[:-1]
_SCREAMING_SNAKE_CASE = get_indent(lines[1] ) + """, """.join([F"\"{k}\"" for k in sort_objects(__snake_case )] )
return "\n".join(__snake_case )
else:
# Finally we have to deal with imports fitting on one line
_SCREAMING_SNAKE_CASE = _re_bracket_content.sub(_replace , __snake_case )
return import_statement
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=True ) -> int:
"""simple docstring"""
with open(__snake_case , """r""" ) as f:
_SCREAMING_SNAKE_CASE = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
_SCREAMING_SNAKE_CASE = split_code_in_indented_blocks(
__snake_case , start_prompt="""_import_structure = {""" , end_prompt="""if TYPE_CHECKING:""" )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(__snake_case ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
_SCREAMING_SNAKE_CASE = main_blocks[block_idx]
_SCREAMING_SNAKE_CASE = block.split("""\n""" )
# Get to the start of the imports.
_SCREAMING_SNAKE_CASE = 0
while line_idx < len(__snake_case ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
_SCREAMING_SNAKE_CASE = len(__snake_case )
else:
line_idx += 1
if line_idx >= len(__snake_case ):
continue
# Ignore beginning and last line: they don't contain anything.
_SCREAMING_SNAKE_CASE = """\n""".join(block_lines[line_idx:-1] )
_SCREAMING_SNAKE_CASE = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
_SCREAMING_SNAKE_CASE = split_code_in_indented_blocks(__snake_case , indent_level=__snake_case )
# We have two categories of import key: list or _import_structure[key].append/extend
_SCREAMING_SNAKE_CASE = _re_direct_key if """_import_structure""" in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
_SCREAMING_SNAKE_CASE = [(pattern.search(__snake_case ).groups()[0] if pattern.search(__snake_case ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
_SCREAMING_SNAKE_CASE = [(i, key) for i, key in enumerate(__snake_case ) if key is not None]
_SCREAMING_SNAKE_CASE = [x[0] for x in sorted(__snake_case , key=lambda SCREAMING_SNAKE_CASE_ : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = []
for i in range(len(__snake_case ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
_SCREAMING_SNAKE_CASE = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(__snake_case )
count += 1
# And we put our main block back together with its first and last line.
_SCREAMING_SNAKE_CASE = """\n""".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(__snake_case ):
if check_only:
return True
else:
print(F"Overwriting {file}." )
with open(__snake_case , """w""" ) as f:
f.write("""\n""".join(__snake_case ) )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_=True ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
for root, _, files in os.walk(__snake_case ):
if "__init__.py" in files:
_SCREAMING_SNAKE_CASE = sort_imports(os.path.join(__snake_case , """__init__.py""" ) , check_only=__snake_case )
if result:
_SCREAMING_SNAKE_CASE = [os.path.join(__snake_case , """__init__.py""" )]
if len(__snake_case ) > 0:
raise ValueError(F"Would overwrite {len(__snake_case )} files, run `make style`." )
if __name__ == "__main__":
UpperCamelCase__ : List[Any] = argparse.ArgumentParser()
parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
UpperCamelCase__ : Any = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 704 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE = 'ChineseCLIPImageProcessor'
SCREAMING_SNAKE_CASE = ('BertTokenizer', 'BertTokenizerFast')
def __init__( self , A__=None , A__=None , **A__ ) -> int:
_SCREAMING_SNAKE_CASE = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , A__ , )
_SCREAMING_SNAKE_CASE = kwargs.pop("""feature_extractor""" )
_SCREAMING_SNAKE_CASE = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("""You need to specify an `image_processor`.""" )
if tokenizer is None:
raise ValueError("""You need to specify a `tokenizer`.""" )
super().__init__(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.image_processor
def __call__( self , A__=None , A__=None , A__=None , **A__ ) -> Optional[int]:
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
_SCREAMING_SNAKE_CASE = self.tokenizer(A__ , return_tensors=A__ , **A__ )
if images is not None:
_SCREAMING_SNAKE_CASE = self.image_processor(A__ , return_tensors=A__ , **A__ )
if text is not None and images is not None:
_SCREAMING_SNAKE_CASE = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**A__ ) , tensor_type=A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Dict:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Optional[Any]:
return self.tokenizer.decode(*A__ , **A__ )
@property
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.tokenizer.model_input_names
_SCREAMING_SNAKE_CASE = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCamelCase ( self ) -> Optional[int]:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , A__ , )
return self.image_processor_class
| 0 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=12 , A__=7 , A__=True , A__=True , A__=True , A__=99 , A__=32 , A__=32 , A__=2 , A__=4 , A__=37 , A__=0.1 , A__=0.1 , A__=5_12 , A__=0.02 , A__=0 , A__=None , ) -> List[str]:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = seq_length
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_input_mask
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = projection_dim
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = dropout
_SCREAMING_SNAKE_CASE = attention_dropout
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = scope
_SCREAMING_SNAKE_CASE = bos_token_id
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
_SCREAMING_SNAKE_CASE = input_mask.numpy()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = input_mask.shape
_SCREAMING_SNAKE_CASE = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(snake_case__ ):
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = self.get_config()
return config, input_ids, tf.convert_to_tensor(snake_case__ )
def UpperCamelCase ( self ) -> Optional[Any]:
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Dict:
_SCREAMING_SNAKE_CASE = TFBlipTextModel(config=snake_case__ )
_SCREAMING_SNAKE_CASE = model(snake_case__ , attention_mask=snake_case__ , training=snake_case__ )
_SCREAMING_SNAKE_CASE = model(snake_case__ , training=snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class _a (__a , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (TFBlipTextModel,) if is_tf_available() else ()
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = BlipTextModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=snake_case__ , hidden_size=37 )
def UpperCamelCase ( self ) -> int:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def UpperCamelCase ( self ) -> Tuple:
pass
def UpperCamelCase ( self ) -> int:
pass
@unittest.skip(reason="""Blip does not use inputs_embeds""" )
def UpperCamelCase ( self ) -> int:
pass
@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING""" )
def UpperCamelCase ( self ) -> int:
pass
@unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING""" )
def UpperCamelCase ( self ) -> Union[str, Any]:
pass
@slow
def UpperCamelCase ( self ) -> Optional[int]:
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE = TFBlipTextModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def UpperCamelCase ( self , A__=True ) -> Optional[Any]:
super().test_pt_tf_model_equivalence(allow_missing_keys=snake_case__ )
| 705 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase__ : List[str] = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"
UpperCamelCase__ : List[Any] = "\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results['matthews_correlation'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results['matthews_correlation'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results['matthews_correlation'], 2))\n -0.25\n"
UpperCamelCase__ : Any = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _a (datasets.Metric):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def UpperCamelCase ( self , A__ , A__ , A__=None ) -> List[str]:
return {
"matthews_correlation": float(matthews_corrcoef(A__ , A__ , sample_weight=A__ ) ),
}
| 0 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__ : Union[str, Any] = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class _a (__lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = '''lxmert'''
SCREAMING_SNAKE_CASE = {}
def __init__( self , A__=3_05_22 , A__=7_68 , A__=12 , A__=95_00 , A__=16_00 , A__=4_00 , A__=30_72 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=2 , A__=0.02 , A__=1E-12 , A__=9 , A__=5 , A__=5 , A__=20_48 , A__=4 , A__=6.67 , A__=True , A__=True , A__=True , A__=True , A__=True , A__=True , A__=True , **A__ , ) -> List[Any]:
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = type_vocab_size
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = num_qa_labels
_SCREAMING_SNAKE_CASE = num_object_labels
_SCREAMING_SNAKE_CASE = num_attr_labels
_SCREAMING_SNAKE_CASE = l_layers
_SCREAMING_SNAKE_CASE = x_layers
_SCREAMING_SNAKE_CASE = r_layers
_SCREAMING_SNAKE_CASE = visual_feat_dim
_SCREAMING_SNAKE_CASE = visual_pos_dim
_SCREAMING_SNAKE_CASE = visual_loss_normalizer
_SCREAMING_SNAKE_CASE = task_matched
_SCREAMING_SNAKE_CASE = task_mask_lm
_SCREAMING_SNAKE_CASE = task_obj_predict
_SCREAMING_SNAKE_CASE = task_qa
_SCREAMING_SNAKE_CASE = visual_obj_loss
_SCREAMING_SNAKE_CASE = visual_attr_loss
_SCREAMING_SNAKE_CASE = visual_feat_loss
_SCREAMING_SNAKE_CASE = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
super().__init__(**a_ )
| 706 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
print(F"Vertex\tShortest Distance from vertex {src}" )
for i, d in enumerate(SCREAMING_SNAKE_CASE_ ):
print(F"{i}\t\t{d}" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
for j in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
return True
return False
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list[float]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [float("""inf""" )] * vertex_count
_SCREAMING_SNAKE_CASE = 0.0
for _ in range(vertex_count - 1 ):
for j in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
_SCREAMING_SNAKE_CASE = distance[u] + w
_SCREAMING_SNAKE_CASE = check_negative_cycle(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if negative_cycle_exists:
raise Exception("""Negative cycle found""" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ : int = int(input("Enter number of vertices: ").strip())
UpperCamelCase__ : int = int(input("Enter number of edges: ").strip())
UpperCamelCase__ : list[dict[str, int]] = [{} for _ in range(E)]
for i in range(E):
print("Edge ", i + 1)
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Dict = (
int(x)
for x in input("Enter source, destination, weight: ").strip().split(" ")
)
UpperCamelCase__ : Optional[Any] = {"src": src, "dst": dest, "weight": weight}
UpperCamelCase__ : Optional[Any] = int(input("\nEnter shortest path source:").strip())
UpperCamelCase__ : Any = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 0 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import (
TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaubertConfig,
TFFlaubertForMultipleChoice,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForSequenceClassification,
TFFlaubertForTokenClassification,
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
)
class _a :
"""simple docstring"""
def __init__( self , A__ , ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = 13
_SCREAMING_SNAKE_CASE = 7
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 99
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 5_12
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 0.02
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = """last"""
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = 0
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] , dtype=tf.floataa )
_SCREAMING_SNAKE_CASE = None
if self.use_input_lengths:
_SCREAMING_SNAKE_CASE = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , 2 , dtype=tf.floataa )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE = FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , bos_token_id=self.bos_token_id , )
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> List[Any]:
_SCREAMING_SNAKE_CASE = TFFlaubertModel(config=A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = [input_ids, input_mask]
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> List[str]:
_SCREAMING_SNAKE_CASE = TFFlaubertWithLMHeadModel(A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> List[Any]:
_SCREAMING_SNAKE_CASE = TFFlaubertForQuestionAnsweringSimple(A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """lengths""": input_lengths}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> Dict:
_SCREAMING_SNAKE_CASE = TFFlaubertForSequenceClassification(A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """lengths""": input_lengths}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFFlaubertForTokenClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> int:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = TFFlaubertForMultipleChoice(config=A__ )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""token_type_ids""": token_type_ids,
"""langs""": token_type_ids,
"""lengths""": input_lengths,
}
return config, inputs_dict
@require_tf
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
TFFlaubertForSequenceClassification,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForTokenClassification,
TFFlaubertForMultipleChoice,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE = (
(TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': TFFlaubertModel,
'fill-mask': TFFlaubertWithLMHeadModel,
'question-answering': TFFlaubertForQuestionAnsweringSimple,
'text-classification': TFFlaubertForSequenceClassification,
'token-classification': TFFlaubertForTokenClassification,
'zero-shot': TFFlaubertForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> Tuple:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("""Fast""" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = TFFlaubertModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , emb_dim=37 )
def UpperCamelCase ( self ) -> Dict:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*A__ )
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*A__ )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_token_classification(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_multiple_choice(*A__ )
@slow
def UpperCamelCase ( self ) -> Union[str, Any]:
for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE = TFFlaubertModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
@require_tf
@require_sentencepiece
@require_tokenizers
class _a (unittest.TestCase):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased""" )
_SCREAMING_SNAKE_CASE = tf.convert_to_tensor(
[[0, 1_58, 7_35, 25_92, 14_24, 67_27, 82, 1]] , dtype=tf.intaa , ) # "J'aime flaubert !"
_SCREAMING_SNAKE_CASE = model(A__ )[0]
_SCREAMING_SNAKE_CASE = tf.TensorShape((1, 8, 5_12) )
self.assertEqual(output.shape , A__ )
# compare the actual values for a slice.
_SCREAMING_SNAKE_CASE = tf.convert_to_tensor(
[
[
[-1.876_8773, -1.56_6555, 0.2707_2418],
[-1.692_0038, -0.587_3505, 1.932_9599],
[-2.956_3985, -1.699_3835, 1.797_2052],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 707 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForMultipleChoice,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerModel,
)
from transformers.models.roformer.modeling_tf_roformer import (
TFRoFormerSelfAttention,
TFRoFormerSinusoidalPositionalEmbedding,
)
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=True , A__=True , A__=99 , A__=32 , A__=2 , A__=4 , A__=37 , A__="gelu" , A__=0.1 , A__=0.1 , A__=5_12 , A__=16 , A__=2 , A__=0.02 , A__=3 , A__=4 , A__=None , ) -> int:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = 13
_SCREAMING_SNAKE_CASE = 7
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = 99
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = 37
_SCREAMING_SNAKE_CASE = """gelu"""
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = 5_12
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 0.02
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
_SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
_SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=A__ , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModel(config=A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
_SCREAMING_SNAKE_CASE = [input_ids, input_mask]
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> str:
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = TFRoFormerForCausalLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )["""logits"""]
self.parent.assertListEqual(
list(prediction_scores.numpy().shape ) , [self.batch_size, self.seq_length, self.vocab_size] )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Dict:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForSequenceClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = TFRoFormerForMultipleChoice(config=A__ )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(A__ , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForTokenClassification(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = TFRoFormerForQuestionAnswering(config=A__ )
_SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) , (
_SCREAMING_SNAKE_CASE
) ,
) = config_and_inputs
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(
TFRoFormerModel,
TFRoFormerForCausalLM,
TFRoFormerForMaskedLM,
TFRoFormerForQuestionAnswering,
TFRoFormerForSequenceClassification,
TFRoFormerForTokenClassification,
TFRoFormerForMultipleChoice,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': TFRoFormerModel,
'fill-mask': TFRoFormerForMaskedLM,
'question-answering': TFRoFormerForQuestionAnswering,
'text-classification': TFRoFormerForSequenceClassification,
'text-generation': TFRoFormerForCausalLM,
'token-classification': TFRoFormerForTokenClassification,
'zero-shot': TFRoFormerForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> str:
if pipeline_test_casse_name == "TextGenerationPipelineTests":
return True
return False
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*A__ )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A__ )
@slow
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base""" )
self.assertIsNotNone(A__ )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base""" )
_SCREAMING_SNAKE_CASE = tf.constant([[0, 1, 2, 3, 4, 5]] )
_SCREAMING_SNAKE_CASE = model(A__ )[0]
# TODO Replace vocab size
_SCREAMING_SNAKE_CASE = 5_00_00
_SCREAMING_SNAKE_CASE = [1, 6, vocab_size]
self.assertEqual(output.shape , A__ )
print(output[:, :3, :3] )
# TODO Replace values below with what was printed above.
_SCREAMING_SNAKE_CASE = tf.constant(
[
[
[-0.1205_3341, -1.026_4901, 0.2922_1946],
[-1.513_3783, 0.19_7433, 0.1519_0607],
[-5.013_5403, -3.90_0256, -0.8403_8764],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , A__ , atol=1E-4 )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant([[4, 10]] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=6 , embedding_dim=6 )
_SCREAMING_SNAKE_CASE = emba(input_ids.shape )
_SCREAMING_SNAKE_CASE = tf.constant(
[[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]] )
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
[0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
] )
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12 , embedding_dim=5_12 )
emba([2, 16, 5_12] )
_SCREAMING_SNAKE_CASE = emba.weight[:3, :5]
tf.debugging.assert_near(A__ , A__ , atol=self.tolerance )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> int:
# 2,12,16,64
_SCREAMING_SNAKE_CASE = tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = -tf.reshape(tf.range(2 * 12 * 16 * 64 , dtype=tf.floataa ) , shape=(2, 12, 16, 64) ) / 1_00
_SCREAMING_SNAKE_CASE = TFRoFormerSinusoidalPositionalEmbedding(num_positions=32 , embedding_dim=64 )
_SCREAMING_SNAKE_CASE = embed_positions([2, 16, 7_68] )[None, None, :, :]
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
A__ , A__ , A__ )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
[-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
[-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
[-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
[0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
[3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
] )
_SCREAMING_SNAKE_CASE = tf.constant(
[
[0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
[0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
[1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
[2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
[-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
[-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
] )
tf.debugging.assert_near(query_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , A__ , atol=self.tolerance )
| 0 | 0 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = int(math.sqrt(_A ) ) # Size of every segment
_SCREAMING_SNAKE_CASE = [True] * (end + 1)
_SCREAMING_SNAKE_CASE = []
while start <= end:
if temp[start] is True:
in_prime.append(_A )
for i in range(start * start , end + 1 , _A ):
_SCREAMING_SNAKE_CASE = False
start += 1
prime += in_prime
_SCREAMING_SNAKE_CASE = end + 1
_SCREAMING_SNAKE_CASE = min(2 * end , _A )
while low <= n:
_SCREAMING_SNAKE_CASE = [True] * (high - low + 1)
for each in in_prime:
_SCREAMING_SNAKE_CASE = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_A , high + 1 , _A ):
_SCREAMING_SNAKE_CASE = False
for j in range(len(_A ) ):
if temp[j] is True:
prime.append(j + low )
_SCREAMING_SNAKE_CASE = high + 1
_SCREAMING_SNAKE_CASE = min(high + end , _A )
return prime
print(sieve(10**6))
| 708 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCamelCase__ : int = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = ["HerbertTokenizerFast"]
if TYPE_CHECKING:
from .tokenization_herbert import HerbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_herbert_fast import HerbertTokenizerFast
else:
import sys
UpperCamelCase__ : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 0 | 0 |
'''simple docstring'''
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : Tuple = '▁'
UpperCamelCase__ : Any = {'vocab_file': 'prophetnet.tokenizer'}
UpperCamelCase__ : List[str] = {
'vocab_file': {
'microsoft/xprophetnet-large-wiki100-cased': (
'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
),
}
}
UpperCamelCase__ : str = {
'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}
UpperCamelCase__ : Any = {
'microsoft/xprophetnet-large-wiki100-cased': 512,
}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = collections.OrderedDict()
with open(A_ , """r""" , encoding="""utf-8""" ) as reader:
_SCREAMING_SNAKE_CASE = reader.readlines()
for index, token in enumerate(A_ ):
_SCREAMING_SNAKE_CASE = token.rstrip("""\n""" )
_SCREAMING_SNAKE_CASE = index
return vocab
class _a (_A):
"""simple docstring"""
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self , A__ , A__="[SEP]" , A__="[SEP]" , A__="[SEP]" , A__="[UNK]" , A__="[PAD]" , A__="[CLS]" , A__="[MASK]" , A__ = None , **A__ , ) -> None:
_SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , sep_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , cls_token=UpperCamelCase__ , mask_token=UpperCamelCase__ , sp_model_kwargs=self.sp_model_kwargs , **UpperCamelCase__ , )
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"""You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
""" pip install sentencepiece""" )
raise
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(UpperCamelCase__ ) )
_SCREAMING_SNAKE_CASE = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# put special tokens and [unused] tokens into the vocab
_SCREAMING_SNAKE_CASE = {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}
for i in range(10 ):
_SCREAMING_SNAKE_CASE = F"[unused{i}]"
_SCREAMING_SNAKE_CASE = 5 + i
# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
_SCREAMING_SNAKE_CASE = 12
_SCREAMING_SNAKE_CASE = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
for k in self.fairseq_tokens_to_ids.keys():
self.unique_no_split_tokens.append(UpperCamelCase__ )
def __getstate__( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.__dict__.copy()
_SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self , A__ ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
"""You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
""" pip install sentencepiece""" )
raise
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase ( self , A__ , A__ = None , A__ = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase__ , token_ids_a=UpperCamelCase__ , already_has_special_tokens=UpperCamelCase__ )
if token_ids_a is None:
return ([0] * len(UpperCamelCase__ )) + [1]
return ([0] * len(UpperCamelCase__ )) + [1] + ([0] * len(UpperCamelCase__ )) + [1]
def UpperCamelCase ( self , A__ , A__ = None ) -> List[int]:
_SCREAMING_SNAKE_CASE = [self.sep_token_id]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0]
return len(token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def UpperCamelCase ( self ) -> str:
return len(self.sp_model ) + self.fairseq_offset
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(UpperCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCamelCase ( self , A__ ) -> str:
return self.sp_model.encode(UpperCamelCase__ , out_type=UpperCamelCase__ )
def UpperCamelCase ( self , A__ ) -> Dict:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_SCREAMING_SNAKE_CASE = self.sp_model.PieceToId(UpperCamelCase__ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def UpperCamelCase ( self , A__ ) -> List[Any]:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def UpperCamelCase ( self , A__ ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = """""".join(UpperCamelCase__ ).replace(UpperCamelCase__ , """ """ ).strip()
return out_string
def UpperCamelCase ( self , A__ , A__ = None ) -> Tuple[str]:
if not os.path.isdir(UpperCamelCase__ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
_SCREAMING_SNAKE_CASE = os.path.join(
UpperCamelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , UpperCamelCase__ )
elif not os.path.isfile(self.vocab_file ):
with open(UpperCamelCase__ , """wb""" ) as fi:
_SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(UpperCamelCase__ )
return (out_vocab_file,)
def UpperCamelCase ( self , A__ , A__ = None ) -> List[int]:
if token_ids_a is None:
return token_ids_a + [self.sep_token_id]
_SCREAMING_SNAKE_CASE = [self.sep_token_id]
return token_ids_a + sep + token_ids_a + sep
| 709 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = XCLIPTextConfig()
# derive patch size from model name
_SCREAMING_SNAKE_CASE = model_name.find("""patch""" )
_SCREAMING_SNAKE_CASE = int(model_name[start_idx + len("""patch""" ) : start_idx + len("""patch""" ) + 2] )
_SCREAMING_SNAKE_CASE = XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE_ , num_frames=SCREAMING_SNAKE_CASE_ )
if "large" in model_name:
_SCREAMING_SNAKE_CASE = 7_68
_SCREAMING_SNAKE_CASE = 30_72
_SCREAMING_SNAKE_CASE = 12
_SCREAMING_SNAKE_CASE = 10_24
_SCREAMING_SNAKE_CASE = 40_96
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 24
_SCREAMING_SNAKE_CASE = 7_68
_SCREAMING_SNAKE_CASE = 30_72
if model_name == "xclip-large-patch14-16-frames":
_SCREAMING_SNAKE_CASE = 3_36
_SCREAMING_SNAKE_CASE = XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if "large" in model_name:
_SCREAMING_SNAKE_CASE = 7_68
return config
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
# text encoder
if name == "token_embedding.weight":
_SCREAMING_SNAKE_CASE = name.replace("""token_embedding.weight""" , """text_model.embeddings.token_embedding.weight""" )
if name == "positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "ln_1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
_SCREAMING_SNAKE_CASE = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""c_proj""" , """fc2""" )
if name.startswith("""transformer.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""transformer.resblocks""" , """text_model.encoder.layers""" )
if "attn.out_proj" in name and "message" not in name:
_SCREAMING_SNAKE_CASE = name.replace("""attn.out_proj""" , """self_attn.out_proj""" )
if "ln_final" in name:
_SCREAMING_SNAKE_CASE = name.replace("""ln_final""" , """text_model.final_layer_norm""" )
# visual encoder
if name == "visual.class_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""visual.class_embedding""" , """vision_model.embeddings.class_embedding""" )
if name == "visual.positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""visual.positional_embedding""" , """vision_model.embeddings.position_embedding.weight""" )
if name.startswith("""visual.transformer.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""visual.transformer.resblocks""" , """vision_model.encoder.layers""" )
if "visual.conv1" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.conv1""" , """vision_model.embeddings.patch_embedding""" )
if "visual.ln_pre" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.ln_pre""" , """vision_model.pre_layernorm""" )
if "visual.ln_post" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.ln_post""" , """vision_model.post_layernorm""" )
if "visual.proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""visual.proj""" , """visual_projection.weight""" )
if "text_projection" in name:
_SCREAMING_SNAKE_CASE = name.replace("""text_projection""" , """text_projection.weight""" )
# things on top
if "prompts_visual_proj" in name:
_SCREAMING_SNAKE_CASE = name.replace("""prompts_visual_proj""" , """prompts_visual_projection""" )
if "prompts_visual_ln" in name:
_SCREAMING_SNAKE_CASE = name.replace("""prompts_visual_ln""" , """prompts_visual_layernorm""" )
# mit
if name == "mit.positional_embedding":
_SCREAMING_SNAKE_CASE = name.replace("""positional""" , """position""" )
if name.startswith("""mit.resblocks""" ):
_SCREAMING_SNAKE_CASE = name.replace("""mit.resblocks""" , """mit.encoder.layers""" )
# prompts generator
if name.startswith("""prompts_generator.norm""" ):
_SCREAMING_SNAKE_CASE = name.replace("""prompts_generator.norm""" , """prompts_generator.layernorm""" )
return name
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_SCREAMING_SNAKE_CASE = orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )
if "attn.in_proj" in key:
_SCREAMING_SNAKE_CASE = key.split(""".""" )
if key.startswith("""visual""" ):
_SCREAMING_SNAKE_CASE = key_split[3]
_SCREAMING_SNAKE_CASE = config.vision_config.hidden_size
if "message_attn" in key:
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[
:dim, :
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[
-dim:, :
]
else:
_SCREAMING_SNAKE_CASE = val[
:dim
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[
:dim, :
]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[
-dim:, :
]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[-dim:]
elif key.startswith("""mit""" ):
_SCREAMING_SNAKE_CASE = key_split[2]
_SCREAMING_SNAKE_CASE = config.vision_config.mit_hidden_size
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[:dim, :]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
_SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[dim : dim * 2]
_SCREAMING_SNAKE_CASE = val[-dim:]
else:
_SCREAMING_SNAKE_CASE = key_split[2]
_SCREAMING_SNAKE_CASE = config.text_config.hidden_size
if "weight" in key:
_SCREAMING_SNAKE_CASE = val[:dim, :]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
_SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
_SCREAMING_SNAKE_CASE = val[:dim]
_SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
_SCREAMING_SNAKE_CASE = val[-dim:]
else:
_SCREAMING_SNAKE_CASE = rename_key(SCREAMING_SNAKE_CASE_ )
if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
_SCREAMING_SNAKE_CASE = val.T
_SCREAMING_SNAKE_CASE = val
return orig_state_dict
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
if num_frames == 8:
_SCREAMING_SNAKE_CASE = """eating_spaghetti_8_frames.npy"""
elif num_frames == 16:
_SCREAMING_SNAKE_CASE = """eating_spaghetti.npy"""
elif num_frames == 32:
_SCREAMING_SNAKE_CASE = """eating_spaghetti_32_frames.npy"""
_SCREAMING_SNAKE_CASE = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename=SCREAMING_SNAKE_CASE_ , repo_type="""dataset""" , )
_SCREAMING_SNAKE_CASE = np.load(SCREAMING_SNAKE_CASE_ )
return list(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , SCREAMING_SNAKE_CASE_=False ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {
# fully supervised kinetics-400 checkpoints
"""xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
"""xclip-base-patch32-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
),
"""xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
"""xclip-base-patch16-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
),
"""xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&export=download&confirm=t&uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
"""xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&export=download&confirm=t&uuid=538fa810-e671-4050-b385-9a623f89804f""",
# fully supervised kinetics-600 checkpoints
"""xclip-base-patch16-kinetics-600""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
),
"""xclip-base-patch16-kinetics-600-16-frames""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
),
"""xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&export=download&confirm=t&uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
# few shot
"""xclip-base-patch16-hmdb-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
),
"""xclip-base-patch16-hmdb-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
),
"""xclip-base-patch16-hmdb-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
),
"""xclip-base-patch16-hmdb-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
),
"""xclip-base-patch16-ucf-2-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
),
"""xclip-base-patch16-ucf-4-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
),
"""xclip-base-patch16-ucf-8-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
),
"""xclip-base-patch16-ucf-16-shot""": (
"""https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
),
# zero shot
"""xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
}
_SCREAMING_SNAKE_CASE = model_to_url[model_name]
_SCREAMING_SNAKE_CASE = 8
if "16-frames" in model_name:
_SCREAMING_SNAKE_CASE = 16
elif "shot" in model_name:
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = get_xclip_config(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = XCLIPModel(SCREAMING_SNAKE_CASE_ )
model.eval()
if "drive" in checkpoint_url:
_SCREAMING_SNAKE_CASE = """pytorch_model.bin"""
gdown.cached_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , quiet=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )["""model"""]
else:
_SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ )["""model"""]
_SCREAMING_SNAKE_CASE = convert_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = XCLIPModel(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
model.eval()
_SCREAMING_SNAKE_CASE = 3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
_SCREAMING_SNAKE_CASE = VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
_SCREAMING_SNAKE_CASE = CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32""" )
_SCREAMING_SNAKE_CASE = XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE_ , tokenizer=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = prepare_video(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = processor(
text=["""playing sports""", """eating spaghetti""", """go shopping"""] , videos=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , padding=SCREAMING_SNAKE_CASE_ )
print("""Shape of pixel values:""" , inputs.pixel_values.shape )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
# Verify outputs
_SCREAMING_SNAKE_CASE = outputs.logits_per_video
_SCREAMING_SNAKE_CASE = logits_per_video.softmax(dim=1 )
print("""Probs:""" , SCREAMING_SNAKE_CASE_ )
# kinetics-400
if model_name == "xclip-base-patch32":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0019, 0.9951, 0.0030]] )
elif model_name == "xclip-base-patch32-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]] )
elif model_name == "xclip-base-patch16":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0083, 0.9681, 0.0236]] )
elif model_name == "xclip-base-patch16-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]] )
elif model_name == "xclip-large-patch14":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0062, 0.9864, 0.0075]] )
elif model_name == "xclip-large-patch14-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]] )
# kinetics-600
elif model_name == "xclip-base-patch16-kinetics-600":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0555, 0.8914, 0.0531]] )
elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]] )
elif model_name == "xclip-large-patch14-kinetics-600":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0036, 0.9920, 0.0045]] )
# few shot
elif model_name == "xclip-base-patch16-hmdb-2-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]] )
elif model_name == "xclip-base-patch16-hmdb-4-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]] )
elif model_name == "xclip-base-patch16-hmdb-8-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]] )
elif model_name == "xclip-base-patch16-hmdb-16-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]] )
elif model_name == "xclip-base-patch16-ucf-2-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-4-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]] )
elif model_name == "xclip-base-patch16-ucf-8-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[0.0027, 0.9904, 0.0070]] )
elif model_name == "xclip-base-patch16-ucf-16-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]] )
# zero shot
elif model_name == "xclip-base-patch16-zero-shot":
_SCREAMING_SNAKE_CASE = torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]] )
else:
raise ValueError(F"Model name {model_name} not supported" )
assert torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , atol=1e-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(F"Saving model {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
print("""Pushing model, processor and slow tokenizer files to the hub...""" )
model.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
processor.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
slow_tokenizer.push_to_hub(SCREAMING_SNAKE_CASE_ , organization="""nielsr""" )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="xclip-base-patch32",
type=str,
help="Name of the model.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
UpperCamelCase__ : str = parser.parse_args()
convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 0 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
return number | (1 << position)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
return number & ~(1 << position)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
return number ^ (1 << position)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> bool:
"""simple docstring"""
return ((number >> position) & 1) == 1
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
return int((number & (1 << position)) != 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 710 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = params
_SCREAMING_SNAKE_CASE = np.array(A__ )
_SCREAMING_SNAKE_CASE = np.array([len(A__ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self , A__ ) -> Dict:
return (self.token_ids[index], self.lengths[index])
def __len__( self ) -> Tuple:
return len(self.lengths )
def UpperCamelCase ( self ) -> Dict:
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.params.max_model_input_size
_SCREAMING_SNAKE_CASE = self.lengths > max_len
logger.info(F"Splitting {sum(A__ )} too long sequences." )
def divide_chunks(A__ , A__ ):
return [l[i : i + n] for i in range(0 , len(A__ ) , A__ )]
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
if self.params.mlm:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
_SCREAMING_SNAKE_CASE = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
_SCREAMING_SNAKE_CASE = np.insert(A__ , 0 , A__ )
if sub_s[-1] != sep_id:
_SCREAMING_SNAKE_CASE = np.insert(A__ , len(A__ ) , A__ )
assert len(A__ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(A__ )
new_tok_ids.extend(A__ )
new_lengths.extend([len(A__ ) for l in sub_seqs] )
_SCREAMING_SNAKE_CASE = np.array(A__ )
_SCREAMING_SNAKE_CASE = np.array(A__ )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = len(self )
_SCREAMING_SNAKE_CASE = self.lengths > 11
_SCREAMING_SNAKE_CASE = self.token_ids[indices]
_SCREAMING_SNAKE_CASE = self.lengths[indices]
_SCREAMING_SNAKE_CASE = len(self )
logger.info(F"Remove {init_size - new_size} too short (<=11 tokens) sequences." )
def UpperCamelCase ( self ) -> int:
if "unk_token" not in self.params.special_tok_ids:
return
else:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""unk_token"""]
_SCREAMING_SNAKE_CASE = len(self )
_SCREAMING_SNAKE_CASE = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
_SCREAMING_SNAKE_CASE = (unk_occs / self.lengths) < 0.5
_SCREAMING_SNAKE_CASE = self.token_ids[indices]
_SCREAMING_SNAKE_CASE = self.lengths[indices]
_SCREAMING_SNAKE_CASE = len(self )
logger.info(F"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)." )
def UpperCamelCase ( self ) -> Optional[Any]:
if not self.params.is_master:
return
logger.info(F"{len(self )} sequences" )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def UpperCamelCase ( self , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = [t[0] for t in batch]
_SCREAMING_SNAKE_CASE = [t[1] for t in batch]
assert len(A__ ) == len(A__ )
# Max for paddings
_SCREAMING_SNAKE_CASE = max(A__ )
# Pad token ids
if self.params.mlm:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""pad_token"""]
else:
_SCREAMING_SNAKE_CASE = self.params.special_tok_ids["""unk_token"""]
_SCREAMING_SNAKE_CASE = [list(t.astype(A__ ) ) + [pad_idx] * (max_seq_len_ - len(A__ )) for t in token_ids]
assert len(tk_ ) == len(A__ )
assert all(len(A__ ) == max_seq_len_ for t in tk_ )
_SCREAMING_SNAKE_CASE = torch.tensor(tk_ ) # (bs, max_seq_len_)
_SCREAMING_SNAKE_CASE = torch.tensor(A__ ) # (bs)
return tk_t, lg_t
| 0 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : Dict = logging.get_logger(__name__)
UpperCamelCase__ : Optional[Any] = {"vocab_file": "sentencepiece.model"}
UpperCamelCase__ : Any = {
"vocab_file": {
"google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
},
}
UpperCamelCase__ : List[str] = {
"google/rembert": 256,
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , A__ , A__=False , A__=True , A__=True , A__="[CLS]" , A__="[SEP]" , A__="[UNK]" , A__="[SEP]" , A__="[PAD]" , A__="[CLS]" , A__="[MASK]" , **A__ , ) -> Optional[int]:
super().__init__(
do_lower_case=__a , remove_space=__a , keep_accents=__a , bos_token=__a , eos_token=__a , unk_token=__a , sep_token=__a , pad_token=__a , cls_token=__a , mask_token=__a , **__a , )
_SCREAMING_SNAKE_CASE = do_lower_case
_SCREAMING_SNAKE_CASE = remove_space
_SCREAMING_SNAKE_CASE = keep_accents
_SCREAMING_SNAKE_CASE = vocab_file
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor()
self.sp_model.Load(__a )
@property
def UpperCamelCase ( self ) -> Dict:
return len(self.sp_model )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(__a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.__dict__.copy()
_SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = d
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file )
def UpperCamelCase ( self , A__ , A__=False ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.sp_model.EncodeAsPieces(__a )
return pieces
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
return self.sp_model.PieceToId(__a )
def UpperCamelCase ( self , A__ ) -> Dict:
return self.sp_model.IdToPiece(__a )
def UpperCamelCase ( self , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.sp_model.decode_pieces(__a )
return out_string
def UpperCamelCase ( self , A__ , A__ = None ) -> List[int]:
_SCREAMING_SNAKE_CASE = [self.sep_token_id]
_SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCamelCase ( self , A__ , A__ = None , A__ = False ) -> List[int]:
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"""You should not supply a second sequence if the provided sequence of """
"""ids is already formatted with special tokens for the model.""" )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(__a )) + [1] + ([0] * len(__a )) + [1]
return [1] + ([0] * len(__a )) + [1]
def UpperCamelCase ( self , A__ , A__ = None ) -> List[int]:
_SCREAMING_SNAKE_CASE = [self.sep_token_id]
_SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase ( self , A__ , A__ = None ) -> Tuple[str]:
if not os.path.isdir(__a ):
logger.error("""Vocabulary path ({}) should be a directory""".format(__a ) )
return
_SCREAMING_SNAKE_CASE = os.path.join(
__a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__a ):
copyfile(self.vocab_file , __a )
return (out_vocab_file,)
| 711 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : Any = "▁"
UpperCamelCase__ : Any = {"vocab_file": "spiece.model"}
UpperCamelCase__ : int = {
"vocab_file": {
"google/reformer-crime-and-punishment": (
"https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
)
}
}
UpperCamelCase__ : Optional[int] = {
"google/reformer-crime-and-punishment": 524_288,
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = VOCAB_FILES_NAMES
SCREAMING_SNAKE_CASE = PRETRAINED_VOCAB_FILES_MAP
SCREAMING_SNAKE_CASE = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
SCREAMING_SNAKE_CASE = ['input_ids', 'attention_mask']
def __init__( self , A__ , A__="</s>" , A__="<unk>" , A__=[] , A__ = None , **A__ , ) -> None:
_SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=A__ , unk_token=A__ , additional_special_tokens=A__ , sp_model_kwargs=self.sp_model_kwargs , **A__ , )
_SCREAMING_SNAKE_CASE = vocab_file
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A__ )
@property
def UpperCamelCase ( self ) -> Any:
return self.sp_model.get_piece_size()
def UpperCamelCase ( self ) -> Dict[str, int]:
_SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(A__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> int:
_SCREAMING_SNAKE_CASE = self.__dict__.copy()
_SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase ( self , A__ ) -> List[str]:
return self.sp_model.encode(A__ , out_type=A__ )
def UpperCamelCase ( self , A__ ) -> Union[str, Any]:
return self.sp_model.piece_to_id(A__ )
def UpperCamelCase ( self , A__ ) -> List[Any]:
if index < self.sp_model.get_piece_size():
_SCREAMING_SNAKE_CASE = self.sp_model.IdToPiece(A__ )
return token
def UpperCamelCase ( self , A__ ) -> str:
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A__ ) + token
_SCREAMING_SNAKE_CASE = []
else:
current_sub_tokens.append(A__ )
out_string += self.sp_model.decode(A__ )
return out_string.strip()
def UpperCamelCase ( self , A__ , A__ = None ) -> Tuple[str]:
if not os.path.isdir(A__ ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
_SCREAMING_SNAKE_CASE = os.path.join(
A__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A__ )
elif not os.path.isfile(self.vocab_file ):
with open(A__ , """wb""" ) as fi:
_SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(A__ )
return (out_vocab_file,)
| 0 | 0 |
'''simple docstring'''
import math
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
if (
not isinstance(lowerCamelCase_ , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("""power_factor must be a valid float value between -1 and 1.""" )
return apparent_power * power_factor
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
if (
not isinstance(lowerCamelCase_ , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("""power_factor must be a valid float value between -1 and 1.""" )
return apparent_power * math.sqrt(1 - power_factor**2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 712 |
'''simple docstring'''
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class _a (_lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = MobileBertTokenizer
SCREAMING_SNAKE_CASE = MobileBertTokenizerFast
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = filter_non_english
SCREAMING_SNAKE_CASE = 'google/mobilebert-uncased'
def UpperCamelCase ( self ) -> Any:
super().setUp()
_SCREAMING_SNAKE_CASE = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
_SCREAMING_SNAKE_CASE = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def UpperCamelCase ( self , A__ ) -> List[str]:
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = """unwanted, running"""
return input_text, output_text
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.tokenizer_class(self.vocab_file )
_SCREAMING_SNAKE_CASE = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(A__ , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(A__ ) , [9, 6, 7, 12, 10, 11] )
def UpperCamelCase ( self ) -> Optional[int]:
if not self.test_rust_tokenizer:
return
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
# With lower casing
_SCREAMING_SNAKE_CASE = self.get_tokenizer(do_lower_case=A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer(do_lower_case=A__ )
_SCREAMING_SNAKE_CASE = """UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.tokenize(A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ )
_SCREAMING_SNAKE_CASE = rust_tokenizer.encode(A__ )
self.assertListEqual(A__ , A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz""" ) , ["""ah""", """\u535A""", """\u63A8""", """zz"""] )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hällo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""h\u00E9llo"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""hallo""", """!""", """how""", """are""", """you""", """?"""] )
self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""" ) , ["""hello"""] )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , strip_accents=A__ )
self.assertListEqual(
tokenizer.tokenize(""" \tHäLLo!how \n Are yoU? """ ) , ["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = BasicTokenizer(do_lower_case=A__ , never_split=["""[UNK]"""] )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo!how \n Are yoU? [UNK]""" ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""] )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
_SCREAMING_SNAKE_CASE = {}
for i, token in enumerate(A__ ):
_SCREAMING_SNAKE_CASE = i
_SCREAMING_SNAKE_CASE = WordpieceTokenizer(vocab=A__ , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""unwanted running""" ) , ["""un""", """##want""", """##ed""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.tokenize("""unwantedX running""" ) , ["""[UNK]""", """runn""", """##ing"""] )
def UpperCamelCase ( self ) -> str:
self.assertTrue(_is_whitespace(""" """ ) )
self.assertTrue(_is_whitespace("""\t""" ) )
self.assertTrue(_is_whitespace("""\r""" ) )
self.assertTrue(_is_whitespace("""\n""" ) )
self.assertTrue(_is_whitespace("""\u00A0""" ) )
self.assertFalse(_is_whitespace("""A""" ) )
self.assertFalse(_is_whitespace("""-""" ) )
def UpperCamelCase ( self ) -> Union[str, Any]:
self.assertTrue(_is_control("""\u0005""" ) )
self.assertFalse(_is_control("""A""" ) )
self.assertFalse(_is_control(""" """ ) )
self.assertFalse(_is_control("""\t""" ) )
self.assertFalse(_is_control("""\r""" ) )
def UpperCamelCase ( self ) -> Dict:
self.assertTrue(_is_punctuation("""-""" ) )
self.assertTrue(_is_punctuation("""$""" ) )
self.assertTrue(_is_punctuation("""`""" ) )
self.assertTrue(_is_punctuation(""".""" ) )
self.assertFalse(_is_punctuation("""A""" ) )
self.assertFalse(_is_punctuation(""" """ ) )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
self.assertListEqual(
[rust_tokenizer.tokenize(A__ ) for t in ["""Test""", """\xad""", """test"""]] , [["""[UNK]"""], [], ["""[UNK]"""]] )
@slow
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained("""google/mobilebert-uncased""" )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""sequence builders""" , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode("""multi-sequence build""" , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(A__ , A__ )
assert encoded_sentence == [1_01] + text + [1_02]
assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
def UpperCamelCase ( self ) -> List[str]:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = F"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
_SCREAMING_SNAKE_CASE = tokenizer_r.encode_plus(
A__ , return_attention_mask=A__ , return_token_type_ids=A__ , return_offsets_mapping=A__ , add_special_tokens=A__ , )
_SCREAMING_SNAKE_CASE = tokenizer_r.do_lower_case if hasattr(A__ , """do_lower_case""" ) else False
_SCREAMING_SNAKE_CASE = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), """A"""),
((1, 2), ""","""),
((3, 5), """na"""),
((5, 6), """##ï"""),
((6, 8), """##ve"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """Allen"""),
((21, 23), """##NL"""),
((23, 24), """##P"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), """a"""),
((1, 2), ""","""),
((3, 8), """naive"""),
((9, 15), tokenizer_r.mask_token),
((16, 21), """allen"""),
((21, 23), """##nl"""),
((23, 24), """##p"""),
((25, 33), """sentence"""),
((33, 34), """."""),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["""offset_mapping"""] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = ["""的""", """人""", """有"""]
_SCREAMING_SNAKE_CASE = """""".join(A__ )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(A__ )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(A__ , A__ )
self.assertListEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(A__ , **A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer_r.convert_ids_to_tokens(A__ )
_SCREAMING_SNAKE_CASE = tokenizer_p.convert_ids_to_tokens(A__ )
# it is expected that only the first Chinese character is not preceded by "##".
_SCREAMING_SNAKE_CASE = [
F"##{token}" if idx != 0 else token for idx, token in enumerate(A__ )
]
self.assertListEqual(A__ , A__ )
self.assertListEqual(A__ , A__ )
| 0 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class _a (__lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ShapEPipeline
SCREAMING_SNAKE_CASE = ['prompt']
SCREAMING_SNAKE_CASE = ['prompt']
SCREAMING_SNAKE_CASE = [
'num_images_per_prompt',
'num_inference_steps',
'generator',
'latents',
'guidance_scale',
'frame_size',
'output_type',
'return_dict',
]
SCREAMING_SNAKE_CASE = False
@property
def UpperCamelCase ( self ) -> str:
return 32
@property
def UpperCamelCase ( self ) -> str:
return 32
@property
def UpperCamelCase ( self ) -> Union[str, Any]:
return self.time_input_dim * 4
@property
def UpperCamelCase ( self ) -> Dict:
return 8
@property
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
return tokenizer
@property
def UpperCamelCase ( self ) -> Optional[Any]:
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModelWithProjection(UpperCamelCase_ )
@property
def UpperCamelCase ( self ) -> List[Any]:
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = {
"""num_attention_heads""": 2,
"""attention_head_dim""": 16,
"""embedding_dim""": self.time_input_dim,
"""num_embeddings""": 32,
"""embedding_proj_dim""": self.text_embedder_hidden_size,
"""time_embed_dim""": self.time_embed_dim,
"""num_layers""": 1,
"""clip_embed_dim""": self.time_input_dim * 2,
"""additional_embeddings""": 0,
"""time_embed_act_fn""": """gelu""",
"""norm_in_type""": """layer""",
"""encoder_hid_proj_type""": None,
"""added_emb_type""": None,
}
_SCREAMING_SNAKE_CASE = PriorTransformer(**UpperCamelCase_ )
return model
@property
def UpperCamelCase ( self ) -> Optional[int]:
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = {
"""param_shapes""": (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
"""d_latent""": self.time_input_dim,
"""d_hidden""": self.renderer_dim,
"""n_output""": 12,
"""background""": (
0.1,
0.1,
0.1,
),
}
_SCREAMING_SNAKE_CASE = ShapERenderer(**UpperCamelCase_ )
return model
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.dummy_prior
_SCREAMING_SNAKE_CASE = self.dummy_text_encoder
_SCREAMING_SNAKE_CASE = self.dummy_tokenizer
_SCREAMING_SNAKE_CASE = self.dummy_renderer
_SCREAMING_SNAKE_CASE = HeunDiscreteScheduler(
beta_schedule="""exp""" , num_train_timesteps=10_24 , prediction_type="""sample""" , use_karras_sigmas=UpperCamelCase_ , clip_sample=UpperCamelCase_ , clip_sample_range=1.0 , )
_SCREAMING_SNAKE_CASE = {
"""prior""": prior,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""renderer""": renderer,
"""scheduler""": scheduler,
}
return components
def UpperCamelCase ( self , A__ , A__=0 ) -> Dict:
if str(UpperCamelCase_ ).startswith("""mps""" ):
_SCREAMING_SNAKE_CASE = torch.manual_seed(UpperCamelCase_ )
else:
_SCREAMING_SNAKE_CASE = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
_SCREAMING_SNAKE_CASE = {
"""prompt""": """horse""",
"""generator""": generator,
"""num_inference_steps""": 1,
"""frame_size""": 32,
"""output_type""": """np""",
}
return inputs
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = """cpu"""
_SCREAMING_SNAKE_CASE = self.get_dummy_components()
_SCREAMING_SNAKE_CASE = self.pipeline_class(**UpperCamelCase_ )
_SCREAMING_SNAKE_CASE = pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
_SCREAMING_SNAKE_CASE = pipe(**self.get_dummy_inputs(UpperCamelCase_ ) )
_SCREAMING_SNAKE_CASE = output.images[0]
_SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
_SCREAMING_SNAKE_CASE = np.array(
[
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
0.0003_9216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def UpperCamelCase ( self ) -> Any:
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = torch_device == """cpu"""
_SCREAMING_SNAKE_CASE = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=UpperCamelCase_ , relax_max_difference=UpperCamelCase_ , )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.get_dummy_components()
_SCREAMING_SNAKE_CASE = self.pipeline_class(**UpperCamelCase_ )
_SCREAMING_SNAKE_CASE = pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = self.get_dummy_inputs(UpperCamelCase_ )
for key in inputs.keys():
if key in self.batch_params:
_SCREAMING_SNAKE_CASE = batch_size * [inputs[key]]
_SCREAMING_SNAKE_CASE = pipe(**UpperCamelCase_ , num_images_per_prompt=UpperCamelCase_ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/shap_e/test_shap_e_np_out.npy""" )
_SCREAMING_SNAKE_CASE = ShapEPipeline.from_pretrained("""openai/shap-e""" )
_SCREAMING_SNAKE_CASE = pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
_SCREAMING_SNAKE_CASE = torch.Generator(device=UpperCamelCase_ ).manual_seed(0 )
_SCREAMING_SNAKE_CASE = pipe(
"""a shark""" , generator=UpperCamelCase_ , guidance_scale=15.0 , num_inference_steps=64 , frame_size=64 , output_type="""np""" , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(UpperCamelCase_ , UpperCamelCase_ )
| 713 |
'''simple docstring'''
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
UpperCamelCase__ : Tuple = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , *A__ , A__=None , A__=None , A__=None , **A__ ) -> Optional[int]:
super().__init__(*A__ , **A__ )
_SCREAMING_SNAKE_CASE = eval_examples
_SCREAMING_SNAKE_CASE = post_process_function
_SCREAMING_SNAKE_CASE = quant_trainer_args
_SCREAMING_SNAKE_CASE = 1_28 # default number of calibration samples
def UpperCamelCase ( self , A__=None ) -> Union[str, Any]:
if calib_dataset is None and self.calib_dataset is None:
raise ValueError("""Trainer: calibration requires an calib_dataset.""" )
_SCREAMING_SNAKE_CASE = calib_dataset if calib_dataset is not None else self.calib_dataset
_SCREAMING_SNAKE_CASE = self._remove_unused_columns(A__ , description="""Calibration""" )
return DataLoader(
A__ , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=A__ , )
def UpperCamelCase ( self , A__=None ) -> str:
_SCREAMING_SNAKE_CASE = self.train_dataset if calib_dataset is None else calib_dataset
_SCREAMING_SNAKE_CASE = self.get_calib_dataloader(A__ )
_SCREAMING_SNAKE_CASE = self.model
quant_trainer.configure_model(A__ , self.quant_trainer_args , calib=A__ )
model.eval()
quant_trainer.enable_calibration(A__ )
logger.info("""***** Running calibration *****""" )
logger.info(F" Num examples = {self.calib_num}" )
logger.info(F" Batch size = {calib_dataloader.batch_size}" )
for step, inputs in enumerate(A__ ):
# Prediction step
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.prediction_step(A__ , A__ , prediction_loss_only=A__ )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(A__ , self.quant_trainer_args )
_SCREAMING_SNAKE_CASE = model
def UpperCamelCase ( self , A__=None , A__=None , A__=None , A__ = "eval" ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.eval_dataset if eval_dataset is None else eval_dataset
_SCREAMING_SNAKE_CASE = self.get_eval_dataloader(A__ )
_SCREAMING_SNAKE_CASE = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_SCREAMING_SNAKE_CASE = self.compute_metrics
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_SCREAMING_SNAKE_CASE = self.post_process_function(A__ , A__ , output.predictions )
_SCREAMING_SNAKE_CASE = self.compute_metrics(A__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
_SCREAMING_SNAKE_CASE = metrics.pop(A__ )
self.log(A__ )
else:
_SCREAMING_SNAKE_CASE = {}
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
_SCREAMING_SNAKE_CASE = self.callback_handler.on_evaluate(self.args , self.state , self.control , A__ )
return metrics
def UpperCamelCase ( self , A__ , A__ , A__=None , A__ = "test" ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.get_test_dataloader(A__ )
# Temporarily disable metric computation, we will do it in the loop here.
_SCREAMING_SNAKE_CASE = self.compute_metrics
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_SCREAMING_SNAKE_CASE = self.post_process_function(A__ , A__ , output.predictions , """predict""" )
_SCREAMING_SNAKE_CASE = self.compute_metrics(A__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"{metric_key_prefix}_" ):
_SCREAMING_SNAKE_CASE = metrics.pop(A__ )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=A__ )
def UpperCamelCase ( self , A__="./" ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.eval_dataset
_SCREAMING_SNAKE_CASE = self.get_eval_dataloader(A__ )
_SCREAMING_SNAKE_CASE = next(iter(A__ ) )
# saving device - to make it consistent
_SCREAMING_SNAKE_CASE = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
# convert to tuple
_SCREAMING_SNAKE_CASE = tuple(v.to(A__ ) for k, v in batch.items() )
logger.info("""Converting model to be onnx compatible""" )
from pytorch_quantization.nn import TensorQuantizer
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = self.model.to(A__ )
model.eval()
model.float()
_SCREAMING_SNAKE_CASE = model.module if hasattr(A__ , """module""" ) else model
quant_trainer.configure_model(A__ , self.quant_trainer_args )
_SCREAMING_SNAKE_CASE = os.path.join(A__ , """model.onnx""" )
logger.info(F"exporting model to {output_model_file}" )
_SCREAMING_SNAKE_CASE = {0: """batch_size""", 1: """seq_len"""}
torch.onnx.export(
A__ , A__ , A__ , export_params=A__ , opset_version=13 , do_constant_folding=A__ , input_names=["""input_ids""", """attention_mask""", """token_type_ids"""] , output_names=["""output_start_logits""", """output_end_logits"""] , dynamic_axes={
"""input_ids""": axes,
"""attention_mask""": axes,
"""token_type_ids""": axes,
"""output_start_logits""": axes,
"""output_end_logits""": axes,
} , verbose=A__ , )
logger.info("""onnx export finished""" )
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