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import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
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 transformers import (
MODEL_MAPPING,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerModel,
)
from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class _a (_UpperCamelCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(A__ , """hidden_sizes""" ) )
self.parent.assertTrue(hasattr(A__ , """num_attention_heads""" ) )
self.parent.assertTrue(hasattr(A__ , """num_encoder_blocks""" ) )
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=64 , A__=3 , A__=4 , A__=[2, 2, 2, 2] , A__=[8, 4, 2, 1] , A__=[16, 32, 64, 1_28] , A__=[1, 4, 8, 16] , A__=[1, 2, 4, 8] , A__=True , A__=True , A__="gelu" , A__=0.1 , A__=0.1 , A__=0.02 , 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 = num_encoder_blocks
_SCREAMING_SNAKE_CASE = sr_ratios
_SCREAMING_SNAKE_CASE = depths
_SCREAMING_SNAKE_CASE = hidden_sizes
_SCREAMING_SNAKE_CASE = downsampling_rates
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_labels
_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 = num_labels
_SCREAMING_SNAKE_CASE = scope
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.image_size, self.image_size] , self.num_labels )
_SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self ) -> Any:
return SegformerConfig(
image_size=self.image_size , num_channels=self.num_channels , num_encoder_blocks=self.num_encoder_blocks , depths=self.depths , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = SegformerModel(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Dict:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = SegformerForSemanticSegmentation(A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
_SCREAMING_SNAKE_CASE = model(A__ , labels=A__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
self.parent.assertGreater(result.loss , 0.0 )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = SegformerForSemanticSegmentation(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = torch.randint(0 , 1 , (self.batch_size, self.image_size, self.image_size) ).to(A__ )
_SCREAMING_SNAKE_CASE = model(A__ , labels=A__ )
self.parent.assertGreater(result.loss , 0.0 )
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE = config_and_inputs
_SCREAMING_SNAKE_CASE = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _a (_UpperCamelCase , _UpperCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(
SegformerModel,
SegformerForSemanticSegmentation,
SegformerForImageClassification,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': SegformerModel,
'image-classification': SegformerForImageClassification,
'image-segmentation': SegformerForSemanticSegmentation,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = SegformerModelTester(self )
_SCREAMING_SNAKE_CASE = SegformerConfigTester(self , config_class=A__ )
def UpperCamelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> str:
_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_binary_image_segmentation(*A__ )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*A__ )
@unittest.skip("""SegFormer does not use inputs_embeds""" )
def UpperCamelCase ( self ) -> List[str]:
pass
@unittest.skip("""SegFormer does not have get_input_embeddings method and get_output_embeddings methods""" )
def UpperCamelCase ( self ) -> Optional[Any]:
pass
def UpperCamelCase ( self ) -> Union[str, Any]:
_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 ) -> Tuple:
_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 = 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
_SCREAMING_SNAKE_CASE = sum(self.model_tester.depths )
self.assertEqual(len(A__ ) , A__ )
# 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__ ) , A__ )
# verify the first attentions (first block, first layer)
_SCREAMING_SNAKE_CASE = (self.model_tester.image_size // 4) ** 2
_SCREAMING_SNAKE_CASE = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
# verify the last attentions (last block, last layer)
_SCREAMING_SNAKE_CASE = (self.model_tester.image_size // 32) ** 2
_SCREAMING_SNAKE_CASE = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:] ) , [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_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__ ) , A__ )
# verify the first attentions (first block, first layer)
_SCREAMING_SNAKE_CASE = (self.model_tester.image_size // 4) ** 2
_SCREAMING_SNAKE_CASE = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
def UpperCamelCase ( self ) -> Optional[Any]:
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_encoder_blocks
self.assertEqual(len(A__ ) , A__ )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.hidden_sizes[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
_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 ) -> Dict:
if not self.model_tester.is_training:
return
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
_SCREAMING_SNAKE_CASE = True
for model_class in self.all_model_classes:
if model_class in get_values(A__ ):
continue
_SCREAMING_SNAKE_CASE = model_class(A__ )
model.to(A__ )
model.train()
_SCREAMING_SNAKE_CASE = self._prepare_for_class(A__ , A__ , return_labels=A__ )
_SCREAMING_SNAKE_CASE = model(**A__ ).loss
loss.backward()
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def UpperCamelCase ( self ) -> str:
pass
@slow
def UpperCamelCase ( self ) -> Dict:
for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE = SegformerModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
def lowerCAmelCase_ ( ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
class _a (unittest.TestCase):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> Any:
# only resize + normalize
_SCREAMING_SNAKE_CASE = SegformerImageProcessor(
image_scale=(5_12, 5_12) , keep_ratio=A__ , align=A__ , do_random_crop=A__ )
_SCREAMING_SNAKE_CASE = SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512""" ).to(
A__ )
_SCREAMING_SNAKE_CASE = prepare_img()
_SCREAMING_SNAKE_CASE = image_processor(images=A__ , return_tensors="""pt""" )
_SCREAMING_SNAKE_CASE = encoded_inputs.pixel_values.to(A__ )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = torch.Size((1, model.config.num_labels, 1_28, 1_28) )
self.assertEqual(outputs.logits.shape , A__ )
_SCREAMING_SNAKE_CASE = torch.tensor(
[
[[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
[[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
[[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
] ).to(A__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , A__ , atol=1E-4 ) )
@slow
def UpperCamelCase ( self ) -> Any:
# only resize + normalize
_SCREAMING_SNAKE_CASE = SegformerImageProcessor(
image_scale=(5_12, 5_12) , keep_ratio=A__ , align=A__ , do_random_crop=A__ )
_SCREAMING_SNAKE_CASE = SegformerForSemanticSegmentation.from_pretrained(
"""nvidia/segformer-b1-finetuned-cityscapes-1024-1024""" ).to(A__ )
_SCREAMING_SNAKE_CASE = prepare_img()
_SCREAMING_SNAKE_CASE = image_processor(images=A__ , return_tensors="""pt""" )
_SCREAMING_SNAKE_CASE = encoded_inputs.pixel_values.to(A__ )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = torch.Size((1, model.config.num_labels, 1_28, 1_28) )
self.assertEqual(outputs.logits.shape , A__ )
_SCREAMING_SNAKE_CASE = torch.tensor(
[
[[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
[[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
[[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
] ).to(A__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , A__ , atol=1E-1 ) )
@slow
def UpperCamelCase ( self ) -> int:
# only resize + normalize
_SCREAMING_SNAKE_CASE = SegformerImageProcessor(
image_scale=(5_12, 5_12) , keep_ratio=A__ , align=A__ , do_random_crop=A__ )
_SCREAMING_SNAKE_CASE = SegformerForSemanticSegmentation.from_pretrained("""nvidia/segformer-b0-finetuned-ade-512-512""" ).to(
A__ )
_SCREAMING_SNAKE_CASE = prepare_img()
_SCREAMING_SNAKE_CASE = image_processor(images=A__ , return_tensors="""pt""" )
_SCREAMING_SNAKE_CASE = encoded_inputs.pixel_values.to(A__ )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = outputs.logits.detach().cpu()
_SCREAMING_SNAKE_CASE = image_processor.post_process_semantic_segmentation(outputs=A__ , target_sizes=[(5_00, 3_00)] )
_SCREAMING_SNAKE_CASE = torch.Size((5_00, 3_00) )
self.assertEqual(segmentation[0].shape , A__ )
_SCREAMING_SNAKE_CASE = image_processor.post_process_semantic_segmentation(outputs=A__ )
_SCREAMING_SNAKE_CASE = torch.Size((1_28, 1_28) )
self.assertEqual(segmentation[0].shape , A__ )
| 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 __future__ import annotations
import math
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(a__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
UpperCamelCase__ : int = [num for num in range(3, 100_001, 2) if not is_prime(num)]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list[int]:
"""simple docstring"""
if not isinstance(a__ , a__ ):
raise ValueError("""n must be an integer""" )
if n <= 0:
raise ValueError("""n must be >= 0""" )
_SCREAMING_SNAKE_CASE = []
for num in range(len(a__ ) ):
_SCREAMING_SNAKE_CASE = 0
while 2 * i * i <= odd_composites[num]:
_SCREAMING_SNAKE_CASE = odd_composites[num] - 2 * i * i
if is_prime(a__ ):
break
i += 1
else:
list_nums.append(odd_composites[num] )
if len(a__ ) == n:
return list_nums
return []
def lowerCAmelCase_ ( ) -> int:
"""simple docstring"""
return compute_nums(1 )[0]
if __name__ == "__main__":
print(f"""{solution() = }""")
| 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 |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
UpperCamelCase__ : List[str] = "pytorch_model.bin"
UpperCamelCase__ : Optional[Any] = "pytorch_model.bin.index.json"
UpperCamelCase__ : Tuple = "adapter_config.json"
UpperCamelCase__ : Union[str, Any] = "adapter_model.bin"
UpperCamelCase__ : List[str] = "adapter_model.safetensors"
UpperCamelCase__ : int = "tf_model.h5"
UpperCamelCase__ : Any = "tf_model.h5.index.json"
UpperCamelCase__ : Any = "model.ckpt"
UpperCamelCase__ : Tuple = "flax_model.msgpack"
UpperCamelCase__ : Optional[int] = "flax_model.msgpack.index.json"
UpperCamelCase__ : Union[str, Any] = "model.safetensors"
UpperCamelCase__ : List[str] = "model.safetensors.index.json"
UpperCamelCase__ : List[Any] = "config.json"
UpperCamelCase__ : Optional[int] = "preprocessor_config.json"
UpperCamelCase__ : List[Any] = FEATURE_EXTRACTOR_NAME
UpperCamelCase__ : Dict = "generation_config.json"
UpperCamelCase__ : Tuple = "modelcard.json"
UpperCamelCase__ : Tuple = "▁"
UpperCamelCase__ : Union[str, Any] = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
UpperCamelCase__ : Optional[Any] = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
UpperCamelCase__ : Union[str, Any] = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
UpperCamelCase__ : Optional[int] = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
if version.parse(snake_case__ ) < version.parse(snake_case__ ):
if "dev" in min_version:
_SCREAMING_SNAKE_CASE = (
"""This example requires a source install from HuggingFace Transformers (see """
"""`https://huggingface.co/docs/transformers/installation#install-from-source`),"""
)
else:
_SCREAMING_SNAKE_CASE = F"This example requires a minimum version of {min_version},"
error_message += F" but the version found is {__version__}.\n"
raise ImportError(
error_message
+ """Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other """
"""versions of HuggingFace Transformers.""" )
| 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'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = 1_00 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 2
for i in range(2 , max_n + 1 ):
_SCREAMING_SNAKE_CASE = pre_numerator
_SCREAMING_SNAKE_CASE = 2 * i // 3 if i % 3 == 0 else 1
_SCREAMING_SNAKE_CASE = cur_numerator
_SCREAMING_SNAKE_CASE = e_cont * pre_numerator + temp
return sum_digits(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 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 __future__ import annotations
from fractions import Fraction
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> bool:
"""simple docstring"""
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = 11
_SCREAMING_SNAKE_CASE = int("""1""" + """0""" * digit_len )
for num in range(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
solutions.append(F"{num}/{den}" )
den += 1
num += 1
_SCREAMING_SNAKE_CASE = 10
return solutions
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = 2 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 1.0
for fraction in fraction_list(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = Fraction(SCREAMING_SNAKE_CASE_ )
result *= frac.denominator / frac.numerator
return int(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
print(solution())
| 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 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {
"""en""": """Machine learning is great, isn't it?""",
"""ru""": """Машинное обучение - это здорово, не так ли?""",
"""de""": """Maschinelles Lernen ist großartig, oder?""",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
_SCREAMING_SNAKE_CASE = {
"""ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
"""en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
"""en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
"""de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
}
_SCREAMING_SNAKE_CASE = F"{src_lang}-{tgt_lang}"
_SCREAMING_SNAKE_CASE = F"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair | fairseq | transformers\n-------|---------|----------\n{pair} | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n year={{2020}},\n title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
os.makedirs(__a , exist_ok=__a )
_SCREAMING_SNAKE_CASE = os.path.join(__a , """README.md""" )
print(F"Generating {path}" )
with open(__a , """w""" , encoding="""utf-8""" ) as f:
f.write(__a )
# make sure we are under the root of the project
UpperCamelCase__ : int = Path(__file__).resolve().parent.parent.parent
UpperCamelCase__ : Optional[Any] = repo_dir / "model_cards"
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ : Any = model_name.split("-")
UpperCamelCase__ : Union[str, Any] = model_cards_dir / "facebook" / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 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'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("""Input value must be an 'int' type""" )
_SCREAMING_SNAKE_CASE = 0
while number:
position += 1
number >>= 1
return position
if __name__ == "__main__":
import doctest
doctest.testmod() | 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'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class _a :
"""simple docstring"""
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__=3 , 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 , ) -> Union[str, Any]:
_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 = text_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 = 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 )
# 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 = t
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 = t
_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__ , A__ , A__ ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = LayoutLMvaModel(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
# text + image
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ , pixel_values=UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = model(
UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
_SCREAMING_SNAKE_CASE = model(UpperCAmelCase__ )
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=UpperCAmelCase__ )
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__ , A__ ) -> int:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = LayoutLMvaForSequenceClassification(UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(
UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = LayoutLMvaForTokenClassification(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(
UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , labels=UpperCAmelCase__ , )
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__ , A__ ) -> int:
_SCREAMING_SNAKE_CASE = LayoutLMvaForQuestionAnswering(config=UpperCAmelCase__ )
model.to(UpperCAmelCase__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(
UpperCAmelCase__ , bbox=UpperCAmelCase__ , pixel_values=UpperCAmelCase__ , attention_mask=UpperCAmelCase__ , token_type_ids=UpperCAmelCase__ , start_positions=UpperCAmelCase__ , end_positions=UpperCAmelCase__ , )
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 ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
_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_torch
class _a (lowercase__ , lowercase__ , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
if is_torch_available()
else {}
)
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> Optional[Any]:
return True
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = LayoutLMvaModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=UpperCAmelCase__ , hidden_size=37 )
def UpperCamelCase ( self , A__ , A__ , A__=False ) -> Tuple:
_SCREAMING_SNAKE_CASE = copy.deepcopy(UpperCAmelCase__ )
if model_class in get_values(UpperCAmelCase__ ):
_SCREAMING_SNAKE_CASE = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(UpperCAmelCase__ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(UpperCAmelCase__ ):
_SCREAMING_SNAKE_CASE = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
elif model_class in get_values(UpperCAmelCase__ ):
_SCREAMING_SNAKE_CASE = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
elif model_class in [
*get_values(UpperCAmelCase__ ),
]:
_SCREAMING_SNAKE_CASE = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase__ )
elif model_class in [
*get_values(UpperCAmelCase__ ),
]:
_SCREAMING_SNAKE_CASE = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=UpperCAmelCase__ , )
return inputs_dict
def UpperCamelCase ( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Optional[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()
for type in ["absolute", "relative_key", "relative_key_query"]:
_SCREAMING_SNAKE_CASE = type
self.model_tester.create_and_check_model(*UpperCAmelCase__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__ )
@slow
def UpperCamelCase ( self ) -> str:
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE = LayoutLMvaModel.from_pretrained(UpperCAmelCase__ )
self.assertIsNotNone(UpperCAmelCase__ )
def lowerCAmelCase_ ( ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
class _a (unittest.TestCase):
"""simple docstring"""
@cached_property
def UpperCamelCase ( self ) -> Optional[int]:
return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase__ ) if is_vision_available() else None
@slow
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" ).to(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = self.default_image_processor
_SCREAMING_SNAKE_CASE = prepare_img()
_SCREAMING_SNAKE_CASE = image_processor(images=UpperCAmelCase__ , return_tensors="""pt""" ).pixel_values.to(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = torch.tensor([[1, 2]] )
_SCREAMING_SNAKE_CASE = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
_SCREAMING_SNAKE_CASE = model(
input_ids=input_ids.to(UpperCAmelCase__ ) , bbox=bbox.to(UpperCAmelCase__ ) , pixel_values=pixel_values.to(UpperCAmelCase__ ) , )
# verify the logits
_SCREAMING_SNAKE_CASE = torch.Size((1, 1_99, 7_68) )
self.assertEqual(outputs.last_hidden_state.shape , UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = torch.tensor(
[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] ).to(UpperCAmelCase__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase__ , atol=1E-4 ) )
| 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 random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
UpperCamelCase__ : Tuple = "0.12" # assumed parallelism: 8
if is_torch_available():
import torch
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> Union[str, Any]:
"""simple docstring"""
if rng is None:
_SCREAMING_SNAKE_CASE = random.Random()
_SCREAMING_SNAKE_CASE = 1
for dim in shape:
total_dims *= dim
_SCREAMING_SNAKE_CASE = []
for _ in range(_lowerCAmelCase ):
values.append(rng.randint(0 , vocab_size - 1 ) )
_SCREAMING_SNAKE_CASE = np.array(_lowerCAmelCase , dtype=jnp.intaa ).reshape(_lowerCAmelCase )
return output
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ids_tensor(_lowerCAmelCase , vocab_size=2 , rng=_lowerCAmelCase )
# make sure that at least one token is attended to for each batch
_SCREAMING_SNAKE_CASE = 1
return attn_mask
@require_flax
class _a :
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = ()
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = inputs["""input_ids"""].shape[-1] // 2
_SCREAMING_SNAKE_CASE = inputs["""input_ids"""][:max_batch_size, :sequence_length]
_SCREAMING_SNAKE_CASE = jnp.ones_like(_lowerCamelCase )
_SCREAMING_SNAKE_CASE = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
_SCREAMING_SNAKE_CASE = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
_SCREAMING_SNAKE_CASE = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self._get_input_ids_and_config()
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = max_length
_SCREAMING_SNAKE_CASE = 0
for model_class in self.all_generative_model_classes:
_SCREAMING_SNAKE_CASE = model_class(_lowerCamelCase )
_SCREAMING_SNAKE_CASE = model_class.__name__[4:] # Skip the "Flax" at the beginning
_SCREAMING_SNAKE_CASE = getattr(_lowerCamelCase , _lowerCamelCase )
_SCREAMING_SNAKE_CASE = pt_model_class(_lowerCamelCase ).eval()
_SCREAMING_SNAKE_CASE = load_flax_weights_in_pytorch_model(_lowerCamelCase , flax_model.params )
_SCREAMING_SNAKE_CASE = flax_model.generate(_lowerCamelCase ).sequences
_SCREAMING_SNAKE_CASE = pt_model.generate(torch.tensor(_lowerCamelCase , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
_SCREAMING_SNAKE_CASE = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self._get_input_ids_and_config()
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = max_length
for model_class in self.all_generative_model_classes:
_SCREAMING_SNAKE_CASE = model_class(_lowerCamelCase )
_SCREAMING_SNAKE_CASE = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
_SCREAMING_SNAKE_CASE = jit(model.generate )
_SCREAMING_SNAKE_CASE = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self._get_input_ids_and_config()
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = max_length
for model_class in self.all_generative_model_classes:
_SCREAMING_SNAKE_CASE = model_class(_lowerCamelCase )
_SCREAMING_SNAKE_CASE = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
_SCREAMING_SNAKE_CASE = jit(model.generate )
_SCREAMING_SNAKE_CASE = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self._get_input_ids_and_config()
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = max_length
_SCREAMING_SNAKE_CASE = 2
for model_class in self.all_generative_model_classes:
_SCREAMING_SNAKE_CASE = model_class(_lowerCamelCase )
_SCREAMING_SNAKE_CASE = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
_SCREAMING_SNAKE_CASE = jit(model.generate )
_SCREAMING_SNAKE_CASE = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self._get_input_ids_and_config()
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = max_length
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 2
for model_class in self.all_generative_model_classes:
_SCREAMING_SNAKE_CASE = model_class(_lowerCamelCase )
_SCREAMING_SNAKE_CASE = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self._get_input_ids_and_config()
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = max_length
_SCREAMING_SNAKE_CASE = 0.8
_SCREAMING_SNAKE_CASE = 10
_SCREAMING_SNAKE_CASE = 0.3
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 8
_SCREAMING_SNAKE_CASE = 9
for model_class in self.all_generative_model_classes:
_SCREAMING_SNAKE_CASE = model_class(_lowerCamelCase )
_SCREAMING_SNAKE_CASE = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
_SCREAMING_SNAKE_CASE = jit(model.generate )
_SCREAMING_SNAKE_CASE = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self._get_input_ids_and_config()
_SCREAMING_SNAKE_CASE = max_length
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 8
_SCREAMING_SNAKE_CASE = 9
for model_class in self.all_generative_model_classes:
_SCREAMING_SNAKE_CASE = model_class(_lowerCamelCase )
_SCREAMING_SNAKE_CASE = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
_SCREAMING_SNAKE_CASE = jit(model.generate )
_SCREAMING_SNAKE_CASE = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self._get_input_ids_and_config()
_SCREAMING_SNAKE_CASE = max_length
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 8
_SCREAMING_SNAKE_CASE = 9
for model_class in self.all_generative_model_classes:
_SCREAMING_SNAKE_CASE = model_class(_lowerCamelCase )
_SCREAMING_SNAKE_CASE = model.generate(_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
_SCREAMING_SNAKE_CASE = jit(model.generate )
_SCREAMING_SNAKE_CASE = jit_generate(_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self._get_input_ids_and_config()
# pad attention mask on the left
_SCREAMING_SNAKE_CASE = attention_mask.at[(0, 0)].set(0 )
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = max_length
for model_class in self.all_generative_model_classes:
_SCREAMING_SNAKE_CASE = model_class(_lowerCamelCase )
_SCREAMING_SNAKE_CASE = model.generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
_SCREAMING_SNAKE_CASE = jit(model.generate )
_SCREAMING_SNAKE_CASE = jit_generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self._get_input_ids_and_config()
# pad attention mask on the left
_SCREAMING_SNAKE_CASE = attention_mask.at[(0, 0)].set(0 )
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = max_length
for model_class in self.all_generative_model_classes:
_SCREAMING_SNAKE_CASE = model_class(_lowerCamelCase )
_SCREAMING_SNAKE_CASE = model.generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
_SCREAMING_SNAKE_CASE = jit(model.generate )
_SCREAMING_SNAKE_CASE = jit_generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self._get_input_ids_and_config()
# pad attention mask on the left
_SCREAMING_SNAKE_CASE = attention_mask.at[(0, 0)].set(0 )
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = max_length
for model_class in self.all_generative_model_classes:
_SCREAMING_SNAKE_CASE = model_class(_lowerCamelCase )
_SCREAMING_SNAKE_CASE = model.generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertEqual(generation_outputs.shape[-1] , _lowerCamelCase )
_SCREAMING_SNAKE_CASE = jit(model.generate )
_SCREAMING_SNAKE_CASE = jit_generate(_lowerCamelCase , attention_mask=_lowerCamelCase ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class _a (unittest.TestCase):
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert""" )
_SCREAMING_SNAKE_CASE = FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only""" )
_SCREAMING_SNAKE_CASE = """Hello world"""
_SCREAMING_SNAKE_CASE = tokenizer(_lowerCamelCase , return_tensors="""np""" ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(_lowerCamelCase , """do_samples""" ):
model.generate(_lowerCamelCase , do_samples=_lowerCamelCase )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(_lowerCamelCase , """foo""" ):
_SCREAMING_SNAKE_CASE = {"""foo""": """bar"""}
model.generate(_lowerCamelCase , **_lowerCamelCase )
| 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 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 , ) -> 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 = 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 ) -> 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] )
_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=__lowerCamelCase , )
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__ ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = TFRoFormerModel(config=__lowerCamelCase )
_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(__lowerCamelCase )
_SCREAMING_SNAKE_CASE = model(__lowerCamelCase )
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__ ) -> int:
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = TFRoFormerForCausalLM(config=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(__lowerCamelCase )['''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__ ) -> Any:
_SCREAMING_SNAKE_CASE = TFRoFormerForMaskedLM(config=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(__lowerCamelCase )
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[Any]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForSequenceClassification(config=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = TFRoFormerForMultipleChoice(config=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(__lowerCamelCase , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(__lowerCamelCase , 1 ) , (1, self.num_choices, 1) )
_SCREAMING_SNAKE_CASE = tf.tile(tf.expand_dims(__lowerCamelCase , 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(__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFRoFormerForTokenClassification(config=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(__lowerCamelCase )
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__ ) -> Dict:
_SCREAMING_SNAKE_CASE = TFRoFormerForQuestionAnswering(config=__lowerCamelCase )
_SCREAMING_SNAKE_CASE = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
_SCREAMING_SNAKE_CASE = model(__lowerCamelCase )
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 ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
_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 ) -> Any:
_SCREAMING_SNAKE_CASE = TFRoFormerModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=__lowerCamelCase , hidden_size=37 )
def UpperCamelCase ( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head(*__lowerCamelCase )
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase )
@slow
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base""" )
self.assertIsNotNone(__lowerCamelCase )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> Tuple:
_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(__lowerCamelCase )[0]
# TODO Replace vocab size
_SCREAMING_SNAKE_CASE = 5_00_00
_SCREAMING_SNAKE_CASE = [1, 6, vocab_size]
self.assertEqual(output.shape , __lowerCamelCase )
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] , __lowerCamelCase , atol=1E-4 )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> Optional[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(__lowerCamelCase , __lowerCamelCase , atol=self.tolerance )
def UpperCamelCase ( self ) -> Union[str, 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(__lowerCamelCase , __lowerCamelCase , atol=self.tolerance )
@require_tf
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 1E-4
def UpperCamelCase ( self ) -> Optional[Any]:
_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 = TFRoFormerSelfAttention.apply_rotary_position_embeddings(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
_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] , __lowerCamelCase , atol=self.tolerance )
tf.debugging.assert_near(key_layer[0, 0, :6, :8] , __lowerCamelCase , atol=self.tolerance )
| 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_sentencepiece_available
UpperCamelCase__ : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : int = ["MLukeTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mluke import MLukeTokenizer
else:
import sys
UpperCamelCase__ : 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 os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=7 ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = None
if token is not None:
_SCREAMING_SNAKE_CASE = {"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}
# The id of a workflow (not of a workflow run)
_SCREAMING_SNAKE_CASE = "636036"
_SCREAMING_SNAKE_CASE = F"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"
_SCREAMING_SNAKE_CASE = requests.get(__SCREAMING_SNAKE_CASE , headers=__SCREAMING_SNAKE_CASE ).json()
return result["workflow_runs"]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = get_daily_ci_runs(__SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
_SCREAMING_SNAKE_CASE = workflow_run["id"]
break
return workflow_run_id
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = get_last_daily_ci_runs(__SCREAMING_SNAKE_CASE )
if workflow_run_id is not None:
_SCREAMING_SNAKE_CASE = get_artifacts_links(worflow_run_id=__SCREAMING_SNAKE_CASE , token=__SCREAMING_SNAKE_CASE )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
_SCREAMING_SNAKE_CASE = artifacts_links[artifact_name]
download_artifact(
artifact_name=__SCREAMING_SNAKE_CASE , artifact_url=__SCREAMING_SNAKE_CASE , output_dir=__SCREAMING_SNAKE_CASE , token=__SCREAMING_SNAKE_CASE )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
get_last_daily_ci_artifacts(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
_SCREAMING_SNAKE_CASE = {}
for artifact_name in artifact_names:
_SCREAMING_SNAKE_CASE = os.path.join(__SCREAMING_SNAKE_CASE , F"{artifact_name}.zip" )
if os.path.isfile(__SCREAMING_SNAKE_CASE ):
_SCREAMING_SNAKE_CASE = {}
with zipfile.ZipFile(__SCREAMING_SNAKE_CASE ) as z:
for filename in z.namelist():
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
# read the file
with z.open(__SCREAMING_SNAKE_CASE ) as f:
_SCREAMING_SNAKE_CASE = f.read().decode("""UTF-8""" )
return results
| 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 |
from __future__ import annotations
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int: # noqa: E741
"""simple docstring"""
while r - l > 1:
_SCREAMING_SNAKE_CASE = (l + r) // 2
if v[m] >= key:
_SCREAMING_SNAKE_CASE = m
else:
_SCREAMING_SNAKE_CASE = m # noqa: E741
return r
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return 0
_SCREAMING_SNAKE_CASE = [0] * len(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = v[0]
for i in range(1 , len(SCREAMING_SNAKE_CASE_ ) ):
if v[i] < tail[0]:
_SCREAMING_SNAKE_CASE = v[i]
elif v[i] > tail[length - 1]:
_SCREAMING_SNAKE_CASE = v[i]
length += 1
else:
_SCREAMING_SNAKE_CASE = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_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__=13 , A__=32 , A__=2 , A__=3 , A__=16 , A__=[32, 64, 1_28] , A__=[1, 2, 1] , A__=[2, 2, 4] , A__=2 , A__=2.0 , A__=True , A__=0.0 , A__=0.0 , A__=0.1 , A__="gelu" , A__=False , A__=True , A__=0.02 , A__=1E-5 , A__=True , A__=None , A__=True , A__=10 , A__=8 , A__=["stage1", "stage2"] , A__=[1, 2] , ) -> List[Any]:
_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 = embed_dim
_SCREAMING_SNAKE_CASE = hidden_sizes
_SCREAMING_SNAKE_CASE = depths
_SCREAMING_SNAKE_CASE = num_heads
_SCREAMING_SNAKE_CASE = window_size
_SCREAMING_SNAKE_CASE = mlp_ratio
_SCREAMING_SNAKE_CASE = qkv_bias
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE = drop_path_rate
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = use_absolute_embeddings
_SCREAMING_SNAKE_CASE = patch_norm
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = scope
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = type_sequence_label_size
_SCREAMING_SNAKE_CASE = encoder_stride
_SCREAMING_SNAKE_CASE = out_features
_SCREAMING_SNAKE_CASE = out_indices
def UpperCamelCase ( self ) -> Dict:
_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 ) -> Optional[int]:
return FocalNetConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , hidden_sizes=self.hidden_sizes , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> List[Any]:
_SCREAMING_SNAKE_CASE = FocalNetModel(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
_SCREAMING_SNAKE_CASE = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = FocalNetBackbone(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = FocalNetBackbone(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Tuple:
_SCREAMING_SNAKE_CASE = FocalNetForMaskedImageModeling(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = FocalNetForMaskedImageModeling(A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Any:
_SCREAMING_SNAKE_CASE = self.type_sequence_label_size
_SCREAMING_SNAKE_CASE = FocalNetForImageClassification(A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ , labels=A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = FocalNetForImageClassification(A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE = config_and_inputs
_SCREAMING_SNAKE_CASE = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _a (__lowercase , __lowercase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
if is_torch_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 ) -> Tuple:
_SCREAMING_SNAKE_CASE = FocalNetModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , embed_dim=37 , has_text_modality=A__ )
def UpperCamelCase ( self ) -> Tuple:
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 ) -> Any:
return
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*A__ )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A__ )
@unittest.skip(reason="""FocalNet does not use inputs_embeds""" )
def UpperCamelCase ( self ) -> Any:
pass
@unittest.skip(reason="""FocalNet does not use feedforward chunking""" )
def UpperCamelCase ( self ) -> str:
pass
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_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 ) -> Any:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
_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 , A__ , A__ , A__ , A__ ) -> List[str]:
_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 = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(A__ ) , A__ )
# FocalNet has a different seq_length
_SCREAMING_SNAKE_CASE = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_SCREAMING_SNAKE_CASE = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
_SCREAMING_SNAKE_CASE = outputs.reshaped_hidden_states
self.assertEqual(len(A__ ) , A__ )
_SCREAMING_SNAKE_CASE = reshaped_hidden_states[0].shape
_SCREAMING_SNAKE_CASE = (
reshaped_hidden_states[0].view(A__ , A__ , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
_SCREAMING_SNAKE_CASE = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
_SCREAMING_SNAKE_CASE = True
self.check_hidden_states_output(A__ , A__ , A__ , A__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_SCREAMING_SNAKE_CASE = True
self.check_hidden_states_output(A__ , A__ , A__ , A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
_SCREAMING_SNAKE_CASE = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
_SCREAMING_SNAKE_CASE = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
_SCREAMING_SNAKE_CASE = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
_SCREAMING_SNAKE_CASE = True
self.check_hidden_states_output(A__ , A__ , A__ , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_SCREAMING_SNAKE_CASE = True
self.check_hidden_states_output(A__ , A__ , A__ , (padded_height, padded_width) )
@slow
def UpperCamelCase ( self ) -> Optional[int]:
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE = FocalNetModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
_SCREAMING_SNAKE_CASE = _config_zero_init(A__ )
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE = model_class(config=A__ )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F"Parameter {name} of model {model_class} seems not properly initialized" , )
@require_vision
@require_torch
class _a (unittest.TestCase):
"""simple docstring"""
@cached_property
def UpperCamelCase ( self ) -> List[str]:
# TODO update organization
return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny""" ) if is_vision_available() else None
@slow
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny""" ).to(A__ )
_SCREAMING_SNAKE_CASE = self.default_image_processor
_SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
_SCREAMING_SNAKE_CASE = image_processor(images=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, 10_00) )
self.assertEqual(outputs.logits.shape , A__ )
_SCREAMING_SNAKE_CASE = torch.tensor([0.2166, -0.4368, 0.2191] ).to(A__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A__ , atol=1E-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item() , 2_81 )
@require_torch
class _a (__lowercase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (FocalNetBackbone,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE = FocalNetConfig
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = FocalNetModelTester(self )
| 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 |
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
UpperCamelCase__ : Any = int(input("Enter number: ").strip())
print(f"""{number} is {"" if perfect(number) else "not "}a Perfect Number.""")
| 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'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 __future__ import annotations
from math import pi, sqrt
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
if inductance <= 0:
raise ValueError("""Inductance cannot be 0 or negative""" )
elif capacitance <= 0:
raise ValueError("""Capacitance cannot be 0 or negative""" )
else:
return (
"Resonant frequency",
float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = inspect.getfile(accelerate.test_utils )
_SCREAMING_SNAKE_CASE = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["""scripts""", """test_script.py"""] )
_SCREAMING_SNAKE_CASE = os.path.sep.join(inspect.getfile(self.__class__ ).split(os.path.sep )[:-1] )
@require_tpu
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = F"\n {self.test_dir}/xla_spawn.py\n --num_cores 8\n {self.test_file_path}\n ".split()
_SCREAMING_SNAKE_CASE = [sys.executable] + distributed_args
execute_subprocess_async(lowerCamelCase_ , env=os.environ.copy() )
| 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'''
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE_ , params=SCREAMING_SNAKE_CASE_ ).content , """html.parser""" )
_SCREAMING_SNAKE_CASE = soup.find("""div""" , attrs={"""class""": """gs_ri"""} )
_SCREAMING_SNAKE_CASE = div.find("""div""" , attrs={"""class""": """gs_fl"""} ).find_all("""a""" )
return anchors[2].get_text()
if __name__ == "__main__":
UpperCamelCase__ = {
"title": (
"Precisely geometry controlled microsupercapacitors for ultrahigh areal "
"capacitance, volumetric capacitance, and energy density"
),
"journal": "Chem. Mater.",
"volume": 30,
"pages": "3979-3990",
"year": 2_018,
"hl": "en",
}
print(get_citation("https://scholar.google.com/scholar_lookup", params=params))
| 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 random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class _a (__UpperCAmelCase , __UpperCAmelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = IFInpaintingPipeline
SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
SCREAMING_SNAKE_CASE = PipelineTesterMixin.required_optional_params - {'latents'}
def UpperCamelCase ( self ) -> Optional[Any]:
return self._get_dummy_components()
def UpperCamelCase ( self , A__ , A__=0 ) -> List[Any]:
if str(lowerCAmelCase_ ).startswith("""mps""" ):
_SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCAmelCase_ )
else:
_SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCAmelCase_ ).manual_seed(lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase_ ) ).to(lowerCAmelCase_ )
_SCREAMING_SNAKE_CASE = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def UpperCamelCase ( self ) -> str:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def UpperCamelCase ( self ) -> int:
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def UpperCamelCase ( self ) -> List[str]:
super().test_save_load_floataa(expected_max_diff=1E-1 )
def UpperCamelCase ( self ) -> List[Any]:
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def UpperCamelCase ( self ) -> Union[str, Any]:
self._test_save_load_local()
def UpperCamelCase ( self ) -> List[Any]:
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 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_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
def update_area_of_max_square(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
_SCREAMING_SNAKE_CASE = update_area_of_max_square(a_ , col + 1 )
_SCREAMING_SNAKE_CASE = update_area_of_max_square(row + 1 , col + 1 )
_SCREAMING_SNAKE_CASE = update_area_of_max_square(row + 1 , a_ )
if mat[row][col]:
_SCREAMING_SNAKE_CASE = 1 + min([right, diagonal, down] )
_SCREAMING_SNAKE_CASE = max(largest_square_area[0] , a_ )
return sub_problem_sol
else:
return 0
_SCREAMING_SNAKE_CASE = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
def update_area_of_max_square_using_dp_array(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
_SCREAMING_SNAKE_CASE = update_area_of_max_square_using_dp_array(a_ , col + 1 , a_ )
_SCREAMING_SNAKE_CASE = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , a_ )
_SCREAMING_SNAKE_CASE = update_area_of_max_square_using_dp_array(row + 1 , a_ , a_ )
if mat[row][col]:
_SCREAMING_SNAKE_CASE = 1 + min([right, diagonal, down] )
_SCREAMING_SNAKE_CASE = max(largest_square_area[0] , a_ )
_SCREAMING_SNAKE_CASE = sub_problem_sol
return sub_problem_sol
else:
return 0
_SCREAMING_SNAKE_CASE = [0]
_SCREAMING_SNAKE_CASE = [[-1] * cols for _ in range(a_ )]
update_area_of_max_square_using_dp_array(0 , 0 , a_ )
return largest_square_area[0]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [[0] * (cols + 1) for _ in range(rows + 1 )]
_SCREAMING_SNAKE_CASE = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
_SCREAMING_SNAKE_CASE = dp_array[row][col + 1]
_SCREAMING_SNAKE_CASE = dp_array[row + 1][col + 1]
_SCREAMING_SNAKE_CASE = dp_array[row + 1][col]
if mat[row][col] == 1:
_SCREAMING_SNAKE_CASE = 1 + min(a_ , a_ , a_ )
_SCREAMING_SNAKE_CASE = max(dp_array[row][col] , a_ )
else:
_SCREAMING_SNAKE_CASE = 0
return largest_square_area
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [0] * (cols + 1)
_SCREAMING_SNAKE_CASE = [0] * (cols + 1)
_SCREAMING_SNAKE_CASE = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
_SCREAMING_SNAKE_CASE = current_row[col + 1]
_SCREAMING_SNAKE_CASE = next_row[col + 1]
_SCREAMING_SNAKE_CASE = next_row[col]
if mat[row][col] == 1:
_SCREAMING_SNAKE_CASE = 1 + min(a_ , a_ , a_ )
_SCREAMING_SNAKE_CASE = max(current_row[col] , a_ )
else:
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 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 TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase__ : List[str] = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Optional[Any] = [
"UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
"UniSpeechForCTC",
"UniSpeechForPreTraining",
"UniSpeechForSequenceClassification",
"UniSpeechModel",
"UniSpeechPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 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 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
UpperCamelCase__ : Dict = "tiny-wmt19-en-ru"
# Build
# borrowed from a test
UpperCamelCase__ : str = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
UpperCamelCase__ : Optional[Any] = dict(zip(vocab, range(len(vocab))))
UpperCamelCase__ : Optional[Any] = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase__ : int = Path(tmpdirname)
UpperCamelCase__ : Union[str, Any] = build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
UpperCamelCase__ : List[str] = build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
UpperCamelCase__ : Union[str, Any] = build_dir / VOCAB_FILES_NAMES["merges_file"]
with open(src_vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, "w") as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, "w") as fp:
fp.write("\n".join(merges))
UpperCamelCase__ : str = FSMTTokenizer(
langs=["en", "ru"],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
UpperCamelCase__ : Dict = FSMTConfig(
langs=["ru", "en"],
src_vocab_size=1_000,
tgt_vocab_size=1_000,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
UpperCamelCase__ : int = FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")
# Test
UpperCamelCase__ : Optional[Any] = tokenizer(["Making tiny model"], return_tensors="pt")
UpperCamelCase__ : Tuple = tiny_model(**batch)
print("test output:", len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(f"""Generated {mname_tiny}""")
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 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 ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
from .camera import create_pan_cameras
from .pipeline_shap_e import ShapEPipeline
from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
from .renderer import (
BoundingBoxVolume,
ImportanceRaySampler,
MLPNeRFModelOutput,
MLPNeRSTFModel,
ShapEParamsProjModel,
ShapERenderer,
StratifiedRaySampler,
VoidNeRFModel,
)
| 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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _a (__UpperCAmelCase):
"""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__ ) -> List[Any]:
_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__ ) -> List[str]:
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__ ) -> int:
return self.tokenizer.batch_decode(*A__ , **A__ )
def UpperCamelCase ( self , *A__ , **A__ ) -> Any:
return self.tokenizer.decode(*A__ , **A__ )
@property
def UpperCamelCase ( self ) -> Optional[int]:
_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 ) -> List[Any]:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , A__ , )
return self.image_processor_class
| 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 fire
from utils import calculate_rouge, save_json
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ ) -> List[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)
| 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'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
UpperCamelCase__ : Optional[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
UpperCamelCase__ : Optional[Any] = [file for file in filepaths if file != file.lower()]
if upper_files:
print(f"""{len(upper_files)} files contain uppercase characters:""")
print("\n".join(upper_files) + "\n")
UpperCamelCase__ : int = [file for file in filepaths if ' ' in file]
if space_files:
print(f"""{len(space_files)} files contain space characters:""")
print("\n".join(space_files) + "\n")
UpperCamelCase__ : Any = [file for file in filepaths if '-' in file]
if hyphen_files:
print(f"""{len(hyphen_files)} files contain hyphen characters:""")
print("\n".join(hyphen_files) + "\n")
UpperCamelCase__ : Optional[int] = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(f"""{len(nodir_files)} files are not in a directory:""")
print("\n".join(nodir_files) + "\n")
UpperCamelCase__ : List[str] = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 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 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class _a (lowercase_ , lowercase_ , lowercase_ , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = StableDiffusionControlNetImgaImgPipeline
SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
SCREAMING_SNAKE_CASE = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'})
SCREAMING_SNAKE_CASE = IMAGE_TO_IMAGE_IMAGE_PARAMS
def UpperCamelCase ( self ) -> Union[str, Any]:
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=A__ , set_alpha_to_one=A__ , )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
_SCREAMING_SNAKE_CASE = CLIPTextModel(A__ )
_SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
_SCREAMING_SNAKE_CASE = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def UpperCamelCase ( self , A__ , A__=0 ) -> List[str]:
if str(A__ ).startswith("""mps""" ):
_SCREAMING_SNAKE_CASE = torch.manual_seed(A__ )
else:
_SCREAMING_SNAKE_CASE = torch.Generator(device=A__ ).manual_seed(A__ )
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=A__ , device=torch.device(A__ ) , )
_SCREAMING_SNAKE_CASE = floats_tensor(control_image.shape , rng=random.Random(A__ ) ).to(A__ )
_SCREAMING_SNAKE_CASE = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_SCREAMING_SNAKE_CASE = Image.fromarray(np.uinta(A__ ) ).convert("""RGB""" ).resize((64, 64) )
_SCREAMING_SNAKE_CASE = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def UpperCamelCase ( self ) -> str:
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def UpperCamelCase ( self ) -> Tuple:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def UpperCamelCase ( self ) -> List[str]:
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
class _a (lowercase_ , lowercase_ , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = StableDiffusionControlNetImgaImgPipeline
SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
SCREAMING_SNAKE_CASE = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
SCREAMING_SNAKE_CASE = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def UpperCamelCase ( self ) -> Optional[Any]:
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
torch.manual_seed(0 )
def init_weights(A__ ):
if isinstance(A__ , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
_SCREAMING_SNAKE_CASE = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(A__ )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(A__ )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=A__ , set_alpha_to_one=A__ , )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
_SCREAMING_SNAKE_CASE = CLIPTextModel(A__ )
_SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
_SCREAMING_SNAKE_CASE = MultiControlNetModel([controlneta, controlneta] )
_SCREAMING_SNAKE_CASE = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def UpperCamelCase ( self , A__ , A__=0 ) -> List[str]:
if str(A__ ).startswith("""mps""" ):
_SCREAMING_SNAKE_CASE = torch.manual_seed(A__ )
else:
_SCREAMING_SNAKE_CASE = torch.Generator(device=A__ ).manual_seed(A__ )
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=A__ , device=torch.device(A__ ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=A__ , device=torch.device(A__ ) , ),
]
_SCREAMING_SNAKE_CASE = floats_tensor(control_image[0].shape , rng=random.Random(A__ ) ).to(A__ )
_SCREAMING_SNAKE_CASE = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_SCREAMING_SNAKE_CASE = Image.fromarray(np.uinta(A__ ) ).convert("""RGB""" ).resize((64, 64) )
_SCREAMING_SNAKE_CASE = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.get_dummy_components()
_SCREAMING_SNAKE_CASE = self.pipeline_class(**A__ )
pipe.to(A__ )
_SCREAMING_SNAKE_CASE = 10.0
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = self.get_dummy_inputs(A__ )
_SCREAMING_SNAKE_CASE = steps
_SCREAMING_SNAKE_CASE = scale
_SCREAMING_SNAKE_CASE = pipe(**A__ )[0]
_SCREAMING_SNAKE_CASE = self.get_dummy_inputs(A__ )
_SCREAMING_SNAKE_CASE = steps
_SCREAMING_SNAKE_CASE = scale
_SCREAMING_SNAKE_CASE = pipe(**A__ , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
_SCREAMING_SNAKE_CASE = self.get_dummy_inputs(A__ )
_SCREAMING_SNAKE_CASE = steps
_SCREAMING_SNAKE_CASE = scale
_SCREAMING_SNAKE_CASE = pipe(**A__ , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
_SCREAMING_SNAKE_CASE = self.get_dummy_inputs(A__ )
_SCREAMING_SNAKE_CASE = steps
_SCREAMING_SNAKE_CASE = scale
_SCREAMING_SNAKE_CASE = pipe(**A__ , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
def UpperCamelCase ( self ) -> List[Any]:
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def UpperCamelCase ( self ) -> Optional[Any]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def UpperCamelCase ( self ) -> Union[str, Any]:
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.get_dummy_components()
_SCREAMING_SNAKE_CASE = self.pipeline_class(**A__ )
pipe.to(A__ )
pipe.set_progress_bar_config(disable=A__ )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(A__ )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> str:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny""" )
_SCREAMING_SNAKE_CASE = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , safety_checker=A__ , controlnet=A__ )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=A__ )
_SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
_SCREAMING_SNAKE_CASE = """evil space-punk bird"""
_SCREAMING_SNAKE_CASE = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" ).resize((5_12, 5_12) )
_SCREAMING_SNAKE_CASE = load_image(
"""https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png""" ).resize((5_12, 5_12) )
_SCREAMING_SNAKE_CASE = pipe(
A__ , A__ , control_image=A__ , generator=A__ , output_type="""np""" , num_inference_steps=50 , strength=0.6 , )
_SCREAMING_SNAKE_CASE = output.images[0]
assert image.shape == (5_12, 5_12, 3)
_SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy""" )
assert np.abs(expected_image - image ).max() < 9E-2
| 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'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ) -> Optional[Any]:
"""simple docstring"""
if isinstance(lowerCamelCase__ , lowerCamelCase__ ) and isinstance(lowerCamelCase__ , lowerCamelCase__ ):
_SCREAMING_SNAKE_CASE = len(set_a.intersection(lowerCamelCase__ ) )
if alternative_union:
_SCREAMING_SNAKE_CASE = len(lowerCamelCase__ ) + len(lowerCamelCase__ )
else:
_SCREAMING_SNAKE_CASE = len(set_a.union(lowerCamelCase__ ) )
return intersection / union
if isinstance(lowerCamelCase__ , (list, tuple) ) and isinstance(lowerCamelCase__ , (list, tuple) ):
_SCREAMING_SNAKE_CASE = [element for element in set_a if element in set_b]
if alternative_union:
_SCREAMING_SNAKE_CASE = len(lowerCamelCase__ ) + len(lowerCamelCase__ )
return len(lowerCamelCase__ ) / union
else:
_SCREAMING_SNAKE_CASE = set_a + [element for element in set_b if element not in set_a]
return len(lowerCamelCase__ ) / len(lowerCamelCase__ )
return len(lowerCamelCase__ ) / len(lowerCamelCase__ )
return None
if __name__ == "__main__":
UpperCamelCase__ : List[str] = {"a", "b", "c", "d", "e"}
UpperCamelCase__ : List[Any] = {"c", "d", "e", "f", "h", "i"}
print(jaccard_similarity(set_a, set_b)) | 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 TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : List[str] = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapTextConfig",
],
"processing_clap": ["ClapProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : List[Any] = [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapModel",
"ClapPreTrainedModel",
"ClapTextModel",
"ClapTextModelWithProjection",
"ClapAudioModel",
"ClapAudioModelWithProjection",
]
UpperCamelCase__ : Dict = ["ClapFeatureExtractor"]
if TYPE_CHECKING:
from .configuration_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioConfig,
ClapConfig,
ClapTextConfig,
)
from .processing_clap import ClapProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clap import ClapFeatureExtractor
from .modeling_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioModel,
ClapAudioModelWithProjection,
ClapModel,
ClapPreTrainedModel,
ClapTextModel,
ClapTextModelWithProjection,
)
else:
import sys
UpperCamelCase__ : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 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 math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class _a (_lowerCamelCase):
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = None
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=0.999 , SCREAMING_SNAKE_CASE_="cosine" , ) -> List[Any]:
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(SCREAMING_SNAKE_CASE_ ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(SCREAMING_SNAKE_CASE_ ):
return math.exp(t * -12.0 )
else:
raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}" )
_SCREAMING_SNAKE_CASE = []
for i in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = i / num_diffusion_timesteps
_SCREAMING_SNAKE_CASE = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE_ ) / alpha_bar_fn(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ ) )
return torch.tensor(SCREAMING_SNAKE_CASE_ , dtype=torch.floataa )
class _a (_lowerCamelCase , _lowerCamelCase):
SCREAMING_SNAKE_CASE = 1
@register_to_config
def __init__( self , A__ = 10_00 , A__ = 0.0001 , A__ = 0.02 , A__ = "linear" , A__ = None , A__ = True , A__ = True , A__ = 0 , A__ = "epsilon" , A__ = 1.0 , **A__ , ) -> Any:
if kwargs.get("""set_alpha_to_one""" , A__ ) is not None:
_SCREAMING_SNAKE_CASE = (
"""The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
)
deprecate("""set_alpha_to_one""" , """1.0.0""" , A__ , standard_warn=A__ )
_SCREAMING_SNAKE_CASE = kwargs["""set_alpha_to_one"""]
if trained_betas is not None:
_SCREAMING_SNAKE_CASE = torch.tensor(A__ , dtype=torch.floataa )
elif beta_schedule == "linear":
_SCREAMING_SNAKE_CASE = torch.linspace(A__ , A__ , A__ , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
_SCREAMING_SNAKE_CASE = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , A__ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_SCREAMING_SNAKE_CASE = betas_for_alpha_bar(A__ )
else:
raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}" )
_SCREAMING_SNAKE_CASE = 1.0 - self.betas
_SCREAMING_SNAKE_CASE = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
_SCREAMING_SNAKE_CASE = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
_SCREAMING_SNAKE_CASE = 1.0
# setable values
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = torch.from_numpy(np.arange(0 , A__ ).copy().astype(np.intaa ) )
def UpperCamelCase ( self , A__ , A__ = None ) -> torch.FloatTensor:
return sample
def UpperCamelCase ( self , A__ , A__ = None ) -> List[Any]:
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
F" maximal {self.config.num_train_timesteps} timesteps." )
_SCREAMING_SNAKE_CASE = num_inference_steps
_SCREAMING_SNAKE_CASE = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
_SCREAMING_SNAKE_CASE = (np.arange(0 , A__ ) * step_ratio).round().copy().astype(np.intaa )
_SCREAMING_SNAKE_CASE = torch.from_numpy(A__ ).to(A__ )
self.timesteps += self.config.steps_offset
def UpperCamelCase ( self , A__ , A__ , A__ , A__ = 0.0 , A__ = False , A__ = None , A__ = True , ) -> Union[DDIMSchedulerOutput, Tuple]:
# 1. get previous step value (=t+1)
_SCREAMING_SNAKE_CASE = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
_SCREAMING_SNAKE_CASE = self.alphas_cumprod[timestep]
_SCREAMING_SNAKE_CASE = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
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
if self.config.prediction_type == "epsilon":
_SCREAMING_SNAKE_CASE = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
_SCREAMING_SNAKE_CASE = model_output
elif self.config.prediction_type == "sample":
_SCREAMING_SNAKE_CASE = model_output
_SCREAMING_SNAKE_CASE = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
_SCREAMING_SNAKE_CASE = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
_SCREAMING_SNAKE_CASE = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
""" `v_prediction`""" )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
_SCREAMING_SNAKE_CASE = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. 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) ** 0.5 * pred_epsilon
# 6. 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 not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=A__ , pred_original_sample=A__ )
def __len__( self ) -> List[Any]:
return self.config.num_train_timesteps
| 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__ : List[str] = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
"studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'luke'
def __init__( self , A__=5_02_67 , A__=50_00_00 , A__=7_68 , A__=2_56 , 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__=True , A__=None , A__=1 , A__=0 , A__=2 , **A__ , ) -> int:
super().__init__(pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ , **A__ )
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = entity_vocab_size
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = entity_emb_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 = use_entity_aware_attention
_SCREAMING_SNAKE_CASE = classifier_dropout
| 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'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False ) -> bool:
"""simple docstring"""
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_31_70_44_06_46_79_88_73_85_96_19_81 and not allow_probable:
raise ValueError(
"""Warning: upper bound of deterministic test is exceeded. """
"""Pass allow_probable=True to allow probabilistic test. """
"""A return value of True indicates a probable prime.""" )
# array bounds provided by analysis
_SCREAMING_SNAKE_CASE = [
20_47,
1_37_36_53,
25_32_60_01,
32_15_03_17_51,
2_15_23_02_89_87_47,
3_47_47_49_66_03_83,
3_41_55_00_71_72_83_21,
1,
3_82_51_23_05_65_46_41_30_51,
1,
1,
31_86_65_85_78_34_03_11_51_16_74_61,
3_31_70_44_06_46_79_88_73_85_96_19_81,
]
_SCREAMING_SNAKE_CASE = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(SCREAMING_SNAKE_CASE_ , 1 ):
if n < _p:
# then we have our last prime to check
_SCREAMING_SNAKE_CASE = primes[:idx]
break
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
_SCREAMING_SNAKE_CASE = False
for r in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = pow(SCREAMING_SNAKE_CASE_ , d * 2**r , SCREAMING_SNAKE_CASE_ )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
_SCREAMING_SNAKE_CASE = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def lowerCAmelCase_ ( ) -> None:
"""simple docstring"""
assert not miller_rabin(5_61 )
assert miller_rabin(5_63 )
# 2047
assert not miller_rabin(83_82_01 )
assert miller_rabin(83_82_07 )
# 1_373_653
assert not miller_rabin(17_31_60_01 )
assert miller_rabin(17_31_60_17 )
# 25_326_001
assert not miller_rabin(30_78_38_66_41 )
assert miller_rabin(30_78_38_66_53 )
# 3_215_031_751
assert not miller_rabin(1_71_30_45_57_48_01 )
assert miller_rabin(1_71_30_45_57_48_19 )
# 2_152_302_898_747
assert not miller_rabin(2_77_97_99_72_83_07 )
assert miller_rabin(2_77_97_99_72_83_27 )
# 3_474_749_660_383
assert not miller_rabin(1_13_85_00_23_90_94_41 )
assert miller_rabin(1_13_85_00_23_90_95_27 )
# 341_550_071_728_321
assert not miller_rabin(1_27_50_41_01_88_48_80_43_51 )
assert miller_rabin(1_27_50_41_01_88_48_80_43_91 )
# 3_825_123_056_546_413_051
assert not miller_rabin(7_96_66_46_44_58_50_77_87_79_18_67 )
assert miller_rabin(7_96_66_46_44_58_50_77_87_79_19_51 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(55_28_40_67_74_46_64_78_97_66_03_33 )
assert miller_rabin(55_28_40_67_74_46_64_78_97_66_03_59 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 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 copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class _a :
"""simple docstring"""
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> "DownloadConfig":
return self.__class__(**{k: copy.deepcopy(A__ ) for k, v in self.__dict__.items()} )
| 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 |
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive""" )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""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_ ) -> Optional[int]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_SCREAMING_SNAKE_CASE = orig_state_dict.pop(SCREAMING_SNAKE_CASE_ )
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(SCREAMING_SNAKE_CASE_ ).expand((1, -1) ) + 2
return orig_state_dict
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )["""model_state_dict"""]
_SCREAMING_SNAKE_CASE = YosoConfig.from_json_file(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = YosoForMaskedLM(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = convert_checkpoint_helper(config.max_position_embeddings , SCREAMING_SNAKE_CASE_ )
print(model.load_state_dict(SCREAMING_SNAKE_CASE_ ) )
model.eval()
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}" )
if __name__ == "__main__":
UpperCamelCase__ : int = 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__ : int = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 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 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)
| 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 argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = TapasConfig.from_json_file(SCREAMING_SNAKE_CASE_ )
# set absolute/relative position embeddings parameter
_SCREAMING_SNAKE_CASE = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
_SCREAMING_SNAKE_CASE = TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
elif task == "WTQ":
# run_task_main.py hparams
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = True
# hparam_utils.py hparams
_SCREAMING_SNAKE_CASE = 0.66_4694
_SCREAMING_SNAKE_CASE = 0.20_7951
_SCREAMING_SNAKE_CASE = 0.12_1194
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = 0.035_2513
_SCREAMING_SNAKE_CASE = TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = False
# hparam_utils.py hparams
_SCREAMING_SNAKE_CASE = 36.4519
_SCREAMING_SNAKE_CASE = 0.90_3421
_SCREAMING_SNAKE_CASE = 222.088
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = 0.76_3141
_SCREAMING_SNAKE_CASE = TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
elif task == "TABFACT":
_SCREAMING_SNAKE_CASE = TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE_ )
elif task == "MLM":
_SCREAMING_SNAKE_CASE = TapasForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
elif task == "INTERMEDIATE_PRETRAINING":
_SCREAMING_SNAKE_CASE = TapasModel(config=SCREAMING_SNAKE_CASE_ )
else:
raise ValueError(F"Task {task} not supported." )
print(F"Building PyTorch model from configuration: {config}" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Save pytorch-model (weights and configuration)
print(F"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Save tokenizer files
print(F"Save tokenizer files to {pytorch_dump_path}" )
_SCREAMING_SNAKE_CASE = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt""" , model_max_length=5_12 )
tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
print("""Used relative position embeddings:""" , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
UpperCamelCase__ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
)
parser.add_argument(
"--reset_position_index_per_cell",
default=False,
action="store_true",
help="Whether to use relative position embeddings or not. Defaults to True.",
)
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--tapas_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained TAPAS model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
UpperCamelCase__ : Optional[int] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 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 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"
| 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 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"""
_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"""
main()
if __name__ == "__main__":
main()
| 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 .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 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'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : Optional[Any] = {
"snap-research/efficientformer-l1-300": (
"https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
),
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'efficientformer'
def __init__( self , A__ = [3, 2, 6, 4] , A__ = [48, 96, 2_24, 4_48] , A__ = [True, True, True, True] , A__ = 4_48 , A__ = 32 , A__ = 4 , A__ = 7 , A__ = 5 , A__ = 8 , A__ = 4 , A__ = 0.0 , A__ = 16 , A__ = 3 , A__ = 3 , A__ = 3 , A__ = 2 , A__ = 1 , A__ = 0.0 , A__ = 1 , A__ = True , A__ = True , A__ = 1E-5 , A__ = "gelu" , A__ = 0.02 , A__ = 1E-12 , A__ = 2_24 , A__ = 1E-05 , **A__ , ) -> None:
super().__init__(**A__ )
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dropout_prob
_SCREAMING_SNAKE_CASE = hidden_sizes
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = patch_size
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = depths
_SCREAMING_SNAKE_CASE = mlp_expansion_ratio
_SCREAMING_SNAKE_CASE = downsamples
_SCREAMING_SNAKE_CASE = dim
_SCREAMING_SNAKE_CASE = key_dim
_SCREAMING_SNAKE_CASE = attention_ratio
_SCREAMING_SNAKE_CASE = resolution
_SCREAMING_SNAKE_CASE = pool_size
_SCREAMING_SNAKE_CASE = downsample_patch_size
_SCREAMING_SNAKE_CASE = downsample_stride
_SCREAMING_SNAKE_CASE = downsample_pad
_SCREAMING_SNAKE_CASE = drop_path_rate
_SCREAMING_SNAKE_CASE = num_metaad_blocks
_SCREAMING_SNAKE_CASE = distillation
_SCREAMING_SNAKE_CASE = use_layer_scale
_SCREAMING_SNAKE_CASE = layer_scale_init_value
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = batch_norm_eps
| 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
from typing import Generic, TypeVar
UpperCamelCase__ : Any = TypeVar("T")
UpperCamelCase__ : Optional[int] = TypeVar("U")
class _a (Generic[T, U]):
"""simple docstring"""
def __init__( self , A__ , A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = key
_SCREAMING_SNAKE_CASE = val
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
def __repr__( self ) -> str:
return (
F"Node: key: {self.key}, val: {self.val}, "
F"has next: {bool(self.next )}, has prev: {bool(self.prev )}"
)
class _a (Generic[T, U]):
"""simple docstring"""
def __init__( self ) -> None:
_SCREAMING_SNAKE_CASE = DoubleLinkedListNode(A__ , A__ )
_SCREAMING_SNAKE_CASE = DoubleLinkedListNode(A__ , A__ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.rear, self.head
def __repr__( self ) -> str:
_SCREAMING_SNAKE_CASE = ["""DoubleLinkedList"""]
_SCREAMING_SNAKE_CASE = self.head
while node.next is not None:
rep.append(str(A__ ) )
_SCREAMING_SNAKE_CASE = node.next
rep.append(str(self.rear ) )
return ",\n ".join(A__ )
def UpperCamelCase ( self , A__ ) -> None:
_SCREAMING_SNAKE_CASE = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
_SCREAMING_SNAKE_CASE = node
_SCREAMING_SNAKE_CASE = previous
_SCREAMING_SNAKE_CASE = node
_SCREAMING_SNAKE_CASE = self.rear
def UpperCamelCase ( self , A__ ) -> DoubleLinkedListNode[T, U] | None:
if node.prev is None or node.next is None:
return None
_SCREAMING_SNAKE_CASE = node.next
_SCREAMING_SNAKE_CASE = node.prev
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
return node
class _a (Generic[T, U]):
"""simple docstring"""
SCREAMING_SNAKE_CASE = {}
def __init__( self , A__ ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = DoubleLinkedList()
_SCREAMING_SNAKE_CASE = capacity
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = {}
def __repr__( self ) -> str:
return (
F"CacheInfo(hits={self.hits}, misses={self.miss}, "
F"capacity={self.capacity}, current size={self.num_keys})"
)
def __contains__( self , A__ ) -> bool:
return key in self.cache
def UpperCamelCase ( self , A__ ) -> U | None:
# Note: pythonic interface would throw KeyError rather than return None
if key in self.cache:
self.hits += 1
_SCREAMING_SNAKE_CASE = self.cache[key]
_SCREAMING_SNAKE_CASE = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(A__ )
return node.val
self.miss += 1
return None
def UpperCamelCase ( self , A__ , A__ ) -> None:
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
_SCREAMING_SNAKE_CASE = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(A__ ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
_SCREAMING_SNAKE_CASE = DoubleLinkedListNode(A__ , A__ )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
_SCREAMING_SNAKE_CASE = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
_SCREAMING_SNAKE_CASE = value
self.list.add(A__ )
@classmethod
def UpperCamelCase ( cls , A__ = 1_28 ) -> Callable[[Callable[[T], U]], Callable[..., U]]:
def cache_decorator_inner(A__ ) -> Callable[..., U]:
def cache_decorator_wrapper(*A__ ) -> U:
if func not in cls.decorator_function_to_instance_map:
_SCREAMING_SNAKE_CASE = LRUCache(A__ )
_SCREAMING_SNAKE_CASE = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
_SCREAMING_SNAKE_CASE = func(*A__ )
cls.decorator_function_to_instance_map[func].put(args[0] , A__ )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(A__ , """cache_info""" , A__ ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
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'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase__ : str = {
"configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = [
"LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
"LongT5EncoderModel",
"LongT5ForConditionalGeneration",
"LongT5Model",
"LongT5PreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Dict = [
"FlaxLongT5ForConditionalGeneration",
"FlaxLongT5Model",
"FlaxLongT5PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longta import (
LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
LongTaEncoderModel,
LongTaForConditionalGeneration,
LongTaModel,
LongTaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_longta import (
FlaxLongTaForConditionalGeneration,
FlaxLongTaModel,
FlaxLongTaPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 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 collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_="attention" ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
_SCREAMING_SNAKE_CASE = params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
_SCREAMING_SNAKE_CASE = params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
_SCREAMING_SNAKE_CASE = params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
return k, o, q, v
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ) -> Dict:
"""simple docstring"""
if split_mlp_wi:
_SCREAMING_SNAKE_CASE = params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
_SCREAMING_SNAKE_CASE = params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
_SCREAMING_SNAKE_CASE = (wi_a, wi_a)
else:
_SCREAMING_SNAKE_CASE = params[F"{prefix}/layers_{i}/mlp/wi/kernel"]
_SCREAMING_SNAKE_CASE = params[F"{prefix}/layers_{i}/mlp/wo/kernel"]
return wi, wo
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
return params[F"{prefix}/layers_{i}/{layer_name}/scale"]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , *, SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = traverse_util.flatten_dict(variables["""target"""] )
_SCREAMING_SNAKE_CASE = {"""/""".join(SCREAMING_SNAKE_CASE_ ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
_SCREAMING_SNAKE_CASE = """encoder/layers_0/mlp/wi_0/kernel""" in old
print("""Split MLP:""" , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = collections.OrderedDict()
# Shared embeddings.
_SCREAMING_SNAKE_CASE = old["""token_embedder/embedding"""]
# Encoder.
for i in range(SCREAMING_SNAKE_CASE_ ):
# Block i, layer 0 (Self Attention).
_SCREAMING_SNAKE_CASE = tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , """encoder""" , """pre_attention_layer_norm""" )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = tax_attention_lookup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , """encoder""" , """attention""" )
_SCREAMING_SNAKE_CASE = layer_norm
_SCREAMING_SNAKE_CASE = k.T
_SCREAMING_SNAKE_CASE = o.T
_SCREAMING_SNAKE_CASE = q.T
_SCREAMING_SNAKE_CASE = v.T
# Block i, layer 1 (MLP).
_SCREAMING_SNAKE_CASE = tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , """encoder""" , """pre_mlp_layer_norm""" )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = tax_mlp_lookup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , """encoder""" , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = layer_norm
if split_mlp_wi:
_SCREAMING_SNAKE_CASE = wi[0].T
_SCREAMING_SNAKE_CASE = wi[1].T
else:
_SCREAMING_SNAKE_CASE = wi.T
_SCREAMING_SNAKE_CASE = wo.T
_SCREAMING_SNAKE_CASE = old[
"""encoder/relpos_bias/rel_embedding"""
].T
_SCREAMING_SNAKE_CASE = old["""encoder/encoder_norm/scale"""]
if not is_encoder_only:
# Decoder.
for i in range(SCREAMING_SNAKE_CASE_ ):
# Block i, layer 0 (Self Attention).
_SCREAMING_SNAKE_CASE = tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , """decoder""" , """pre_self_attention_layer_norm""" )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = tax_attention_lookup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , """decoder""" , """self_attention""" )
_SCREAMING_SNAKE_CASE = layer_norm
_SCREAMING_SNAKE_CASE = k.T
_SCREAMING_SNAKE_CASE = o.T
_SCREAMING_SNAKE_CASE = q.T
_SCREAMING_SNAKE_CASE = v.T
# Block i, layer 1 (Cross Attention).
_SCREAMING_SNAKE_CASE = tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , """decoder""" , """pre_cross_attention_layer_norm""" )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = tax_attention_lookup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , """decoder""" , """encoder_decoder_attention""" )
_SCREAMING_SNAKE_CASE = layer_norm
_SCREAMING_SNAKE_CASE = k.T
_SCREAMING_SNAKE_CASE = o.T
_SCREAMING_SNAKE_CASE = q.T
_SCREAMING_SNAKE_CASE = v.T
# Block i, layer 2 (MLP).
_SCREAMING_SNAKE_CASE = tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , """decoder""" , """pre_mlp_layer_norm""" )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = tax_mlp_lookup(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , """decoder""" , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = layer_norm
if split_mlp_wi:
_SCREAMING_SNAKE_CASE = wi[0].T
_SCREAMING_SNAKE_CASE = wi[1].T
else:
_SCREAMING_SNAKE_CASE = wi.T
_SCREAMING_SNAKE_CASE = wo.T
_SCREAMING_SNAKE_CASE = old["""decoder/decoder_norm/scale"""]
_SCREAMING_SNAKE_CASE = old[
"""decoder/relpos_bias/rel_embedding"""
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
_SCREAMING_SNAKE_CASE = old["""decoder/logits_dense/kernel"""].T
return new
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
_SCREAMING_SNAKE_CASE = state_dict["""shared.weight"""]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
_SCREAMING_SNAKE_CASE = state_dict["""shared.weight"""]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("""Using shared word embeddings as lm_head.""" )
_SCREAMING_SNAKE_CASE = state_dict["""shared.weight"""]
return state_dict
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = convert_tax_to_pytorch(SCREAMING_SNAKE_CASE_ , num_layers=config.num_layers , is_encoder_only=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = make_state_dict(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
model.load_state_dict(SCREAMING_SNAKE_CASE_ , strict=SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = False ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = TaConfig.from_json_file(SCREAMING_SNAKE_CASE_ )
print(F"Building PyTorch model from configuration: {config}" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
_SCREAMING_SNAKE_CASE = TaEncoderModel(SCREAMING_SNAKE_CASE_ )
else:
_SCREAMING_SNAKE_CASE = TaForConditionalGeneration(SCREAMING_SNAKE_CASE_ )
# Load weights from tf checkpoint
load_tax_weights_in_ta(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Verify that we can load the checkpoint.
model.from_pretrained(SCREAMING_SNAKE_CASE_ )
print("""Done""" )
if __name__ == "__main__":
UpperCamelCase__ : Optional[Any] = argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
# Required parameters
parser.add_argument(
"--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
)
UpperCamelCase__ : int = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 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_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ : Any = {
"configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Dict = ["MobileViTFeatureExtractor"]
UpperCamelCase__ : int = ["MobileViTImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Any = [
"MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"MobileViTForImageClassification",
"MobileViTForSemanticSegmentation",
"MobileViTModel",
"MobileViTPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Any = [
"TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFMobileViTForImageClassification",
"TFMobileViTForSemanticSegmentation",
"TFMobileViTModel",
"TFMobileViTPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilevit import MobileViTFeatureExtractor
from .image_processing_mobilevit import MobileViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilevit import (
MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilevit import (
TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 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 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__ )
| 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_torch_available, is_vision_available
UpperCamelCase__ : int = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",
"PoolFormerOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Optional[int] = ["PoolFormerFeatureExtractor"]
UpperCamelCase__ : Dict = ["PoolFormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Tuple = [
"POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"PoolFormerForImageClassification",
"PoolFormerModel",
"PoolFormerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_poolformer import (
POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
PoolFormerConfig,
PoolFormerOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_poolformer import PoolFormerFeatureExtractor
from .image_processing_poolformer import PoolFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_poolformer import (
POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
PoolFormerForImageClassification,
PoolFormerModel,
PoolFormerPreTrainedModel,
)
else:
import sys
UpperCamelCase__ : Any = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 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'''
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> str:
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
_SCREAMING_SNAKE_CASE = FlaxDiffusionPipeline.from_pretrained(
"""hf-internal-testing/tiny-stable-diffusion-pipe""" , safety_checker=A__ , cache_dir=A__ )
_SCREAMING_SNAKE_CASE = [t[-1] for t in os.walk(os.path.join(A__ , os.listdir(A__ )[0] , """snapshots""" ) )]
_SCREAMING_SNAKE_CASE = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith(""".bin""" ) for f in files )
@slow
@require_flax
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
"""hf-internal-testing/tiny-stable-diffusion-pipe""" , safety_checker=A__ )
_SCREAMING_SNAKE_CASE = (
"""A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
""" field, close up, split lighting, cinematic"""
)
_SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0 )
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = jax.device_count()
_SCREAMING_SNAKE_CASE = num_samples * [prompt]
_SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(A__ )
# shard inputs and rng
_SCREAMING_SNAKE_CASE = replicate(A__ )
_SCREAMING_SNAKE_CASE = jax.random.split(A__ , A__ )
_SCREAMING_SNAKE_CASE = shard(A__ )
_SCREAMING_SNAKE_CASE = pipeline(A__ , A__ , A__ , A__ , jit=A__ ).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 4.151_4745 ) < 1E-3
assert np.abs(np.abs(A__ , dtype=np.floataa ).sum() - 4_99_47.8_75 ) < 5E-1
_SCREAMING_SNAKE_CASE = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(A__ ) == num_samples
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""flax""" , safety_checker=A__ )
_SCREAMING_SNAKE_CASE = (
"""A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
""" field, close up, split lighting, cinematic"""
)
_SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0 )
_SCREAMING_SNAKE_CASE = 50
_SCREAMING_SNAKE_CASE = jax.device_count()
_SCREAMING_SNAKE_CASE = num_samples * [prompt]
_SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(A__ )
# shard inputs and rng
_SCREAMING_SNAKE_CASE = replicate(A__ )
_SCREAMING_SNAKE_CASE = jax.random.split(A__ , A__ )
_SCREAMING_SNAKE_CASE = shard(A__ )
_SCREAMING_SNAKE_CASE = pipeline(A__ , A__ , A__ , A__ , jit=A__ ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0565_2401) ) < 1E-3
assert np.abs((np.abs(A__ , dtype=np.floataa ).sum() - 2_38_38_08.2) ) < 5E-1
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""bf16""" , dtype=jnp.bfloataa , safety_checker=A__ )
_SCREAMING_SNAKE_CASE = (
"""A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
""" field, close up, split lighting, cinematic"""
)
_SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0 )
_SCREAMING_SNAKE_CASE = 50
_SCREAMING_SNAKE_CASE = jax.device_count()
_SCREAMING_SNAKE_CASE = num_samples * [prompt]
_SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(A__ )
# shard inputs and rng
_SCREAMING_SNAKE_CASE = replicate(A__ )
_SCREAMING_SNAKE_CASE = jax.random.split(A__ , A__ )
_SCREAMING_SNAKE_CASE = shard(A__ )
_SCREAMING_SNAKE_CASE = pipeline(A__ , A__ , A__ , A__ , jit=A__ ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0400_3906) ) < 1E-3
assert np.abs((np.abs(A__ , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""bf16""" , dtype=jnp.bfloataa )
_SCREAMING_SNAKE_CASE = (
"""A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
""" field, close up, split lighting, cinematic"""
)
_SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0 )
_SCREAMING_SNAKE_CASE = 50
_SCREAMING_SNAKE_CASE = jax.device_count()
_SCREAMING_SNAKE_CASE = num_samples * [prompt]
_SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(A__ )
# shard inputs and rng
_SCREAMING_SNAKE_CASE = replicate(A__ )
_SCREAMING_SNAKE_CASE = jax.random.split(A__ , A__ )
_SCREAMING_SNAKE_CASE = shard(A__ )
_SCREAMING_SNAKE_CASE = pipeline(A__ , A__ , A__ , A__ , jit=A__ ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0400_3906) ) < 1E-3
assert np.abs((np.abs(A__ , dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = FlaxDDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , set_alpha_to_one=A__ , steps_offset=1 , )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""bf16""" , dtype=jnp.bfloataa , scheduler=A__ , safety_checker=A__ , )
_SCREAMING_SNAKE_CASE = scheduler.create_state()
_SCREAMING_SNAKE_CASE = scheduler_state
_SCREAMING_SNAKE_CASE = (
"""A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
""" field, close up, split lighting, cinematic"""
)
_SCREAMING_SNAKE_CASE = jax.random.PRNGKey(0 )
_SCREAMING_SNAKE_CASE = 50
_SCREAMING_SNAKE_CASE = jax.device_count()
_SCREAMING_SNAKE_CASE = num_samples * [prompt]
_SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(A__ )
# shard inputs and rng
_SCREAMING_SNAKE_CASE = replicate(A__ )
_SCREAMING_SNAKE_CASE = jax.random.split(A__ , A__ )
_SCREAMING_SNAKE_CASE = shard(A__ )
_SCREAMING_SNAKE_CASE = pipeline(A__ , A__ , A__ , A__ , jit=A__ ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] , dtype=np.floataa ).sum() - 0.0_4504_3945) ) < 1E-3
assert np.abs((np.abs(A__ , dtype=np.floataa ).sum() - 2_34_76_93.5) ) < 5E-1
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = (
"""A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of"""
""" field, close up, split lighting, cinematic"""
)
_SCREAMING_SNAKE_CASE = jax.device_count()
_SCREAMING_SNAKE_CASE = num_samples * [prompt]
_SCREAMING_SNAKE_CASE = jax.random.split(jax.random.PRNGKey(0 ) , A__ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""bf16""" , dtype=jnp.bfloataa , safety_checker=A__ , )
_SCREAMING_SNAKE_CASE = replicate(A__ )
_SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(A__ )
_SCREAMING_SNAKE_CASE = shard(A__ )
_SCREAMING_SNAKE_CASE = pipeline(A__ , A__ , A__ , jit=A__ ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
_SCREAMING_SNAKE_CASE = images[2, 0, 2_56, 10:17, 1]
# With memory efficient attention
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = FlaxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""bf16""" , dtype=jnp.bfloataa , safety_checker=A__ , use_memory_efficient_attention=A__ , )
_SCREAMING_SNAKE_CASE = replicate(A__ )
_SCREAMING_SNAKE_CASE = pipeline.prepare_inputs(A__ )
_SCREAMING_SNAKE_CASE = shard(A__ )
_SCREAMING_SNAKE_CASE = pipeline(A__ , A__ , A__ , jit=A__ ).images
assert images_eff.shape == (num_samples, 1, 5_12, 5_12, 3)
_SCREAMING_SNAKE_CASE = images[2, 0, 2_56, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1E-2
| 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 |
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] )}
| 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
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__ : 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) | 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'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
for line in lines:
_SCREAMING_SNAKE_CASE = re.sub(r"""#.*""" , """""" , SCREAMING_SNAKE_CASE_ ) # remove comments
if line:
filtered_lines.append(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = """\n""".join(SCREAMING_SNAKE_CASE_ )
# Make a hash from all this code
_SCREAMING_SNAKE_CASE = full_str.encode("""utf-8""" )
return shaaaa(SCREAMING_SNAKE_CASE_ ).hexdigest()
# get importable module names and hash for caching
UpperCamelCase__ : List[str] = {
"csv": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"json": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"pandas": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"parquet": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"arrow": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"text": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"imagefolder": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"audiofolder": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
UpperCamelCase__ : str = {
".csv": ("csv", {}),
".tsv": ("csv", {"sep": "\t"}),
".json": ("json", {}),
".jsonl": ("json", {}),
".parquet": ("parquet", {}),
".arrow": ("arrow", {}),
".txt": ("text", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
UpperCamelCase__ : Union[str, Any] = {"imagefolder", "audiofolder"}
# Used to filter data files based on extensions given a module name
UpperCamelCase__ : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(".zip")
_MODULE_TO_EXTENSIONS["audiofolder"].append(".zip")
| 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 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):
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,)
| 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
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase__ : Tuple = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paws""", """csv"""] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
inspect_dataset(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = path + """.py"""
assert script_name in os.listdir(SCREAMING_SNAKE_CASE_ )
assert "__pycache__" not in os.listdir(SCREAMING_SNAKE_CASE_ )
@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.parametrize("""path""" , ["""accuracy"""] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
inspect_metric(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = path + """.py"""
assert script_name in os.listdir(SCREAMING_SNAKE_CASE_ )
assert "__pycache__" not in os.listdir(SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize(
"""path, config_name, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = get_dataset_config_info(SCREAMING_SNAKE_CASE_ , config_name=SCREAMING_SNAKE_CASE_ )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
get_dataset_config_info(SCREAMING_SNAKE_CASE_ , config_name=SCREAMING_SNAKE_CASE_ )
@pytest.mark.parametrize(
"""path, expected""" , [
("""squad""", """plain_text"""),
("""acronym_identification""", """default"""),
("""lhoestq/squad""", """plain_text"""),
("""lhoestq/test""", """default"""),
("""lhoestq/demo1""", """lhoestq--demo1"""),
("""dalle-mini/wit""", """dalle-mini--wit"""),
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = get_dataset_config_names(SCREAMING_SNAKE_CASE_ )
assert expected in config_names
@pytest.mark.parametrize(
"""path, expected_configs, expected_splits_in_first_config""" , [
("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = get_dataset_infos(SCREAMING_SNAKE_CASE_ )
assert list(infos.keys() ) == expected_configs
_SCREAMING_SNAKE_CASE = expected_configs[0]
assert expected_config in infos
_SCREAMING_SNAKE_CASE = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"""path, expected_config, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = get_dataset_infos(SCREAMING_SNAKE_CASE_ )
assert expected_config in infos
_SCREAMING_SNAKE_CASE = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
with pytest.raises(SCREAMING_SNAKE_CASE_ ):
get_dataset_split_names(SCREAMING_SNAKE_CASE_ , config_name=SCREAMING_SNAKE_CASE_ )
| 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 __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
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 (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class _a :
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=True , A__=True , A__=99 , A__=[1, 1, 2] , A__=1 , A__=32 , A__=4 , A__=8 , A__=37 , A__="gelu_new" , A__=0.1 , A__=0.1 , A__=0.0 , A__=5_12 , A__=3 , A__=0.02 , A__=3 , A__=4 , A__=None , A__=False , ) -> Dict:
_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 = block_sizes
_SCREAMING_SNAKE_CASE = num_decoder_layers
_SCREAMING_SNAKE_CASE = d_model
_SCREAMING_SNAKE_CASE = n_head
_SCREAMING_SNAKE_CASE = d_head
_SCREAMING_SNAKE_CASE = d_inner
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dropout
_SCREAMING_SNAKE_CASE = attention_dropout
_SCREAMING_SNAKE_CASE = activation_dropout
_SCREAMING_SNAKE_CASE = max_position_embeddings
_SCREAMING_SNAKE_CASE = type_vocab_size
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = num_labels
_SCREAMING_SNAKE_CASE = num_choices
_SCREAMING_SNAKE_CASE = scope
_SCREAMING_SNAKE_CASE = initializer_std
# Used in the tests to check the size of the first attention layer
_SCREAMING_SNAKE_CASE = n_head
# Used in the tests to check the size of the first hidden state
_SCREAMING_SNAKE_CASE = self.d_model
# Used in the tests to check the number of output hidden states/attentions
_SCREAMING_SNAKE_CASE = sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
_SCREAMING_SNAKE_CASE = self.num_hidden_layers + 2
def UpperCamelCase ( self ) -> int:
_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 = FunnelConfig(
vocab_size=self.vocab_size , block_sizes=self.block_sizes , num_decoder_layers=self.num_decoder_layers , d_model=self.d_model , n_head=self.n_head , d_head=self.d_head , d_inner=self.d_inner , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , activation_dropout=self.activation_dropout , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_std=self.initializer_std , )
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__ , ) -> List[str]:
_SCREAMING_SNAKE_CASE = TFFunnelModel(config=A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
_SCREAMING_SNAKE_CASE = model(A__ )
_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.d_model) )
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = TFFunnelModel(config=A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = TFFunnelModel(config=A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.d_model) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = TFFunnelBaseModel(config=A__ )
_SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
_SCREAMING_SNAKE_CASE = model(A__ )
_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, 2, self.d_model) )
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = TFFunnelBaseModel(config=A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 3, self.d_model) )
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = TFFunnelBaseModel(config=A__ )
_SCREAMING_SNAKE_CASE = model(A__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, 2, self.d_model) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = TFFunnelForPreTraining(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) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = TFFunnelForMaskedLM(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__ , ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFFunnelForSequenceClassification(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__ , ) -> Dict:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = TFFunnelForMultipleChoice(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[Any]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = TFFunnelForTokenClassification(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__ , ) -> Dict:
_SCREAMING_SNAKE_CASE = TFFunnelForQuestionAnswering(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 ) -> Optional[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
) ,
) = 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 = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
'fill-mask': TFFunnelForMaskedLM,
'question-answering': TFFunnelForQuestionAnswering,
'text-classification': TFFunnelForSequenceClassification,
'token-classification': TFFunnelForTokenClassification,
'zero-shot': TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = TFFunnelModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ )
def UpperCamelCase ( self ) -> List[Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A__ )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A__ )
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A__ )
@require_tf
class _a (_lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = TFFunnelModelTester(self , base=A__ )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ )
def UpperCamelCase ( self ) -> str:
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_base_model(*A__ )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*A__ )
| 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_ , SCREAMING_SNAKE_CASE_ ) -> float:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def lowerCAmelCase_ ( ) -> Optional[int]:
"""simple docstring"""
print(sum_of_series(1 , 1 , 10 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 math
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0 , SCREAMING_SNAKE_CASE_ = 0 ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = end or len(SCREAMING_SNAKE_CASE_ )
for i in range(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = i
_SCREAMING_SNAKE_CASE = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
_SCREAMING_SNAKE_CASE = array[temp_index - 1]
temp_index -= 1
_SCREAMING_SNAKE_CASE = temp_index_value
return array
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> None: # Max Heap
"""simple docstring"""
_SCREAMING_SNAKE_CASE = index
_SCREAMING_SNAKE_CASE = 2 * index + 1 # Left Node
_SCREAMING_SNAKE_CASE = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
_SCREAMING_SNAKE_CASE = left_index
if right_index < heap_size and array[largest] < array[right_index]:
_SCREAMING_SNAKE_CASE = right_index
if largest != index:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = array[largest], array[index]
heapify(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(SCREAMING_SNAKE_CASE_ )
for i in range(n // 2 , -1 , -1 ):
heapify(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
for i in range(n - 1 , 0 , -1 ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = array[0], array[i]
heapify(SCREAMING_SNAKE_CASE_ , 0 , SCREAMING_SNAKE_CASE_ )
return array
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = low
_SCREAMING_SNAKE_CASE = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = array[j], array[i]
i += 1
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return array
_SCREAMING_SNAKE_CASE = 2 * math.ceil(math.loga(len(SCREAMING_SNAKE_CASE_ ) ) )
_SCREAMING_SNAKE_CASE = 16
return intro_sort(SCREAMING_SNAKE_CASE_ , 0 , len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> list:
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(SCREAMING_SNAKE_CASE_ )
max_depth -= 1
_SCREAMING_SNAKE_CASE = median_of_a(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , start + ((end - start) // 2) + 1 , end - 1 )
_SCREAMING_SNAKE_CASE = partition(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
intro_sort(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = p
return insertion_sort(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ : Optional[Any] = input("Enter numbers separated by a comma : ").strip()
UpperCamelCase__ : Optional[int] = [float(item) for item in user_input.split(",")]
print(sort(unsorted))
| 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 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Dict = logging.get_logger(__name__)
UpperCamelCase__ : List[str] = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# See all SEW models at https://huggingface.co/models?filter=sew
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'sew'
def __init__( self , A__=32 , A__=7_68 , A__=12 , A__=12 , A__=30_72 , A__=2 , A__="gelu" , A__=0.1 , A__=0.1 , A__=0.1 , A__=0.0 , A__=0.1 , A__=0.1 , A__=0.02 , A__=1E-5 , A__="group" , A__="gelu" , A__=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12) , A__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , A__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , A__=False , A__=1_28 , A__=16 , A__=True , A__=0.05 , A__=10 , A__=2 , A__=0.0 , A__=10 , A__=0 , A__="mean" , A__=False , A__=False , A__=2_56 , A__=0 , A__=1 , A__=2 , **A__ , ) -> List[Any]:
super().__init__(**A__ , pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ )
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = feat_extract_norm
_SCREAMING_SNAKE_CASE = feat_extract_activation
_SCREAMING_SNAKE_CASE = list(A__ )
_SCREAMING_SNAKE_CASE = list(A__ )
_SCREAMING_SNAKE_CASE = list(A__ )
_SCREAMING_SNAKE_CASE = conv_bias
_SCREAMING_SNAKE_CASE = num_conv_pos_embeddings
_SCREAMING_SNAKE_CASE = num_conv_pos_embedding_groups
_SCREAMING_SNAKE_CASE = len(self.conv_dim )
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = squeeze_factor
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = hidden_dropout
_SCREAMING_SNAKE_CASE = attention_dropout
_SCREAMING_SNAKE_CASE = activation_dropout
_SCREAMING_SNAKE_CASE = feat_proj_dropout
_SCREAMING_SNAKE_CASE = final_dropout
_SCREAMING_SNAKE_CASE = layerdrop
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"""Configuration for convolutional layers is incorrect."""
"""It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
F"but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"
F"= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`." )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_SCREAMING_SNAKE_CASE = apply_spec_augment
_SCREAMING_SNAKE_CASE = mask_time_prob
_SCREAMING_SNAKE_CASE = mask_time_length
_SCREAMING_SNAKE_CASE = mask_time_min_masks
_SCREAMING_SNAKE_CASE = mask_feature_prob
_SCREAMING_SNAKE_CASE = mask_feature_length
_SCREAMING_SNAKE_CASE = mask_feature_min_masks
# ctc loss
_SCREAMING_SNAKE_CASE = ctc_loss_reduction
_SCREAMING_SNAKE_CASE = ctc_zero_infinity
# sequence classification
_SCREAMING_SNAKE_CASE = use_weighted_layer_sum
_SCREAMING_SNAKE_CASE = classifier_proj_size
@property
def UpperCamelCase ( self ) -> Optional[int]:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 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 math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = True , SCREAMING_SNAKE_CASE_ = math.inf , SCREAMING_SNAKE_CASE_ = -math.inf , SCREAMING_SNAKE_CASE_ = math.inf , SCREAMING_SNAKE_CASE_ = -math.inf , SCREAMING_SNAKE_CASE_ = False , SCREAMING_SNAKE_CASE_ = 1_00 , SCREAMING_SNAKE_CASE_ = 0.01 , SCREAMING_SNAKE_CASE_ = 1 , ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = search_prob
_SCREAMING_SNAKE_CASE = start_temperate
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = None
while not search_end:
_SCREAMING_SNAKE_CASE = current_state.score()
if best_state is None or current_score > best_state.score():
_SCREAMING_SNAKE_CASE = current_state
scores.append(SCREAMING_SNAKE_CASE_ )
iterations += 1
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
_SCREAMING_SNAKE_CASE = random.randint(0 , len(SCREAMING_SNAKE_CASE_ ) - 1 ) # picking a random neighbor
_SCREAMING_SNAKE_CASE = neighbors.pop(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
_SCREAMING_SNAKE_CASE = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
_SCREAMING_SNAKE_CASE = picked_neighbor
else:
_SCREAMING_SNAKE_CASE = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
_SCREAMING_SNAKE_CASE = picked_neighbor
_SCREAMING_SNAKE_CASE = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
_SCREAMING_SNAKE_CASE = True
else:
_SCREAMING_SNAKE_CASE = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
plt.xlabel("""Iterations""" )
plt.ylabel("""Function values""" )
plt.show()
return best_state
if __name__ == "__main__":
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
UpperCamelCase__ : int = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
UpperCamelCase__ : List[str] = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
# starting the problem with initial coordinates (12, 47)
UpperCamelCase__ : int = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
UpperCamelCase__ : int = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
return (3 * x**2) - (6 * y)
UpperCamelCase__ : List[Any] = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
UpperCamelCase__ : int = simulated_annealing(prob, find_max=False, visualization=True)
print(
"The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"""{local_min.score()}"""
)
UpperCamelCase__ : Union[str, Any] = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
UpperCamelCase__ : Dict = simulated_annealing(prob, find_max=True, visualization=True)
print(
"The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"""{local_min.score()}"""
)
| 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 collections import namedtuple
import requests
from lxml import html # type: ignore
UpperCamelCase__ : Union[str, Any] = namedtuple("covid_data", "cases deaths recovered")
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """//div[@class = \"maincounter-number\"]/span/text()"""
return covid_data(*html.fromstring(requests.get(SCREAMING_SNAKE_CASE_ ).content ).xpath(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase__ : Tuple = "Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}"
print(fmt.format(*covid_stats()))
| 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 argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = old_name
if "patch_embed" in old_name:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = old_name.split(""".""" )
if layer == "0":
_SCREAMING_SNAKE_CASE = old_name.replace("""0""" , """convolution1""" )
elif layer == "1":
_SCREAMING_SNAKE_CASE = old_name.replace("""1""" , """batchnorm_before""" )
elif layer == "3":
_SCREAMING_SNAKE_CASE = old_name.replace("""3""" , """convolution2""" )
else:
_SCREAMING_SNAKE_CASE = old_name.replace("""4""" , """batchnorm_after""" )
if "network" in old_name and re.search(r"""\d\.\d""" , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = r"""\b\d{2}\b"""
if bool(re.search(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ):
_SCREAMING_SNAKE_CASE = re.search(r"""\d\.\d\d.""" , SCREAMING_SNAKE_CASE_ ).group()
else:
_SCREAMING_SNAKE_CASE = re.search(r"""\d\.\d.""" , SCREAMING_SNAKE_CASE_ ).group()
if int(match[0] ) < 6:
_SCREAMING_SNAKE_CASE = old_name.replace(SCREAMING_SNAKE_CASE_ , """""" )
_SCREAMING_SNAKE_CASE = trimmed_name.replace("""network""" , match[0] + """.meta4D_layers.blocks.""" + match[2:-1] )
_SCREAMING_SNAKE_CASE = """intermediate_stages.""" + trimmed_name
else:
_SCREAMING_SNAKE_CASE = old_name.replace(SCREAMING_SNAKE_CASE_ , """""" )
if int(match[2] ) < num_meta4D_last_stage:
_SCREAMING_SNAKE_CASE = trimmed_name.replace("""network""" , """meta4D_layers.blocks.""" + match[2] )
else:
_SCREAMING_SNAKE_CASE = str(int(match[2] ) - num_meta4D_last_stage )
_SCREAMING_SNAKE_CASE = trimmed_name.replace("""network""" , """meta3D_layers.blocks.""" + layer_index )
if "norm1" in old_name:
_SCREAMING_SNAKE_CASE = trimmed_name.replace("""norm1""" , """layernorm1""" )
elif "norm2" in old_name:
_SCREAMING_SNAKE_CASE = trimmed_name.replace("""norm2""" , """layernorm2""" )
elif "fc1" in old_name:
_SCREAMING_SNAKE_CASE = trimmed_name.replace("""fc1""" , """linear_in""" )
elif "fc2" in old_name:
_SCREAMING_SNAKE_CASE = trimmed_name.replace("""fc2""" , """linear_out""" )
_SCREAMING_SNAKE_CASE = """last_stage.""" + trimmed_name
elif "network" in old_name and re.search(r""".\d.""" , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = old_name.replace("""network""" , """intermediate_stages""" )
if "fc" in new_name:
_SCREAMING_SNAKE_CASE = new_name.replace("""fc""" , """convolution""" )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
_SCREAMING_SNAKE_CASE = new_name.replace("""norm1""" , """batchnorm_before""" )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
_SCREAMING_SNAKE_CASE = new_name.replace("""norm2""" , """batchnorm_after""" )
if "proj" in new_name:
_SCREAMING_SNAKE_CASE = new_name.replace("""proj""" , """projection""" )
if "dist_head" in new_name:
_SCREAMING_SNAKE_CASE = new_name.replace("""dist_head""" , """distillation_classifier""" )
elif "head" in new_name:
_SCREAMING_SNAKE_CASE = new_name.replace("""head""" , """classifier""" )
elif "patch_embed" in new_name:
_SCREAMING_SNAKE_CASE = """efficientformer.""" + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
_SCREAMING_SNAKE_CASE = new_name.replace("""norm""" , """layernorm""" )
_SCREAMING_SNAKE_CASE = """efficientformer.""" + new_name
else:
_SCREAMING_SNAKE_CASE = """efficientformer.encoder.""" + new_name
return new_name
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
for key in checkpoint.copy().keys():
_SCREAMING_SNAKE_CASE = checkpoint.pop(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = val
return checkpoint
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 image
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = torch.load(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )["""model"""]
_SCREAMING_SNAKE_CASE = EfficientFormerConfig.from_json_file(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = EfficientFormerForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = """_""".join(checkpoint_path.split("""/""" )[-1].split(""".""" )[0].split("""_""" )[:-1] )
_SCREAMING_SNAKE_CASE = config.depths[-1] - config.num_metaad_blocks + 1
_SCREAMING_SNAKE_CASE = convert_torch_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
model.eval()
_SCREAMING_SNAKE_CASE = {
"""bilinear""": PILImageResampling.BILINEAR,
"""bicubic""": PILImageResampling.BICUBIC,
"""nearest""": PILImageResampling.NEAREST,
}
# prepare image
_SCREAMING_SNAKE_CASE = prepare_img()
_SCREAMING_SNAKE_CASE = 2_56
_SCREAMING_SNAKE_CASE = 2_24
_SCREAMING_SNAKE_CASE = EfficientFormerImageProcessor(
size={"""shortest_edge""": image_size} , crop_size={"""height""": crop_size, """width""": crop_size} , resample=pillow_resamplings["""bicubic"""] , )
_SCREAMING_SNAKE_CASE = processor(images=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).pixel_values
# original processing pipeline
_SCREAMING_SNAKE_CASE = Compose(
[
Resize(SCREAMING_SNAKE_CASE_ , interpolation=pillow_resamplings["""bicubic"""] ),
CenterCrop(SCREAMING_SNAKE_CASE_ ),
ToTensor(),
Normalize(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ),
] )
_SCREAMING_SNAKE_CASE = image_transforms(SCREAMING_SNAKE_CASE_ ).unsqueeze(0 )
assert torch.allclose(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = model(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = outputs.logits
_SCREAMING_SNAKE_CASE = (1, 10_00)
if "l1" in model_name:
_SCREAMING_SNAKE_CASE = torch.Tensor(
[-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328] )
assert torch.allclose(logits[0, :10] , SCREAMING_SNAKE_CASE_ , atol=1e-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
_SCREAMING_SNAKE_CASE = torch.Tensor(
[-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127] )
assert torch.allclose(logits[0, :10] , SCREAMING_SNAKE_CASE_ , atol=1e-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
_SCREAMING_SNAKE_CASE = torch.Tensor(
[-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878] )
assert logits.shape == expected_shape
else:
raise ValueError(
F"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7" )
# Save Checkpoints
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}" )
processor.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(F"Processor successfuly saved at {pytorch_dump_path}" )
if push_to_hub:
print("""Pushing model to the hub...""" )
model.push_to_hub(
repo_id=F"Bearnardd/{pytorch_dump_path}" , commit_message="""Add model""" , use_temp_dir=SCREAMING_SNAKE_CASE_ , )
processor.push_to_hub(
repo_id=F"Bearnardd/{pytorch_dump_path}" , commit_message="""Add image processor""" , use_temp_dir=SCREAMING_SNAKE_CASE_ , )
if __name__ == "__main__":
UpperCamelCase__ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--pytorch_model_path",
default=None,
type=str,
required=True,
help="Path to EfficientFormer pytorch checkpoint.",
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The json file for EfficientFormer model config.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
parser.add_argument(
"--no-push_to_hub",
dest="push_to_hub",
action="store_false",
help="Do not push model and image processor to the hub",
)
parser.set_defaults(push_to_hub=True)
UpperCamelCase__ : str = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
)
| 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 warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE = 'FlavaImageProcessor'
SCREAMING_SNAKE_CASE = ('BertTokenizer', 'BertTokenizerFast')
def __init__( self , A__=None , A__=None , **A__ ) -> str:
_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__ = True , A__ = False , A__ = False , A__ = None , A__ = 0 , A__ = None , A__ = None , A__ = None , A__ = None , A__ = None , A__ = False , A__ = False , A__ = False , A__ = False , A__ = True , A__ = None , **A__ , ) -> str:
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(
text=A__ , add_special_tokens=A__ , padding=A__ , truncation=A__ , max_length=A__ , stride=A__ , pad_to_multiple_of=A__ , return_token_type_ids=A__ , return_attention_mask=A__ , return_overflowing_tokens=A__ , return_special_tokens_mask=A__ , return_offsets_mapping=A__ , return_length=A__ , verbose=A__ , return_tensors=A__ , **A__ , )
if images is not None:
_SCREAMING_SNAKE_CASE = self.image_processor(
A__ , return_image_mask=A__ , return_codebook_pixels=A__ , return_tensors=A__ , **A__ , )
if text is not None and images is not None:
encoding.update(A__ )
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__ ) -> Any:
return self.tokenizer.decode(*A__ , **A__ )
@property
def UpperCamelCase ( self ) -> Optional[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 ) -> List[Any]:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , A__ , )
return self.image_processor_class
@property
def UpperCamelCase ( self ) -> List[Any]:
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , A__ , )
return self.image_processor
| 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'''
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import 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 GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel
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__=5 , A__=4 , A__=4 , A__="gelu" , A__=0.0 , A__=0.1 , A__=True , A__=5_12 , A__=16 , A__=2 , A__=0.02 , A__=3 , A__=4 , A__=None , ) -> Dict:
_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_multiple_size
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dropout
_SCREAMING_SNAKE_CASE = attention_dropout
_SCREAMING_SNAKE_CASE = weight_tying
_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
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
if self.use_labels:
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_SCREAMING_SNAKE_CASE = self.get_config()
return config, input_ids, input_mask, token_labels
def UpperCamelCase ( self ) -> Any:
return GPTNeoXJapaneseConfig(
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_multiple_size=self.intermediate_multiple_size , hidden_act=self.hidden_act , hidden_dropout=self.hidden_dropout , attention_dropout=self.attention_dropout , weight_tying=self.weight_tying , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A__ , initializer_range=self.initializer_range , )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE = True
return config, input_ids, input_mask, token_labels
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Dict:
_SCREAMING_SNAKE_CASE = GPTNeoXJapaneseModel(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ , attention_mask=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__ ) -> str:
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = GPTNeoXJapaneseModel(A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ , attention_mask=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__ ) -> Any:
_SCREAMING_SNAKE_CASE = GPTNeoXJapaneseForCausalLM(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__ ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = GPTNeoXJapaneseForCausalLM(config=A__ )
model.to(A__ )
model.eval()
# first forward pass
_SCREAMING_SNAKE_CASE = model(A__ , attention_mask=A__ , use_cache=A__ )
_SCREAMING_SNAKE_CASE = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_SCREAMING_SNAKE_CASE = ids_tensor((self.batch_size, 3) , config.vocab_size )
_SCREAMING_SNAKE_CASE = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_SCREAMING_SNAKE_CASE = torch.cat([input_ids, next_tokens] , dim=-1 )
_SCREAMING_SNAKE_CASE = torch.cat([input_mask, next_mask] , dim=-1 )
_SCREAMING_SNAKE_CASE = model(A__ , attention_mask=A__ , output_hidden_states=A__ )
_SCREAMING_SNAKE_CASE = output_from_no_past["""hidden_states"""][0]
_SCREAMING_SNAKE_CASE = model(
A__ , attention_mask=A__ , past_key_values=A__ , output_hidden_states=A__ , )["""hidden_states"""][0]
# select random slice
_SCREAMING_SNAKE_CASE = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_SCREAMING_SNAKE_CASE = output_from_no_past[:, -3:, random_slice_idx].detach()
_SCREAMING_SNAKE_CASE = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A__ , A__ , atol=1E-3 ) )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
_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 = (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
SCREAMING_SNAKE_CASE = (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE = (
{'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = GPTNeoXJapaneseModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , hidden_size=37 )
def UpperCamelCase ( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Dict:
_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__ )
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(A__ , A__ , A__ )
def UpperCamelCase ( self ) -> List[Any]:
# This regression test was failing with PyTorch < 1.3
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_decoder()
_SCREAMING_SNAKE_CASE = None
self.model_tester.create_and_check_model_as_decoder(A__ , A__ , A__ )
def UpperCamelCase ( self ) -> Dict:
_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_decoder_model_past_large_inputs(A__ , A__ , A__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*A__ )
@slow
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = """abeja/gpt-neox-japanese-2.7b"""
_SCREAMING_SNAKE_CASE = ["""データサイエンティストとは、""", """100年後に必要とされる会社は、""", """フルリモートの環境で働くために必要なことは、""", """国境の長いトンネルを抜けると""", """美味しい日本食といえば、"""]
_SCREAMING_SNAKE_CASE = [
"""データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。""",
"""100年後に必要とされる会社は、「人」が中心の会社です。""",
"""フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。""",
"""国境の長いトンネルを抜けると、そこは雪国だった。""",
"""美味しい日本食といえば、やっぱりお寿司ですよね。""",
]
_SCREAMING_SNAKE_CASE = GPTNeoXJapaneseTokenizer.from_pretrained(A__ )
_SCREAMING_SNAKE_CASE = GPTNeoXJapaneseForCausalLM.from_pretrained(A__ )
_SCREAMING_SNAKE_CASE = []
for prompt in prompts:
_SCREAMING_SNAKE_CASE = tokenizer(A__ , return_tensors="""pt""" ).input_ids
_SCREAMING_SNAKE_CASE = model.generate(A__ , max_length=50 )
_SCREAMING_SNAKE_CASE = tokenizer.batch_decode(A__ , skip_special_tokens=A__ )
predicted_outputs += generated_string
self.assertListEqual(A__ , A__ )
| 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_ ) -> int:
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("""only integers accepted as input""" )
else:
_SCREAMING_SNAKE_CASE = str(abs(SCREAMING_SNAKE_CASE_ ) )
_SCREAMING_SNAKE_CASE = [list(SCREAMING_SNAKE_CASE_ ) for char in range(len(SCREAMING_SNAKE_CASE_ ) )]
for index in range(len(SCREAMING_SNAKE_CASE_ ) ):
num_transpositions[index].pop(SCREAMING_SNAKE_CASE_ )
return max(
int("""""".join(list(SCREAMING_SNAKE_CASE_ ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("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 __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 ) )
| 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 List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = 42
def __init__( self , A__ , A__ ) -> List[str]:
super().__init__()
self.register_modules(unet=A__ , scheduler=A__ )
@torch.no_grad()
def __call__( self , A__ = 1 , A__ = 50 , A__ = None , A__ = "pil" , A__ = True , **A__ , ) -> Union[Tuple, ImagePipelineOutput]:
_SCREAMING_SNAKE_CASE = self.unet.config.sample_size
_SCREAMING_SNAKE_CASE = (batch_size, 3, img_size, img_size)
_SCREAMING_SNAKE_CASE = self.unet
# sample x_0 ~ N(0, sigma_0^2 * I)
_SCREAMING_SNAKE_CASE = randn_tensor(A__ , generator=A__ , device=self.device ) * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(A__ )
for t in self.progress_bar(self.scheduler.timesteps ):
# here sigma_t == t_i from the paper
_SCREAMING_SNAKE_CASE = self.scheduler.schedule[t]
_SCREAMING_SNAKE_CASE = self.scheduler.schedule[t - 1] if t > 0 else 0
# 1. Select temporarily increased noise level sigma_hat
# 2. Add new noise to move from sample_i to sample_hat
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.scheduler.add_noise_to_input(A__ , A__ , generator=A__ )
# 3. Predict the noise residual given the noise magnitude `sigma_hat`
# The model inputs and output are adjusted by following eq. (213) in [1].
_SCREAMING_SNAKE_CASE = (sigma_hat / 2) * model((sample_hat + 1) / 2 , sigma_hat / 2 ).sample
# 4. Evaluate dx/dt at sigma_hat
# 5. Take Euler step from sigma to sigma_prev
_SCREAMING_SNAKE_CASE = self.scheduler.step(A__ , A__ , A__ , A__ )
if sigma_prev != 0:
# 6. Apply 2nd order correction
# The model inputs and output are adjusted by following eq. (213) in [1].
_SCREAMING_SNAKE_CASE = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2 , sigma_prev / 2 ).sample
_SCREAMING_SNAKE_CASE = self.scheduler.step_correct(
A__ , A__ , A__ , A__ , step_output.prev_sample , step_output["""derivative"""] , )
_SCREAMING_SNAKE_CASE = step_output.prev_sample
_SCREAMING_SNAKE_CASE = (sample / 2 + 0.5).clamp(0 , 1 )
_SCREAMING_SNAKE_CASE = sample.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,)
return ImagePipelineOutput(images=A__ )
| 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 |
from collections.abc import Sequence
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = None ) -> int:
"""simple docstring"""
if nums is None or not nums:
raise ValueError("""Input sequence should not be empty""" )
_SCREAMING_SNAKE_CASE = nums[0]
for i in range(1 , len(SCREAMING_SNAKE_CASE_ ) ):
_SCREAMING_SNAKE_CASE = nums[i]
_SCREAMING_SNAKE_CASE = max(SCREAMING_SNAKE_CASE_ , ans + num , SCREAMING_SNAKE_CASE_ )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
UpperCamelCase__ : List[str] = int(input("Enter number of elements : ").strip())
UpperCamelCase__ : Tuple = list(map(int, input("\nEnter the numbers : ").strip().split()))[:n]
print(max_subsequence_sum(array))
| 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__ : List[Any] = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}
class _a (_lowerCamelCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = 'markuplm'
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__=0 , A__=0 , A__=2 , A__=2_56 , A__=10_24 , A__=2_16 , A__=10_01 , A__=32 , A__=50 , A__="absolute" , A__=True , A__=None , **A__ , ) -> int:
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 = use_cache
_SCREAMING_SNAKE_CASE = classifier_dropout
# additional properties
_SCREAMING_SNAKE_CASE = max_depth
_SCREAMING_SNAKE_CASE = max_xpath_tag_unit_embeddings
_SCREAMING_SNAKE_CASE = max_xpath_subs_unit_embeddings
_SCREAMING_SNAKE_CASE = tag_pad_id
_SCREAMING_SNAKE_CASE = subs_pad_id
_SCREAMING_SNAKE_CASE = xpath_unit_hidden_size
| 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 typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict[str, torch.Tensor]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
for rt in rc.restypes:
_SCREAMING_SNAKE_CASE = rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names] )
_SCREAMING_SNAKE_CASE = {name: i for i, name in enumerate(SCREAMING_SNAKE_CASE_ )}
restype_atomaa_to_atomaa_list.append(
[(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types] )
restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names] )
# Add dummy mapping for restype 'UNK'
restype_atomaa_to_atomaa_list.append([0] * 14 )
restype_atomaa_to_atomaa_list.append([0] * 37 )
restype_atomaa_mask_list.append([0.0] * 14 )
_SCREAMING_SNAKE_CASE = torch.tensor(
SCREAMING_SNAKE_CASE_ , dtype=torch.intaa , device=protein["""aatype"""].device , )
_SCREAMING_SNAKE_CASE = torch.tensor(
SCREAMING_SNAKE_CASE_ , dtype=torch.intaa , device=protein["""aatype"""].device , )
_SCREAMING_SNAKE_CASE = torch.tensor(
SCREAMING_SNAKE_CASE_ , dtype=torch.floataa , device=protein["""aatype"""].device , )
_SCREAMING_SNAKE_CASE = protein["""aatype"""].to(torch.long )
# create the mapping for (residx, atom14) --> atom37, i.e. an array
# with shape (num_res, 14) containing the atom37 indices for this protein
_SCREAMING_SNAKE_CASE = restype_atomaa_to_atomaa[protein_aatype]
_SCREAMING_SNAKE_CASE = restype_atomaa_mask[protein_aatype]
_SCREAMING_SNAKE_CASE = residx_atomaa_mask
_SCREAMING_SNAKE_CASE = residx_atomaa_to_atomaa.long()
# create the gather indices for mapping back
_SCREAMING_SNAKE_CASE = restype_atomaa_to_atomaa[protein_aatype]
_SCREAMING_SNAKE_CASE = residx_atomaa_to_atomaa.long()
# create the corresponding mask
_SCREAMING_SNAKE_CASE = torch.zeros([21, 37] , dtype=torch.floataa , device=protein["""aatype"""].device )
for restype, restype_letter in enumerate(rc.restypes ):
_SCREAMING_SNAKE_CASE = rc.restype_atoa[restype_letter]
_SCREAMING_SNAKE_CASE = rc.residue_atoms[restype_name]
for atom_name in atom_names:
_SCREAMING_SNAKE_CASE = rc.atom_order[atom_name]
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = restype_atomaa_mask[protein_aatype]
_SCREAMING_SNAKE_CASE = residx_atomaa_mask
return protein
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict[str, np.ndarray]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = tree_map(lambda SCREAMING_SNAKE_CASE_ : torch.tensor(SCREAMING_SNAKE_CASE_ , device=batch["""aatype"""].device ) , SCREAMING_SNAKE_CASE_ , np.ndarray )
_SCREAMING_SNAKE_CASE = tensor_tree_map(lambda SCREAMING_SNAKE_CASE_ : np.array(SCREAMING_SNAKE_CASE_ ) , make_atomaa_masks(SCREAMING_SNAKE_CASE_ ) )
return out
| 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 logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCamelCase__ : Any = logging.getLogger(__name__)
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__=-1 ) -> str:
# in NER datasets, the last column is usually reserved for NER label
_SCREAMING_SNAKE_CASE = label_idx
def UpperCamelCase ( self , A__ , A__ ) -> List[InputExample]:
if isinstance(A__ , A__ ):
_SCREAMING_SNAKE_CASE = mode.value
_SCREAMING_SNAKE_CASE = os.path.join(A__ , F"{mode}.txt" )
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = []
with open(A__ , encoding="""utf-8""" ) as f:
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
for line in f:
if line.startswith("""-DOCSTART-""" ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=A__ , labels=A__ ) )
guid_index += 1
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
else:
_SCREAMING_SNAKE_CASE = line.split(""" """ )
words.append(splits[0] )
if len(A__ ) > 1:
labels.append(splits[self.label_idx].replace("""\n""" , """""" ) )
else:
# Examples could have no label for mode = "test"
labels.append("""O""" )
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=A__ , labels=A__ ) )
return examples
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = 0
for line in test_input_reader:
if line.startswith("""-DOCSTART-""" ) or line == "" or line == "\n":
writer.write(A__ )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
_SCREAMING_SNAKE_CASE = line.split()[0] + """ """ + preds_list[example_id].pop(0 ) + """\n"""
writer.write(A__ )
else:
logger.warning("""Maximum sequence length exceeded: No prediction for '%s'.""" , line.split()[0] )
def UpperCamelCase ( self , A__ ) -> List[str]:
if path:
with open(A__ , """r""" ) as f:
_SCREAMING_SNAKE_CASE = f.read().splitlines()
if "O" not in labels:
_SCREAMING_SNAKE_CASE = ["""O"""] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self ) -> List[str]:
# in CONLL2003 dataset chunk column is second-to-last
super().__init__(label_idx=-2 )
def UpperCamelCase ( self , A__ ) -> List[str]:
if path:
with open(A__ , """r""" ) as f:
_SCREAMING_SNAKE_CASE = f.read().splitlines()
if "O" not in labels:
_SCREAMING_SNAKE_CASE = ["""O"""] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class _a (_lowerCamelCase):
"""simple docstring"""
def UpperCamelCase ( self , A__ , A__ ) -> List[InputExample]:
if isinstance(A__ , A__ ):
_SCREAMING_SNAKE_CASE = mode.value
_SCREAMING_SNAKE_CASE = os.path.join(A__ , F"{mode}.txt" )
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = []
with open(A__ , encoding="""utf-8""" ) as f:
for sentence in parse_incr(A__ ):
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
for token in sentence:
words.append(token["""form"""] )
labels.append(token["""upos"""] )
assert len(A__ ) == len(A__ )
if words:
examples.append(InputExample(guid=F"{mode}-{guid_index}" , words=A__ , labels=A__ ) )
guid_index += 1
return examples
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Dict:
_SCREAMING_SNAKE_CASE = 0
for sentence in parse_incr(A__ ):
_SCREAMING_SNAKE_CASE = preds_list[example_id]
_SCREAMING_SNAKE_CASE = """"""
for token in sentence:
out += F"{token['form']} ({token['upos']}|{s_p.pop(0 )}) "
out += "\n"
writer.write(A__ )
example_id += 1
def UpperCamelCase ( self , A__ ) -> List[str]:
if path:
with open(A__ , """r""" ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 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'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list[list]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = current_set.copy()
for row_index, row in enumerate(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = row[0]
for column_index, column in enumerate(SCREAMING_SNAKE_CASE_ ):
if magnitude == 0:
_SCREAMING_SNAKE_CASE = column
continue
_SCREAMING_SNAKE_CASE = column / magnitude
# Subtract to cancel term
_SCREAMING_SNAKE_CASE = current_set[0]
_SCREAMING_SNAKE_CASE = [first_row]
_SCREAMING_SNAKE_CASE = current_set[1::]
for row in current_set:
_SCREAMING_SNAKE_CASE = []
# If first term is 0, it is already in form we want, so we preserve it
if row[0] == 0:
final_set.append(SCREAMING_SNAKE_CASE_ )
continue
for column_index in range(len(SCREAMING_SNAKE_CASE_ ) ):
temp_row.append(first_row[column_index] - row[column_index] )
final_set.append(SCREAMING_SNAKE_CASE_ )
# Create next recursion iteration set
if len(final_set[0] ) != 3:
_SCREAMING_SNAKE_CASE = final_set[0]
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
for row in final_set[1::]:
current_first_column.append(row[0] )
next_iteration.append(row[1::] )
_SCREAMING_SNAKE_CASE = simplify(SCREAMING_SNAKE_CASE_ )
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
resultant[i].insert(0 , current_first_column[i] )
resultant.insert(0 , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = resultant
return final_set
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) == 0:
raise IndexError("""solve_simultaneous() requires n lists of length n+1""" )
_SCREAMING_SNAKE_CASE = len(SCREAMING_SNAKE_CASE_ ) + 1
if any(len(SCREAMING_SNAKE_CASE_ ) != _length for item in equations ):
raise IndexError("""solve_simultaneous() requires n lists of length n+1""" )
for row in equations:
if any(not isinstance(SCREAMING_SNAKE_CASE_ , (int, float) ) for column in row ):
raise ValueError("""solve_simultaneous() requires lists of integers""" )
if len(SCREAMING_SNAKE_CASE_ ) == 1:
return [equations[0][-1] / equations[0][0]]
_SCREAMING_SNAKE_CASE = equations.copy()
if any(0 in row for row in data_set ):
_SCREAMING_SNAKE_CASE = data_set.copy()
_SCREAMING_SNAKE_CASE = []
for row_index, row in enumerate(SCREAMING_SNAKE_CASE_ ):
if 0 not in row:
_SCREAMING_SNAKE_CASE = data_set.pop(SCREAMING_SNAKE_CASE_ )
break
if not full_row:
raise ValueError("""solve_simultaneous() requires at least 1 full equation""" )
data_set.insert(0 , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = data_set.copy()
_SCREAMING_SNAKE_CASE = simplify(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = simplified[::-1]
_SCREAMING_SNAKE_CASE = []
for row in simplified:
_SCREAMING_SNAKE_CASE = row[-1]
if not solutions:
if row[-2] == 0:
solutions.append(0 )
continue
solutions.append(current_solution / row[-2] )
continue
_SCREAMING_SNAKE_CASE = row.copy()[: len(SCREAMING_SNAKE_CASE_ ) - 1 :]
while temp_row[0] == 0:
temp_row.pop(0 )
if len(SCREAMING_SNAKE_CASE_ ) == 0:
solutions.append(0 )
continue
_SCREAMING_SNAKE_CASE = temp_row[1::]
_SCREAMING_SNAKE_CASE = temp_row[::-1]
for column_index, column in enumerate(SCREAMING_SNAKE_CASE_ ):
current_solution -= column * solutions[column_index]
solutions.append(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = []
for item in solutions:
final.append(float(round(SCREAMING_SNAKE_CASE_ , 5 ) ) )
return final[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ : Union[str, Any] = [
[2, 1, 1, 1, 1, 4],
[1, 2, 1, 1, 1, 5],
[1, 1, 2, 1, 1, 6],
[1, 1, 1, 2, 1, 7],
[1, 1, 1, 1, 2, 8],
]
print(solve_simultaneous(eq))
print(solve_simultaneous([[4, 2]]))
| 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 |
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__)
| 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 copy
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {}
with open(SCREAMING_SNAKE_CASE_ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_SCREAMING_SNAKE_CASE = []
_list.append([line.split()[1], line.split()[2]] )
_SCREAMING_SNAKE_CASE = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_SCREAMING_SNAKE_CASE = []
_list.append([line.split()[0], line.split()[2]] )
_SCREAMING_SNAKE_CASE = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE_ ) as f:
_SCREAMING_SNAKE_CASE = f.read(1 )
_SCREAMING_SNAKE_CASE = start_node
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = start_node
_SCREAMING_SNAKE_CASE = 0
while visiting not in first_solution:
_SCREAMING_SNAKE_CASE = 1_00_00
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(SCREAMING_SNAKE_CASE_ ) and k[0] not in first_solution:
_SCREAMING_SNAKE_CASE = k[1]
_SCREAMING_SNAKE_CASE = k[0]
first_solution.append(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = distance_of_first_solution + int(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = best_node
first_solution.append(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_SCREAMING_SNAKE_CASE = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_00_00
)
return first_solution, distance_of_first_solution
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
for n in solution[1:-1]:
_SCREAMING_SNAKE_CASE = solution.index(SCREAMING_SNAKE_CASE_ )
for kn in solution[1:-1]:
_SCREAMING_SNAKE_CASE = solution.index(SCREAMING_SNAKE_CASE_ )
if n == kn:
continue
_SCREAMING_SNAKE_CASE = copy.deepcopy(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = kn
_SCREAMING_SNAKE_CASE = n
_SCREAMING_SNAKE_CASE = 0
for k in _tmp[:-1]:
_SCREAMING_SNAKE_CASE = _tmp[_tmp.index(SCREAMING_SNAKE_CASE_ ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_SCREAMING_SNAKE_CASE = distance + int(i[1] )
_tmp.append(SCREAMING_SNAKE_CASE_ )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_SCREAMING_SNAKE_CASE = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda SCREAMING_SNAKE_CASE_ : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = first_solution
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = distance_of_first_solution
_SCREAMING_SNAKE_CASE = solution
while count <= iters:
_SCREAMING_SNAKE_CASE = find_neighborhood(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = neighborhood[index_of_best_solution]
_SCREAMING_SNAKE_CASE = len(SCREAMING_SNAKE_CASE_ ) - 1
_SCREAMING_SNAKE_CASE = False
while not found:
_SCREAMING_SNAKE_CASE = 0
while i < len(SCREAMING_SNAKE_CASE_ ):
if best_solution[i] != solution[i]:
_SCREAMING_SNAKE_CASE = best_solution[i]
_SCREAMING_SNAKE_CASE = solution[i]
break
_SCREAMING_SNAKE_CASE = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = best_solution[:-1]
_SCREAMING_SNAKE_CASE = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_SCREAMING_SNAKE_CASE = cost
_SCREAMING_SNAKE_CASE = solution
else:
_SCREAMING_SNAKE_CASE = index_of_best_solution + 1
_SCREAMING_SNAKE_CASE = neighborhood[index_of_best_solution]
if len(SCREAMING_SNAKE_CASE_ ) >= size:
tabu_list.pop(0 )
_SCREAMING_SNAKE_CASE = count + 1
return best_solution_ever, best_cost
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_=None ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = generate_neighbours(args.File )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = generate_first_solution(
args.File , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = tabu_search(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , args.Iterations , args.Size , )
print(F"Best solution: {best_sol}, with total distance: {best_cost}." )
if __name__ == "__main__":
UpperCamelCase__ : Tuple = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args()) | 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'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
UpperCamelCase__ : Optional[int] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
UpperCamelCase__ : Optional[int] = " \"\"\"\n Output class for the scheduler's step function output.\n\n Args:\n prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n denoising loop.\n pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n `pred_original_sample` can be used to preview progress or for guidance.\n \"\"\"\n\n prev_sample: torch.FloatTensor\n pred_original_sample: Optional[torch.FloatTensor] = None\n"
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , """schedulers/""" ) )
_SCREAMING_SNAKE_CASE = self.diffusers_dir
shutil.copy(
os.path.join(A__ , """src/diffusers/schedulers/scheduling_ddpm.py""" ) , os.path.join(self.diffusers_dir , """schedulers/scheduling_ddpm.py""" ) , )
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = """src/diffusers"""
shutil.rmtree(self.diffusers_dir )
def UpperCamelCase ( self , A__ , A__ , A__ , A__=None ) -> str:
_SCREAMING_SNAKE_CASE = comment + F"\nclass {class_name}(nn.Module):\n" + class_code
if overwrite_result is not None:
_SCREAMING_SNAKE_CASE = comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
_SCREAMING_SNAKE_CASE = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 )
_SCREAMING_SNAKE_CASE = black.format_str(A__ , mode=A__ )
_SCREAMING_SNAKE_CASE = os.path.join(self.diffusers_dir , """new_code.py""" )
with open(A__ , """w""" , newline="""\n""" ) as f:
f.write(A__ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(A__ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=A__ )
with open(A__ , """r""" ) as f:
self.assertTrue(f.read() , A__ )
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput""" )
self.assertEqual(A__ , A__ )
def UpperCamelCase ( self ) -> str:
# Base copy consistency
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , REFERENCE_CODE + """\n""" , )
# With no empty line at the end
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , A__ , )
# Copy consistency with rename
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , re.sub("""DDPM""" , """Test""" , A__ ) , )
# Copy consistency with a really long name
_SCREAMING_SNAKE_CASE = """TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
self.check_copy_consistency(
F"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}" , F"{long_class_name}SchedulerOutput" , re.sub("""Bert""" , A__ , A__ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , A__ , overwrite_result=re.sub("""DDPM""" , """Test""" , 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'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _a (_lowerCamelCase):
def __init__( self , A__ , A__ = None , A__ = None , A__ = None , A__ = False , A__ = False , A__ = None , A__ = None , **A__ , ) -> int:
super().__init__(
A__ , split=A__ , features=A__ , cache_dir=A__ , keep_in_memory=A__ , streaming=A__ , num_proc=A__ , **A__ , )
_SCREAMING_SNAKE_CASE = field
_SCREAMING_SNAKE_CASE = path_or_paths if isinstance(A__ , A__ ) else {self.split: path_or_paths}
_SCREAMING_SNAKE_CASE = Json(
cache_dir=A__ , data_files=A__ , features=A__ , field=A__ , **A__ , )
def UpperCamelCase ( self ) -> int:
# Build iterable dataset
if self.streaming:
_SCREAMING_SNAKE_CASE = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
self.builder.download_and_prepare(
download_config=A__ , download_mode=A__ , verification_mode=A__ , base_path=A__ , num_proc=self.num_proc , )
_SCREAMING_SNAKE_CASE = self.builder.as_dataset(
split=self.split , verification_mode=A__ , in_memory=self.keep_in_memory )
return dataset
class _a :
def __init__( self , A__ , A__ , A__ = None , A__ = None , **A__ , ) -> List[str]:
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 = path_or_buf
_SCREAMING_SNAKE_CASE = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
_SCREAMING_SNAKE_CASE = num_proc
_SCREAMING_SNAKE_CASE = """utf-8"""
_SCREAMING_SNAKE_CASE = to_json_kwargs
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop("""path_or_buf""" , A__ )
_SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop("""orient""" , """records""" )
_SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop("""lines""" , True if orient == """records""" else False )
_SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop("""index""" , False if orient in ["""split""", """table"""] else True )
_SCREAMING_SNAKE_CASE = self.to_json_kwargs.pop("""compression""" , A__ )
if compression not in [None, "infer", "gzip", "bz2", "xz"]:
raise NotImplementedError(F"`datasets` currently does not support {compression} compression" )
if isinstance(self.path_or_buf , (str, bytes, os.PathLike) ):
with fsspec.open(self.path_or_buf , """wb""" , compression=A__ ) as buffer:
_SCREAMING_SNAKE_CASE = self._write(file_obj=A__ , orient=A__ , lines=A__ , index=A__ , **self.to_json_kwargs )
else:
if compression:
raise NotImplementedError(
F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
""" was passed. Please provide a local path instead.""" )
_SCREAMING_SNAKE_CASE = self._write(
file_obj=self.path_or_buf , orient=A__ , lines=A__ , index=A__ , **self.to_json_kwargs )
return written
def UpperCamelCase ( self , A__ ) -> List[Any]:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = args
_SCREAMING_SNAKE_CASE = query_table(
table=self.dataset.data , key=slice(A__ , offset + self.batch_size ) , indices=self.dataset._indices , )
_SCREAMING_SNAKE_CASE = batch.to_pandas().to_json(
path_or_buf=A__ , orient=A__ , lines=A__ , index=A__ , **A__ )
if not json_str.endswith("""\n""" ):
json_str += "\n"
return json_str.encode(self.encoding )
def UpperCamelCase ( self , A__ , A__ , A__ , 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 json from Arrow format""" , ):
_SCREAMING_SNAKE_CASE = self._batch_json((offset, orient, lines, index, to_json_kwargs) )
written += file_obj.write(A__ )
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = len(self.dataset ), self.batch_size
with multiprocessing.Pool(self.num_proc ) as pool:
for json_str in logging.tqdm(
pool.imap(
self._batch_json , [(offset, orient, lines, index, to_json_kwargs) for offset in range(0 , A__ , A__ )] , ) , 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 json from Arrow format""" , ):
written += file_obj.write(A__ )
return written
| 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'''
UpperCamelCase__ : List[str] = 256
# Modulus to hash a string
UpperCamelCase__ : List[Any] = 1_000_003
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> bool:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = len(SCREAMING_SNAKE_CASE_ )
if p_len > t_len:
return False
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 1
# Calculating the hash of pattern and substring of text
for i in range(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_SCREAMING_SNAKE_CASE = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_SCREAMING_SNAKE_CASE = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_SCREAMING_SNAKE_CASE = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def lowerCAmelCase_ ( ) -> None:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """abc1abc12"""
_SCREAMING_SNAKE_CASE = """alskfjaldsabc1abc1abc12k23adsfabcabc"""
_SCREAMING_SNAKE_CASE = """alskfjaldsk23adsfabcabc"""
assert rabin_karp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) and not rabin_karp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Test 2)
_SCREAMING_SNAKE_CASE = """ABABX"""
_SCREAMING_SNAKE_CASE = """ABABZABABYABABX"""
assert rabin_karp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Test 3)
_SCREAMING_SNAKE_CASE = """AAAB"""
_SCREAMING_SNAKE_CASE = """ABAAAAAB"""
assert rabin_karp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Test 4)
_SCREAMING_SNAKE_CASE = """abcdabcy"""
_SCREAMING_SNAKE_CASE = """abcxabcdabxabcdabcdabcy"""
assert rabin_karp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Test 5)
_SCREAMING_SNAKE_CASE = """Lü"""
_SCREAMING_SNAKE_CASE = """Lüsai"""
assert rabin_karp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = """Lue"""
assert not rabin_karp(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
print("""Success.""" )
if __name__ == "__main__":
test_rabin_karp()
| 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 math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
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__ ) -> Union[str, Any]:
super().__init__(*A__ , **A__ )
_SCREAMING_SNAKE_CASE = eval_examples
_SCREAMING_SNAKE_CASE = post_process_function
def UpperCamelCase ( self , A__=None , A__=None , A__=None , A__ = "eval" ) -> Optional[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
_SCREAMING_SNAKE_CASE = time.time()
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , metric_key_prefix=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
_SCREAMING_SNAKE_CASE = self.args.eval_batch_size * self.args.world_size
if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
output.metrics.update(
speed_metrics(
A__ , A__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
_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__ )
metrics.update(output.metrics )
else:
_SCREAMING_SNAKE_CASE = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(A__ )
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
_SCREAMING_SNAKE_CASE = time.time()
try:
_SCREAMING_SNAKE_CASE = eval_loop(
A__ , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=A__ , metric_key_prefix=A__ , )
finally:
_SCREAMING_SNAKE_CASE = compute_metrics
_SCREAMING_SNAKE_CASE = self.args.eval_batch_size * self.args.world_size
if F"{metric_key_prefix}_jit_compilation_time" in output.metrics:
start_time += output.metrics[F"{metric_key_prefix}_jit_compilation_time"]
output.metrics.update(
speed_metrics(
A__ , A__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
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__ )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=A__ )
| 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 random
from typing import Any
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list[Any]:
"""simple docstring"""
for _ in range(len(SCREAMING_SNAKE_CASE_ ) ):
_SCREAMING_SNAKE_CASE = random.randint(0 , len(SCREAMING_SNAKE_CASE_ ) - 1 )
_SCREAMING_SNAKE_CASE = random.randint(0 , len(SCREAMING_SNAKE_CASE_ ) - 1 )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = data[b], data[a]
return data
if __name__ == "__main__":
UpperCamelCase__ : str = [0, 1, 2, 3, 4, 5, 6, 7]
UpperCamelCase__ : Optional[int] = ["python", "says", "hello", "!"]
print("Fisher-Yates Shuffle:")
print("List", integers, strings)
print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 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 |
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()
| 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 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = FlaxAutoModelForSeqaSeqLM.from_config(config=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]
if config.model_type == "t5":
_SCREAMING_SNAKE_CASE = """SelfAttention"""
if config.model_type == "longt5" and config.encoder_attention_type == "local":
_SCREAMING_SNAKE_CASE = """LocalSelfAttention"""
elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
_SCREAMING_SNAKE_CASE = """TransientGlobalSelfAttention"""
else:
raise ValueError(
"""Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"""
""" attribute with a value from ['local', 'transient-global].""" )
# Encoder
for layer_index in range(config.num_layers ):
_SCREAMING_SNAKE_CASE = F"layers_{str(SCREAMING_SNAKE_CASE_ )}"
# Self-Attention
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]
# Layer Normalization
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]
if split_mlp_wi:
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
else:
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
# Layer Normalization
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
# Assigning
_SCREAMING_SNAKE_CASE = flax_model.params["""encoder"""]["""block"""][str(SCREAMING_SNAKE_CASE_ )]["""layer"""]
_SCREAMING_SNAKE_CASE = tax_attention_key
_SCREAMING_SNAKE_CASE = tax_attention_out
_SCREAMING_SNAKE_CASE = tax_attention_query
_SCREAMING_SNAKE_CASE = tax_attention_value
_SCREAMING_SNAKE_CASE = tax_attention_layer_norm
# Global input layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
_SCREAMING_SNAKE_CASE = tax_global_layer_norm
if split_mlp_wi:
_SCREAMING_SNAKE_CASE = tax_mlp_wi_a
_SCREAMING_SNAKE_CASE = tax_mlp_wi_a
else:
_SCREAMING_SNAKE_CASE = tax_mlp_wi
_SCREAMING_SNAKE_CASE = tax_mlp_wo
_SCREAMING_SNAKE_CASE = tax_mlp_layer_norm
_SCREAMING_SNAKE_CASE = flax_model_encoder_layer_block
# Only for layer 0:
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
_SCREAMING_SNAKE_CASE = tax_encoder_rel_embedding
# Side/global relative position_bias + layer norm
if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
_SCREAMING_SNAKE_CASE = tax_encoder_global_rel_embedding
# Assigning
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
_SCREAMING_SNAKE_CASE = tax_encoder_norm
# Decoder
for layer_index in range(config.num_layers ):
_SCREAMING_SNAKE_CASE = F"layers_{str(SCREAMING_SNAKE_CASE_ )}"
# Self-Attention
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]
# Layer Normalization
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
"""scale"""
]
# Encoder-Decoder-Attention
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]
_SCREAMING_SNAKE_CASE = tax_enc_dec_attention_module["""key"""]["""kernel"""]
_SCREAMING_SNAKE_CASE = tax_enc_dec_attention_module["""out"""]["""kernel"""]
_SCREAMING_SNAKE_CASE = tax_enc_dec_attention_module["""query"""]["""kernel"""]
_SCREAMING_SNAKE_CASE = tax_enc_dec_attention_module["""value"""]["""kernel"""]
# Layer Normalization
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]
# MLP
if split_mlp_wi:
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
else:
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]
# Layer Normalization
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]
# Assigning
_SCREAMING_SNAKE_CASE = flax_model.params["""decoder"""]["""block"""][str(SCREAMING_SNAKE_CASE_ )]["""layer"""]
_SCREAMING_SNAKE_CASE = tax_attention_key
_SCREAMING_SNAKE_CASE = tax_attention_out
_SCREAMING_SNAKE_CASE = tax_attention_query
_SCREAMING_SNAKE_CASE = tax_attention_value
_SCREAMING_SNAKE_CASE = tax_pre_attention_layer_norm
_SCREAMING_SNAKE_CASE = tax_enc_dec_attention_key
_SCREAMING_SNAKE_CASE = tax_enc_dec_attention_out
_SCREAMING_SNAKE_CASE = tax_enc_dec_attention_query
_SCREAMING_SNAKE_CASE = tax_enc_dec_attention_value
_SCREAMING_SNAKE_CASE = tax_cross_layer_norm
if split_mlp_wi:
_SCREAMING_SNAKE_CASE = tax_mlp_wi_a
_SCREAMING_SNAKE_CASE = tax_mlp_wi_a
else:
_SCREAMING_SNAKE_CASE = tax_mlp_wi
_SCREAMING_SNAKE_CASE = tax_mlp_wo
_SCREAMING_SNAKE_CASE = txa_mlp_layer_norm
_SCREAMING_SNAKE_CASE = flax_model_decoder_layer_block
# Decoder Normalization
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
_SCREAMING_SNAKE_CASE = txa_decoder_norm
# Only for layer 0:
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
_SCREAMING_SNAKE_CASE = tax_decoder_rel_embedding
# Token Embeddings
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""token_embedder"""]["""embedding"""]
_SCREAMING_SNAKE_CASE = txa_token_embeddings
# LM Head (only in v1.1 and LongT5 checkpoints)
if "logits_dense" in tax_model["target"]["decoder"]:
_SCREAMING_SNAKE_CASE = tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]
flax_model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print("""T5X Model was sucessfully converted!""" )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
)
parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
parser.add_argument(
"--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
)
UpperCamelCase__ : int = parser.parse_args()
convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
| 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 os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__=13 , A__=7 , A__=True , A__=True , A__=True , A__=True , A__=True , A__=False , A__=False , A__=False , A__=2 , A__=99 , A__=0 , A__=32 , A__=5 , A__=4 , A__=0.1 , A__=0.1 , A__=5_12 , A__=12 , A__=2 , A__=0.02 , A__=3 , A__=4 , A__="last" , A__=None , A__=None , ) -> Dict:
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = seq_length
_SCREAMING_SNAKE_CASE = is_training
_SCREAMING_SNAKE_CASE = use_input_lengths
_SCREAMING_SNAKE_CASE = use_token_type_ids
_SCREAMING_SNAKE_CASE = use_labels
_SCREAMING_SNAKE_CASE = gelu_activation
_SCREAMING_SNAKE_CASE = sinusoidal_embeddings
_SCREAMING_SNAKE_CASE = causal
_SCREAMING_SNAKE_CASE = asm
_SCREAMING_SNAKE_CASE = n_langs
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = n_special
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = num_attention_heads
_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 = summary_type
_SCREAMING_SNAKE_CASE = use_proj
_SCREAMING_SNAKE_CASE = scope
def UpperCamelCase ( self ) -> Tuple:
_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] )
_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 ).float()
_SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] , self.num_choices )
_SCREAMING_SNAKE_CASE = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCamelCase ( self ) -> Optional[Any]:
return 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 , )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> Tuple:
_SCREAMING_SNAKE_CASE = FlaubertModel(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ , lengths=A__ , langs=A__ )
_SCREAMING_SNAKE_CASE = model(A__ , langs=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__ , A__ , A__ , ) -> Dict:
_SCREAMING_SNAKE_CASE = FlaubertWithLMHeadModel(A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ , token_type_ids=A__ , labels=A__ )
self.parent.assertEqual(result.loss.shape , () )
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__ , ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = FlaubertForQuestionAnsweringSimple(A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = model(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__ , A__ , A__ , A__ , ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = FlaubertForQuestionAnswering(A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = model(
A__ , start_positions=A__ , end_positions=A__ , cls_index=A__ , is_impossible=A__ , p_mask=A__ , )
_SCREAMING_SNAKE_CASE = model(
A__ , start_positions=A__ , end_positions=A__ , cls_index=A__ , is_impossible=A__ , )
((_SCREAMING_SNAKE_CASE ) , ) = result_with_labels.to_tuple()
_SCREAMING_SNAKE_CASE = model(A__ , start_positions=A__ , end_positions=A__ )
((_SCREAMING_SNAKE_CASE ) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , A__ , ) -> List[str]:
_SCREAMING_SNAKE_CASE = FlaubertForSequenceClassification(A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = model(A__ )
_SCREAMING_SNAKE_CASE = model(A__ , labels=A__ )
self.parent.assertEqual(result.loss.shape , () )
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__ , ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.num_labels
_SCREAMING_SNAKE_CASE = FlaubertForTokenClassification(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__ , A__ , A__ , A__ , ) -> int:
_SCREAMING_SNAKE_CASE = self.num_choices
_SCREAMING_SNAKE_CASE = FlaubertForMultipleChoice(config=A__ )
model.to(A__ )
model.eval()
_SCREAMING_SNAKE_CASE = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_SCREAMING_SNAKE_CASE = token_type_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__ , token_type_ids=A__ , labels=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,
"""lengths""": input_lengths,
"""attention_mask""": input_mask,
}
return config, inputs_dict
@require_torch
class _a (_lowerCamelCase , _lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE = (
{
'feature-extraction': FlaubertModel,
'fill-mask': FlaubertWithLMHeadModel,
'question-answering': FlaubertForQuestionAnsweringSimple,
'text-classification': FlaubertForSequenceClassification,
'token-classification': FlaubertForTokenClassification,
'zero-shot': FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCamelCase ( self , A__ , A__ , A__ , A__ , A__ ) -> Optional[Any]:
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 , A__ , A__ , A__=False ) -> List[Any]:
_SCREAMING_SNAKE_CASE = super()._prepare_for_class(A__ , A__ , return_labels=A__ )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
_SCREAMING_SNAKE_CASE = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A__ )
_SCREAMING_SNAKE_CASE = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=A__ )
return inputs_dict
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = FlaubertModelTester(self )
_SCREAMING_SNAKE_CASE = ConfigTester(self , config_class=A__ , emb_dim=37 )
def UpperCamelCase ( self ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*A__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*A__ )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*A__ )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*A__ )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*A__ )
@slow
def UpperCamelCase ( self ) -> Optional[int]:
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_SCREAMING_SNAKE_CASE = FlaubertModel.from_pretrained(A__ )
self.assertIsNotNone(A__ )
@slow
@require_torch_gpu
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:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = model_class(config=A__ )
_SCREAMING_SNAKE_CASE = self._prepare_for_class(A__ , A__ )
_SCREAMING_SNAKE_CASE = torch.jit.trace(
A__ , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(A__ , os.path.join(A__ , """traced_model.pt""" ) )
_SCREAMING_SNAKE_CASE = torch.jit.load(os.path.join(A__ , """traced_model.pt""" ) , map_location=A__ )
loaded(inputs_dict["""input_ids"""].to(A__ ) , inputs_dict["""attention_mask"""].to(A__ ) )
@require_torch
class _a (unittest.TestCase):
"""simple docstring"""
@slow
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased""" )
_SCREAMING_SNAKE_CASE = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
with torch.no_grad():
_SCREAMING_SNAKE_CASE = model(A__ )[0]
_SCREAMING_SNAKE_CASE = torch.Size((1, 11, 7_68) )
self.assertEqual(output.shape , A__ )
_SCREAMING_SNAKE_CASE = torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A__ , atol=1E-4 ) )
| 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 os
def lowerCAmelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
with open(os.path.dirname(SCREAMING_SNAKE_CASE_ ) + """/grid.txt""" ) as f:
_SCREAMING_SNAKE_CASE = [] # noqa: E741
for _ in range(20 ):
l.append([int(SCREAMING_SNAKE_CASE_ ) for x in f.readline().split()] )
_SCREAMING_SNAKE_CASE = 0
# right
for i in range(20 ):
for j in range(17 ):
_SCREAMING_SNAKE_CASE = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
_SCREAMING_SNAKE_CASE = temp
# down
for i in range(17 ):
for j in range(20 ):
_SCREAMING_SNAKE_CASE = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
_SCREAMING_SNAKE_CASE = temp
# diagonal 1
for i in range(17 ):
for j in range(17 ):
_SCREAMING_SNAKE_CASE = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
_SCREAMING_SNAKE_CASE = temp
# diagonal 2
for i in range(17 ):
for j in range(3 , 20 ):
_SCREAMING_SNAKE_CASE = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
_SCREAMING_SNAKE_CASE = temp
return maximum
if __name__ == "__main__":
print(solution())
| 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 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)
| 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 json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
UpperCamelCase__ : Union[str, Any] = logging.getLogger(__name__)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = {
"""repo_id""": str(SCREAMING_SNAKE_CASE_ ),
"""repo_sha""": str(repo.head.object.hexsha ),
"""repo_branch""": str(repo.active_branch ),
}
with open(os.path.join(SCREAMING_SNAKE_CASE_ , """git_log.json""" ) , """w""" ) as f:
json.dump(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , indent=4 )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
if params.n_gpu <= 0:
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = -1
_SCREAMING_SNAKE_CASE = True
_SCREAMING_SNAKE_CASE = False
return
assert torch.cuda.is_available()
logger.info("""Initializing GPUs""" )
if params.n_gpu > 1:
assert params.local_rank != -1
_SCREAMING_SNAKE_CASE = int(os.environ["""WORLD_SIZE"""] )
_SCREAMING_SNAKE_CASE = int(os.environ["""N_GPU_NODE"""] )
_SCREAMING_SNAKE_CASE = int(os.environ["""RANK"""] )
# number of nodes / node ID
_SCREAMING_SNAKE_CASE = params.world_size // params.n_gpu_per_node
_SCREAMING_SNAKE_CASE = params.global_rank // params.n_gpu_per_node
_SCREAMING_SNAKE_CASE = True
assert params.n_nodes == int(os.environ["""N_NODES"""] )
assert params.node_id == int(os.environ["""NODE_RANK"""] )
# local job (single GPU)
else:
assert params.local_rank == -1
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
_SCREAMING_SNAKE_CASE = params.node_id == 0 and params.local_rank == 0
_SCREAMING_SNAKE_CASE = params.n_nodes > 1
# summary
_SCREAMING_SNAKE_CASE = F"--- Global rank: {params.global_rank} - "
logger.info(PREFIX + """Number of nodes: %i""" % params.n_nodes )
logger.info(PREFIX + """Node ID : %i""" % params.node_id )
logger.info(PREFIX + """Local rank : %i""" % params.local_rank )
logger.info(PREFIX + """World size : %i""" % params.world_size )
logger.info(PREFIX + """GPUs per node : %i""" % params.n_gpu_per_node )
logger.info(PREFIX + """Master : %s""" % str(params.is_master ) )
logger.info(PREFIX + """Multi-node : %s""" % str(params.multi_node ) )
logger.info(PREFIX + """Multi-GPU : %s""" % str(params.multi_gpu ) )
logger.info(PREFIX + """Hostname : %s""" % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info("""Initializing PyTorch distributed""" )
torch.distributed.init_process_group(
init_method="""env://""" , backend="""nccl""" , )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 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'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"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
| 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'''
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()
| 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 re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = int(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = t // 36_00, (t // 60) % 60, t % 60
return F"{h}:{m:02d}:{s:02d}" if h != 0 else F"{m:02d}:{s:02d}"
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=3_00 ) -> Union[str, Any]:
"""simple docstring"""
return F"\n <div>\n {prefix}\n <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>\n {label}\n </div>\n "
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """<table border=\"1\" class=\"dataframe\">\n"""
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += F" <th>{i}</th>\n"
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
_SCREAMING_SNAKE_CASE = F"{elt:.6f}" if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) else str(SCREAMING_SNAKE_CASE_ )
html_code += F" <td>{elt}</td>\n"
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class _a :
"""simple docstring"""
SCREAMING_SNAKE_CASE = 5
SCREAMING_SNAKE_CASE = 0.2
def __init__( self , A__ , A__ = None , A__ = True , A__ = None , A__ = 3_00 , ) -> int:
_SCREAMING_SNAKE_CASE = total
_SCREAMING_SNAKE_CASE = """""" if prefix is None else prefix
_SCREAMING_SNAKE_CASE = leave
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = width
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self , A__ , A__ = False , A__ = None ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = value
if comment is not None:
_SCREAMING_SNAKE_CASE = comment
if self.last_value is None:
_SCREAMING_SNAKE_CASE = _SCREAMING_SNAKE_CASE = time.time()
_SCREAMING_SNAKE_CASE = _SCREAMING_SNAKE_CASE = value
_SCREAMING_SNAKE_CASE = _SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = self.warmup
_SCREAMING_SNAKE_CASE = 1
self.update_bar(A__ )
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total ):
if self.first_calls > 0:
self.first_calls -= 1
_SCREAMING_SNAKE_CASE = time.time()
_SCREAMING_SNAKE_CASE = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
_SCREAMING_SNAKE_CASE = self.elapsed_time / (value - self.start_value)
else:
_SCREAMING_SNAKE_CASE = None
if value >= self.total:
_SCREAMING_SNAKE_CASE = self.total
_SCREAMING_SNAKE_CASE = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
_SCREAMING_SNAKE_CASE = self.average_time_per_item * (self.total - value)
self.update_bar(A__ )
_SCREAMING_SNAKE_CASE = value
_SCREAMING_SNAKE_CASE = current_time
if self.average_time_per_item is None:
_SCREAMING_SNAKE_CASE = 1
else:
_SCREAMING_SNAKE_CASE = max(int(self.update_every / self.average_time_per_item ) , 1 )
def UpperCamelCase ( self , A__ , A__=None ) -> List[Any]:
_SCREAMING_SNAKE_CASE = """ """ * (len(str(self.total ) ) - len(str(A__ ) )) + str(A__ )
if self.elapsed_time is None:
_SCREAMING_SNAKE_CASE = F"[{spaced_value}/{self.total} : < :"
elif self.predicted_remaining is None:
_SCREAMING_SNAKE_CASE = F"[{spaced_value}/{self.total} {format_time(self.elapsed_time )}"
else:
_SCREAMING_SNAKE_CASE = (
F"[{spaced_value}/{self.total} {format_time(self.elapsed_time )} <"
F" {format_time(self.predicted_remaining )}"
)
self.label += F", {1/self.average_time_per_item:.2f} it/s"
self.label += "]" if self.comment is None or len(self.comment ) == 0 else F", {self.comment}]"
self.display()
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
_SCREAMING_SNAKE_CASE = disp.display(disp.HTML(self.html_code ) , display_id=A__ )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCamelCase ( self ) -> Optional[Any]:
if self.parent is None and self.output is not None:
self.output.update(disp.HTML("""""" ) )
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self , A__ , A__=None ) -> Optional[int]:
super().__init__(A__ )
_SCREAMING_SNAKE_CASE = None if column_names is None else [column_names]
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width )
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table )
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
_SCREAMING_SNAKE_CASE = disp.display(disp.HTML(self.html_code ) , display_id=A__ )
else:
self.output.update(disp.HTML(self.html_code ) )
def UpperCamelCase ( self , A__ ) -> Any:
if self.inner_table is None:
_SCREAMING_SNAKE_CASE = [list(values.keys() ), list(values.values() )]
else:
_SCREAMING_SNAKE_CASE = self.inner_table[0]
if len(self.inner_table ) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(A__ )
_SCREAMING_SNAKE_CASE = columns
self.inner_table.append([values[c] for c in columns] )
def UpperCamelCase ( self , A__ , A__=None , A__=3_00 ) -> str:
_SCREAMING_SNAKE_CASE = NotebookProgressBar(A__ , prefix=A__ , parent=self , width=A__ )
return self.child_bar
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = None
self.display()
class _a (_lowerCamelCase):
"""simple docstring"""
def __init__( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ , A__ , A__ , **A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = """Epoch""" if args.evaluation_strategy == IntervalStrategy.EPOCH else """Step"""
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = [self.first_column] + ["""Training Loss"""]
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append("""Validation Loss""" )
_SCREAMING_SNAKE_CASE = NotebookTrainingTracker(state.max_steps , A__ )
def UpperCamelCase ( self , A__ , A__ , A__ , **A__ ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = int(state.epoch ) if int(state.epoch ) == state.epoch else F"{state.epoch:.2f}"
self.training_tracker.update(
state.global_step + 1 , comment=F"Epoch {epoch}/{state.num_train_epochs}" , force_update=self._force_next_update , )
_SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self , A__ , A__ , A__ , A__=None , **A__ ) -> Union[str, Any]:
if not has_length(A__ ):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
_SCREAMING_SNAKE_CASE = self.training_tracker.add_child(len(A__ ) )
else:
_SCREAMING_SNAKE_CASE = NotebookProgressBar(len(A__ ) )
self.prediction_bar.update(1 )
else:
self.prediction_bar.update(self.prediction_bar.value + 1 )
def UpperCamelCase ( self , A__ , A__ , A__ , **A__ ) -> List[Any]:
if self.prediction_bar is not None:
self.prediction_bar.close()
_SCREAMING_SNAKE_CASE = None
def UpperCamelCase ( self , A__ , A__ , A__ , A__=None , **A__ ) -> Optional[int]:
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
_SCREAMING_SNAKE_CASE = {"""Training Loss""": logs["""loss"""]}
# First column is necessarily Step sine we're not in epoch eval strategy
_SCREAMING_SNAKE_CASE = state.global_step
self.training_tracker.write_line(A__ )
def UpperCamelCase ( self , A__ , A__ , A__ , A__=None , **A__ ) -> Dict:
if self.training_tracker is not None:
_SCREAMING_SNAKE_CASE = {"""Training Loss""": """No log""", """Validation Loss""": """No log"""}
for log in reversed(state.log_history ):
if "loss" in log:
_SCREAMING_SNAKE_CASE = log["""loss"""]
break
if self.first_column == "Epoch":
_SCREAMING_SNAKE_CASE = int(state.epoch )
else:
_SCREAMING_SNAKE_CASE = state.global_step
_SCREAMING_SNAKE_CASE = """eval"""
for k in metrics:
if k.endswith("""_loss""" ):
_SCREAMING_SNAKE_CASE = re.sub(R"""\_loss$""" , """""" , A__ )
_SCREAMING_SNAKE_CASE = metrics.pop("""total_flos""" , A__ )
_SCREAMING_SNAKE_CASE = metrics.pop("""epoch""" , A__ )
_SCREAMING_SNAKE_CASE = metrics.pop(F"{metric_key_prefix}_runtime" , A__ )
_SCREAMING_SNAKE_CASE = metrics.pop(F"{metric_key_prefix}_samples_per_second" , A__ )
_SCREAMING_SNAKE_CASE = metrics.pop(F"{metric_key_prefix}_steps_per_second" , A__ )
_SCREAMING_SNAKE_CASE = metrics.pop(F"{metric_key_prefix}_jit_compilation_time" , A__ )
for k, v in metrics.items():
if k == F"{metric_key_prefix}_loss":
_SCREAMING_SNAKE_CASE = v
else:
_SCREAMING_SNAKE_CASE = k.split("""_""" )
_SCREAMING_SNAKE_CASE = """ """.join([part.capitalize() for part in splits[1:]] )
_SCREAMING_SNAKE_CASE = v
self.training_tracker.write_line(A__ )
self.training_tracker.remove_child()
_SCREAMING_SNAKE_CASE = None
# Evaluation takes a long time so we should force the next update.
_SCREAMING_SNAKE_CASE = True
def UpperCamelCase ( self , A__ , A__ , A__ , **A__ ) -> Dict:
self.training_tracker.update(
state.global_step , comment=F"Epoch {int(state.epoch )}/{state.num_train_epochs}" , force_update=A__ )
_SCREAMING_SNAKE_CASE = None
| 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 json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class _a (unittest.TestCase):
"""simple docstring"""
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
# fmt: off
_SCREAMING_SNAKE_CASE = ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
_SCREAMING_SNAKE_CASE = dict(zip(A__ , range(len(A__ ) ) ) )
_SCREAMING_SNAKE_CASE = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
_SCREAMING_SNAKE_CASE = {"""unk_token""": """<unk>"""}
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(A__ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(A__ ) )
_SCREAMING_SNAKE_CASE = {
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
"""image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
}
_SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname , A__ )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(A__ , A__ )
def UpperCamelCase ( self , **A__ ) -> Any:
return CLIPTokenizer.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self , **A__ ) -> str:
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self , **A__ ) -> Dict:
return ViTImageProcessor.from_pretrained(self.tmpdirname , **A__ )
def UpperCamelCase ( self ) -> Any:
shutil.rmtree(self.tmpdirname )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = [np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
_SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(A__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE = self.get_image_processor()
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A__ , image_processor=A__ )
processor_slow.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=A__ )
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A__ , image_processor=A__ )
processor_fast.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE = CLIPSegProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , A__ )
self.assertIsInstance(processor_fast.tokenizer , A__ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , A__ )
self.assertIsInstance(processor_fast.image_processor , A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
_SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=A__ , padding_value=1.0 )
_SCREAMING_SNAKE_CASE = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=A__ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , A__ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A__ )
def UpperCamelCase ( self ) -> Dict:
_SCREAMING_SNAKE_CASE = self.get_image_processor()
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A__ , image_processor=A__ )
_SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE = image_processor(A__ , return_tensors="""np""" )
_SCREAMING_SNAKE_CASE = processor(images=A__ , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCamelCase ( self ) -> Tuple:
_SCREAMING_SNAKE_CASE = self.get_image_processor()
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A__ , image_processor=A__ )
_SCREAMING_SNAKE_CASE = """lower newer"""
_SCREAMING_SNAKE_CASE = processor(text=A__ )
_SCREAMING_SNAKE_CASE = tokenizer(A__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCamelCase ( self ) -> Any:
_SCREAMING_SNAKE_CASE = self.get_image_processor()
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A__ , image_processor=A__ )
_SCREAMING_SNAKE_CASE = """lower newer"""
_SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE = processor(text=A__ , images=A__ )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(A__ ):
processor()
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.get_image_processor()
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A__ , image_processor=A__ )
_SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE = processor(images=A__ , visual_prompt=A__ )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """conditional_pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(A__ ):
processor()
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.get_image_processor()
_SCREAMING_SNAKE_CASE = self.get_tokenizer()
_SCREAMING_SNAKE_CASE = CLIPSegProcessor(tokenizer=A__ , image_processor=A__ )
_SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_SCREAMING_SNAKE_CASE = processor.batch_decode(A__ )
_SCREAMING_SNAKE_CASE = tokenizer.batch_decode(A__ )
self.assertListEqual(A__ , A__ )
| 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 json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ) -> Any:
"""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"module.blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight") )
rename_keys.append((F"module.blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias") )
rename_keys.append(
(F"module.blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight") )
rename_keys.append((F"module.blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias") )
rename_keys.append((F"module.blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight") )
rename_keys.append((F"module.blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias") )
rename_keys.append((F"module.blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight") )
rename_keys.append((F"module.blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias") )
rename_keys.append((F"module.blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight") )
rename_keys.append((F"module.blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias") )
# projection layer + position embeddings
rename_keys.extend(
[
("""module.cls_token""", """vit.embeddings.cls_token"""),
("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
("""module.norm.weight""", """layernorm.weight"""),
("""module.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 ) -> int:
"""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"module.blocks.{i}.attn.qkv.weight" )
_SCREAMING_SNAKE_CASE = state_dict.pop(F"module.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_ ) -> Union[str, Any]:
"""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_ ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [
"""module.fc.fc1.weight""",
"""module.fc.fc1.bias""",
"""module.fc.bn1.weight""",
"""module.fc.bn1.bias""",
"""module.fc.bn1.running_mean""",
"""module.fc.bn1.running_var""",
"""module.fc.bn1.num_batches_tracked""",
"""module.fc.fc2.weight""",
"""module.fc.fc2.bias""",
"""module.fc.bn2.weight""",
"""module.fc.bn2.bias""",
"""module.fc.bn2.running_mean""",
"""module.fc.bn2.running_var""",
"""module.fc.bn2.num_batches_tracked""",
"""module.fc.fc3.weight""",
"""module.fc.fc3.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_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = dct.pop(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = val
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ViTMSNConfig()
_SCREAMING_SNAKE_CASE = 10_00
_SCREAMING_SNAKE_CASE = """datasets/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_ ) , """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()}
if "s16" in checkpoint_url:
_SCREAMING_SNAKE_CASE = 3_84
_SCREAMING_SNAKE_CASE = 15_36
_SCREAMING_SNAKE_CASE = 6
elif "l16" in checkpoint_url:
_SCREAMING_SNAKE_CASE = 10_24
_SCREAMING_SNAKE_CASE = 40_96
_SCREAMING_SNAKE_CASE = 24
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 0.1
elif "b4" in checkpoint_url:
_SCREAMING_SNAKE_CASE = 4
elif "l7" in checkpoint_url:
_SCREAMING_SNAKE_CASE = 7
_SCREAMING_SNAKE_CASE = 10_24
_SCREAMING_SNAKE_CASE = 40_96
_SCREAMING_SNAKE_CASE = 24
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = 0.1
_SCREAMING_SNAKE_CASE = ViTMSNModel(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )["""target_encoder"""]
_SCREAMING_SNAKE_CASE = ViTImageProcessor(size=config.image_size )
remove_projection_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_ , base_model=SCREAMING_SNAKE_CASE_ )
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
model.eval()
_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 )
_SCREAMING_SNAKE_CASE = ViTImageProcessor(
size=config.image_size , image_mean=SCREAMING_SNAKE_CASE_ , image_std=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
_SCREAMING_SNAKE_CASE = model(**SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
_SCREAMING_SNAKE_CASE = torch.tensor([[-1.0915, -1.4876, -1.1809]] )
elif "b16" in checkpoint_url:
_SCREAMING_SNAKE_CASE = torch.tensor([[14.2889, -18.9045, 11.7281]] )
elif "l16" in checkpoint_url:
_SCREAMING_SNAKE_CASE = torch.tensor([[41.5028, -22.8681, 45.6475]] )
elif "b4" in checkpoint_url:
_SCREAMING_SNAKE_CASE = torch.tensor([[-4.3868, 5.2932, -0.4137]] )
else:
_SCREAMING_SNAKE_CASE = torch.tensor([[-0.1792, -0.6465, 2.4263]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 )
print(F"Saving model 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__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
UpperCamelCase__ : List[Any] = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 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_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__)
| 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 math import isqrt
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> list[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = False
return [i for i in range(2 , SCREAMING_SNAKE_CASE_ ) if is_prime[i]]
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ = 10**8 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = calculate_prime_numbers(max_number // 2 )
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = len(SCREAMING_SNAKE_CASE_ ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 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 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__ : Optional[int] = "base_with_context"
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""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=SCREAMING_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=SCREAMING_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_ ) -> Optional[int]:
"""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=SCREAMING_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_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = checkpoints.load_tax_checkpoint(args.checkpoint_path )
_SCREAMING_SNAKE_CASE = jnp.tree_util.tree_map(onp.array , SCREAMING_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(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = inference.InferenceModel(args.checkpoint_path , SCREAMING_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"""] , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""] , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = load_decoder(ta_checkpoint["""target"""]["""decoder"""] , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder""" )
_SCREAMING_SNAKE_CASE = SpectrogramDiffusionPipeline(
notes_encoder=SCREAMING_SNAKE_CASE_ , continuous_encoder=SCREAMING_SNAKE_CASE_ , decoder=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ , melgan=SCREAMING_SNAKE_CASE_ , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
UpperCamelCase__ : List[Any] = 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[Any] = parser.parse_args()
main(args)
| 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'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class _a (unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def UpperCamelCase ( self , A__ , A__ , A__ ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = TextaTextGenerationPipeline(model=A__ , tokenizer=A__ )
return generator, ["Something to write", "Something else"]
def UpperCamelCase ( self , A__ , A__ ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = generator("""Something there""" )
self.assertEqual(A__ , [{"""generated_text""": ANY(A__ )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )
_SCREAMING_SNAKE_CASE = generator(["""This is great !""", """Something else"""] , num_return_sequences=2 , do_sample=A__ )
self.assertEqual(
A__ , [
[{"""generated_text""": ANY(A__ )}, {"""generated_text""": ANY(A__ )}],
[{"""generated_text""": ANY(A__ )}, {"""generated_text""": ANY(A__ )}],
] , )
_SCREAMING_SNAKE_CASE = generator(
["""This is great !""", """Something else"""] , num_return_sequences=2 , batch_size=2 , do_sample=A__ )
self.assertEqual(
A__ , [
[{"""generated_text""": ANY(A__ )}, {"""generated_text""": ANY(A__ )}],
[{"""generated_text""": ANY(A__ )}, {"""generated_text""": ANY(A__ )}],
] , )
with self.assertRaises(A__ ):
generator(4 )
@require_torch
def UpperCamelCase ( self ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = pipeline("""text2text-generation""" , model="""patrickvonplaten/t5-tiny-random""" , framework="""pt""" )
# do_sample=False necessary for reproducibility
_SCREAMING_SNAKE_CASE = generator("""Something there""" , do_sample=A__ )
self.assertEqual(A__ , [{"""generated_text""": """"""}] )
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = generator(
"""Something there""" , num_return_sequences=A__ , num_beams=A__ , )
_SCREAMING_SNAKE_CASE = [
{"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
{"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
{"""generated_text""": """"""},
]
self.assertEqual(A__ , A__ )
_SCREAMING_SNAKE_CASE = generator("""This is a test""" , do_sample=A__ , num_return_sequences=2 , return_tensors=A__ )
self.assertEqual(
A__ , [
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
] , )
_SCREAMING_SNAKE_CASE = generator.model.config.eos_token_id
_SCREAMING_SNAKE_CASE = """<pad>"""
_SCREAMING_SNAKE_CASE = generator(
["""This is a test""", """This is a second test"""] , do_sample=A__ , num_return_sequences=2 , batch_size=2 , return_tensors=A__ , )
self.assertEqual(
A__ , [
[
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
],
[
{"""generated_token_ids""": ANY(torch.Tensor )},
{"""generated_token_ids""": ANY(torch.Tensor )},
],
] , )
@require_tf
def UpperCamelCase ( self ) -> str:
_SCREAMING_SNAKE_CASE = pipeline("""text2text-generation""" , model="""patrickvonplaten/t5-tiny-random""" , framework="""tf""" )
# do_sample=False necessary for reproducibility
_SCREAMING_SNAKE_CASE = generator("""Something there""" , do_sample=A__ )
self.assertEqual(A__ , [{"""generated_text""": """"""}] )
| 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 math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
UpperCamelCase__ : Any = {
"gwf-440k": {
"url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
"sample_rate": 48_000,
"sample_size": 65_536,
},
"jmann-small-190k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
"sample_rate": 48_000,
"sample_size": 65_536,
},
"jmann-large-580k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
"sample_rate": 48_000,
"sample_size": 131_072,
},
"maestro-uncond-150k": {
"url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
"sample_rate": 16_000,
"sample_size": 65_536,
},
"unlocked-uncond-250k": {
"url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
"sample_rate": 16_000,
"sample_size": 65_536,
},
"honk-140k": {
"url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
"sample_rate": 16_000,
"sample_size": 65_536,
},
}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
return torch.atana(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) / math.pi * 2
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = torch.sin(t * math.pi / 2 ) ** 2
_SCREAMING_SNAKE_CASE = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
class _a (_lowerCamelCase):
"""simple docstring"""
pass
class _a (nn.Module):
"""simple docstring"""
def __init__( self , A__ ) -> Dict:
super().__init__()
_SCREAMING_SNAKE_CASE = DiffusionAttnUnetaD(A__ , n_attn_layers=4 )
_SCREAMING_SNAKE_CASE = deepcopy(self.diffusion )
_SCREAMING_SNAKE_CASE = torch.quasirandom.SobolEngine(1 , scramble=A__ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = MODELS_MAP[model_name]["""url"""]
os.system(F"wget {url} ./" )
return F"./{model_name}.ckpt"
UpperCamelCase__ : Optional[int] = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
}
UpperCamelCase__ : List[str] = {
"8": "resnets.0",
"9": "attentions.0",
"10": "resnets.1",
"11": "attentions.1",
"12": "resnets.2",
"13": "attentions.2",
}
UpperCamelCase__ : str = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
"8": "resnets.3",
"9": "attentions.3",
"10": "resnets.4",
"11": "attentions.4",
"12": "resnets.5",
"13": "attentions.5",
}
UpperCamelCase__ : Union[str, Any] = {
"0": "resnets.0",
"1": "resnets.1",
"2": "resnets.2",
"4": "resnets.0",
"5": "resnets.1",
"6": "resnets.2",
}
UpperCamelCase__ : Optional[int] = {
"skip": "conv_skip",
"main.0": "conv_1",
"main.1": "group_norm_1",
"main.3": "conv_2",
"main.4": "group_norm_2",
}
UpperCamelCase__ : Any = {
"norm": "group_norm",
"qkv_proj": ["query", "key", "value"],
"out_proj": ["proj_attn"],
}
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
if name.startswith("""skip""" ):
return name.replace("""skip""" , RES_CONV_MAP["""skip"""] )
# name has to be of format main.{digit}
if not name.startswith("""main.""" ):
raise ValueError(F"ResConvBlock error with {name}" )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
for key, value in ATTN_MAP.items():
if name.startswith(SCREAMING_SNAKE_CASE_ ) and not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return name.replace(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif name.startswith(SCREAMING_SNAKE_CASE_ ):
return [name.replace(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for v in value]
raise ValueError(F"Attn error with {name}" )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=13 ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = input_string
if string.split(""".""" )[0] == "timestep_embed":
return string.replace("""timestep_embed""" , """time_proj""" )
_SCREAMING_SNAKE_CASE = 0
if string.startswith("""net.3.""" ):
depth += 1
_SCREAMING_SNAKE_CASE = string[6:]
elif string.startswith("""net.""" ):
_SCREAMING_SNAKE_CASE = string[4:]
while string.startswith("""main.7.""" ):
depth += 1
_SCREAMING_SNAKE_CASE = string[7:]
if string.startswith("""main.""" ):
_SCREAMING_SNAKE_CASE = string[5:]
# mid block
if string[:2].isdigit():
_SCREAMING_SNAKE_CASE = string[:2]
_SCREAMING_SNAKE_CASE = string[2:]
else:
_SCREAMING_SNAKE_CASE = string[0]
_SCREAMING_SNAKE_CASE = string[1:]
if depth == max_depth:
_SCREAMING_SNAKE_CASE = MID_NUM_TO_LAYER[layer_num]
_SCREAMING_SNAKE_CASE = """mid_block"""
elif depth > 0 and int(SCREAMING_SNAKE_CASE_ ) < 7:
_SCREAMING_SNAKE_CASE = DOWN_NUM_TO_LAYER[layer_num]
_SCREAMING_SNAKE_CASE = F"down_blocks.{depth}"
elif depth > 0 and int(SCREAMING_SNAKE_CASE_ ) > 7:
_SCREAMING_SNAKE_CASE = UP_NUM_TO_LAYER[layer_num]
_SCREAMING_SNAKE_CASE = F"up_blocks.{max_depth - depth - 1}"
elif depth == 0:
_SCREAMING_SNAKE_CASE = DEPTH_0_TO_LAYER[layer_num]
_SCREAMING_SNAKE_CASE = F"up_blocks.{max_depth - 1}" if int(SCREAMING_SNAKE_CASE_ ) > 3 else """down_blocks.0"""
if not string_left.startswith(""".""" ):
raise ValueError(F"Naming error with {input_string} and string_left: {string_left}." )
_SCREAMING_SNAKE_CASE = string_left[1:]
if "resnets" in new_layer:
_SCREAMING_SNAKE_CASE = convert_resconv_naming(SCREAMING_SNAKE_CASE_ )
elif "attentions" in new_layer:
_SCREAMING_SNAKE_CASE = convert_attn_naming(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = new_string_left
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = prefix + """.""" + new_layer + """.""" + string_left
else:
_SCREAMING_SNAKE_CASE = [prefix + """.""" + new_layer + """.""" + s for s in string_left]
return new_string
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = {}
for k, v in state_dict.items():
if k.endswith("""kernel""" ):
# up- and downsample layers, don't have trainable weights
continue
_SCREAMING_SNAKE_CASE = rename(SCREAMING_SNAKE_CASE_ )
# check if we need to transform from Conv => Linear for attention
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = transform_conv_attns(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
_SCREAMING_SNAKE_CASE = v
return new_state_dict
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) == 1:
if len(v.shape ) == 3:
# weight
_SCREAMING_SNAKE_CASE = v[:, :, 0]
else:
# bias
_SCREAMING_SNAKE_CASE = v
else:
# qkv matrices
_SCREAMING_SNAKE_CASE = v.shape[0]
_SCREAMING_SNAKE_CASE = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
_SCREAMING_SNAKE_CASE = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
_SCREAMING_SNAKE_CASE = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
_SCREAMING_SNAKE_CASE = args.model_path.split("""/""" )[-1].split(""".""" )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), F"Make sure to provide one of the official model names {MODELS_MAP.keys()}"
_SCREAMING_SNAKE_CASE = download(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = MODELS_MAP[model_name]["""sample_rate"""]
_SCREAMING_SNAKE_CASE = MODELS_MAP[model_name]["""sample_size"""]
_SCREAMING_SNAKE_CASE = Object()
_SCREAMING_SNAKE_CASE = sample_size
_SCREAMING_SNAKE_CASE = sample_rate
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_ , sample_rate=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = diffusers_model.state_dict()
_SCREAMING_SNAKE_CASE = DiffusionUncond(SCREAMING_SNAKE_CASE_ )
orig_model.load_state_dict(torch.load(args.model_path , map_location=SCREAMING_SNAKE_CASE_ )["""state_dict"""] )
_SCREAMING_SNAKE_CASE = orig_model.diffusion_ema.eval()
_SCREAMING_SNAKE_CASE = orig_model.state_dict()
_SCREAMING_SNAKE_CASE = rename_orig_weights(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
_SCREAMING_SNAKE_CASE = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(SCREAMING_SNAKE_CASE_ ) == 0, F"Problem with {renamed_minus_diffusers}"
assert all(k.endswith("""kernel""" ) for k in list(SCREAMING_SNAKE_CASE_ ) ), F"Problem with {diffusers_minus_renamed}"
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), F"Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"
if key == "time_proj.weight":
_SCREAMING_SNAKE_CASE = value.squeeze()
_SCREAMING_SNAKE_CASE = value
diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = 1_00
_SCREAMING_SNAKE_CASE = 33
_SCREAMING_SNAKE_CASE = IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = torch.randn([1, 2, config.sample_size] , generator=SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = torch.linspace(1 , 0 , steps + 1 , device=SCREAMING_SNAKE_CASE_ )[:-1]
_SCREAMING_SNAKE_CASE = get_crash_schedule(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = torch.manual_seed(33 )
_SCREAMING_SNAKE_CASE = pipe(num_inference_steps=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ ).audios
_SCREAMING_SNAKE_CASE = sampling.iplms_sample(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , {} )
_SCREAMING_SNAKE_CASE = generated.clamp(-1 , 1 )
_SCREAMING_SNAKE_CASE = (generated - audio).abs().sum()
_SCREAMING_SNAKE_CASE = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print("""Diff sum""" , SCREAMING_SNAKE_CASE_ )
print("""Diff max""" , SCREAMING_SNAKE_CASE_ )
assert diff_max < 1e-3, F"Diff max: {diff_max} is too much :-/"
print(F"Conversion for {model_name} successful!" )
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
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=None, type=str, required=True, help="Path to the output model.")
UpperCamelCase__ : int = parser.parse_args()
main(args)
| 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'''
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
while b:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = b, a % b
return a
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(SCREAMING_SNAKE_CASE_ , a % b )
def lowerCAmelCase_ ( ) -> Tuple:
"""simple docstring"""
print(F"euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}" )
print(F"euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}" )
print(F"euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}" )
print(F"euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}" )
print(F"euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}" )
print(F"euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}" )
print(F"euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}" )
print(F"euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}" )
print(F"euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}" )
print(F"euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}" )
if __name__ == "__main__":
main() | 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'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Dict:
"""simple docstring"""
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
_SCREAMING_SNAKE_CASE = flax_key_tuple[:-1] + ("""weight""",)
_SCREAMING_SNAKE_CASE = torch.permute(SCREAMING_SNAKE_CASE_ , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(SCREAMING_SNAKE_CASE_ ):
# linear layer
_SCREAMING_SNAKE_CASE = flax_key_tuple[:-1] + ("""weight""",)
_SCREAMING_SNAKE_CASE = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_SCREAMING_SNAKE_CASE = flax_key_tuple[:-1] + ("""weight""",)
return flax_key_tuple, flax_tensor
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
"""simple docstring"""
if "metadata" in layer:
_SCREAMING_SNAKE_CASE = layer.split("""metadata""" )
_SCREAMING_SNAKE_CASE = """""".join(split_layer[0] )[:-1]
_SCREAMING_SNAKE_CASE = [tuple(("""metadata""" + split_layer[1]).split("""/""" ) )]
elif "kvstore" in layer:
_SCREAMING_SNAKE_CASE = layer.split("""kvstore""" )
_SCREAMING_SNAKE_CASE = """""".join(split_layer[0] )[:-1]
_SCREAMING_SNAKE_CASE = [tuple(("""kvstore""" + split_layer[1]).split("""/""" ) )]
else:
_SCREAMING_SNAKE_CASE = layer.split("""/""" )
_SCREAMING_SNAKE_CASE = """/""".join(split_layer[:-1] )
_SCREAMING_SNAKE_CASE = (split_layer[-1],)
if "kvstore/path" in layer:
_SCREAMING_SNAKE_CASE = F"{switch_checkpoint_path}/{checkpoint_info[layer]}"
elif "kvstore/driver" in layer:
_SCREAMING_SNAKE_CASE = """file"""
else:
_SCREAMING_SNAKE_CASE = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = rename_keys(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = {}
for k, v in current_block.items():
_SCREAMING_SNAKE_CASE = v
_SCREAMING_SNAKE_CASE = new_current_block
torch.save(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = WEIGHTS_NAME ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = convert_file_size_to_int(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
os.makedirs(SCREAMING_SNAKE_CASE_ , exist_ok=SCREAMING_SNAKE_CASE_ )
with gfile.GFile(switch_checkpoint_path + """/checkpoint""" , """rb""" ) as fp:
_SCREAMING_SNAKE_CASE = serialization.msgpack_restore(fp.read() )["""optimizer"""]["""target"""]
_SCREAMING_SNAKE_CASE = flatten_dict(SCREAMING_SNAKE_CASE_ , sep="""/""" )
_SCREAMING_SNAKE_CASE = {}
for layer in checkpoint_info.keys():
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = get_key_and_tensorstore_dict(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if curr_real_layer_name in all_layers:
_SCREAMING_SNAKE_CASE = content
else:
_SCREAMING_SNAKE_CASE = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
_SCREAMING_SNAKE_CASE = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
_SCREAMING_SNAKE_CASE = torch.tensor(SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = rename_base_flax_keys(tuple(key.split("""/""" ) ) , SCREAMING_SNAKE_CASE_ )
_SCREAMING_SNAKE_CASE = """/""".join(SCREAMING_SNAKE_CASE_ )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
_SCREAMING_SNAKE_CASE = os.path.join(
SCREAMING_SNAKE_CASE_ , weights_name.replace(""".bin""" , F"-{len(SCREAMING_SNAKE_CASE_ )+1:05d}-of-???.bin" ) )
rename_and_save_block(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
sharded_state_dicts.append(current_block.keys() )
del current_block
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = raw_weights.to(getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
_SCREAMING_SNAKE_CASE = os.path.join(SCREAMING_SNAKE_CASE_ , weights_name.replace(""".bin""" , F"-{len(SCREAMING_SNAKE_CASE_ )+1:05d}-of-???.bin" ) )
rename_and_save_block(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(SCREAMING_SNAKE_CASE_ ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = {}
for idx, shard in enumerate(SCREAMING_SNAKE_CASE_ ):
_SCREAMING_SNAKE_CASE = weights_name.replace(
""".bin""" , F"-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_ ):05d}.bin" ) # len(sharded_state_dicts):05d}
_SCREAMING_SNAKE_CASE = os.path.join(SCREAMING_SNAKE_CASE_ , weights_name.replace(""".bin""" , F"-{idx+1:05d}-of-???.bin" ) )
os.rename(SCREAMING_SNAKE_CASE_ , os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
_SCREAMING_SNAKE_CASE = shard
for key in shard:
_SCREAMING_SNAKE_CASE = shard_file
# Add the metadata
_SCREAMING_SNAKE_CASE = {"""total_size""": total_size}
_SCREAMING_SNAKE_CASE = {"""metadata""": metadata, """weight_map""": weight_map}
with open(os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , """w""" , encoding="""utf-8""" ) as f:
_SCREAMING_SNAKE_CASE = json.dumps(SCREAMING_SNAKE_CASE_ , indent=2 , sort_keys=SCREAMING_SNAKE_CASE_ ) + """\n"""
f.write(SCREAMING_SNAKE_CASE_ )
return metadata, index
if __name__ == "__main__":
UpperCamelCase__ : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--switch_t5x_checkpoint_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600",
type=str,
required=False,
help="Path to a directory containing a folder per layer. Follows the original Google format.",
)
parser.add_argument("--max_shard_size", default="10GB", required=False, help="Max shard size")
parser.add_argument("--dtype", default="bfloat16", type=str, required=False, help="dtype of the saved model")
parser.add_argument(
"--pytorch_dump_folder_path",
default="/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted",
type=str,
required=False,
help="Path to the output pytorch model.",
)
UpperCamelCase__ : Optional[Any] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def lowerCAmelCase_ ( ) -> Tuple:
"""simple docstring"""
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
_SCREAMING_SNAKE_CASE = SwitchTransformersConfig.from_pretrained("""google/switch-base-8""" )
config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted""" )
_SCREAMING_SNAKE_CASE = SwitchTransformersForConditionalGeneration.from_pretrained(
"""/home/arthur_huggingface_co/transformers/switch_converted""" , device_map="""auto""" )
_SCREAMING_SNAKE_CASE = TaTokenizer.from_pretrained("""t5-small""" )
_SCREAMING_SNAKE_CASE = """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""
_SCREAMING_SNAKE_CASE = tokenizer(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" ).input_ids
_SCREAMING_SNAKE_CASE = model.generate(SCREAMING_SNAKE_CASE_ , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 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 functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Tuple = logging.get_logger(__name__)
UpperCamelCase__ : str = {
"microsoft/unispeech-sat-base-100h-libri-ft": (
"https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"
),
# See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}
class _a (_lowerCamelCase):
SCREAMING_SNAKE_CASE = 'unispeech-sat'
def __init__( self , A__=32 , A__=7_68 , A__=12 , A__=12 , A__=30_72 , A__="gelu" , A__=0.1 , A__=0.1 , A__=0.1 , A__=0.0 , A__=0.0 , A__=0.1 , A__=0.1 , A__=0.02 , A__=1E-5 , A__="group" , A__="gelu" , A__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , A__=(5, 2, 2, 2, 2, 2, 2) , A__=(10, 3, 3, 3, 3, 2, 2) , A__=False , A__=1_28 , A__=16 , A__=False , A__=True , A__=0.05 , A__=10 , A__=2 , A__=0.0 , A__=10 , A__=0 , A__=3_20 , A__=2 , A__=0.1 , A__=1_00 , A__=2_56 , A__=2_56 , A__=0.1 , A__="mean" , A__=False , A__=False , A__=2_56 , A__=(5_12, 5_12, 5_12, 5_12, 15_00) , A__=(5, 3, 3, 1, 1) , A__=(1, 2, 3, 1, 1) , A__=5_12 , A__=0 , A__=1 , A__=2 , A__=5_04 , **A__ , ) -> List[str]:
super().__init__(**A__ , pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ )
_SCREAMING_SNAKE_CASE = hidden_size
_SCREAMING_SNAKE_CASE = feat_extract_norm
_SCREAMING_SNAKE_CASE = feat_extract_activation
_SCREAMING_SNAKE_CASE = list(A__ )
_SCREAMING_SNAKE_CASE = list(A__ )
_SCREAMING_SNAKE_CASE = list(A__ )
_SCREAMING_SNAKE_CASE = conv_bias
_SCREAMING_SNAKE_CASE = num_conv_pos_embeddings
_SCREAMING_SNAKE_CASE = num_conv_pos_embedding_groups
_SCREAMING_SNAKE_CASE = len(self.conv_dim )
_SCREAMING_SNAKE_CASE = num_hidden_layers
_SCREAMING_SNAKE_CASE = intermediate_size
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = num_attention_heads
_SCREAMING_SNAKE_CASE = hidden_dropout
_SCREAMING_SNAKE_CASE = attention_dropout
_SCREAMING_SNAKE_CASE = activation_dropout
_SCREAMING_SNAKE_CASE = feat_proj_dropout
_SCREAMING_SNAKE_CASE = final_dropout
_SCREAMING_SNAKE_CASE = layerdrop
_SCREAMING_SNAKE_CASE = layer_norm_eps
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = vocab_size
_SCREAMING_SNAKE_CASE = num_clusters
_SCREAMING_SNAKE_CASE = do_stable_layer_norm
_SCREAMING_SNAKE_CASE = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"""Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
""" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
F" {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,"
F" `len(config.conv_kernel) = {len(self.conv_kernel )}`." )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_SCREAMING_SNAKE_CASE = apply_spec_augment
_SCREAMING_SNAKE_CASE = mask_time_prob
_SCREAMING_SNAKE_CASE = mask_time_length
_SCREAMING_SNAKE_CASE = mask_time_min_masks
_SCREAMING_SNAKE_CASE = mask_feature_prob
_SCREAMING_SNAKE_CASE = mask_feature_length
_SCREAMING_SNAKE_CASE = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_SCREAMING_SNAKE_CASE = num_codevectors_per_group
_SCREAMING_SNAKE_CASE = num_codevector_groups
_SCREAMING_SNAKE_CASE = contrastive_logits_temperature
_SCREAMING_SNAKE_CASE = feat_quantizer_dropout
_SCREAMING_SNAKE_CASE = num_negatives
_SCREAMING_SNAKE_CASE = codevector_dim
_SCREAMING_SNAKE_CASE = proj_codevector_dim
_SCREAMING_SNAKE_CASE = diversity_loss_weight
# ctc loss
_SCREAMING_SNAKE_CASE = ctc_loss_reduction
_SCREAMING_SNAKE_CASE = ctc_zero_infinity
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_SCREAMING_SNAKE_CASE = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_SCREAMING_SNAKE_CASE = list(A__ )
_SCREAMING_SNAKE_CASE = list(A__ )
_SCREAMING_SNAKE_CASE = list(A__ )
_SCREAMING_SNAKE_CASE = xvector_output_dim
@property
def UpperCamelCase ( self ) -> Dict:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 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_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
ImageTextPipelineOutput,
UniDiffuserPipeline,
)
else:
from .modeling_text_decoder import UniDiffuserTextDecoder
from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline
| 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'''
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()
| 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 json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class _a (_lowerCamelCase , unittest.TestCase):
"""simple docstring"""
SCREAMING_SNAKE_CASE = CanineTokenizer
SCREAMING_SNAKE_CASE = False
def UpperCamelCase ( self ) -> str:
super().setUp()
_SCREAMING_SNAKE_CASE = CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def UpperCamelCase ( self ) -> str:
return CanineTokenizer.from_pretrained("""google/canine-s""" )
def UpperCamelCase ( self , **A__ ) -> CanineTokenizer:
_SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(self.tmpdirname , **A__ )
_SCREAMING_SNAKE_CASE = 10_24
return tokenizer
@require_torch
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.canine_tokenizer
_SCREAMING_SNAKE_CASE = ["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
# fmt: off
_SCREAMING_SNAKE_CASE = [5_73_44, 76, 1_05, 1_02, 1_01, 32, 1_05, 1_15, 32, 1_08, 1_05, 1_07, 1_01, 32, 97, 32, 98, 1_11, 1_20, 32, 1_11, 1_02, 32, 99, 1_04, 1_11, 99, 1_11, 1_08, 97, 1_16, 1_01, 1_15, 46, 5_73_45, 0, 0, 0, 0]
# fmt: on
_SCREAMING_SNAKE_CASE = tokenizer(A__ , padding=A__ , return_tensors="""pt""" )
self.assertIsInstance(A__ , A__ )
_SCREAMING_SNAKE_CASE = list(batch.input_ids.numpy()[0] )
self.assertListEqual(A__ , A__ )
self.assertEqual((2, 39) , batch.input_ids.shape )
self.assertEqual((2, 39) , batch.attention_mask.shape )
@require_torch
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.canine_tokenizer
_SCREAMING_SNAKE_CASE = ["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
_SCREAMING_SNAKE_CASE = tokenizer(A__ , padding=A__ , return_tensors="""pt""" )
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn("""input_ids""" , A__ )
self.assertIn("""attention_mask""" , A__ )
self.assertIn("""token_type_ids""" , A__ )
@require_torch
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.canine_tokenizer
_SCREAMING_SNAKE_CASE = [
"""What's the weater?""",
"""It's about 25 degrees.""",
]
_SCREAMING_SNAKE_CASE = tokenizer(
text_target=A__ , max_length=32 , padding="""max_length""" , truncation=A__ , return_tensors="""pt""" )
self.assertEqual(32 , targets["""input_ids"""].shape[1] )
def UpperCamelCase ( self ) -> Optional[Any]:
# safety check on max_len default value so we are sure the test works
_SCREAMING_SNAKE_CASE = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
self.assertNotEqual(tokenizer.model_max_length , 42 )
# Now let's start the test
_SCREAMING_SNAKE_CASE = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
_SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
_SCREAMING_SNAKE_CASE = """ He is very happy, UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
tokenizer.save_pretrained(A__ )
_SCREAMING_SNAKE_CASE = tokenizer.__class__.from_pretrained(A__ )
_SCREAMING_SNAKE_CASE = after_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
shutil.rmtree(A__ )
_SCREAMING_SNAKE_CASE = self.get_tokenizers(model_max_length=42 )
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
# Isolate this from the other tests because we save additional tokens/etc
_SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
_SCREAMING_SNAKE_CASE = """ He is very happy, UNwant\u00E9d,running"""
_SCREAMING_SNAKE_CASE = tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
_SCREAMING_SNAKE_CASE = chr(0Xe_0_0_7 )
additional_special_tokens.append(A__ )
tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
tokenizer.save_pretrained(A__ )
_SCREAMING_SNAKE_CASE = tokenizer.__class__.from_pretrained(A__ )
_SCREAMING_SNAKE_CASE = after_tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertListEqual(A__ , A__ )
self.assertIn(A__ , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
_SCREAMING_SNAKE_CASE = tokenizer.__class__.from_pretrained(A__ , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(A__ )
def UpperCamelCase ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = self.get_tokenizers(do_lower_case=A__ )
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.get_clean_sequence(A__ )
# a special token for Canine can be defined as follows:
_SCREAMING_SNAKE_CASE = 0Xe_0_0_5
_SCREAMING_SNAKE_CASE = chr(A__ )
tokenizer.add_special_tokens({"""cls_token""": special_token} )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertEqual(len(A__ ) , 1 )
_SCREAMING_SNAKE_CASE = tokenizer.decode(ids + encoded_special_token , clean_up_tokenization_spaces=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
self.assertEqual(A__ , input_encoded + special_token_id )
_SCREAMING_SNAKE_CASE = tokenizer.decode(A__ , skip_special_tokens=A__ )
self.assertTrue(special_token not in decoded )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.get_tokenizers(do_lower_case=A__ )
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
_SCREAMING_SNAKE_CASE = chr(0Xe_0_0_5 )
_SCREAMING_SNAKE_CASE = chr(0Xe_0_0_6 )
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1] , special_tokens=A__ )
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({"""additional_special_tokens""": [SPECIAL_TOKEN_2]} )
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
_SCREAMING_SNAKE_CASE = tokenizer.tokenize(A__ )
self.assertEqual(len(A__ ) , 1 )
self.assertEqual(len(A__ ) , 1 )
self.assertEqual(token_a[0] , A__ )
self.assertEqual(token_a[0] , A__ )
@require_tokenizers
def UpperCamelCase ( self ) -> int:
_SCREAMING_SNAKE_CASE = self.get_tokenizers(do_lower_case=A__ )
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
# a special token for Canine can be defined as follows:
_SCREAMING_SNAKE_CASE = 0Xe_0_0_6
_SCREAMING_SNAKE_CASE = chr(A__ )
_SCREAMING_SNAKE_CASE = AddedToken(A__ , lstrip=A__ )
tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]} )
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(A__ )
tokenizer.from_pretrained(A__ )
def UpperCamelCase ( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()) )
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()) )
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(A__ )
with open(os.path.join(A__ , """special_tokens_map.json""" ) , encoding="""utf-8""" ) as json_file:
_SCREAMING_SNAKE_CASE = json.load(A__ )
with open(os.path.join(A__ , """tokenizer_config.json""" ) , encoding="""utf-8""" ) as json_file:
_SCREAMING_SNAKE_CASE = json.load(A__ )
# a special token for Canine can be defined as follows:
_SCREAMING_SNAKE_CASE = 0Xe_0_0_6
_SCREAMING_SNAKE_CASE = chr(A__ )
_SCREAMING_SNAKE_CASE = [new_token_a]
_SCREAMING_SNAKE_CASE = [new_token_a]
with open(os.path.join(A__ , """special_tokens_map.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(A__ , A__ )
with open(os.path.join(A__ , """tokenizer_config.json""" ) , """w""" , encoding="""utf-8""" ) as outfile:
json.dump(A__ , A__ )
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
_SCREAMING_SNAKE_CASE = tokenizer_class.from_pretrained(A__ , extra_ids=0 )
self.assertIn(A__ , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) ) , )
_SCREAMING_SNAKE_CASE = 0Xe_0_0_7
_SCREAMING_SNAKE_CASE = chr(A__ )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
_SCREAMING_SNAKE_CASE = [AddedToken(A__ , lstrip=A__ )]
_SCREAMING_SNAKE_CASE = tokenizer_class.from_pretrained(
A__ , additional_special_tokens=A__ , extra_ids=0 )
self.assertIn(A__ , tokenizer.additional_special_tokens )
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) )
@require_tokenizers
def UpperCamelCase ( self ) -> List[str]:
_SCREAMING_SNAKE_CASE = self.get_tokenizers(do_lower_case=A__ )
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
_SCREAMING_SNAKE_CASE = """hello world"""
if self.space_between_special_tokens:
_SCREAMING_SNAKE_CASE = """[CLS] hello world [SEP]"""
else:
_SCREAMING_SNAKE_CASE = input
_SCREAMING_SNAKE_CASE = tokenizer.encode(A__ , add_special_tokens=A__ )
_SCREAMING_SNAKE_CASE = tokenizer.decode(A__ , spaces_between_special_tokens=self.space_between_special_tokens )
self.assertIn(A__ , [output, output.lower()] )
def UpperCamelCase ( self ) -> Union[str, Any]:
_SCREAMING_SNAKE_CASE = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}" ):
_SCREAMING_SNAKE_CASE = [
"""bos_token""",
"""eos_token""",
"""unk_token""",
"""sep_token""",
"""pad_token""",
"""cls_token""",
"""mask_token""",
]
_SCREAMING_SNAKE_CASE = """a"""
_SCREAMING_SNAKE_CASE = ord(A__ )
for attr in attributes_list:
setattr(A__ , attr + """_id""" , A__ )
self.assertEqual(getattr(A__ , A__ ) , A__ )
self.assertEqual(getattr(A__ , attr + """_id""" ) , A__ )
setattr(A__ , attr + """_id""" , A__ )
self.assertEqual(getattr(A__ , A__ ) , A__ )
self.assertEqual(getattr(A__ , attr + """_id""" ) , A__ )
setattr(A__ , """additional_special_tokens_ids""" , [] )
self.assertListEqual(getattr(A__ , """additional_special_tokens""" ) , [] )
self.assertListEqual(getattr(A__ , """additional_special_tokens_ids""" ) , [] )
_SCREAMING_SNAKE_CASE = 0Xe_0_0_6
_SCREAMING_SNAKE_CASE = chr(A__ )
setattr(A__ , """additional_special_tokens_ids""" , [additional_special_token_id] )
self.assertListEqual(getattr(A__ , """additional_special_tokens""" ) , [additional_special_token] )
self.assertListEqual(getattr(A__ , """additional_special_tokens_ids""" ) , [additional_special_token_id] )
def UpperCamelCase ( self ) -> Union[str, Any]:
pass
def UpperCamelCase ( self ) -> List[str]:
pass
def UpperCamelCase ( self ) -> Optional[Any]:
pass
def UpperCamelCase ( self ) -> List[Any]:
pass
def UpperCamelCase ( self ) -> Optional[int]:
pass
def UpperCamelCase ( self ) -> List[Any]:
pass
def UpperCamelCase ( self ) -> int:
pass
def UpperCamelCase ( self ) -> Optional[int]:
pass
| 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 |