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# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class __snake_case ( a , a ):
UpperCAmelCase__ : int = 1
@register_to_config
def __init__( self : Optional[Any] , _snake_case : Tuple=2000 , _snake_case : Tuple=0.1 , _snake_case : Optional[int]=20 , _snake_case : Dict=1e-3):
"""simple docstring"""
UpperCAmelCase_ = None
UpperCAmelCase_ = None
UpperCAmelCase_ = None
def lowerCamelCase ( self : List[str] , _snake_case : Dict , _snake_case : List[str] = None):
"""simple docstring"""
UpperCAmelCase_ = torch.linspace(1 , self.config.sampling_eps , _snake_case , device=_snake_case)
def lowerCamelCase ( self : List[str] , _snake_case : Optional[int] , _snake_case : Any , _snake_case : List[str] , _snake_case : List[Any]=None):
"""simple docstring"""
if self.timesteps is None:
raise ValueError(
'''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''')
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
UpperCAmelCase_ = (
-0.2_5 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
UpperCAmelCase_ = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
UpperCAmelCase_ = std.flatten()
while len(std.shape) < len(score.shape):
UpperCAmelCase_ = std.unsqueeze(-1)
UpperCAmelCase_ = -score / std
# compute
UpperCAmelCase_ = -1.0 / len(self.timesteps)
UpperCAmelCase_ = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
UpperCAmelCase_ = beta_t.flatten()
while len(beta_t.shape) < len(x.shape):
UpperCAmelCase_ = beta_t.unsqueeze(-1)
UpperCAmelCase_ = -0.5 * beta_t * x
UpperCAmelCase_ = torch.sqrt(_snake_case)
UpperCAmelCase_ = drift - diffusion**2 * score
UpperCAmelCase_ = x + drift * dt
# add noise
UpperCAmelCase_ = randn_tensor(x.shape , layout=x.layout , generator=_snake_case , device=x.device , dtype=x.dtype)
UpperCAmelCase_ = x_mean + diffusion * math.sqrt(-dt) * noise
return x, x_mean
def __len__( self : Optional[Any]):
"""simple docstring"""
return self.config.num_train_timesteps
| 51 |
from math import isqrt
def UpperCAmelCase_ ( __snake_case ) -> list[int]:
"""simple docstring"""
_lowercase =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __snake_case , __snake_case ):
_lowercase =False
return [i for i in range(2 , __snake_case ) if is_prime[i]]
def UpperCAmelCase_ ( __snake_case = 10**8 ) -> int:
"""simple docstring"""
_lowercase =calculate_prime_numbers(max_number // 2 )
_lowercase =0
_lowercase =0
_lowercase =len(__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() = }''')
| 5 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a =logging.get_logger(__name__)
a ={
"""facebook/data2vec-vision-base-ft""": (
"""https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"""
),
}
class A_ ( SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : Optional[int] = '''data2vec-vision'''
def __init__( self : List[str] ,SCREAMING_SNAKE_CASE__ : Any=7_6_8 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=1_2 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=1_2 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=3_0_7_2 ,SCREAMING_SNAKE_CASE__ : Optional[int]="gelu" ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.0 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=0.0 ,SCREAMING_SNAKE_CASE__ : Tuple=0.02 ,SCREAMING_SNAKE_CASE__ : List[str]=1E-12 ,SCREAMING_SNAKE_CASE__ : List[Any]=2_2_4 ,SCREAMING_SNAKE_CASE__ : Any=1_6 ,SCREAMING_SNAKE_CASE__ : str=3 ,SCREAMING_SNAKE_CASE__ : str=False ,SCREAMING_SNAKE_CASE__ : str=False ,SCREAMING_SNAKE_CASE__ : str=False ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=False ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.1 ,SCREAMING_SNAKE_CASE__ : List[Any]=0.1 ,SCREAMING_SNAKE_CASE__ : str=True ,SCREAMING_SNAKE_CASE__ : List[Any]=[3, 5, 7, 1_1] ,SCREAMING_SNAKE_CASE__ : List[str]=[1, 2, 3, 6] ,SCREAMING_SNAKE_CASE__ : Dict=True ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.4 ,SCREAMING_SNAKE_CASE__ : int=2_5_6 ,SCREAMING_SNAKE_CASE__ : Any=1 ,SCREAMING_SNAKE_CASE__ : Optional[int]=False ,SCREAMING_SNAKE_CASE__ : Tuple=2_5_5 ,**SCREAMING_SNAKE_CASE__ : Tuple ,):
super().__init__(**SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[int] = hidden_size
__lowerCamelCase : List[Any] = num_hidden_layers
__lowerCamelCase : Union[str, Any] = num_attention_heads
__lowerCamelCase : List[Any] = intermediate_size
__lowerCamelCase : List[Any] = hidden_act
__lowerCamelCase : int = hidden_dropout_prob
__lowerCamelCase : Dict = attention_probs_dropout_prob
__lowerCamelCase : str = initializer_range
__lowerCamelCase : str = layer_norm_eps
__lowerCamelCase : Optional[Any] = image_size
__lowerCamelCase : List[Any] = patch_size
__lowerCamelCase : Optional[Any] = num_channels
__lowerCamelCase : int = use_mask_token
__lowerCamelCase : Tuple = use_absolute_position_embeddings
__lowerCamelCase : Dict = use_relative_position_bias
__lowerCamelCase : Optional[Any] = use_shared_relative_position_bias
__lowerCamelCase : Optional[int] = layer_scale_init_value
__lowerCamelCase : List[str] = drop_path_rate
__lowerCamelCase : Optional[int] = use_mean_pooling
# decode head attributes (semantic segmentation)
__lowerCamelCase : Any = out_indices
__lowerCamelCase : Any = pool_scales
# auxiliary head attributes (semantic segmentation)
__lowerCamelCase : Any = use_auxiliary_head
__lowerCamelCase : int = auxiliary_loss_weight
__lowerCamelCase : Dict = auxiliary_channels
__lowerCamelCase : Optional[int] = auxiliary_num_convs
__lowerCamelCase : List[Any] = auxiliary_concat_input
__lowerCamelCase : Dict = semantic_loss_ignore_index
class A_ ( SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : Dict = version.parse('''1.11''' )
@property
def lowerCAmelCase ( self : Tuple):
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
])
@property
def lowerCAmelCase ( self : Any):
return 1E-4
| 73 |
UpperCAmelCase__ = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase__ = {value: key for key, value in MORSE_CODE_DICT.items()}
def UpperCAmelCase_ ( __snake_case ) -> str:
"""simple docstring"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def UpperCAmelCase_ ( __snake_case ) -> str:
"""simple docstring"""
return "".join(REVERSE_DICT[char] for char in message.split() )
def UpperCAmelCase_ ( ) -> None:
"""simple docstring"""
_lowercase ='''Morse code here!'''
print(__snake_case )
_lowercase =encrypt(__snake_case )
print(__snake_case )
_lowercase =decrypt(__snake_case )
print(__snake_case )
if __name__ == "__main__":
main()
| 5 | 0 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , A : List[str] , A : str=sys.maxsize ) ->str:
lowerCamelCase__ : str = '''bilinear'''
lowerCamelCase__ : Tuple = max_size
lowerCamelCase__ : Optional[Any] = short_edge_length
def __call__( self : Optional[int] , A : List[Any] ) ->int:
lowerCamelCase__ : Optional[int] = []
for img in imgs:
lowerCamelCase__ , lowerCamelCase__ : str = img.shape[:2]
# later: provide list and randomly choose index for resize
lowerCamelCase__ : Union[str, Any] = np.random.randint(self.short_edge_length[0] , self.short_edge_length[1] + 1 )
if size == 0:
return img
lowerCamelCase__ : List[str] = size * 1.0 / min(A , A )
if h < w:
lowerCamelCase__ , lowerCamelCase__ : Any = size, scale * w
else:
lowerCamelCase__ , lowerCamelCase__ : List[Any] = scale * h, size
if max(A , A ) > self.max_size:
lowerCamelCase__ : List[Any] = self.max_size * 1.0 / max(A , A )
lowerCamelCase__ : List[str] = newh * scale
lowerCamelCase__ : List[str] = neww * scale
lowerCamelCase__ : Tuple = int(neww + 0.5 )
lowerCamelCase__ : Dict = int(newh + 0.5 )
if img.dtype == np.uinta:
lowerCamelCase__ : Union[str, Any] = Image.fromarray(A )
lowerCamelCase__ : str = pil_image.resize((neww, newh) , PILImageResampling.BILINEAR )
lowerCamelCase__ : List[Any] = np.asarray(A )
else:
lowerCamelCase__ : Union[str, Any] = img.permute(2 , 0 , 1 ).unsqueeze(0 ) # 3, 0, 1) # hw(c) -> nchw
lowerCamelCase__ : Any = nn.functional.interpolate(
A , (newh, neww) , mode=self.interp_method , align_corners=A ).squeeze(0 )
img_augs.append(A )
return img_augs
class __SCREAMING_SNAKE_CASE :
def __init__( self : str , A : List[str] ) ->Tuple:
lowerCamelCase__ : Union[str, Any] = ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] , cfg.INPUT.MAX_SIZE_TEST )
lowerCamelCase__ : Tuple = cfg.INPUT.FORMAT
lowerCamelCase__ : List[Any] = cfg.SIZE_DIVISIBILITY
lowerCamelCase__ : Any = cfg.PAD_VALUE
lowerCamelCase__ : List[str] = cfg.INPUT.MAX_SIZE_TEST
lowerCamelCase__ : List[Any] = cfg.MODEL.DEVICE
lowerCamelCase__ : Tuple = torch.tensor(cfg.MODEL.PIXEL_STD ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
lowerCamelCase__ : Optional[int] = torch.tensor(cfg.MODEL.PIXEL_MEAN ).to(self.device ).view(len(cfg.MODEL.PIXEL_STD ) , 1 , 1 )
lowerCamelCase__ : List[str] = lambda A : (x - self.pixel_mean) / self.pixel_std
def __lowerCamelCase ( self : Dict , A : Union[str, Any] ) ->List[str]:
lowerCamelCase__ : List[str] = tuple(max(A ) for s in zip(*[img.shape for img in images] ) )
lowerCamelCase__ : List[Any] = [im.shape[-2:] for im in images]
lowerCamelCase__ : Union[str, Any] = [
nn.functional.pad(
A , [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] , value=self.pad_value , )
for size, im in zip(A , A )
]
return torch.stack(A ), torch.tensor(A )
def __call__( self : str , A : int , A : List[str]=False ) ->str:
with torch.no_grad():
if not isinstance(A , A ):
lowerCamelCase__ : List[str] = [images]
if single_image:
assert len(A ) == 1
for i in range(len(A ) ):
if isinstance(images[i] , torch.Tensor ):
images.insert(A , images.pop(A ).to(self.device ).float() )
elif not isinstance(images[i] , torch.Tensor ):
images.insert(
A , torch.as_tensor(img_tensorize(images.pop(A ) , input_format=self.input_format ) )
.to(self.device )
.float() , )
# resize smallest edge
lowerCamelCase__ : str = torch.tensor([im.shape[:2] for im in images] )
lowerCamelCase__ : str = self.aug(A )
# transpose images and convert to torch tensors
# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
# now normalize before pad to avoid useless arithmetic
lowerCamelCase__ : Dict = [self.normalizer(A ) for x in images]
# now pad them to do the following operations
lowerCamelCase__ , lowerCamelCase__ : List[Any] = self.pad(A )
# Normalize
if self.size_divisibility > 0:
raise NotImplementedError()
# pad
lowerCamelCase__ : List[Any] = torch.true_divide(A , A )
if single_image:
return images[0], sizes[0], scales_yx[0]
else:
return images, sizes, scales_yx
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Tuple:
"""simple docstring"""
boxes[:, 0::2] *= scale_yx[:, 1]
boxes[:, 1::2] *= scale_yx[:, 0]
return boxes
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Optional[int]:
"""simple docstring"""
assert torch.isfinite(__snake_case ).all(), "Box tensor contains infinite or NaN!"
lowerCamelCase__ , lowerCamelCase__ : int = box_size
tensor[:, 0].clamp_(min=0 , max=__snake_case )
tensor[:, 1].clamp_(min=0 , max=__snake_case )
tensor[:, 2].clamp_(min=0 , max=__snake_case )
tensor[:, 3].clamp_(min=0 , max=__snake_case )
| 142 |
from typing import Any
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> list:
"""simple docstring"""
_validation(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
# Creates data structures and fill initial step
_lowercase ={}
_lowercase ={}
for state in states_space:
_lowercase =observations_space[0]
_lowercase =(
initial_probabilities[state] * emission_probabilities[state][observation]
)
_lowercase =None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(__snake_case ) ):
_lowercase =observations_space[o]
_lowercase =observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
_lowercase =''''''
_lowercase =-1
for k_state in states_space:
_lowercase =(
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
_lowercase =probability
_lowercase =k_state
# Update probabilities and pointers dicts
_lowercase =(
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
_lowercase =arg_max
# The final observation
_lowercase =observations_space[len(__snake_case ) - 1]
# argmax for given final observation
_lowercase =''''''
_lowercase =-1
for k_state in states_space:
_lowercase =probabilities[(k_state, final_observation)]
if probability > max_probability:
_lowercase =probability
_lowercase =k_state
_lowercase =arg_max
# Process pointers backwards
_lowercase =last_state
_lowercase =[]
for o in range(len(__snake_case ) - 1 , -1 , -1 ):
result.append(__snake_case )
_lowercase =pointers[previous, observations_space[o]]
result.reverse()
return result
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> None:
"""simple docstring"""
_validate_not_empty(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
_validate_lists(__snake_case , __snake_case )
_validate_dicts(
__snake_case , __snake_case , __snake_case )
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> None:
"""simple docstring"""
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError('''There\'s an empty parameter''' )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> None:
"""simple docstring"""
_validate_list(__snake_case , '''observations_space''' )
_validate_list(__snake_case , '''states_space''' )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> None:
"""simple docstring"""
if not isinstance(_object , __snake_case ):
_lowercase =F"{var_name} must be a list"
raise ValueError(__snake_case )
else:
for x in _object:
if not isinstance(__snake_case , __snake_case ):
_lowercase =F"{var_name} must be a list of strings"
raise ValueError(__snake_case )
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , ) -> None:
"""simple docstring"""
_validate_dict(__snake_case , '''initial_probabilities''' , __snake_case )
_validate_nested_dict(__snake_case , '''transition_probabilities''' )
_validate_nested_dict(__snake_case , '''emission_probabilities''' )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> None:
"""simple docstring"""
_validate_dict(_object , __snake_case , __snake_case )
for x in _object.values():
_validate_dict(__snake_case , __snake_case , __snake_case , __snake_case )
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case = False ) -> None:
"""simple docstring"""
if not isinstance(_object , __snake_case ):
_lowercase =F"{var_name} must be a dict"
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object ):
_lowercase =F"{var_name} all keys must be strings"
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object.values() ):
_lowercase ='''nested dictionary ''' if nested else ''''''
_lowercase =F"{var_name} {nested_text}all values must be {value_type.__name__}"
raise ValueError(__snake_case )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 5 | 0 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def __UpperCAmelCase ( a_: List[Any], a_: int, a_: List[str], a_: Union[str, Any] ):
_UpperCAmelCase : List[str] = sorted(zip(__snake_case, __snake_case ), key=lambda a_ : x[0] / x[1], reverse=__snake_case )
_UpperCAmelCase , _UpperCAmelCase : List[str] = [i[0] for i in r], [i[1] for i in r]
_UpperCAmelCase : Union[str, Any] = list(accumulate(__snake_case ) )
_UpperCAmelCase : List[Any] = bisect(__snake_case, __snake_case )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod() | 145 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
# TODO Update this
UpperCAmelCase__ = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class lowerCamelCase__ ( lowerCAmelCase):
SCREAMING_SNAKE_CASE__ = '''esm'''
def __init__(self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=7_6_8 , UpperCAmelCase=1_2 , UpperCAmelCase=1_2 , UpperCAmelCase=3_0_7_2 , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=1_0_2_6 , UpperCAmelCase=0.02 , UpperCAmelCase=1e-12 , UpperCAmelCase="absolute" , UpperCAmelCase=True , UpperCAmelCase=None , UpperCAmelCase=False , UpperCAmelCase=False , UpperCAmelCase=None , UpperCAmelCase=None , **UpperCAmelCase , ) -> Tuple:
super().__init__(pad_token_id=UpperCAmelCase , mask_token_id=UpperCAmelCase , **UpperCAmelCase )
_lowercase =vocab_size
_lowercase =hidden_size
_lowercase =num_hidden_layers
_lowercase =num_attention_heads
_lowercase =intermediate_size
_lowercase =hidden_dropout_prob
_lowercase =attention_probs_dropout_prob
_lowercase =max_position_embeddings
_lowercase =initializer_range
_lowercase =layer_norm_eps
_lowercase =position_embedding_type
_lowercase =use_cache
_lowercase =emb_layer_norm_before
_lowercase =token_dropout
_lowercase =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
_lowercase =EsmFoldConfig()
elif isinstance(UpperCAmelCase , UpperCAmelCase ):
_lowercase =EsmFoldConfig(**UpperCAmelCase )
_lowercase =esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
_lowercase =get_default_vocab_list()
else:
_lowercase =vocab_list
else:
_lowercase =None
_lowercase =None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , UpperCAmelCase ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def __A (self ) -> List[str]:
_lowercase =super().to_dict()
if isinstance(self.esmfold_config , UpperCAmelCase ):
_lowercase =self.esmfold_config.to_dict()
return output
@dataclass
class lowerCamelCase__ :
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = None
def __A (self ) -> Union[str, Any]:
if self.trunk is None:
_lowercase =TrunkConfig()
elif isinstance(self.trunk , UpperCAmelCase ):
_lowercase =TrunkConfig(**self.trunk )
def __A (self ) -> Tuple:
_lowercase =asdict(self )
_lowercase =self.trunk.to_dict()
return output
@dataclass
class lowerCamelCase__ :
SCREAMING_SNAKE_CASE__ = 48
SCREAMING_SNAKE_CASE__ = 1024
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = 32
SCREAMING_SNAKE_CASE__ = 32
SCREAMING_SNAKE_CASE__ = 32
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = 4
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = None
def __A (self ) -> List[str]:
if self.structure_module is None:
_lowercase =StructureModuleConfig()
elif isinstance(self.structure_module , UpperCAmelCase ):
_lowercase =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f"`max_recycles` should be positive, got {self.max_recycles}." )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
f" {self.sequence_state_dim} and {self.sequence_state_dim}." )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
f" {self.pairwise_state_dim} and {self.pairwise_state_dim}." )
_lowercase =self.sequence_state_dim // self.sequence_head_width
_lowercase =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}." )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}." )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}." )
if self.dropout >= 0.4:
raise ValueError(f"`dropout` should not be greater than 0.4, got {self.dropout}." )
def __A (self ) -> Dict:
_lowercase =asdict(self )
_lowercase =self.structure_module.to_dict()
return output
@dataclass
class lowerCamelCase__ :
SCREAMING_SNAKE_CASE__ = 384
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = 16
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = 12
SCREAMING_SNAKE_CASE__ = 4
SCREAMING_SNAKE_CASE__ = 8
SCREAMING_SNAKE_CASE__ = 0.1
SCREAMING_SNAKE_CASE__ = 8
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 2
SCREAMING_SNAKE_CASE__ = 7
SCREAMING_SNAKE_CASE__ = 10
SCREAMING_SNAKE_CASE__ = 1E-8
SCREAMING_SNAKE_CASE__ = 1E5
def __A (self ) -> List[Any]:
return asdict(self )
def UpperCAmelCase_ ( ) -> Tuple:
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 5 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( _SCREAMING_SNAKE_CASE ,unittest.TestCase):
"""simple docstring"""
lowercase = CTRLTokenizer
lowercase = False
lowercase = False
def __lowercase ( self : Optional[int] ) -> int:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ : Dict = ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
lowerCAmelCase_ : int = dict(zip(lowerCamelCase , range(len(lowerCamelCase ) ) ) )
lowerCAmelCase_ : Tuple = ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
lowerCAmelCase_ : Dict = {"""unk_token""": """<unk>"""}
lowerCAmelCase_ : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase_ : str = 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(lowerCamelCase ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowerCamelCase ) )
def __lowercase ( self : List[str] , **lowerCamelCase : Any ) -> str:
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase )
def __lowercase ( self : str , lowerCamelCase : str ) -> Union[str, Any]:
lowerCAmelCase_ : Dict = """adapt react readapt apt"""
lowerCAmelCase_ : Any = """adapt react readapt apt"""
return input_text, output_text
def __lowercase ( self : int ) -> Dict:
lowerCAmelCase_ : int = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowerCAmelCase_ : int = """adapt react readapt apt"""
lowerCAmelCase_ : Dict = """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
lowerCAmelCase_ : Tuple = tokenizer.tokenize(lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
lowerCAmelCase_ : Optional[Any] = tokens + [tokenizer.unk_token]
lowerCAmelCase_ : Dict = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase ) , lowerCamelCase )
| 120 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
UpperCAmelCase__ = ['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']
class lowerCamelCase__ ( lowerCAmelCase):
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=1 ) -> Dict:
_lowercase =tokenizer
_lowercase =dataset
_lowercase =len(UpperCAmelCase ) if n_tasks is None else n_tasks
_lowercase =n_copies
def __iter__(self ) -> Optional[Any]:
_lowercase =[]
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip() )
_lowercase =self.tokenizer(UpperCAmelCase , padding=UpperCAmelCase , return_tensors='''pt''' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class lowerCamelCase__ ( lowerCAmelCase):
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
_lowercase =start_length
_lowercase =eof_strings
_lowercase =tokenizer
def __call__(self , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ) -> Dict:
_lowercase =self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
_lowercase =[]
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(UpperCAmelCase )
def UpperCAmelCase_ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
_lowercase =re.split('''(%s)''' % '''|'''.join(__snake_case ) , __snake_case )
# last string should be ""
return "".join(string_list[:-2] )
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=20 , **__snake_case ) -> Tuple:
"""simple docstring"""
_lowercase =defaultdict(__snake_case ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(__snake_case ) ):
with torch.no_grad():
_lowercase =batch['''ids'''].shape[-1]
_lowercase =accelerator.unwrap_model(__snake_case ).generate(
input_ids=batch['''ids'''][:, : batch['''input_len''']] , num_return_sequences=__snake_case , **__snake_case )
# each task is generated batch_size times
_lowercase =batch['''task_id'''].repeat(__snake_case )
_lowercase =accelerator.pad_across_processes(
__snake_case , dim=1 , pad_index=tokenizer.pad_token_id )
_lowercase , _lowercase =accelerator.gather((generated_tokens, generated_tasks) )
_lowercase =generated_tokens.cpu().numpy()
_lowercase =generated_tasks.cpu().numpy()
for task, generated_tokens in zip(__snake_case , __snake_case ):
gen_token_dict[task].append(__snake_case )
_lowercase =[[] for _ in range(__snake_case )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
_lowercase =tokenizer.decode(__snake_case , skip_special_tokens=__snake_case , clean_up_tokenization_spaces=__snake_case )
code_gens[task].append(remove_last_block(__snake_case ) )
return code_gens
def UpperCAmelCase_ ( ) -> str:
"""simple docstring"""
_lowercase =HfArgumentParser(__snake_case )
_lowercase =parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
_lowercase =args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
_lowercase ='''false'''
if args.num_workers is None:
_lowercase =multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
_lowercase =Accelerator()
set_seed(args.seed , device_specific=__snake_case )
# Load model and tokenizer
_lowercase =AutoTokenizer.from_pretrained(args.model_ckpt )
_lowercase =tokenizer.eos_token
_lowercase =AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
_lowercase ={
'''do_sample''': args.do_sample,
'''temperature''': args.temperature,
'''max_new_tokens''': args.max_new_tokens,
'''top_p''': args.top_p,
'''top_k''': args.top_k,
'''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0 , __snake_case , __snake_case )] ),
}
# Load evaluation dataset and metric
_lowercase =load_dataset('''openai_humaneval''' )
_lowercase =load_metric('''code_eval''' )
_lowercase =args.num_tasks if args.num_tasks is not None else len(human_eval['''test'''] )
_lowercase =args.n_samples // args.batch_size
_lowercase =TokenizedDataset(__snake_case , human_eval['''test'''] , n_copies=__snake_case , n_tasks=__snake_case )
# do not confuse args.batch_size, which is actually the num_return_sequences
_lowercase =DataLoader(__snake_case , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
_lowercase =code_eval_metric.compute(references=[''''''] , predictions=[['''''']] )
except ValueError as exception:
print(
'''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
''' flag to enable code evaluation.''' )
raise exception
_lowercase , _lowercase =accelerator.prepare(__snake_case , __snake_case )
_lowercase =complete_code(
__snake_case , __snake_case , __snake_case , __snake_case , n_tasks=__snake_case , batch_size=args.batch_size , **__snake_case , )
if accelerator.is_main_process:
_lowercase =[]
for task in tqdm(range(__snake_case ) ):
_lowercase =human_eval['''test'''][task]['''test''']
_lowercase =F"check({human_eval['test'][task]['entry_point']})"
references.append('''\n''' + test_func + '''\n''' + entry_point )
# Evaluate completions with "code_eval" metric
_lowercase , _lowercase =code_eval_metric.compute(
references=__snake_case , predictions=__snake_case , num_workers=args.num_workers )
print(F"Results: {pass_at_k}" )
# Save results to json file
with open(args.output_file , '''w''' ) as fp:
json.dump(__snake_case , __snake_case )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 5 | 0 |
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> List[Any]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__snake_case , int(b / 2)) * actual_power(__snake_case , int(b / 2))
else:
return a * actual_power(__snake_case , int(b / 2)) * actual_power(__snake_case , int(b / 2))
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> float:
if b < 0:
return 1 / actual_power(__snake_case , __snake_case)
return actual_power(__snake_case , __snake_case)
if __name__ == "__main__":
print(power(-2, -3))
| 111 |
UpperCAmelCase__ = 8.31_44_62 # Unit - J mol-1 K-1
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 5 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase : List[Any] = {
"""microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}
class __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__):
_lowercase : List[Any] = """focalnet"""
def __init__( self , lowerCAmelCase__=2_2_4 , lowerCAmelCase__=4 , lowerCAmelCase__=3 , lowerCAmelCase__=9_6 , lowerCAmelCase__=False , lowerCAmelCase__=[1_9_2, 3_8_4, 7_6_8, 7_6_8] , lowerCAmelCase__=[2, 2, 6, 2] , lowerCAmelCase__=[2, 2, 2, 2] , lowerCAmelCase__=[3, 3, 3, 3] , lowerCAmelCase__="gelu" , lowerCAmelCase__=4.0 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.1 , lowerCAmelCase__=False , lowerCAmelCase__=1E-4 , lowerCAmelCase__=False , lowerCAmelCase__=False , lowerCAmelCase__=False , lowerCAmelCase__=0.02 , lowerCAmelCase__=1E-5 , lowerCAmelCase__=3_2 , lowerCAmelCase__=None , lowerCAmelCase__=None , **lowerCAmelCase__ , ) -> Dict:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
a__ : List[Any] =image_size
a__ : Optional[Any] =patch_size
a__ : List[Any] =num_channels
a__ : List[str] =embed_dim
a__ : str =use_conv_embed
a__ : List[str] =hidden_sizes
a__ : int =depths
a__ : Dict =focal_levels
a__ : Union[str, Any] =focal_windows
a__ : Union[str, Any] =hidden_act
a__ : Union[str, Any] =mlp_ratio
a__ : str =hidden_dropout_prob
a__ : List[str] =drop_path_rate
a__ : List[str] =use_layerscale
a__ : List[str] =layerscale_value
a__ : Dict =use_post_layernorm
a__ : List[Any] =use_post_layernorm_in_modulation
a__ : Dict =normalize_modulator
a__ : Optional[int] =initializer_range
a__ : Optional[Any] =layer_norm_eps
a__ : Dict =encoder_stride
a__ : List[Any] =["stem"] + [F'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
a__ , a__ : Union[str, Any] =get_aligned_output_features_output_indices(
out_features=lowerCAmelCase__ , out_indices=lowerCAmelCase__ , stage_names=self.stage_names )
| 95 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> Matrix:
"""simple docstring"""
_lowercase =len(__snake_case )
_lowercase =[[0 for _ in range(size + 1 )] for _ in range(__snake_case )]
_lowercase =42
_lowercase =42
_lowercase =42
_lowercase =42
_lowercase =42
_lowercase =42
for row in range(__snake_case ):
for col in range(__snake_case ):
_lowercase =matrix[row][col]
_lowercase =vector[row][0]
_lowercase =0
_lowercase =0
while row < size and col < size:
# pivoting
_lowercase =max((abs(augmented[rowa][col] ), rowa) for rowa in range(__snake_case , __snake_case ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_lowercase , _lowercase =augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , __snake_case ):
_lowercase =augmented[rowa][col] / augmented[row][col]
_lowercase =0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , __snake_case ):
for row in range(__snake_case ):
_lowercase =augmented[row][col] / augmented[col][col]
for cola in range(__snake_case , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(__snake_case )
]
def UpperCAmelCase_ ( __snake_case ) -> Callable[[int], int]:
"""simple docstring"""
_lowercase =len(__snake_case )
_lowercase =[[0 for _ in range(__snake_case )] for _ in range(__snake_case )]
_lowercase =[[0] for _ in range(__snake_case )]
_lowercase =42
_lowercase =42
_lowercase =42
_lowercase =42
for x_val, y_val in enumerate(__snake_case ):
for col in range(__snake_case ):
_lowercase =(x_val + 1) ** (size - col - 1)
_lowercase =y_val
_lowercase =solve(__snake_case , __snake_case )
def interpolated_func(__snake_case ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(__snake_case ) )
return interpolated_func
def UpperCAmelCase_ ( __snake_case ) -> int:
"""simple docstring"""
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def UpperCAmelCase_ ( __snake_case = question_function , __snake_case = 10 ) -> int:
"""simple docstring"""
_lowercase =[func(__snake_case ) for x_val in range(1 , order + 1 )]
_lowercase =[
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_lowercase =0
_lowercase =42
_lowercase =42
for poly in polynomials:
_lowercase =1
while func(__snake_case ) == poly(__snake_case ):
x_val += 1
ret += poly(__snake_case )
return ret
if __name__ == "__main__":
print(f'''{solution() = }''')
| 5 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
@dataclass
class A_ ( snake_case__ ):
_lowercase : Optional[Any] = 4_2
class A_ ( snake_case__ , snake_case__ ):
@register_to_config
def __init__( self : List[str] , UpperCAmelCase : Dict = 3 , UpperCAmelCase : Dict = 3 , UpperCAmelCase : List[Any] = ("DownEncoderBlock2D",) , UpperCAmelCase : Union[str, Any] = ("UpDecoderBlock2D",) , UpperCAmelCase : Optional[Any] = (6_4,) , UpperCAmelCase : Optional[Any] = 1 , UpperCAmelCase : List[Any] = "silu" , UpperCAmelCase : str = 3 , UpperCAmelCase : Optional[int] = 3_2 , UpperCAmelCase : int = 2_5_6 , UpperCAmelCase : Tuple = 3_2 , UpperCAmelCase : Optional[int] = None , UpperCAmelCase : Any = 0.18215 , UpperCAmelCase : str = "group" , ) -> Any:
super().__init__()
# pass init params to Encoder
__lowerCAmelCase: List[str] = Encoder(
in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , down_block_types=UpperCAmelCase , block_out_channels=UpperCAmelCase , layers_per_block=UpperCAmelCase , act_fn=UpperCAmelCase , norm_num_groups=UpperCAmelCase , double_z=UpperCAmelCase , )
__lowerCAmelCase: Dict = vq_embed_dim if vq_embed_dim is not None else latent_channels
__lowerCAmelCase: str = nn.Convad(UpperCAmelCase , UpperCAmelCase , 1 )
__lowerCAmelCase: Dict = VectorQuantizer(UpperCAmelCase , UpperCAmelCase , beta=0.25 , remap=UpperCAmelCase , sane_index_shape=UpperCAmelCase )
__lowerCAmelCase: int = nn.Convad(UpperCAmelCase , UpperCAmelCase , 1 )
# pass init params to Decoder
__lowerCAmelCase: List[Any] = Decoder(
in_channels=UpperCAmelCase , out_channels=UpperCAmelCase , up_block_types=UpperCAmelCase , block_out_channels=UpperCAmelCase , layers_per_block=UpperCAmelCase , act_fn=UpperCAmelCase , norm_num_groups=UpperCAmelCase , norm_type=UpperCAmelCase , )
@apply_forward_hook
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] = True ) -> VQEncoderOutput:
__lowerCAmelCase: Optional[Any] = self.encoder(UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = self.quant_conv(UpperCAmelCase )
if not return_dict:
return (h,)
return VQEncoderOutput(latents=UpperCAmelCase )
@apply_forward_hook
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any] = False , UpperCAmelCase : Union[str, Any] = True ) -> Union[DecoderOutput, torch.FloatTensor]:
# also go through quantization layer
if not force_not_quantize:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: List[Any] = self.quantize(UpperCAmelCase )
else:
__lowerCAmelCase: str = h
__lowerCAmelCase: Any = self.post_quant_conv(UpperCAmelCase )
__lowerCAmelCase: List[str] = self.decoder(UpperCAmelCase , quant if self.config.norm_type == 'spatial' else None )
if not return_dict:
return (dec,)
return DecoderOutput(sample=UpperCAmelCase )
def UpperCAmelCase ( self : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : Dict = True ) -> Union[DecoderOutput, torch.FloatTensor]:
__lowerCAmelCase: Optional[int] = sample
__lowerCAmelCase: Optional[Any] = self.encode(UpperCAmelCase ).latents
__lowerCAmelCase: Dict = self.decode(UpperCAmelCase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=UpperCAmelCase )
| 322 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['''XLMTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
'''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMForMultipleChoice''',
'''XLMForQuestionAnswering''',
'''XLMForQuestionAnsweringSimple''',
'''XLMForSequenceClassification''',
'''XLMForTokenClassification''',
'''XLMModel''',
'''XLMPreTrainedModel''',
'''XLMWithLMHeadModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
'''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMForMultipleChoice''',
'''TFXLMForQuestionAnsweringSimple''',
'''TFXLMForSequenceClassification''',
'''TFXLMForTokenClassification''',
'''TFXLMMainLayer''',
'''TFXLMModel''',
'''TFXLMPreTrainedModel''',
'''TFXLMWithLMHeadModel''',
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 5 | 0 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__snake_case : Union[str, Any] = Mapping[str, np.ndarray]
__snake_case : Union[str, Any] = Mapping[str, Any] # Is a nested dict.
__snake_case : List[str] = 0.01
@dataclasses.dataclass(frozen=a_ )
class A__:
"""simple docstring"""
_A : List[str] = 42 # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
_A : str = 42 # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
_A : List[Any] = 42 # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
_A : List[Any] = 42 # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
_A : Optional[int] = 42 # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
_A : List[Any] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
_A : Optional[Any] = None
# Templates used to generate this protein (prediction-only)
_A : str = None
# Chain corresponding to each parent
_A : Dict = None
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : List[Any] = r"""(\[[A-Z]+\]\n)"""
a_ : str = [tag.strip() for tag in re.split(__snake_case , __snake_case) if len(__snake_case) > 0]
a_ : Union[str, Any] = zip(tags[0::2] , [l.split("""\n""") for l in tags[1::2]])
a_ : str = ["""N""", """CA""", """C"""]
a_ : Dict = None
a_ : Tuple = None
a_ : int = None
for g in groups:
if "[PRIMARY]" == g[0]:
a_ : List[Any] = g[1][0].strip()
for i in range(len(__snake_case)):
if seq[i] not in residue_constants.restypes:
a_ : str = """X""" # FIXME: strings are immutable
a_ : Union[str, Any] = np.array(
[residue_constants.restype_order.get(__snake_case , residue_constants.restype_num) for res_symbol in seq])
elif "[TERTIARY]" == g[0]:
a_ : Dict = []
for axis in range(3):
tertiary.append(list(map(__snake_case , g[1][axis].split())))
a_ : Tuple = np.array(__snake_case)
a_ : List[str] = np.zeros((len(tertiary[0]) // 3, residue_constants.atom_type_num, 3)).astype(np.floataa)
for i, atom in enumerate(__snake_case):
a_ : Optional[int] = np.transpose(tertiary_np[:, i::3])
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
a_ : Dict = np.array(list(map({"""-""": 0, """+""": 1}.get , g[1][0].strip())))
a_ : str = np.zeros(
(
len(__snake_case),
residue_constants.atom_type_num,
)).astype(np.floataa)
for i, atom in enumerate(__snake_case):
a_ : Any = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=__snake_case , atom_mask=__snake_case , aatype=__snake_case , residue_index=np.arange(len(__snake_case)) , b_factors=__snake_case , )
def _UpperCAmelCase ( a__ , a__ = 0):
'''simple docstring'''
a_ : Union[str, Any] = []
a_ : Optional[Any] = prot.remark
if remark is not None:
pdb_headers.append(f'''REMARK {remark}''')
a_ : Dict = prot.parents
a_ : int = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
a_ : List[Any] = [p for i, p in zip(__snake_case , __snake_case) if i == chain_id]
if parents is None or len(__snake_case) == 0:
a_ : int = ["""N/A"""]
pdb_headers.append(f'''PARENT {" ".join(__snake_case)}''')
return pdb_headers
def _UpperCAmelCase ( a__ , a__):
'''simple docstring'''
a_ : List[str] = []
a_ : Dict = pdb_str.split("""\n""")
a_ : Tuple = prot.remark
if remark is not None:
out_pdb_lines.append(f'''REMARK {remark}''')
a_ : Optional[int] = 4_2
if prot.parents is not None and len(prot.parents) > 0:
a_ : Optional[int] = []
if prot.parents_chain_index is not None:
a_ : Dict = {}
for p, i in zip(prot.parents , prot.parents_chain_index):
parent_dict.setdefault(str(__snake_case) , [])
parent_dict[str(__snake_case)].append(__snake_case)
a_ : str = max([int(__snake_case) for chain_idx in parent_dict])
for i in range(max_idx + 1):
a_ : int = parent_dict.get(str(__snake_case) , ["""N/A"""])
parents_per_chain.append(__snake_case)
else:
parents_per_chain.append(list(prot.parents))
else:
a_ : Tuple = [["""N/A"""]]
def make_parent_line(a__) -> str:
return f'''PARENT {" ".join(__snake_case)}'''
out_pdb_lines.append(make_parent_line(parents_per_chain[0]))
a_ : Tuple = 0
for i, l in enumerate(__snake_case):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(__snake_case)
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(__snake_case):
a_ : List[str] = parents_per_chain[chain_counter]
else:
a_ : Optional[int] = ["""N/A"""]
out_pdb_lines.append(make_parent_line(__snake_case))
return "\n".join(__snake_case)
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : str = residue_constants.restypes + ["""X"""]
def res_atoa(a__) -> str:
return residue_constants.restype_atoa.get(restypes[r] , """UNK""")
a_ : Any = residue_constants.atom_types
a_ : Optional[int] = []
a_ : Dict = prot.atom_mask
a_ : List[str] = prot.aatype
a_ : Union[str, Any] = prot.atom_positions
a_ : Tuple = prot.residue_index.astype(np.intaa)
a_ : Dict = prot.b_factors
a_ : Union[str, Any] = prot.chain_index
if np.any(aatype > residue_constants.restype_num):
raise ValueError("""Invalid aatypes.""")
a_ : List[str] = get_pdb_headers(__snake_case)
if len(__snake_case) > 0:
pdb_lines.extend(__snake_case)
a_ : Optional[Any] = aatype.shape[0]
a_ : Any = 1
a_ : Tuple = 0
a_ : Optional[Any] = string.ascii_uppercase
a_ : Any = None
# Add all atom sites.
for i in range(__snake_case):
a_ : List[str] = res_atoa(aatype[i])
for atom_name, pos, mask, b_factor in zip(__snake_case , atom_positions[i] , atom_mask[i] , b_factors[i]):
if mask < 0.5:
continue
a_ : List[str] = """ATOM"""
a_ : List[str] = atom_name if len(__snake_case) == 4 else f''' {atom_name}'''
a_ : List[str] = """"""
a_ : str = """"""
a_ : Optional[int] = 1.00
a_ : Any = atom_name[0] # Protein supports only C, N, O, S, this works.
a_ : Optional[int] = """"""
a_ : int = """A"""
if chain_index is not None:
a_ : Tuple = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
a_ : Optional[int] = (
f'''{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'''
f'''{res_name_a:>3} {chain_tag:>1}'''
f'''{residue_index[i]:>4}{insertion_code:>1} '''
f'''{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'''
f'''{occupancy:>6.2f}{b_factor:>6.2f} '''
f'''{element:>2}{charge:>2}'''
)
pdb_lines.append(__snake_case)
atom_index += 1
a_ : int = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
a_ : int = True
a_ : List[str] = chain_index[i + 1]
if should_terminate:
# Close the chain.
a_ : List[str] = """TER"""
a_ : Dict = (
f'''{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i]):>3} {chain_tag:>1}{residue_index[i]:>4}'''
)
pdb_lines.append(__snake_case)
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(__snake_case , __snake_case))
pdb_lines.append("""END""")
pdb_lines.append("""""")
return "\n".join(__snake_case)
def _UpperCAmelCase ( a__):
'''simple docstring'''
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def _UpperCAmelCase ( a__ , a__ , a__ = None , a__ = None , a__ = None , a__ = None , a__ = None , ):
'''simple docstring'''
return Protein(
aatype=features["""aatype"""] , atom_positions=result["""final_atom_positions"""] , atom_mask=result["""final_atom_mask"""] , residue_index=features["""residue_index"""] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result["""final_atom_mask"""]) , chain_index=__snake_case , remark=__snake_case , parents=__snake_case , parents_chain_index=__snake_case , )
| 248 |
# Copyright 2023 The HuggingFace 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 typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ = {
'''configuration_efficientnet''': [
'''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''EfficientNetConfig''',
'''EfficientNetOnnxConfig''',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ['''EfficientNetImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
'''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''EfficientNetForImageClassification''',
'''EfficientNetModel''',
'''EfficientNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 5 | 0 |
from itertools import count
def lowercase_ ( _A : Any = 50 ):
"""simple docstring"""
lowerCamelCase__ : Any = [1] * min_block_length
for n in count(__snake_case ):
fill_count_functions.append(1 )
for block_length in range(__snake_case , n + 1 ):
for block_start in range(n - block_length ):
fill_count_functions[n] += fill_count_functions[
n - block_start - block_length - 1
]
fill_count_functions[n] += 1
if fill_count_functions[n] > 1000000:
break
return n
if __name__ == "__main__":
print(f'{solution() = }')
| 184 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
'''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TimesformerModel''',
'''TimesformerForVideoClassification''',
'''TimesformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 5 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Dict = logging.get_logger(__name__)
a__ : Dict = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = 'wavlm'
def __init__( self , _lowerCamelCase=32 , _lowerCamelCase=768 , _lowerCamelCase=12 , _lowerCamelCase=12 , _lowerCamelCase=3072 , _lowerCamelCase="gelu" , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=0.0 , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=0.0_2 , _lowerCamelCase=1e-5 , _lowerCamelCase="group" , _lowerCamelCase="gelu" , _lowerCamelCase=(512, 512, 512, 512, 512, 512, 512) , _lowerCamelCase=(5, 2, 2, 2, 2, 2, 2) , _lowerCamelCase=(10, 3, 3, 3, 3, 2, 2) , _lowerCamelCase=False , _lowerCamelCase=128 , _lowerCamelCase=16 , _lowerCamelCase=320 , _lowerCamelCase=800 , _lowerCamelCase=False , _lowerCamelCase=True , _lowerCamelCase=0.0_5 , _lowerCamelCase=10 , _lowerCamelCase=2 , _lowerCamelCase=0.0 , _lowerCamelCase=10 , _lowerCamelCase=320 , _lowerCamelCase=2 , _lowerCamelCase=0.1 , _lowerCamelCase=100 , _lowerCamelCase=256 , _lowerCamelCase=256 , _lowerCamelCase=0.1 , _lowerCamelCase="mean" , _lowerCamelCase=False , _lowerCamelCase=False , _lowerCamelCase=256 , _lowerCamelCase=(512, 512, 512, 512, 1500) , _lowerCamelCase=(5, 3, 3, 1, 1) , _lowerCamelCase=(1, 2, 3, 1, 1) , _lowerCamelCase=512 , _lowerCamelCase=80 , _lowerCamelCase=0 , _lowerCamelCase=1 , _lowerCamelCase=2 , _lowerCamelCase=False , _lowerCamelCase=3 , _lowerCamelCase=2 , _lowerCamelCase=3 , _lowerCamelCase=None , **_lowerCamelCase , ) ->Optional[Any]:
super().__init__(**_lowerCamelCase , pad_token_id=_lowerCamelCase , bos_token_id=_lowerCamelCase , eos_token_id=_lowerCamelCase )
SCREAMING_SNAKE_CASE : List[str] = hidden_size
SCREAMING_SNAKE_CASE : Any = feat_extract_norm
SCREAMING_SNAKE_CASE : Dict = feat_extract_activation
SCREAMING_SNAKE_CASE : Union[str, Any] = list(_lowerCamelCase )
SCREAMING_SNAKE_CASE : List[Any] = list(_lowerCamelCase )
SCREAMING_SNAKE_CASE : Optional[int] = list(_lowerCamelCase )
SCREAMING_SNAKE_CASE : Any = conv_bias
SCREAMING_SNAKE_CASE : Optional[int] = num_buckets
SCREAMING_SNAKE_CASE : List[str] = max_bucket_distance
SCREAMING_SNAKE_CASE : Dict = num_conv_pos_embeddings
SCREAMING_SNAKE_CASE : Dict = num_conv_pos_embedding_groups
SCREAMING_SNAKE_CASE : Optional[int] = len(self.conv_dim )
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : Optional[Any] = intermediate_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_act
SCREAMING_SNAKE_CASE : Dict = num_attention_heads
SCREAMING_SNAKE_CASE : int = hidden_dropout
SCREAMING_SNAKE_CASE : Dict = attention_dropout
SCREAMING_SNAKE_CASE : Tuple = activation_dropout
SCREAMING_SNAKE_CASE : Optional[Any] = feat_proj_dropout
SCREAMING_SNAKE_CASE : Any = final_dropout
SCREAMING_SNAKE_CASE : List[Any] = layerdrop
SCREAMING_SNAKE_CASE : Tuple = layer_norm_eps
SCREAMING_SNAKE_CASE : int = initializer_range
SCREAMING_SNAKE_CASE : int = num_ctc_classes
SCREAMING_SNAKE_CASE : Optional[int] = vocab_size
SCREAMING_SNAKE_CASE : int = do_stable_layer_norm
SCREAMING_SNAKE_CASE : List[str] = use_weighted_layer_sum
SCREAMING_SNAKE_CASE : str = classifier_proj_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)`, 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 : List[Any] = apply_spec_augment
SCREAMING_SNAKE_CASE : Tuple = mask_time_prob
SCREAMING_SNAKE_CASE : str = mask_time_length
SCREAMING_SNAKE_CASE : Union[str, Any] = mask_time_min_masks
SCREAMING_SNAKE_CASE : Tuple = mask_feature_prob
SCREAMING_SNAKE_CASE : Union[str, Any] = mask_feature_length
# parameters for pretraining with codevector quantized representations
SCREAMING_SNAKE_CASE : int = num_codevectors_per_group
SCREAMING_SNAKE_CASE : Any = num_codevector_groups
SCREAMING_SNAKE_CASE : Union[str, Any] = contrastive_logits_temperature
SCREAMING_SNAKE_CASE : List[Any] = num_negatives
SCREAMING_SNAKE_CASE : str = codevector_dim
SCREAMING_SNAKE_CASE : Union[str, Any] = proj_codevector_dim
SCREAMING_SNAKE_CASE : Dict = diversity_loss_weight
# ctc loss
SCREAMING_SNAKE_CASE : List[str] = ctc_loss_reduction
SCREAMING_SNAKE_CASE : int = ctc_zero_infinity
# adapter
SCREAMING_SNAKE_CASE : Union[str, Any] = add_adapter
SCREAMING_SNAKE_CASE : List[str] = adapter_kernel_size
SCREAMING_SNAKE_CASE : Tuple = adapter_stride
SCREAMING_SNAKE_CASE : Dict = num_adapter_layers
SCREAMING_SNAKE_CASE : str = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE : Any = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
SCREAMING_SNAKE_CASE : Tuple = list(_lowerCamelCase )
SCREAMING_SNAKE_CASE : Optional[int] = list(_lowerCamelCase )
SCREAMING_SNAKE_CASE : int = list(_lowerCamelCase )
SCREAMING_SNAKE_CASE : Optional[int] = xvector_output_dim
@property
def __lowerCAmelCase ( self ) ->int:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 313 |
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> List[Any]:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__snake_case , int(b / 2 ) ) * actual_power(__snake_case , int(b / 2 ) )
else:
return a * actual_power(__snake_case , int(b / 2 ) ) * actual_power(__snake_case , int(b / 2 ) )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> float:
"""simple docstring"""
if b < 0:
return 1 / actual_power(__snake_case , __snake_case )
return actual_power(__snake_case , __snake_case )
if __name__ == "__main__":
print(power(-2, -3))
| 5 | 0 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
snake_case_ : Tuple = "src/transformers"
snake_case_ : Union[str, Any] = "docs/source/en"
snake_case_ : str = "."
def A (__A : Union[str, Any] , __A : str , __A : Dict ) -> List[Any]:
"""simple docstring"""
with open(__snake_case , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
UpperCAmelCase_ = f.readlines()
# Find the start prompt.
UpperCAmelCase_ = 0
while not lines[start_index].startswith(__snake_case ):
start_index += 1
start_index += 1
UpperCAmelCase_ = start_index
while not lines[end_index].startswith(__snake_case ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
snake_case_ : Dict = "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
snake_case_ : Any = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
snake_case_ : List[str] = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
snake_case_ : List[Any] = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# This is to make sure the transformers module imported is the one in the repo.
snake_case_ : int = direct_transformers_import(TRANSFORMERS_PATH)
def A (__A : List[str] ) -> Tuple:
"""simple docstring"""
UpperCAmelCase_ = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' , __snake_case )
return [m.group(0 ) for m in matches]
def A (__A : Optional[Any] , __A : List[Any] ) -> str:
"""simple docstring"""
UpperCAmelCase_ = 2 if text == '''✅''' or text == '''❌''' else len(__snake_case )
UpperCAmelCase_ = (width - text_length) // 2
UpperCAmelCase_ = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def A () -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase_ = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
UpperCAmelCase_ = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
UpperCAmelCase_ = {name: config.replace('''Config''' , '''''' ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
UpperCAmelCase_ = collections.defaultdict(__snake_case )
UpperCAmelCase_ = collections.defaultdict(__snake_case )
UpperCAmelCase_ = collections.defaultdict(__snake_case )
UpperCAmelCase_ = collections.defaultdict(__snake_case )
UpperCAmelCase_ = collections.defaultdict(__snake_case )
# Let's lookup through all transformers object (once).
for attr_name in dir(__snake_case ):
UpperCAmelCase_ = None
if attr_name.endswith('''Tokenizer''' ):
UpperCAmelCase_ = slow_tokenizers
UpperCAmelCase_ = attr_name[:-9]
elif attr_name.endswith('''TokenizerFast''' ):
UpperCAmelCase_ = fast_tokenizers
UpperCAmelCase_ = attr_name[:-13]
elif _re_tf_models.match(__snake_case ) is not None:
UpperCAmelCase_ = tf_models
UpperCAmelCase_ = _re_tf_models.match(__snake_case ).groups()[0]
elif _re_flax_models.match(__snake_case ) is not None:
UpperCAmelCase_ = flax_models
UpperCAmelCase_ = _re_flax_models.match(__snake_case ).groups()[0]
elif _re_pt_models.match(__snake_case ) is not None:
UpperCAmelCase_ = pt_models
UpperCAmelCase_ = _re_pt_models.match(__snake_case ).groups()[0]
if lookup_dict is not None:
while len(__snake_case ) > 0:
if attr_name in model_name_to_prefix.values():
UpperCAmelCase_ = True
break
# Try again after removing the last word in the name
UpperCAmelCase_ = ''''''.join(camel_case_split(__snake_case )[:-1] )
# Let's build that table!
UpperCAmelCase_ = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
UpperCAmelCase_ = ['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
UpperCAmelCase_ = [len(__snake_case ) + 2 for c in columns]
UpperCAmelCase_ = max([len(__snake_case ) for name in model_names] ) + 2
# Build the table per se
UpperCAmelCase_ = '''|''' + '''|'''.join([_center_text(__snake_case , __snake_case ) for c, w in zip(__snake_case , __snake_case )] ) + '''|\n'''
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths] ) + "|\n"
UpperCAmelCase_ = {True: '''✅''', False: '''❌'''}
for name in model_names:
UpperCAmelCase_ = model_name_to_prefix[name]
UpperCAmelCase_ = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(__snake_case , __snake_case ) for l, w in zip(__snake_case , __snake_case )] ) + "|\n"
return table
def A (__A : Optional[int]=False ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = _find_text_in_file(
filename=os.path.join(__snake_case , '''index.md''' ) , start_prompt='''<!--This table is updated automatically from the auto modules''' , end_prompt='''<!-- End table-->''' , )
UpperCAmelCase_ = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(__snake_case , '''index.md''' ) , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
'''The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.''' )
if __name__ == "__main__":
snake_case_ : Tuple = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
snake_case_ : List[Any] = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 51 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase__ ( nn.Module):
def __init__(self , UpperCAmelCase = 1_6 , UpperCAmelCase = 8_8 , UpperCAmelCase = None , UpperCAmelCase = 1 , UpperCAmelCase = 0.0 , UpperCAmelCase = 3_2 , UpperCAmelCase = None , UpperCAmelCase = False , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = "geglu" , UpperCAmelCase = None , ) -> Any:
super().__init__()
_lowercase =nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=UpperCAmelCase , attention_head_dim=UpperCAmelCase , in_channels=UpperCAmelCase , num_layers=UpperCAmelCase , dropout=UpperCAmelCase , norm_num_groups=UpperCAmelCase , cross_attention_dim=UpperCAmelCase , attention_bias=UpperCAmelCase , sample_size=UpperCAmelCase , num_vector_embeds=UpperCAmelCase , activation_fn=UpperCAmelCase , num_embeds_ada_norm=UpperCAmelCase , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
_lowercase =0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_lowercase =[7_7, 2_5_7]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_lowercase =[1, 0]
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase = True , ) -> str:
_lowercase =hidden_states
_lowercase =[]
_lowercase =0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_lowercase =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_lowercase =self.transformer_index_for_condition[i]
_lowercase =self.transformers[transformer_index](
UpperCAmelCase , encoder_hidden_states=UpperCAmelCase , timestep=UpperCAmelCase , cross_attention_kwargs=UpperCAmelCase , return_dict=UpperCAmelCase , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
_lowercase =encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_lowercase =output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=UpperCAmelCase )
| 5 | 0 |
a ={
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": """--""", """N""": """-.""",
"""O""": """---""", """P""": """.--.""", """Q""": """--.-""", """R""": """.-.""", """S""": """...""", """T""": """-""", """U""": """..-""",
"""V""": """...-""", """W""": """.--""", """X""": """-..-""", """Y""": """-.--""", """Z""": """--..""", """1""": """.----""",
"""2""": """..---""", """3""": """...--""", """4""": """....-""", """5""": """.....""", """6""": """-....""", """7""": """--...""",
"""8""": """---..""", """9""": """----.""", """0""": """-----""", """&""": """.-...""", """@""": """.--.-.""",
""":""": """---...""", """,""": """--..--""", """.""": """.-.-.-""", """\'""": """.----.""", """\"""": """.-..-.""",
"""?""": """..--..""", """/""": """-..-.""", """=""": """-...-""", """+""": """.-.-.""", """-""": """-....-""",
"""(""": """-.--.""", """)""": """-.--.-""", """!""": """-.-.--""", """ """: """/"""
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
a ={value: key for key, value in MORSE_CODE_DICT.items()}
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> str:
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> str:
return "".join(REVERSE_DICT[char] for char in message.split() )
def SCREAMING_SNAKE_CASE__ ( ) -> None:
__lowerCamelCase : Dict = 'Morse code here!'
print(__snake_case )
__lowerCamelCase : List[Any] = encrypt(__snake_case )
print(__snake_case )
__lowerCamelCase : Union[str, Any] = decrypt(__snake_case )
print(__snake_case )
if __name__ == "__main__":
main()
| 73 |
import heapq as hq
import math
from collections.abc import Iterator
class lowerCamelCase__ :
def __init__(self , UpperCAmelCase ) -> Any:
_lowercase =str(id_ )
_lowercase =None
_lowercase =None
_lowercase =[]
_lowercase ={} # {vertex:distance}
def __lt__(self , UpperCAmelCase ) -> List[str]:
return self.key < other.key
def __repr__(self ) -> str:
return self.id
def __A (self , UpperCAmelCase ) -> Dict:
self.neighbors.append(UpperCAmelCase )
def __A (self , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
_lowercase =weight
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case ) -> List[str]:
"""simple docstring"""
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , __snake_case )
graph[b - 1].add_edge(graph[a - 1] , __snake_case )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> list:
"""simple docstring"""
_lowercase =[]
for u in graph:
_lowercase =math.inf
_lowercase =None
_lowercase =0
_lowercase =graph[:]
while q:
_lowercase =min(__snake_case )
q.remove(__snake_case )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
_lowercase =u
_lowercase =u.edges[v.id]
for i in range(1 , len(__snake_case ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> Iterator[tuple]:
"""simple docstring"""
for u in graph:
_lowercase =math.inf
_lowercase =None
_lowercase =0
_lowercase =list(__snake_case )
hq.heapify(__snake_case )
while h:
_lowercase =hq.heappop(__snake_case )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
_lowercase =u
_lowercase =u.edges[v.id]
hq.heapify(__snake_case )
for i in range(1 , len(__snake_case ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def UpperCAmelCase_ ( ) -> None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 | 0 |
class __SCREAMING_SNAKE_CASE :
def __init__( self : Optional[Any] ) ->Tuple:
lowerCamelCase__ : str = ''''''
lowerCamelCase__ : int = ''''''
lowerCamelCase__ : List[str] = []
def __lowerCamelCase ( self : Tuple , A : Optional[Any] , A : Dict ) ->int:
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
lowerCamelCase__ : List[str] = self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
lowerCamelCase__ : str = self.__min_dist_top_down_dp(A , n - 1 )
lowerCamelCase__ : str = self.__min_dist_top_down_dp(m - 1 , A )
lowerCamelCase__ : List[Any] = self.__min_dist_top_down_dp(m - 1 , n - 1 )
lowerCamelCase__ : Any = 1 + min(A , A , A )
return self.dp[m][n]
def __lowerCamelCase ( self : Dict , A : int , A : Any ) ->int:
lowerCamelCase__ : Union[str, Any] = worda
lowerCamelCase__ : Optional[Any] = worda
lowerCamelCase__ : Tuple = [[-1 for _ in range(len(A ) )] for _ in range(len(A ) )]
return self.__min_dist_top_down_dp(len(A ) - 1 , len(A ) - 1 )
def __lowerCamelCase ( self : List[Any] , A : List[Any] , A : Dict ) ->int:
lowerCamelCase__ : Optional[Any] = worda
lowerCamelCase__ : Dict = worda
lowerCamelCase__ : Optional[Any] = len(A )
lowerCamelCase__ : Optional[Any] = len(A )
lowerCamelCase__ : Tuple = [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
lowerCamelCase__ : int = j
elif j == 0: # second string is empty
lowerCamelCase__ : str = i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
lowerCamelCase__ : List[Any] = self.dp[i - 1][j - 1]
else:
lowerCamelCase__ : int = self.dp[i][j - 1]
lowerCamelCase__ : int = self.dp[i - 1][j]
lowerCamelCase__ : str = self.dp[i - 1][j - 1]
lowerCamelCase__ : Union[str, Any] = 1 + min(A , A , A )
return self.dp[m][n]
if __name__ == "__main__":
_A : str = EditDistance()
print('****************** Testing Edit Distance DP Algorithm ******************')
print()
_A : int = input('Enter the first string: ').strip()
_A : int = input('Enter the second string: ').strip()
print()
print(F'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
print(F'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
print()
print('*************** End of Testing Edit Distance DP Algorithm ***************')
| 142 |
# flake8: noqa
# Lint as: python3
UpperCAmelCase__ = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 5 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( a_: str, a_: List[str], a_: Dict ):
_UpperCAmelCase : Union[str, Any] = LxmertConfig.from_json_file(__snake_case )
print(f"""Building PyTorch model from configuration: {config}""" )
_UpperCAmelCase : Optional[int] = LxmertForPreTraining(__snake_case )
# Load weights from tf checkpoint
load_tf_weights_in_lxmert(__snake_case, __snake_case, __snake_case )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict(), __snake_case )
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The config json file corresponding to the pre-trained 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.'
)
__a = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path) | 145 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class lowerCamelCase__ ( lowerCAmelCase):
SCREAMING_SNAKE_CASE__ = '''wavlm'''
def __init__(self , UpperCAmelCase=3_2 , UpperCAmelCase=7_6_8 , UpperCAmelCase=1_2 , UpperCAmelCase=1_2 , UpperCAmelCase=3_0_7_2 , UpperCAmelCase="gelu" , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=0.0 , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=0.02 , UpperCAmelCase=1e-5 , UpperCAmelCase="group" , UpperCAmelCase="gelu" , UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2) , UpperCAmelCase=(1_0, 3, 3, 3, 3, 2, 2) , UpperCAmelCase=False , UpperCAmelCase=1_2_8 , UpperCAmelCase=1_6 , UpperCAmelCase=3_2_0 , UpperCAmelCase=8_0_0 , UpperCAmelCase=False , UpperCAmelCase=True , UpperCAmelCase=0.05 , UpperCAmelCase=1_0 , UpperCAmelCase=2 , UpperCAmelCase=0.0 , UpperCAmelCase=1_0 , UpperCAmelCase=3_2_0 , UpperCAmelCase=2 , UpperCAmelCase=0.1 , UpperCAmelCase=1_0_0 , UpperCAmelCase=2_5_6 , UpperCAmelCase=2_5_6 , UpperCAmelCase=0.1 , UpperCAmelCase="mean" , UpperCAmelCase=False , UpperCAmelCase=False , UpperCAmelCase=2_5_6 , UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) , UpperCAmelCase=(5, 3, 3, 1, 1) , UpperCAmelCase=(1, 2, 3, 1, 1) , UpperCAmelCase=5_1_2 , UpperCAmelCase=8_0 , UpperCAmelCase=0 , UpperCAmelCase=1 , UpperCAmelCase=2 , UpperCAmelCase=False , UpperCAmelCase=3 , UpperCAmelCase=2 , UpperCAmelCase=3 , UpperCAmelCase=None , **UpperCAmelCase , ) -> Optional[Any]:
super().__init__(**UpperCAmelCase , pad_token_id=UpperCAmelCase , bos_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase )
_lowercase =hidden_size
_lowercase =feat_extract_norm
_lowercase =feat_extract_activation
_lowercase =list(UpperCAmelCase )
_lowercase =list(UpperCAmelCase )
_lowercase =list(UpperCAmelCase )
_lowercase =conv_bias
_lowercase =num_buckets
_lowercase =max_bucket_distance
_lowercase =num_conv_pos_embeddings
_lowercase =num_conv_pos_embedding_groups
_lowercase =len(self.conv_dim )
_lowercase =num_hidden_layers
_lowercase =intermediate_size
_lowercase =hidden_act
_lowercase =num_attention_heads
_lowercase =hidden_dropout
_lowercase =attention_dropout
_lowercase =activation_dropout
_lowercase =feat_proj_dropout
_lowercase =final_dropout
_lowercase =layerdrop
_lowercase =layer_norm_eps
_lowercase =initializer_range
_lowercase =num_ctc_classes
_lowercase =vocab_size
_lowercase =do_stable_layer_norm
_lowercase =use_weighted_layer_sum
_lowercase =classifier_proj_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)`, 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
_lowercase =apply_spec_augment
_lowercase =mask_time_prob
_lowercase =mask_time_length
_lowercase =mask_time_min_masks
_lowercase =mask_feature_prob
_lowercase =mask_feature_length
# parameters for pretraining with codevector quantized representations
_lowercase =num_codevectors_per_group
_lowercase =num_codevector_groups
_lowercase =contrastive_logits_temperature
_lowercase =num_negatives
_lowercase =codevector_dim
_lowercase =proj_codevector_dim
_lowercase =diversity_loss_weight
# ctc loss
_lowercase =ctc_loss_reduction
_lowercase =ctc_zero_infinity
# adapter
_lowercase =add_adapter
_lowercase =adapter_kernel_size
_lowercase =adapter_stride
_lowercase =num_adapter_layers
_lowercase =output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_lowercase =classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_lowercase =list(UpperCAmelCase )
_lowercase =list(UpperCAmelCase )
_lowercase =list(UpperCAmelCase )
_lowercase =xvector_output_dim
@property
def __A (self ) -> int:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 5 | 0 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
)
__A : Dict = None
__A : int = {
"7B": 1_1008,
"13B": 1_3824,
"30B": 1_7920,
"65B": 2_2016,
"70B": 2_8672,
}
__A : List[Any] = {
"7B": 1,
"7Bf": 1,
"13B": 2,
"13Bf": 2,
"30B": 4,
"65B": 8,
"70B": 8,
"70Bf": 8,
}
def UpperCamelCase_ ( A__ : List[Any] , A__ : int=1 , A__ : Dict=2_56 ):
'''simple docstring'''
return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3 ) ) + multiple_of - 1) // multiple_of)
def UpperCamelCase_ ( A__ : Tuple ):
'''simple docstring'''
with open(__snake_case , """r""" ) as f:
return json.load(__snake_case )
def UpperCamelCase_ ( A__ : Optional[Any] , A__ : int ):
'''simple docstring'''
with open(__snake_case , """w""" ) as f:
json.dump(__snake_case , __snake_case )
def UpperCamelCase_ ( A__ : List[str] , A__ : Any , A__ : Dict , A__ : int=True ):
'''simple docstring'''
os.makedirs(__snake_case , exist_ok=__snake_case )
lowerCAmelCase_ : Union[str, Any] = os.path.join(__snake_case , """tmp""" )
os.makedirs(__snake_case , exist_ok=__snake_case )
lowerCAmelCase_ : int = read_json(os.path.join(__snake_case , """params.json""" ) )
lowerCAmelCase_ : int = NUM_SHARDS[model_size]
lowerCAmelCase_ : List[str] = params["""n_layers"""]
lowerCAmelCase_ : Optional[int] = params["""n_heads"""]
lowerCAmelCase_ : Tuple = n_heads // num_shards
lowerCAmelCase_ : int = params["""dim"""]
lowerCAmelCase_ : Optional[Any] = dim // n_heads
lowerCAmelCase_ : Tuple = 1_00_00.0
lowerCAmelCase_ : List[str] = 1.0 / (base ** (torch.arange(0 , __snake_case , 2 ).float() / dims_per_head))
if "n_kv_heads" in params:
lowerCAmelCase_ : Any = params["""n_kv_heads"""] # for GQA / MQA
lowerCAmelCase_ : str = n_heads_per_shard // num_key_value_heads
lowerCAmelCase_ : int = dim // num_key_value_heads
else: # compatibility with other checkpoints
lowerCAmelCase_ : int = n_heads
lowerCAmelCase_ : Optional[Any] = n_heads_per_shard
lowerCAmelCase_ : int = dim
# permute for sliced rotary
def permute(A__ : int , A__ : List[Any]=n_heads , A__ : Any=dim , A__ : str=dim ):
return w.view(__snake_case , dima // n_heads // 2 , 2 , __snake_case ).transpose(1 , 2 ).reshape(__snake_case , __snake_case )
print(f'Fetching all parameters from the checkpoint at {input_base_path}.' )
# Load weights
if model_size == "7B":
# Not sharded
# (The sharded implementation would also work, but this is simpler.)
lowerCAmelCase_ : List[str] = torch.load(os.path.join(__snake_case , """consolidated.00.pth""" ) , map_location="""cpu""" )
else:
# Sharded
lowerCAmelCase_ : Tuple = [
torch.load(os.path.join(__snake_case , f'consolidated.{i:02d}.pth' ) , map_location="""cpu""" )
for i in range(__snake_case )
]
lowerCAmelCase_ : int = 0
lowerCAmelCase_ : Any = {"""weight_map""": {}}
for layer_i in range(__snake_case ):
lowerCAmelCase_ : Any = f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
lowerCAmelCase_ : Optional[Any] = {
f'model.layers.{layer_i}.self_attn.q_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wq.weight'] ),
f'model.layers.{layer_i}.self_attn.k_proj.weight': permute(
loaded[f'layers.{layer_i}.attention.wk.weight'] ),
f'model.layers.{layer_i}.self_attn.v_proj.weight': loaded[f'layers.{layer_i}.attention.wv.weight'],
f'model.layers.{layer_i}.self_attn.o_proj.weight': loaded[f'layers.{layer_i}.attention.wo.weight'],
f'model.layers.{layer_i}.mlp.gate_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w1.weight'],
f'model.layers.{layer_i}.mlp.down_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w2.weight'],
f'model.layers.{layer_i}.mlp.up_proj.weight': loaded[f'layers.{layer_i}.feed_forward.w3.weight'],
f'model.layers.{layer_i}.input_layernorm.weight': loaded[f'layers.{layer_i}.attention_norm.weight'],
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[f'layers.{layer_i}.ffn_norm.weight'],
}
else:
# Sharded
# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
lowerCAmelCase_ : List[Any] = {
f'model.layers.{layer_i}.input_layernorm.weight': loaded[0][
f'layers.{layer_i}.attention_norm.weight'
].clone(),
f'model.layers.{layer_i}.post_attention_layernorm.weight': loaded[0][
f'layers.{layer_i}.ffn_norm.weight'
].clone(),
}
lowerCAmelCase_ : Optional[Any] = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wq.weight'].view(__snake_case , __snake_case , __snake_case )
for i in range(__snake_case )
] , dim=0 , ).reshape(__snake_case , __snake_case ) )
lowerCAmelCase_ : Tuple = permute(
torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wk.weight'].view(
__snake_case , __snake_case , __snake_case )
for i in range(__snake_case )
] , dim=0 , ).reshape(__snake_case , __snake_case ) , __snake_case , __snake_case , __snake_case , )
lowerCAmelCase_ : List[Any] = torch.cat(
[
loaded[i][f'layers.{layer_i}.attention.wv.weight'].view(
__snake_case , __snake_case , __snake_case )
for i in range(__snake_case )
] , dim=0 , ).reshape(__snake_case , __snake_case )
lowerCAmelCase_ : Optional[Any] = torch.cat(
[loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(__snake_case )] , dim=1 )
lowerCAmelCase_ : List[str] = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(__snake_case )] , dim=0 )
lowerCAmelCase_ : List[str] = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(__snake_case )] , dim=1 )
lowerCAmelCase_ : Tuple = torch.cat(
[loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(__snake_case )] , dim=0 )
lowerCAmelCase_ : List[Any] = inv_freq
for k, v in state_dict.items():
lowerCAmelCase_ : List[Any] = filename
param_count += v.numel()
torch.save(__snake_case , os.path.join(__snake_case , __snake_case ) )
lowerCAmelCase_ : int = f'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
if model_size == "7B":
# Unsharded
lowerCAmelCase_ : Any = {
"""model.embed_tokens.weight""": loaded["""tok_embeddings.weight"""],
"""model.norm.weight""": loaded["""norm.weight"""],
"""lm_head.weight""": loaded["""output.weight"""],
}
else:
lowerCAmelCase_ : List[str] = {
"""model.norm.weight""": loaded[0]["""norm.weight"""],
"""model.embed_tokens.weight""": torch.cat(
[loaded[i]["""tok_embeddings.weight"""] for i in range(__snake_case )] , dim=1 ),
"""lm_head.weight""": torch.cat([loaded[i]["""output.weight"""] for i in range(__snake_case )] , dim=0 ),
}
for k, v in state_dict.items():
lowerCAmelCase_ : Union[str, Any] = filename
param_count += v.numel()
torch.save(__snake_case , os.path.join(__snake_case , __snake_case ) )
# Write configs
lowerCAmelCase_ : List[str] = {"""total_size""": param_count * 2}
write_json(__snake_case , os.path.join(__snake_case , """pytorch_model.bin.index.json""" ) )
lowerCAmelCase_ : str = params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
lowerCAmelCase_ : Optional[Any] = params["""multiple_of"""] if """multiple_of""" in params else 2_56
lowerCAmelCase_ : str = LlamaConfig(
hidden_size=__snake_case , intermediate_size=compute_intermediate_size(__snake_case , __snake_case , __snake_case ) , num_attention_heads=params["""n_heads"""] , num_hidden_layers=params["""n_layers"""] , rms_norm_eps=params["""norm_eps"""] , num_key_value_heads=__snake_case , )
config.save_pretrained(__snake_case )
# Make space so we can load the model properly now.
del state_dict
del loaded
gc.collect()
print("""Loading the checkpoint in a Llama model.""" )
lowerCAmelCase_ : List[str] = LlamaForCausalLM.from_pretrained(__snake_case , torch_dtype=torch.floataa , low_cpu_mem_usage=__snake_case )
# Avoid saving this as part of the config.
del model.config._name_or_path
print("""Saving in the Transformers format.""" )
model.save_pretrained(__snake_case , safe_serialization=__snake_case )
shutil.rmtree(__snake_case )
def UpperCamelCase_ ( A__ : str , A__ : str ):
'''simple docstring'''
lowerCAmelCase_ : Tuple = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.' )
lowerCAmelCase_ : List[str] = tokenizer_class(__snake_case )
tokenizer.save_pretrained(__snake_case )
def UpperCamelCase_ ( ):
'''simple docstring'''
lowerCAmelCase_ : Dict = argparse.ArgumentParser()
parser.add_argument(
"""--input_dir""" , help="""Location of LLaMA weights, which contains tokenizer.model and model folders""" , )
parser.add_argument(
"""--model_size""" , choices=["""7B""", """7Bf""", """13B""", """13Bf""", """30B""", """65B""", """70B""", """70Bf""", """tokenizer_only"""] , )
parser.add_argument(
"""--output_dir""" , help="""Location to write HF model and tokenizer""" , )
parser.add_argument("""--safe_serialization""" , type=__snake_case , help="""Whether or not to save using `safetensors`.""" )
lowerCAmelCase_ : Union[str, Any] = parser.parse_args()
if args.model_size != "tokenizer_only":
write_model(
model_path=args.output_dir , input_base_path=os.path.join(args.input_dir , args.model_size ) , model_size=args.model_size , safe_serialization=args.safe_serialization , )
lowerCAmelCase_ : Optional[int] = os.path.join(args.input_dir , """tokenizer.model""" )
write_tokenizer(args.output_dir , __snake_case )
if __name__ == "__main__":
main()
| 120 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase__ ( unittest.TestCase):
def __A (self ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def __A (self ) -> Optional[Any]:
_lowercase =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
_lowercase =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
_lowercase ='''xvjiarui/stable-diffusion-2-inpainting'''
_lowercase , _lowercase =FlaxStableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase , safety_checker=UpperCAmelCase )
_lowercase ='''Face of a yellow cat, high resolution, sitting on a park bench'''
_lowercase =jax.random.PRNGKey(0 )
_lowercase =5_0
_lowercase =jax.device_count()
_lowercase =num_samples * [prompt]
_lowercase =num_samples * [init_image]
_lowercase =num_samples * [mask_image]
_lowercase , _lowercase , _lowercase =pipeline.prepare_inputs(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# shard inputs and rng
_lowercase =replicate(UpperCAmelCase )
_lowercase =jax.random.split(UpperCAmelCase , jax.device_count() )
_lowercase =shard(UpperCAmelCase )
_lowercase =shard(UpperCAmelCase )
_lowercase =shard(UpperCAmelCase )
_lowercase =pipeline(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , jit=UpperCAmelCase )
_lowercase =output.images.reshape(UpperCAmelCase , 5_1_2 , 5_1_2 , 3 )
_lowercase =images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
_lowercase =jnp.asarray(jax.device_get(image_slice.flatten() ) )
_lowercase =jnp.array(
[0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084] )
print(f"output_slice: {output_slice}" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 5 | 0 |
import os
def A__ ( SCREAMING_SNAKE_CASE__ = "input.txt") -> int:
with open(os.path.join(os.path.dirname(__snake_case) , __snake_case)) as input_file:
__snake_case: Any = [
[int(__snake_case) for element in line.split(""",""")]
for line in input_file.readlines()
]
__snake_case: List[Any] = len(__snake_case)
__snake_case: List[Any] = len(matrix[0])
__snake_case: Union[str, Any] = [[-1 for _ in range(__snake_case)] for _ in range(__snake_case)]
for i in range(__snake_case):
__snake_case: int = matrix[i][0]
for j in range(1 , __snake_case):
for i in range(__snake_case):
__snake_case: Optional[Any] = minimal_path_sums[i][j - 1] + matrix[i][j]
for i in range(1 , __snake_case):
__snake_case: Any = min(
minimal_path_sums[i][j] , minimal_path_sums[i - 1][j] + matrix[i][j])
for i in range(rows - 2 , -1 , -1):
__snake_case: Optional[Any] = min(
minimal_path_sums[i][j] , minimal_path_sums[i + 1][j] + matrix[i][j])
return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums)
if __name__ == "__main__":
print(f'{solution() = }')
| 111 |
import comet # From: unbabel-comet
import torch
import datasets
UpperCAmelCase__ = datasets.logging.get_logger(__name__)
UpperCAmelCase__ = '''\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
month = {November},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
pages = {909--918},
}
@inproceedings{rei-etal-2020-comet,
title = "{COMET}: A Neural Framework for {MT} Evaluation",
author = "Rei, Ricardo and
Stewart, Craig and
Farinha, Ana C and
Lavie, Alon",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
pages = "2685--2702",
}
'''
UpperCAmelCase__ = '''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.
See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''
UpperCAmelCase__ = '''
COMET score.
Args:
`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.
Returns:
`samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
`scores`: List of scores.
Examples:
>>> comet_metric = datasets.load_metric(\'comet\')
>>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\') # you can also choose which model to use
>>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
>>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
>>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
>>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
>>> print([round(v, 2) for v in results["scores"]])
[0.19, 0.92]
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class lowerCamelCase__ ( datasets.Metric):
def __A (self ) -> Optional[int]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='''https://unbabel.github.io/COMET/html/index.html''' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''sources''': datasets.Value('''string''' , id='''sequence''' ),
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/Unbabel/COMET'''] , reference_urls=[
'''https://github.com/Unbabel/COMET''',
'''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
'''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
] , )
def __A (self , UpperCAmelCase ) -> Dict:
if self.config_name == "default":
_lowercase =comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da''' ) )
else:
_lowercase =comet.load_from_checkpoint(comet.download_model(self.config_name ) )
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=False ) -> int:
if gpus is None:
_lowercase =1 if torch.cuda.is_available() else 0
_lowercase ={'''src''': sources, '''mt''': predictions, '''ref''': references}
_lowercase =[dict(zip(UpperCAmelCase , UpperCAmelCase ) ) for t in zip(*data.values() )]
_lowercase , _lowercase =self.scorer.predict(UpperCAmelCase , gpus=UpperCAmelCase , progress_bar=UpperCAmelCase )
return {"mean_score": mean_score, "scores": scores}
| 5 | 0 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
SeqaSeqDataset,
calculate_bleu,
calculate_rouge,
chunks,
lmap,
load_json,
parse_numeric_n_bool_cl_kwargs,
save_json,
use_task_specific_params,
write_txt_file,
)
UpperCAmelCase : List[str] = getLogger(__name__)
def _A ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Dict = 8 , SCREAMING_SNAKE_CASE : Tuple = 1_024 , SCREAMING_SNAKE_CASE : Any="val" , SCREAMING_SNAKE_CASE : Optional[int]=None , SCREAMING_SNAKE_CASE : Optional[Any]=False , SCREAMING_SNAKE_CASE : int="summarization" , SCREAMING_SNAKE_CASE : Optional[Any]=None , SCREAMING_SNAKE_CASE : List[Any]=1 , SCREAMING_SNAKE_CASE : Optional[int] = None , SCREAMING_SNAKE_CASE : Optional[Any]="" , **SCREAMING_SNAKE_CASE : int , ):
"""simple docstring"""
a__ : List[str] =str(__snake_case )
assert local_rank is not None
torch.distributed.init_process_group(backend="nccl" , rank=__snake_case )
a__ : List[str] =Path(__snake_case )
a__ : List[Any] =save_dir.joinpath(f'''rank_{local_rank}_output.json''' )
torch.cuda.set_device(__snake_case )
a__ : Union[str, Any] =AutoModelForSeqaSeqLM.from_pretrained(__snake_case ).cuda()
if fpaa:
a__ : Dict =model.half()
# determine if we need to increase num_beams
use_task_specific_params(__snake_case , __snake_case ) # update config with task specific params
a__ : Dict =generate_kwargs.pop("num_beams" , model.config.num_beams ) # AttributeError risk?
if num_return_sequences > num_beams:
a__ : List[Any] =num_return_sequences
a__ : Optional[Any] =AutoTokenizer.from_pretrained(__snake_case )
logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}''' ) # if this is wrong, check config.model_type.
if max_source_length is None:
a__ : str =tokenizer.model_max_length
if prefix is None:
a__ : Optional[Any] =prefix or getattr(model.config , "prefix" , "" ) or ""
a__ : Tuple =SeqaSeqDataset(
__snake_case , __snake_case , __snake_case , max_target_length=1_024 , type_path=__snake_case , n_obs=__snake_case , prefix=__snake_case , **__snake_case , )
# I set shuffle=True for a more accurate progress bar.
# If all the longest samples are first, the prog bar estimate is too high at the beginning.
a__ : Tuple =ds.make_sortish_sampler(__snake_case , distributed=__snake_case , add_extra_examples=__snake_case , shuffle=__snake_case )
a__ : Union[str, Any] =DataLoader(__snake_case , sampler=__snake_case , batch_size=__snake_case , collate_fn=ds.collate_fn )
a__ : Optional[int] =[]
for batch in tqdm(__snake_case ):
a__ : List[str] =model.generate(
input_ids=batch["input_ids"].to(model.device ) , attention_mask=batch["attention_mask"].to(model.device ) , num_return_sequences=__snake_case , num_beams=__snake_case , **__snake_case , )
a__ : Union[str, Any] =tokenizer.batch_decode(__snake_case , skip_special_tokens=__snake_case , clean_up_tokenization_spaces=__snake_case )
a__ : Any =batch["ids"]
if num_return_sequences > 1:
a__ : Any =chunks(__snake_case , __snake_case ) # batch size chunks, each of size num_return_seq
for i, pred in enumerate(__snake_case ):
results.append({"pred": pred, "id": ids[i].item()} )
save_json(__snake_case , __snake_case )
return results, sampler.num_replicas
def _A ( ):
"""simple docstring"""
a__ : Optional[Any] =argparse.ArgumentParser(
epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate" )
parser.add_argument("--data_dir" , type=__snake_case , help="like cnn_dm/test.source" )
parser.add_argument(
"--model_name" , type=__snake_case , help="like facebook/bart-large-cnn,t5-base, etc." , default="sshleifer/distilbart-xsum-12-3" , )
parser.add_argument("--save_dir" , type=__snake_case , help="where to save" , default="tmp_gen" )
parser.add_argument("--max_source_length" , type=__snake_case , default=__snake_case )
parser.add_argument(
"--type_path" , type=__snake_case , default="test" , help="which subset to evaluate typically train/val/test" )
parser.add_argument("--task" , type=__snake_case , default="summarization" , help="used for task_specific_params + metrics" )
parser.add_argument("--bs" , type=__snake_case , default=8 , required=__snake_case , help="batch size" )
parser.add_argument(
"--local_rank" , type=__snake_case , default=-1 , required=__snake_case , help="should be passed by distributed.launch" )
parser.add_argument(
"--n_obs" , type=__snake_case , default=__snake_case , required=__snake_case , help="How many observations. Defaults to all." )
parser.add_argument(
"--num_return_sequences" , type=__snake_case , default=1 , required=__snake_case , help="How many sequences to return" )
parser.add_argument(
"--sync_timeout" , type=__snake_case , default=600 , required=__snake_case , help="How long should master process wait for other processes to finish." , )
parser.add_argument("--src_lang" , type=__snake_case , default=__snake_case , required=__snake_case )
parser.add_argument("--tgt_lang" , type=__snake_case , default=__snake_case , required=__snake_case )
parser.add_argument(
"--prefix" , type=__snake_case , required=__snake_case , default=__snake_case , help="will be added to the begininng of src examples" )
parser.add_argument("--fp16" , action="store_true" )
parser.add_argument("--debug" , action="store_true" )
a__ : List[str] =time.time()
a__ , a__ : Union[str, Any] =parser.parse_known_args()
a__ : Optional[int] =parse_numeric_n_bool_cl_kwargs(__snake_case )
if generate_kwargs and args.local_rank <= 0:
print(f'''parsed the following generate kwargs: {generate_kwargs}''' )
a__ : Optional[Any] =Path(args.save_dir + "_tmp" )
Path(__snake_case ).mkdir(exist_ok=__snake_case ) # this handles locking.
a__ : List[str] =list(json_save_dir.glob("rank_*.json" ) )
if intermediate_files:
raise ValueError(f'''Found files at {json_save_dir} please move or remove them.''' )
# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
a__ : Optional[int] ={}
if args.src_lang is not None:
a__ : Union[str, Any] =args.src_lang
if args.tgt_lang is not None:
a__ : List[Any] =args.tgt_lang
Path(args.save_dir ).mkdir(exist_ok=__snake_case )
a__ , a__ : Tuple =eval_data_dir(
args.data_dir , __snake_case , args.model_name , type_path=args.type_path , bs=args.bs , fpaa=args.fpaa , task=args.task , local_rank=args.local_rank , n_obs=args.n_obs , max_source_length=args.max_source_length , num_return_sequences=args.num_return_sequences , prefix=args.prefix , dataset_kwargs=__snake_case , **__snake_case , )
if args.local_rank <= 0:
a__ : List[Any] =Path(args.save_dir )
save_dir.mkdir(exist_ok=__snake_case )
a__ : Optional[Any] =gather_results_from_each_node(__snake_case , __snake_case , args.sync_timeout )
a__ : Tuple =combine_partial_results(__snake_case )
if args.num_return_sequences > 1:
a__ : str =save_dir.joinpath("pseudolabel_results.json" )
print(f'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/''' )
save_json(__snake_case , __snake_case )
return
a__ : List[Any] =Path(args.data_dir ).joinpath(args.type_path + ".target" )
with open(__snake_case ) as f:
a__ : List[Any] =[x.rstrip() for x in f.readlines()][: len(__snake_case )]
# Calculate metrics, save metrics, and save _generations.txt
a__ : Dict ="translation" in args.task
a__ : Tuple =calculate_bleu if calc_bleu else calculate_rouge
a__ : Dict ="bleu" if calc_bleu else "rouge"
a__ : List[str] =score_fn(__snake_case , __snake_case )
a__ : List[Any] =len(__snake_case )
a__ : str =time.time() - start_time
a__ : Optional[int] =round(runtime / metrics["n_obs"] , 4 )
a__ : str =num_replicas
# TODO(@stas00): add whatever metadata to metrics
a__ : str =save_dir.joinpath(f'''{args.type_path}_{metric_name}.json''' )
save_json(__snake_case , __snake_case , indent=__snake_case )
print(__snake_case )
write_txt_file(__snake_case , save_dir.joinpath(f'''{args.type_path}_generations.txt''' ) )
if args.debug:
write_txt_file(__snake_case , save_dir.joinpath(f'''{args.type_path}.target''' ) )
else:
shutil.rmtree(__snake_case )
def _A ( SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
a__ : Dict =[]
for partial_result in partial_results:
records.extend(__snake_case )
a__ : Any =sorted(__snake_case , key=lambda SCREAMING_SNAKE_CASE : x["id"] )
a__ : Union[str, Any] =[x["pred"] for x in records]
return preds
def _A ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
a__ : List[str] =time.time()
logger.info("waiting for all nodes to finish" )
a__ : Dict =None
while (time.time() - start_wait) < timeout:
a__ : List[Any] =list(save_dir.glob("rank_*.json" ) )
if len(__snake_case ) < num_replicas:
continue
try:
# make sure all json files are fully saved
a__ : Optional[int] =lmap(__snake_case , __snake_case )
return json_data
except JSONDecodeError:
continue
else:
raise TimeoutError("Rank 0 gave up on waiting for other processes" )
# Unreachable
if __name__ == "__main__":
# Usage for MT:
run_generate()
| 95 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class lowerCamelCase__ ( lowerCAmelCase , lowerCAmelCase):
SCREAMING_SNAKE_CASE__ = 1
@register_to_config
def __init__(self , UpperCAmelCase=2_0_0_0 , UpperCAmelCase=0.1 , UpperCAmelCase=2_0 , UpperCAmelCase=1e-3 ) -> List[str]:
_lowercase =None
_lowercase =None
_lowercase =None
def __A (self , UpperCAmelCase , UpperCAmelCase = None ) -> str:
_lowercase =torch.linspace(1 , self.config.sampling_eps , UpperCAmelCase , device=UpperCAmelCase )
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None ) -> Optional[int]:
if self.timesteps is None:
raise ValueError(
'''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
_lowercase =(
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
_lowercase =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
_lowercase =std.flatten()
while len(std.shape ) < len(score.shape ):
_lowercase =std.unsqueeze(-1 )
_lowercase =-score / std
# compute
_lowercase =-1.0 / len(self.timesteps )
_lowercase =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
_lowercase =beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
_lowercase =beta_t.unsqueeze(-1 )
_lowercase =-0.5 * beta_t * x
_lowercase =torch.sqrt(UpperCAmelCase )
_lowercase =drift - diffusion**2 * score
_lowercase =x + drift * dt
# add noise
_lowercase =randn_tensor(x.shape , layout=x.layout , generator=UpperCAmelCase , device=x.device , dtype=x.dtype )
_lowercase =x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__(self ) -> str:
return self.config.num_train_timesteps
| 5 | 0 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
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 BertGenerationDecoder, BertGenerationEncoder
class A_ :
def __init__( self : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : Any=1_3 , UpperCAmelCase : int=7 , UpperCAmelCase : Any=True , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : List[Any]=9_9 , UpperCAmelCase : List[str]=3_2 , UpperCAmelCase : Optional[Any]=5 , UpperCAmelCase : List[Any]=4 , UpperCAmelCase : Optional[Any]=3_7 , UpperCAmelCase : Dict="gelu" , UpperCAmelCase : List[Any]=0.1 , UpperCAmelCase : Any=0.1 , UpperCAmelCase : List[str]=5_0 , UpperCAmelCase : str=0.02 , UpperCAmelCase : Tuple=True , UpperCAmelCase : Tuple=None , ) -> str:
__lowerCAmelCase: Optional[int] = parent
__lowerCAmelCase: str = batch_size
__lowerCAmelCase: Optional[Any] = seq_length
__lowerCAmelCase: str = is_training
__lowerCAmelCase: Union[str, Any] = use_input_mask
__lowerCAmelCase: Any = vocab_size
__lowerCAmelCase: Optional[Any] = hidden_size
__lowerCAmelCase: Dict = num_hidden_layers
__lowerCAmelCase: List[Any] = num_attention_heads
__lowerCAmelCase: List[str] = intermediate_size
__lowerCAmelCase: Union[str, Any] = hidden_act
__lowerCAmelCase: List[str] = hidden_dropout_prob
__lowerCAmelCase: Dict = attention_probs_dropout_prob
__lowerCAmelCase: Optional[Any] = max_position_embeddings
__lowerCAmelCase: Any = initializer_range
__lowerCAmelCase: int = use_labels
__lowerCAmelCase: Dict = scope
def UpperCAmelCase ( self : Optional[Any] ) -> int:
__lowerCAmelCase: Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase: str = None
if self.use_input_mask:
__lowerCAmelCase: Dict = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
__lowerCAmelCase: List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase: str = self.get_config()
return config, input_ids, input_mask, token_labels
def UpperCAmelCase ( self : Dict ) -> Any:
return BertGenerationConfig(
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 , is_decoder=UpperCAmelCase , initializer_range=self.initializer_range , )
def UpperCAmelCase ( self : Any ) -> Dict:
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Union[str, Any] = self.prepare_config_and_inputs()
__lowerCAmelCase: List[Any] = True
__lowerCAmelCase: Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__lowerCAmelCase: Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : int , UpperCAmelCase : Tuple , UpperCAmelCase : str , **UpperCAmelCase : List[str] , ) -> Tuple:
__lowerCAmelCase: Optional[Any] = BertGenerationEncoder(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Any = model(UpperCAmelCase , attention_mask=UpperCAmelCase )
__lowerCAmelCase: Optional[Any] = model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : List[str] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : List[Any] , **UpperCAmelCase : List[str] , ) -> List[str]:
__lowerCAmelCase: Any = True
__lowerCAmelCase: int = BertGenerationEncoder(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Dict = model(
UpperCAmelCase , attention_mask=UpperCAmelCase , encoder_hidden_states=UpperCAmelCase , encoder_attention_mask=UpperCAmelCase , )
__lowerCAmelCase: Dict = model(
UpperCAmelCase , attention_mask=UpperCAmelCase , encoder_hidden_states=UpperCAmelCase , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : Tuple , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : Optional[int] , **UpperCAmelCase : str , ) -> Dict:
__lowerCAmelCase: Any = True
__lowerCAmelCase: Dict = True
__lowerCAmelCase: List[Any] = BertGenerationDecoder(config=UpperCAmelCase ).to(UpperCAmelCase ).eval()
# first forward pass
__lowerCAmelCase: Any = model(
UpperCAmelCase , attention_mask=UpperCAmelCase , encoder_hidden_states=UpperCAmelCase , encoder_attention_mask=UpperCAmelCase , use_cache=UpperCAmelCase , )
__lowerCAmelCase: str = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__lowerCAmelCase: Dict = ids_tensor((self.batch_size, 3) , config.vocab_size )
__lowerCAmelCase: Any = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
__lowerCAmelCase: List[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
__lowerCAmelCase: Optional[Any] = torch.cat([input_mask, next_mask] , dim=-1 )
__lowerCAmelCase: int = model(
UpperCAmelCase , attention_mask=UpperCAmelCase , encoder_hidden_states=UpperCAmelCase , encoder_attention_mask=UpperCAmelCase , output_hidden_states=UpperCAmelCase , )['hidden_states'][0]
__lowerCAmelCase: Optional[int] = model(
UpperCAmelCase , attention_mask=UpperCAmelCase , encoder_hidden_states=UpperCAmelCase , encoder_attention_mask=UpperCAmelCase , past_key_values=UpperCAmelCase , output_hidden_states=UpperCAmelCase , )['hidden_states'][0]
# select random slice
__lowerCAmelCase: Union[str, Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__lowerCAmelCase: Any = output_from_no_past[:, -3:, random_slice_idx].detach()
__lowerCAmelCase: List[Any] = 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(UpperCAmelCase , UpperCAmelCase , atol=1E-3 ) )
def UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : str , UpperCAmelCase : Any , UpperCAmelCase : List[str] , *UpperCAmelCase : List[str] , ) -> Optional[int]:
__lowerCAmelCase: int = BertGenerationDecoder(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
__lowerCAmelCase: Optional[Any] = model(UpperCAmelCase , attention_mask=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self : str ) -> Tuple:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: List[str] = self.prepare_config_and_inputs()
__lowerCAmelCase: Union[str, Any] = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class A_ ( snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
_lowercase : Optional[int] = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
_lowercase : Tuple = (BertGenerationDecoder,) if is_torch_available() else ()
_lowercase : Optional[Any] = (
{'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
if is_torch_available()
else {}
)
def UpperCAmelCase ( self : List[str] ) -> Dict:
__lowerCAmelCase: List[Any] = BertGenerationEncoderTester(self )
__lowerCAmelCase: Optional[int] = ConfigTester(self , config_class=UpperCAmelCase , hidden_size=3_7 )
def UpperCAmelCase ( self : Optional[Any] ) -> List[str]:
self.config_tester.run_common_tests()
def UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
__lowerCAmelCase: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[Any]:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase: int = self.model_tester.prepare_config_and_inputs()
__lowerCAmelCase: Union[str, Any] = 'bert'
self.model_tester.create_and_check_model(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def UpperCAmelCase ( self : Dict ) -> Union[str, Any]:
__lowerCAmelCase: List[str] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*UpperCAmelCase )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
__lowerCAmelCase: List[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*UpperCAmelCase )
def UpperCAmelCase ( self : List[str] ) -> int:
# This regression test was failing with PyTorch < 1.3
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
): Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
__lowerCAmelCase: str = None
self.model_tester.create_and_check_model_as_decoder(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , )
def UpperCAmelCase ( self : int ) -> str:
__lowerCAmelCase: List[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*UpperCAmelCase )
@slow
def UpperCAmelCase ( self : Tuple ) -> Union[str, Any]:
__lowerCAmelCase: str = BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
self.assertIsNotNone(UpperCAmelCase )
@require_torch
class A_ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self : List[Any] ) -> List[str]:
__lowerCAmelCase: List[str] = BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
__lowerCAmelCase: str = torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]] )
with torch.no_grad():
__lowerCAmelCase: Union[str, Any] = model(UpperCAmelCase )[0]
__lowerCAmelCase: List[Any] = torch.Size([1, 8, 1_0_2_4] )
self.assertEqual(output.shape , UpperCAmelCase )
__lowerCAmelCase: List[Any] = torch.tensor(
[[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCAmelCase , atol=1E-4 ) )
@require_torch
class A_ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self : Any ) -> Dict:
__lowerCAmelCase: List[Any] = BertGenerationDecoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder' )
__lowerCAmelCase: List[Any] = torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]] )
with torch.no_grad():
__lowerCAmelCase: Dict = model(UpperCAmelCase )[0]
__lowerCAmelCase: Optional[Any] = torch.Size([1, 8, 5_0_3_5_8] )
self.assertEqual(output.shape , UpperCAmelCase )
__lowerCAmelCase: Dict = torch.tensor(
[[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCAmelCase , atol=1E-4 ) )
| 322 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase__ = logging.get_logger(__name__)
def UpperCAmelCase_ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
_lowercase =MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
_lowercase =[144, 192, 240]
_lowercase =[16, 32, 64, 96, 128, 160, 640]
elif "mobilevit_xs" in mobilevit_name:
_lowercase =[96, 120, 144]
_lowercase =[16, 32, 48, 64, 80, 96, 384]
elif "mobilevit_xxs" in mobilevit_name:
_lowercase =[64, 80, 96]
_lowercase =[16, 16, 24, 48, 64, 80, 320]
_lowercase =0.05
_lowercase =2.0
if mobilevit_name.startswith('''deeplabv3_''' ):
_lowercase =512
_lowercase =16
_lowercase =21
_lowercase ='''pascal-voc-id2label.json'''
else:
_lowercase =1000
_lowercase ='''imagenet-1k-id2label.json'''
_lowercase ='''huggingface/label-files'''
_lowercase =json.load(open(hf_hub_download(__snake_case , __snake_case , repo_type='''dataset''' ) , '''r''' ) )
_lowercase ={int(__snake_case ): v for k, v in idalabel.items()}
_lowercase =idalabel
_lowercase ={v: k for k, v in idalabel.items()}
return config
def UpperCAmelCase_ ( __snake_case , __snake_case=False ) -> Tuple:
"""simple docstring"""
for i in range(1 , 6 ):
if F"layer_{i}." in name:
_lowercase =name.replace(F"layer_{i}." , F"encoder.layer.{i - 1}." )
if "conv_1." in name:
_lowercase =name.replace('''conv_1.''' , '''conv_stem.''' )
if ".block." in name:
_lowercase =name.replace('''.block.''' , '''.''' )
if "exp_1x1" in name:
_lowercase =name.replace('''exp_1x1''' , '''expand_1x1''' )
if "red_1x1" in name:
_lowercase =name.replace('''red_1x1''' , '''reduce_1x1''' )
if ".local_rep.conv_3x3." in name:
_lowercase =name.replace('''.local_rep.conv_3x3.''' , '''.conv_kxk.''' )
if ".local_rep.conv_1x1." in name:
_lowercase =name.replace('''.local_rep.conv_1x1.''' , '''.conv_1x1.''' )
if ".norm." in name:
_lowercase =name.replace('''.norm.''' , '''.normalization.''' )
if ".conv." in name:
_lowercase =name.replace('''.conv.''' , '''.convolution.''' )
if ".conv_proj." in name:
_lowercase =name.replace('''.conv_proj.''' , '''.conv_projection.''' )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F".{i}.{j}." in name:
_lowercase =name.replace(F".{i}.{j}." , F".{i}.layer.{j}." )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F".{i}.{j}." in name:
_lowercase =name.replace(F".{i}.{j}." , F".{i}." )
if "expand_1x1" in name:
_lowercase =name.replace('''expand_1x1''' , '''downsampling_layer.expand_1x1''' )
if "conv_3x3" in name:
_lowercase =name.replace('''conv_3x3''' , '''downsampling_layer.conv_3x3''' )
if "reduce_1x1" in name:
_lowercase =name.replace('''reduce_1x1''' , '''downsampling_layer.reduce_1x1''' )
for i in range(2 , 5 ):
if F".global_rep.{i}.weight" in name:
_lowercase =name.replace(F".global_rep.{i}.weight" , '''.layernorm.weight''' )
if F".global_rep.{i}.bias" in name:
_lowercase =name.replace(F".global_rep.{i}.bias" , '''.layernorm.bias''' )
if ".global_rep." in name:
_lowercase =name.replace('''.global_rep.''' , '''.transformer.''' )
if ".pre_norm_mha.0." in name:
_lowercase =name.replace('''.pre_norm_mha.0.''' , '''.layernorm_before.''' )
if ".pre_norm_mha.1.out_proj." in name:
_lowercase =name.replace('''.pre_norm_mha.1.out_proj.''' , '''.attention.output.dense.''' )
if ".pre_norm_ffn.0." in name:
_lowercase =name.replace('''.pre_norm_ffn.0.''' , '''.layernorm_after.''' )
if ".pre_norm_ffn.1." in name:
_lowercase =name.replace('''.pre_norm_ffn.1.''' , '''.intermediate.dense.''' )
if ".pre_norm_ffn.4." in name:
_lowercase =name.replace('''.pre_norm_ffn.4.''' , '''.output.dense.''' )
if ".transformer." in name:
_lowercase =name.replace('''.transformer.''' , '''.transformer.layer.''' )
if ".aspp_layer." in name:
_lowercase =name.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in name:
_lowercase =name.replace('''.aspp_pool.''' , '''.''' )
if "seg_head." in name:
_lowercase =name.replace('''seg_head.''' , '''segmentation_head.''' )
if "segmentation_head.classifier.classifier." in name:
_lowercase =name.replace('''segmentation_head.classifier.classifier.''' , '''segmentation_head.classifier.''' )
if "classifier.fc." in name:
_lowercase =name.replace('''classifier.fc.''' , '''classifier.''' )
elif (not base_model) and ("segmentation_head." not in name):
_lowercase ='''mobilevit.''' + name
return name
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case=False ) -> Optional[Any]:
"""simple docstring"""
if base_model:
_lowercase =''''''
else:
_lowercase ='''mobilevit.'''
for key in orig_state_dict.copy().keys():
_lowercase =orig_state_dict.pop(__snake_case )
if key[:8] == "encoder.":
_lowercase =key[8:]
if "qkv" in key:
_lowercase =key.split('''.''' )
_lowercase =int(key_split[0][6:] ) - 1
_lowercase =int(key_split[3] )
_lowercase =model.get_submodule(F"{model_prefix}encoder.layer.{layer_num}" )
_lowercase =layer.transformer.layer[transformer_num].attention.attention.all_head_size
_lowercase =(
F"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
)
if "weight" in key:
_lowercase =val[:dim, :]
_lowercase =val[dim : dim * 2, :]
_lowercase =val[-dim:, :]
else:
_lowercase =val[:dim]
_lowercase =val[dim : dim * 2]
_lowercase =val[-dim:]
else:
_lowercase =val
return orig_state_dict
def UpperCAmelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
_lowercase ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
_lowercase =Image.open(requests.get(__snake_case , stream=__snake_case ).raw )
return im
@torch.no_grad()
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case=False ) -> int:
"""simple docstring"""
_lowercase =get_mobilevit_config(__snake_case )
# load original state_dict
_lowercase =torch.load(__snake_case , map_location='''cpu''' )
# load 🤗 model
if mobilevit_name.startswith('''deeplabv3_''' ):
_lowercase =MobileViTForSemanticSegmentation(__snake_case ).eval()
else:
_lowercase =MobileViTForImageClassification(__snake_case ).eval()
_lowercase =convert_state_dict(__snake_case , __snake_case )
model.load_state_dict(__snake_case )
# Check outputs on an image, prepared by MobileViTImageProcessor
_lowercase =MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
_lowercase =image_processor(images=prepare_img() , return_tensors='''pt''' )
_lowercase =model(**__snake_case )
_lowercase =outputs.logits
if mobilevit_name.startswith('''deeplabv3_''' ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
_lowercase =torch.tensor(
[
[[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
[[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
[[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
_lowercase =torch.tensor(
[
[[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
[[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
[[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
_lowercase =torch.tensor(
[
[[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
[[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
[[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
] )
else:
raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}" )
assert torch.allclose(logits[0, :3, :3, :3] , __snake_case , atol=1e-4 )
else:
assert logits.shape == (1, 1000)
if mobilevit_name == "mobilevit_s":
_lowercase =torch.tensor([-0.98_66, 0.23_92, -1.12_41] )
elif mobilevit_name == "mobilevit_xs":
_lowercase =torch.tensor([-2.47_61, -0.93_99, -1.95_87] )
elif mobilevit_name == "mobilevit_xxs":
_lowercase =torch.tensor([-1.93_64, -1.23_27, -0.46_53] )
else:
raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}" )
assert torch.allclose(logits[0, :3] , __snake_case , atol=1e-4 )
Path(__snake_case ).mkdir(exist_ok=__snake_case )
print(F"Saving model {mobilevit_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(__snake_case )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__snake_case )
if push_to_hub:
_lowercase ={
'''mobilevit_s''': '''mobilevit-small''',
'''mobilevit_xs''': '''mobilevit-x-small''',
'''mobilevit_xxs''': '''mobilevit-xx-small''',
'''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
'''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
'''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
}
print('''Pushing to the hub...''' )
_lowercase =model_mapping[mobilevit_name]
image_processor.push_to_hub(__snake_case , organization='''apple''' )
model.push_to_hub(__snake_case , organization='''apple''' )
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--mobilevit_name''',
default='''mobilevit_s''',
type=str,
help=(
'''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
),
)
parser.add_argument(
'''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
UpperCAmelCase__ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 5 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
__snake_case : Union[str, Any] = logging.get_logger(__name__)
__snake_case : Any = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__snake_case : List[str] = {
"""vocab_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
),
"""google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
},
"""tokenizer_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-openqa""": (
"""https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-reader""": (
"""https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-openqa""": (
"""https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-reader""": (
"""https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
),
},
}
__snake_case : Optional[Any] = {
"""google/realm-cc-news-pretrained-embedder""": 5_12,
"""google/realm-cc-news-pretrained-encoder""": 5_12,
"""google/realm-cc-news-pretrained-scorer""": 5_12,
"""google/realm-cc-news-pretrained-openqa""": 5_12,
"""google/realm-orqa-nq-openqa""": 5_12,
"""google/realm-orqa-nq-reader""": 5_12,
"""google/realm-orqa-wq-openqa""": 5_12,
"""google/realm-orqa-wq-reader""": 5_12,
}
__snake_case : Optional[Any] = {
"""google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}
class A__(a_ ):
"""simple docstring"""
_A : str = VOCAB_FILES_NAMES
_A : str = PRETRAINED_VOCAB_FILES_MAP
_A : List[Any] = PRETRAINED_INIT_CONFIGURATION
_A : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A : List[str] = RealmTokenizer
def __init__( self , _lowercase=None , _lowercase=None , _lowercase=True , _lowercase="[UNK]" , _lowercase="[SEP]" , _lowercase="[PAD]" , _lowercase="[CLS]" , _lowercase="[MASK]" , _lowercase=True , _lowercase=None , **_lowercase , ) -> Optional[Any]:
super().__init__(
_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , tokenize_chinese_chars=_lowercase , strip_accents=_lowercase , **_lowercase , )
a_ : Tuple = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , _lowercase ) != do_lower_case
or normalizer_state.get("""strip_accents""" , _lowercase ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , _lowercase ) != tokenize_chinese_chars
):
a_ : Dict = getattr(_lowercase , normalizer_state.pop("""type""" ) )
a_ : List[Any] = do_lower_case
a_ : List[str] = strip_accents
a_ : Optional[int] = tokenize_chinese_chars
a_ : Optional[Any] = normalizer_class(**_lowercase )
a_ : str = do_lower_case
def UpperCamelCase__ ( self , _lowercase , **_lowercase ) -> Any:
a_ : Dict = PaddingStrategy.MAX_LENGTH
a_ : List[str] = text
a_ : List[str] = kwargs.pop("""text_pair""" , _lowercase )
a_ : Optional[Any] = kwargs.pop("""return_tensors""" , _lowercase )
a_ : Optional[Any] = {
"""input_ids""": [],
"""attention_mask""": [],
"""token_type_ids""": [],
}
for idx, candidate_text in enumerate(_lowercase ):
if batch_text_pair is not None:
a_ : Optional[int] = batch_text_pair[idx]
else:
a_ : Union[str, Any] = None
a_ : Any = super().__call__(_lowercase , _lowercase , return_tensors=_lowercase , **_lowercase )
a_ : List[str] = encoded_candidates.get("""input_ids""" )
a_ : str = encoded_candidates.get("""attention_mask""" )
a_ : Union[str, Any] = encoded_candidates.get("""token_type_ids""" )
if encoded_input_ids is not None:
output_data["input_ids"].append(_lowercase )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(_lowercase )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(_lowercase )
a_ : Optional[Any] = {key: item for key, item in output_data.items() if len(_lowercase ) != 0}
return BatchEncoding(_lowercase , tensor_type=_lowercase )
def UpperCamelCase__ ( self , _lowercase , _lowercase=None ) -> Dict:
a_ : Tuple = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCamelCase__ ( self , _lowercase , _lowercase = None ) -> List[int]:
a_ : Dict = [self.sep_token_id]
a_ : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase__ ( self , _lowercase , _lowercase = None ) -> Tuple[str]:
a_ : int = self._tokenizer.model.save(_lowercase , name=_lowercase )
return tuple(_lowercase )
| 248 |
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( __snake_case = "https://www.worldometers.info/coronavirus" ) -> dict:
"""simple docstring"""
_lowercase =BeautifulSoup(requests.get(__snake_case ).text , '''html.parser''' )
_lowercase =soup.findAll('''h1''' )
_lowercase =soup.findAll('''div''' , {'''class''': '''maincounter-number'''} )
keys += soup.findAll('''span''' , {'''class''': '''panel-title'''} )
values += soup.findAll('''div''' , {'''class''': '''number-table-main'''} )
return {key.text.strip(): value.text.strip() for key, value in zip(__snake_case , __snake_case )}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(f'''{key}\n{value}\n''')
| 5 | 0 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( _A : Tuple , _A : Any , _A : int=None ):
"""simple docstring"""
assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
lowerCamelCase__ : List[str] = nn.Parameter(__snake_case )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
lowerCamelCase__ : Tuple = nn.Parameter(__snake_case )
def lowercase_ ( _A : Dict , _A : Tuple , _A : List[Any] ):
"""simple docstring"""
lowerCamelCase__ : Optional[Any] = np.asarray(weights[0] )
lowerCamelCase__ : Tuple = np.asarray(weights[1] )
lowerCamelCase__ : str = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(__snake_case ).transpose(1 , 2 ).contiguous().view(-1 , __snake_case ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__snake_case ).transpose(1 , 2 ).contiguous().view(-1 , __snake_case ) , )
set_param(
torch_layer.output.dense , torch.tensor(__snake_case ).view(-1 , __snake_case ).contiguous().transpose(0 , 1 ) , )
def lowercase_ ( _A : List[Any] , _A : List[Any] , _A : Optional[Any] ):
"""simple docstring"""
lowerCamelCase__ : str = np.asarray(weights[0] )
lowerCamelCase__ : Optional[int] = np.asarray(weights[1] )
lowerCamelCase__ : Dict = np.asarray(weights[2] )
lowerCamelCase__ : List[Any] = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(__snake_case ).transpose(1 , 2 ).contiguous().view(-1 , __snake_case ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(__snake_case ).transpose(1 , 2 ).contiguous().view(-1 , __snake_case ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(__snake_case ).transpose(1 , 2 ).contiguous().view(-1 , __snake_case ) , )
set_param(
torch_layer.output.dense , torch.tensor(__snake_case ).view(-1 , __snake_case ).contiguous().transpose(0 , 1 ) , )
def lowercase_ ( _A : List[str] , _A : Any , _A : Optional[Any] ):
"""simple docstring"""
lowerCamelCase__ : List[Any] = weights[0][0][0]
lowerCamelCase__ : Tuple = np.asarray(layer_norm_a[0] )
lowerCamelCase__ : Any = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(__snake_case ) , torch.tensor(__snake_case ) , )
# lsh weights + output
lowerCamelCase__ : Union[str, Any] = weights[0][1]
if len(__snake_case ) < 4:
set_layer_weights_in_torch_lsh(__snake_case , torch_block.attention , __snake_case )
else:
set_layer_weights_in_torch_local(__snake_case , torch_block.attention , __snake_case )
# intermediate weighs
lowerCamelCase__ : int = weights[2][0][1][2]
# Chunked Feed Forward
if len(__snake_case ) == 4:
lowerCamelCase__ : Dict = intermediate_weights[2]
# layernorm 2
lowerCamelCase__ : Any = np.asarray(intermediate_weights[0][0] )
lowerCamelCase__ : str = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(__snake_case ) , torch.tensor(__snake_case ) , )
# intermediate dense
lowerCamelCase__ : Optional[int] = np.asarray(intermediate_weights[1][0] )
lowerCamelCase__ : Tuple = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(__snake_case ).transpose(0 , 1 ).contiguous() , torch.tensor(__snake_case ) , )
# intermediate out
lowerCamelCase__ : List[Any] = np.asarray(intermediate_weights[4][0] )
lowerCamelCase__ : str = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(__snake_case ).transpose(0 , 1 ).contiguous() , torch.tensor(__snake_case ) , )
def lowercase_ ( _A : List[Any] , _A : Optional[Any] , _A : Optional[int] ):
"""simple docstring"""
lowerCamelCase__ : List[Any] = torch_model.reformer
# word embeds
lowerCamelCase__ : Dict = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(__snake_case ) , )
if isinstance(weights[3] , __snake_case ):
lowerCamelCase__ : Optional[Any] = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
lowerCamelCase__ : Optional[Any] = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), F"{position_embeddings[emb_idx]} emb does not match"
lowerCamelCase__ : Any = nn.Parameter(torch.tensor(__snake_case ) )
lowerCamelCase__ : Any = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
__snake_case ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
lowerCamelCase__ : List[str] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(__snake_case , __snake_case , __snake_case )
# output layer norm
lowerCamelCase__ : List[Any] = np.asarray(weights[7][0] )
lowerCamelCase__ : Optional[Any] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(__snake_case ) , torch.tensor(__snake_case ) , )
# output embeddings
lowerCamelCase__ : Union[str, Any] = np.asarray(weights[9][0] )
lowerCamelCase__ : Optional[Any] = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(__snake_case ).transpose(0 , 1 ).contiguous() , torch.tensor(__snake_case ) , )
def lowercase_ ( _A : str , _A : int , _A : List[Any] ):
"""simple docstring"""
lowerCamelCase__ : List[str] = ReformerConfig.from_json_file(__snake_case )
print(F"Building PyTorch model from configuration: {config}" )
lowerCamelCase__ : Optional[int] = ReformerModelWithLMHead(__snake_case )
with open(__snake_case , "rb" ) as f:
lowerCamelCase__ : List[str] = pickle.load(__snake_case )["weights"]
set_model_weights_in_torch(__snake_case , __snake_case , config.hidden_size )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , __snake_case )
if __name__ == "__main__":
A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained Reformer 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."
)
A : int = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 184 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCAmelCase__ = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''PatchingSpec''',
],
'''convert''': ['''export''', '''validate_model_outputs'''],
'''features''': ['''FeaturesManager'''],
'''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 5 | 0 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class a_ :
"""simple docstring"""
def __lowerCAmelCase ( self , _lowerCamelCase ) ->Dict:
raise NotImplementedError()
def __lowerCAmelCase ( self ) ->Optional[int]:
raise NotImplementedError()
class a_ ( a__ ):
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase = False , **_lowerCamelCase ) ->Any:
SCREAMING_SNAKE_CASE : Tuple = tokenizer
SCREAMING_SNAKE_CASE : Tuple = skip_prompt
SCREAMING_SNAKE_CASE : int = decode_kwargs
# variables used in the streaming process
SCREAMING_SNAKE_CASE : List[str] = []
SCREAMING_SNAKE_CASE : Optional[Any] = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = True
def __lowerCAmelCase ( self , _lowerCamelCase ) ->int:
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('''TextStreamer only supports batch size 1''' )
elif len(value.shape ) > 1:
SCREAMING_SNAKE_CASE : str = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
SCREAMING_SNAKE_CASE : Optional[int] = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
SCREAMING_SNAKE_CASE : str = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('''\n''' ):
SCREAMING_SNAKE_CASE : str = text[self.print_len :]
SCREAMING_SNAKE_CASE : Any = []
SCREAMING_SNAKE_CASE : Any = 0
# If the last token is a CJK character, we print the characters.
elif len(_lowerCamelCase ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
SCREAMING_SNAKE_CASE : Tuple = text[self.print_len :]
self.print_len += len(_lowerCamelCase )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
SCREAMING_SNAKE_CASE : Tuple = text[self.print_len : text.rfind(''' ''' ) + 1]
self.print_len += len(_lowerCamelCase )
self.on_finalized_text(_lowerCamelCase )
def __lowerCAmelCase ( self ) ->Any:
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
SCREAMING_SNAKE_CASE : Any = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
SCREAMING_SNAKE_CASE : Any = text[self.print_len :]
SCREAMING_SNAKE_CASE : Optional[int] = []
SCREAMING_SNAKE_CASE : List[Any] = 0
else:
SCREAMING_SNAKE_CASE : Any = ''''''
SCREAMING_SNAKE_CASE : List[Any] = True
self.on_finalized_text(_lowerCamelCase , stream_end=_lowerCamelCase )
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase = False ) ->Union[str, Any]:
print(_lowerCamelCase , flush=_lowerCamelCase , end='''''' if not stream_end else None )
def __lowerCAmelCase ( self , _lowerCamelCase ) ->Optional[int]:
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4_e00 and cp <= 0x9_fff)
or (cp >= 0x3_400 and cp <= 0x4_dbf) #
or (cp >= 0x20_000 and cp <= 0x2a_6df) #
or (cp >= 0x2a_700 and cp <= 0x2b_73f) #
or (cp >= 0x2b_740 and cp <= 0x2b_81f) #
or (cp >= 0x2b_820 and cp <= 0x2c_eaf) #
or (cp >= 0xf_900 and cp <= 0xf_aff)
or (cp >= 0x2f_800 and cp <= 0x2f_a1f) #
): #
return True
return False
class a_ ( a__ ):
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase = False , _lowerCamelCase = None , **_lowerCamelCase ) ->List[str]:
super().__init__(_lowerCamelCase , _lowerCamelCase , **_lowerCamelCase )
SCREAMING_SNAKE_CASE : Optional[int] = Queue()
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : Optional[Any] = timeout
def __lowerCAmelCase ( self , _lowerCamelCase , _lowerCamelCase = False ) ->Any:
self.text_queue.put(_lowerCamelCase , timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal , timeout=self.timeout )
def __iter__( self ) ->List[str]:
return self
def __lowerCAmelCase ( self ) ->Dict:
SCREAMING_SNAKE_CASE : Any = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 313 |
def UpperCAmelCase_ ( __snake_case ) -> str:
"""simple docstring"""
_lowercase =0
# if input_string is "aba" than new_input_string become "a|b|a"
_lowercase =''''''
_lowercase =''''''
# append each character + "|" in new_string for range(0, length-1)
for i in input_string[: len(__snake_case ) - 1]:
new_input_string += i + "|"
# append last character
new_input_string += input_string[-1]
# we will store the starting and ending of previous furthest ending palindromic
# substring
_lowercase , _lowercase =0, 0
# length[i] shows the length of palindromic substring with center i
_lowercase =[1 for i in range(len(__snake_case ) )]
# for each character in new_string find corresponding palindromic string
_lowercase =0
for j in range(len(__snake_case ) ):
_lowercase =1 if j > r else min(length[l + r - j] // 2 , r - j + 1 )
while (
j - k >= 0
and j + k < len(__snake_case )
and new_input_string[k + j] == new_input_string[j - k]
):
k += 1
_lowercase =2 * k - 1
# does this string is ending after the previously explored end (that is r) ?
# if yes the update the new r to the last index of this
if j + k - 1 > r:
_lowercase =j - k + 1 # noqa: E741
_lowercase =j + k - 1
# update max_length and start position
if max_length < length[j]:
_lowercase =length[j]
_lowercase =j
# create that string
_lowercase =new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
for i in s:
if i != "|":
output_string += i
return output_string
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def A (__A : Union[str, Any] , __A : Dict , __A : Dict , __A : int , ) -> list[float]:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = coefficient_matrix.shape
UpperCAmelCase_ , UpperCAmelCase_ = constant_matrix.shape
if rowsa != colsa:
UpperCAmelCase_ = F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
raise ValueError(__snake_case )
if colsa != 1:
UpperCAmelCase_ = F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
raise ValueError(__snake_case )
if rowsa != rowsa:
UpperCAmelCase_ = (
'''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
)
raise ValueError(__snake_case )
if len(__snake_case ) != rowsa:
UpperCAmelCase_ = (
'''Number of initial values must be equal to number of rows in coefficient '''
F"""matrix but received {len(__snake_case )} and {rowsa}"""
)
raise ValueError(__snake_case )
if iterations <= 0:
raise ValueError('''Iterations must be at least 1''' )
UpperCAmelCase_ = np.concatenate(
(coefficient_matrix, constant_matrix) , axis=1 )
UpperCAmelCase_ , UpperCAmelCase_ = table.shape
strictly_diagonally_dominant(__snake_case )
# Iterates the whole matrix for given number of times
for _ in range(__snake_case ):
UpperCAmelCase_ = []
for row in range(__snake_case ):
UpperCAmelCase_ = 0
for col in range(__snake_case ):
if col == row:
UpperCAmelCase_ = table[row][col]
elif col == cols - 1:
UpperCAmelCase_ = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
UpperCAmelCase_ = (temp + val) / denom
new_val.append(__snake_case )
UpperCAmelCase_ = new_val
return [float(__snake_case ) for i in new_val]
def A (__A : Union[str, Any] ) -> bool:
"""simple docstring"""
UpperCAmelCase_ , UpperCAmelCase_ = table.shape
UpperCAmelCase_ = True
for i in range(0 , __snake_case ):
UpperCAmelCase_ = 0
for j in range(0 , cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError('''Coefficient matrix is not strictly diagonally dominant''' )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 51 |
from math import isqrt
def UpperCAmelCase_ ( __snake_case ) -> list[int]:
"""simple docstring"""
_lowercase =[True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __snake_case , __snake_case ):
_lowercase =False
return [i for i in range(2 , __snake_case ) if is_prime[i]]
def UpperCAmelCase_ ( __snake_case = 10**8 ) -> int:
"""simple docstring"""
_lowercase =calculate_prime_numbers(max_number // 2 )
_lowercase =0
_lowercase =0
_lowercase =len(__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() = }''')
| 5 | 0 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class A_ ( SCREAMING_SNAKE_CASE , unittest.TestCase ):
_UpperCAmelCase : List[Any] = ProphetNetTokenizer
_UpperCAmelCase : Any = False
def lowerCAmelCase ( self : Optional[int]):
super().setUp()
__lowerCamelCase : Union[str, Any] = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__lowerCamelCase : str = 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]))
def lowerCAmelCase ( self : Tuple ,SCREAMING_SNAKE_CASE__ : Tuple):
__lowerCamelCase : Union[str, Any] = 'UNwant\u00E9d,running'
__lowerCamelCase : Dict = 'unwanted, running'
return input_text, output_text
def lowerCAmelCase ( self : str):
__lowerCamelCase : List[Any] = self.tokenizer_class(self.vocab_file)
__lowerCamelCase : Dict = tokenizer.tokenize('UNwant\u00E9d,running')
self.assertListEqual(SCREAMING_SNAKE_CASE__ ,['un', '##want', '##ed', ',', 'runn', '##ing'])
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__) ,[9, 6, 7, 1_2, 1_0, 1_1])
def lowerCAmelCase ( self : Any):
__lowerCamelCase : Tuple = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz') ,['ah', '\u535A', '\u63A8', 'zz'])
def lowerCAmelCase ( self : str):
__lowerCamelCase : str = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__)
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ') ,['hello', '!', 'how', 'are', 'you', '?'])
self.assertListEqual(tokenizer.tokenize('H\u00E9llo') ,['hello'])
def lowerCAmelCase ( self : Union[str, Any]):
__lowerCamelCase : Tuple = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ ,strip_accents=SCREAMING_SNAKE_CASE__)
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ') ,['hällo', '!', 'how', 'are', 'you', '?'])
self.assertListEqual(tokenizer.tokenize('H\u00E9llo') ,['h\u00E9llo'])
def lowerCAmelCase ( self : Dict):
__lowerCamelCase : Tuple = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ ,strip_accents=SCREAMING_SNAKE_CASE__)
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ') ,['hallo', '!', 'how', 'are', 'you', '?'])
self.assertListEqual(tokenizer.tokenize('H\u00E9llo') ,['hello'])
def lowerCAmelCase ( self : Optional[Any]):
__lowerCamelCase : Any = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__)
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ') ,['hallo', '!', 'how', 'are', 'you', '?'])
self.assertListEqual(tokenizer.tokenize('H\u00E9llo') ,['hello'])
def lowerCAmelCase ( self : Optional[Any]):
__lowerCamelCase : Optional[int] = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__)
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ') ,['HeLLo', '!', 'how', 'Are', 'yoU', '?'])
def lowerCAmelCase ( self : str):
__lowerCamelCase : Union[str, Any] = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ ,strip_accents=SCREAMING_SNAKE_CASE__)
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ') ,['HäLLo', '!', 'how', 'Are', 'yoU', '?'])
def lowerCAmelCase ( self : Tuple):
__lowerCamelCase : Optional[Any] = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ ,strip_accents=SCREAMING_SNAKE_CASE__)
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ') ,['HaLLo', '!', 'how', 'Are', 'yoU', '?'])
def lowerCAmelCase ( self : List[Any]):
__lowerCamelCase : Optional[Any] = BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__ ,never_split=['[UNK]'])
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]') ,['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'])
def lowerCAmelCase ( self : Any):
__lowerCamelCase : Any = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
__lowerCamelCase : List[Any] = {}
for i, token in enumerate(SCREAMING_SNAKE_CASE__):
__lowerCamelCase : List[str] = i
__lowerCamelCase : Union[str, Any] = WordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE__ ,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'])
@require_torch
def lowerCAmelCase ( self : Union[str, Any]):
__lowerCamelCase : Tuple = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased')
__lowerCamelCase : int = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__lowerCamelCase : Tuple = [1_0_3_7, 2_1_4_6, 2_0_4_2_3, 2_0_0_5, 7_6_8_0, 7_8_4_9, 3_9_8_9, 1_0_1_2, 1_0_2]
__lowerCamelCase : str = tokenizer(SCREAMING_SNAKE_CASE__ ,padding=SCREAMING_SNAKE_CASE__ ,return_tensors='pt')
self.assertIsInstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[str] = list(batch.input_ids.numpy()[0])
self.assertListEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
self.assertEqual((2, 9) ,batch.input_ids.shape)
self.assertEqual((2, 9) ,batch.attention_mask.shape)
def lowerCAmelCase ( 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 lowerCAmelCase ( 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 lowerCAmelCase ( self : List[str]):
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(' '))
@slow
def lowerCAmelCase ( self : Optional[Any]):
__lowerCamelCase : List[str] = self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased')
__lowerCamelCase : Optional[int] = tokenizer.encode('sequence builders' ,add_special_tokens=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[Any] = tokenizer.encode('multi-sequence build' ,add_special_tokens=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Any = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Any = tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
assert encoded_sentence == text + [1_0_2]
assert encoded_pair == text + [1_0_2] + text_a + [1_0_2]
| 73 |
UpperCAmelCase__ = {
'''A''': '''.-''', '''B''': '''-...''', '''C''': '''-.-.''', '''D''': '''-..''', '''E''': '''.''', '''F''': '''..-.''', '''G''': '''--.''',
'''H''': '''....''', '''I''': '''..''', '''J''': '''.---''', '''K''': '''-.-''', '''L''': '''.-..''', '''M''': '''--''', '''N''': '''-.''',
'''O''': '''---''', '''P''': '''.--.''', '''Q''': '''--.-''', '''R''': '''.-.''', '''S''': '''...''', '''T''': '''-''', '''U''': '''..-''',
'''V''': '''...-''', '''W''': '''.--''', '''X''': '''-..-''', '''Y''': '''-.--''', '''Z''': '''--..''', '''1''': '''.----''',
'''2''': '''..---''', '''3''': '''...--''', '''4''': '''....-''', '''5''': '''.....''', '''6''': '''-....''', '''7''': '''--...''',
'''8''': '''---..''', '''9''': '''----.''', '''0''': '''-----''', '''&''': '''.-...''', '''@''': '''.--.-.''',
''':''': '''---...''', ''',''': '''--..--''', '''.''': '''.-.-.-''', '''\'''': '''.----.''', '''"''': '''.-..-.''',
'''?''': '''..--..''', '''/''': '''-..-.''', '''=''': '''-...-''', '''+''': '''.-.-.''', '''-''': '''-....-''',
'''(''': '''-.--.''', ''')''': '''-.--.-''', '''!''': '''-.-.--''', ''' ''': '''/'''
} # Exclamation mark is not in ITU-R recommendation
# fmt: on
UpperCAmelCase__ = {value: key for key, value in MORSE_CODE_DICT.items()}
def UpperCAmelCase_ ( __snake_case ) -> str:
"""simple docstring"""
return " ".join(MORSE_CODE_DICT[char] for char in message.upper() )
def UpperCAmelCase_ ( __snake_case ) -> str:
"""simple docstring"""
return "".join(REVERSE_DICT[char] for char in message.split() )
def UpperCAmelCase_ ( ) -> None:
"""simple docstring"""
_lowercase ='''Morse code here!'''
print(__snake_case )
_lowercase =encrypt(__snake_case )
print(__snake_case )
_lowercase =decrypt(__snake_case )
print(__snake_case )
if __name__ == "__main__":
main()
| 5 | 0 |
_A : str = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_A : Tuple = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> list[int]:
"""simple docstring"""
lowerCamelCase__ : Tuple = True
lowerCamelCase__ : Optional[int] = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(__snake_case , __snake_case , __snake_case )
order.append(__snake_case )
return order
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> list[int]:
"""simple docstring"""
lowerCamelCase__ : Optional[int] = True
lowerCamelCase__ : Optional[int] = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(__snake_case , __snake_case , __snake_case )
return component
def _a ( UpperCAmelCase ) -> list[list[int]]:
"""simple docstring"""
lowerCamelCase__ : Tuple = len(__snake_case ) * [False]
lowerCamelCase__ : Tuple = {vert: [] for vert in range(len(__snake_case ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(__snake_case )
lowerCamelCase__ : Dict = []
for i, was_visited in enumerate(__snake_case ):
if not was_visited:
order += topology_sort(__snake_case , __snake_case , __snake_case )
lowerCamelCase__ : Any = []
lowerCamelCase__ : Optional[int] = len(__snake_case ) * [False]
for i in range(len(__snake_case ) ):
lowerCamelCase__ : Dict = order[len(__snake_case ) - i - 1]
if not visited[vert]:
lowerCamelCase__ : Union[str, Any] = find_components(__snake_case , __snake_case , __snake_case )
components_list.append(__snake_case )
return components_list
| 142 |
from typing import Any
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> list:
"""simple docstring"""
_validation(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
# Creates data structures and fill initial step
_lowercase ={}
_lowercase ={}
for state in states_space:
_lowercase =observations_space[0]
_lowercase =(
initial_probabilities[state] * emission_probabilities[state][observation]
)
_lowercase =None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(__snake_case ) ):
_lowercase =observations_space[o]
_lowercase =observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
_lowercase =''''''
_lowercase =-1
for k_state in states_space:
_lowercase =(
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
_lowercase =probability
_lowercase =k_state
# Update probabilities and pointers dicts
_lowercase =(
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
_lowercase =arg_max
# The final observation
_lowercase =observations_space[len(__snake_case ) - 1]
# argmax for given final observation
_lowercase =''''''
_lowercase =-1
for k_state in states_space:
_lowercase =probabilities[(k_state, final_observation)]
if probability > max_probability:
_lowercase =probability
_lowercase =k_state
_lowercase =arg_max
# Process pointers backwards
_lowercase =last_state
_lowercase =[]
for o in range(len(__snake_case ) - 1 , -1 , -1 ):
result.append(__snake_case )
_lowercase =pointers[previous, observations_space[o]]
result.reverse()
return result
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> None:
"""simple docstring"""
_validate_not_empty(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
_validate_lists(__snake_case , __snake_case )
_validate_dicts(
__snake_case , __snake_case , __snake_case )
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , ) -> None:
"""simple docstring"""
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError('''There\'s an empty parameter''' )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> None:
"""simple docstring"""
_validate_list(__snake_case , '''observations_space''' )
_validate_list(__snake_case , '''states_space''' )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> None:
"""simple docstring"""
if not isinstance(_object , __snake_case ):
_lowercase =F"{var_name} must be a list"
raise ValueError(__snake_case )
else:
for x in _object:
if not isinstance(__snake_case , __snake_case ):
_lowercase =F"{var_name} must be a list of strings"
raise ValueError(__snake_case )
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , ) -> None:
"""simple docstring"""
_validate_dict(__snake_case , '''initial_probabilities''' , __snake_case )
_validate_nested_dict(__snake_case , '''transition_probabilities''' )
_validate_nested_dict(__snake_case , '''emission_probabilities''' )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> None:
"""simple docstring"""
_validate_dict(_object , __snake_case , __snake_case )
for x in _object.values():
_validate_dict(__snake_case , __snake_case , __snake_case , __snake_case )
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case = False ) -> None:
"""simple docstring"""
if not isinstance(_object , __snake_case ):
_lowercase =F"{var_name} must be a dict"
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object ):
_lowercase =F"{var_name} all keys must be strings"
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object.values() ):
_lowercase ='''nested dictionary ''' if nested else ''''''
_lowercase =F"{var_name} {nested_text}all values must be {value_type.__name__}"
raise ValueError(__snake_case )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 5 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = ['''flax''', '''transformers''']
def __init__( self : Dict , *lowerCAmelCase__ : str , **lowerCAmelCase__ : Union[str, Any] ) -> int:
"""simple docstring"""
requires_backends(self , ["flax", "transformers"] )
@classmethod
def _lowerCAmelCase ( cls : List[Any] , *lowerCAmelCase__ : int , **lowerCAmelCase__ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
@classmethod
def _lowerCAmelCase ( cls : Optional[int] , *lowerCAmelCase__ : Optional[int] , **lowerCAmelCase__ : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
class A__ ( metaclass=UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Dict = ['''flax''', '''transformers''']
def __init__( self : str , *lowerCAmelCase__ : List[Any] , **lowerCAmelCase__ : Tuple ) -> Tuple:
"""simple docstring"""
requires_backends(self , ["flax", "transformers"] )
@classmethod
def _lowerCAmelCase ( cls : Union[str, Any] , *lowerCAmelCase__ : List[Any] , **lowerCAmelCase__ : Dict ) -> List[str]:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
@classmethod
def _lowerCAmelCase ( cls : List[str] , *lowerCAmelCase__ : str , **lowerCAmelCase__ : List[Any] ) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
class A__ ( metaclass=UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Tuple = ['''flax''', '''transformers''']
def __init__( self : Any , *lowerCAmelCase__ : List[str] , **lowerCAmelCase__ : Tuple ) -> str:
"""simple docstring"""
requires_backends(self , ["flax", "transformers"] )
@classmethod
def _lowerCAmelCase ( cls : Any , *lowerCAmelCase__ : Tuple , **lowerCAmelCase__ : int ) -> str:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
@classmethod
def _lowerCAmelCase ( cls : Optional[Any] , *lowerCAmelCase__ : str , **lowerCAmelCase__ : List[str] ) -> int:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
class A__ ( metaclass=UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : List[Any] = ['''flax''', '''transformers''']
def __init__( self : Optional[int] , *lowerCAmelCase__ : Dict , **lowerCAmelCase__ : Tuple ) -> Tuple:
"""simple docstring"""
requires_backends(self , ["flax", "transformers"] )
@classmethod
def _lowerCAmelCase ( cls : Optional[Any] , *lowerCAmelCase__ : List[Any] , **lowerCAmelCase__ : Optional[int] ) -> int:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] )
@classmethod
def _lowerCAmelCase ( cls : Tuple , *lowerCAmelCase__ : Optional[int] , **lowerCAmelCase__ : Tuple ) -> int:
"""simple docstring"""
requires_backends(cls , ["flax", "transformers"] ) | 145 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
# TODO Update this
UpperCAmelCase__ = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class lowerCamelCase__ ( lowerCAmelCase):
SCREAMING_SNAKE_CASE__ = '''esm'''
def __init__(self , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=7_6_8 , UpperCAmelCase=1_2 , UpperCAmelCase=1_2 , UpperCAmelCase=3_0_7_2 , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=1_0_2_6 , UpperCAmelCase=0.02 , UpperCAmelCase=1e-12 , UpperCAmelCase="absolute" , UpperCAmelCase=True , UpperCAmelCase=None , UpperCAmelCase=False , UpperCAmelCase=False , UpperCAmelCase=None , UpperCAmelCase=None , **UpperCAmelCase , ) -> Tuple:
super().__init__(pad_token_id=UpperCAmelCase , mask_token_id=UpperCAmelCase , **UpperCAmelCase )
_lowercase =vocab_size
_lowercase =hidden_size
_lowercase =num_hidden_layers
_lowercase =num_attention_heads
_lowercase =intermediate_size
_lowercase =hidden_dropout_prob
_lowercase =attention_probs_dropout_prob
_lowercase =max_position_embeddings
_lowercase =initializer_range
_lowercase =layer_norm_eps
_lowercase =position_embedding_type
_lowercase =use_cache
_lowercase =emb_layer_norm_before
_lowercase =token_dropout
_lowercase =is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info('''No esmfold_config supplied for folding model, using default values.''' )
_lowercase =EsmFoldConfig()
elif isinstance(UpperCAmelCase , UpperCAmelCase ):
_lowercase =EsmFoldConfig(**UpperCAmelCase )
_lowercase =esmfold_config
if vocab_list is None:
logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!''' )
_lowercase =get_default_vocab_list()
else:
_lowercase =vocab_list
else:
_lowercase =None
_lowercase =None
if self.esmfold_config is not None and getattr(self.esmfold_config , '''use_esm_attn_map''' , UpperCAmelCase ):
raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!''' )
def __A (self ) -> List[str]:
_lowercase =super().to_dict()
if isinstance(self.esmfold_config , UpperCAmelCase ):
_lowercase =self.esmfold_config.to_dict()
return output
@dataclass
class lowerCamelCase__ :
SCREAMING_SNAKE_CASE__ = None
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = True
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = None
def __A (self ) -> Union[str, Any]:
if self.trunk is None:
_lowercase =TrunkConfig()
elif isinstance(self.trunk , UpperCAmelCase ):
_lowercase =TrunkConfig(**self.trunk )
def __A (self ) -> Tuple:
_lowercase =asdict(self )
_lowercase =self.trunk.to_dict()
return output
@dataclass
class lowerCamelCase__ :
SCREAMING_SNAKE_CASE__ = 48
SCREAMING_SNAKE_CASE__ = 1024
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = 32
SCREAMING_SNAKE_CASE__ = 32
SCREAMING_SNAKE_CASE__ = 32
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = 4
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = None
def __A (self ) -> List[str]:
if self.structure_module is None:
_lowercase =StructureModuleConfig()
elif isinstance(self.structure_module , UpperCAmelCase ):
_lowercase =StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f"`max_recycles` should be positive, got {self.max_recycles}." )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
'''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
f" {self.sequence_state_dim} and {self.sequence_state_dim}." )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
'''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
f" {self.pairwise_state_dim} and {self.pairwise_state_dim}." )
_lowercase =self.sequence_state_dim // self.sequence_head_width
_lowercase =self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
'''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}." )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
'''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}." )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}." )
if self.dropout >= 0.4:
raise ValueError(f"`dropout` should not be greater than 0.4, got {self.dropout}." )
def __A (self ) -> Dict:
_lowercase =asdict(self )
_lowercase =self.structure_module.to_dict()
return output
@dataclass
class lowerCamelCase__ :
SCREAMING_SNAKE_CASE__ = 384
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = 16
SCREAMING_SNAKE_CASE__ = 128
SCREAMING_SNAKE_CASE__ = 12
SCREAMING_SNAKE_CASE__ = 4
SCREAMING_SNAKE_CASE__ = 8
SCREAMING_SNAKE_CASE__ = 0.1
SCREAMING_SNAKE_CASE__ = 8
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 2
SCREAMING_SNAKE_CASE__ = 7
SCREAMING_SNAKE_CASE__ = 10
SCREAMING_SNAKE_CASE__ = 1E-8
SCREAMING_SNAKE_CASE__ = 1E5
def __A (self ) -> List[Any]:
return asdict(self )
def UpperCAmelCase_ ( ) -> Tuple:
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 5 | 0 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : Union[str, Any] , A__ : List[str] ):
'''simple docstring'''
lowerCAmelCase_ : Any = len(__snake_case ) + 1
lowerCAmelCase_ : List[Any] = len(__snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
lowerCAmelCase_ : Tuple = [[0 for i in range(__snake_case )] for j in range(__snake_case )]
# since string of zero length match pattern of zero length
lowerCAmelCase_ : str = 1
# since pattern of zero length will never match with string of non-zero length
for i in range(1 , __snake_case ):
lowerCAmelCase_ : str = 0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1 , __snake_case ):
lowerCAmelCase_ : List[Any] = dp[0][j - 2] if pattern[j - 1] == """*""" else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1 , __snake_case ):
for j in range(1 , __snake_case ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
lowerCAmelCase_ : str = dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
lowerCAmelCase_ : List[str] = 1
elif pattern[j - 2] in (input_string[i - 1], "."):
lowerCAmelCase_ : Optional[int] = dp[i - 1][j]
else:
lowerCAmelCase_ : Any = 0
else:
lowerCAmelCase_ : Dict = 0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
__A : Union[str, Any] = "aab"
__A : int = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F'''{input_string} matches the given pattern {pattern}''')
else:
print(F'''{input_string} does not match with the given pattern {pattern}''')
| 120 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
UpperCAmelCase__ = ['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']
class lowerCamelCase__ ( lowerCAmelCase):
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=1 ) -> Dict:
_lowercase =tokenizer
_lowercase =dataset
_lowercase =len(UpperCAmelCase ) if n_tasks is None else n_tasks
_lowercase =n_copies
def __iter__(self ) -> Optional[Any]:
_lowercase =[]
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip() )
_lowercase =self.tokenizer(UpperCAmelCase , padding=UpperCAmelCase , return_tensors='''pt''' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class lowerCamelCase__ ( lowerCAmelCase):
def __init__(self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
_lowercase =start_length
_lowercase =eof_strings
_lowercase =tokenizer
def __call__(self , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ) -> Dict:
_lowercase =self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
_lowercase =[]
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(UpperCAmelCase )
def UpperCAmelCase_ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
_lowercase =re.split('''(%s)''' % '''|'''.join(__snake_case ) , __snake_case )
# last string should be ""
return "".join(string_list[:-2] )
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case=20 , **__snake_case ) -> Tuple:
"""simple docstring"""
_lowercase =defaultdict(__snake_case ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(__snake_case ) ):
with torch.no_grad():
_lowercase =batch['''ids'''].shape[-1]
_lowercase =accelerator.unwrap_model(__snake_case ).generate(
input_ids=batch['''ids'''][:, : batch['''input_len''']] , num_return_sequences=__snake_case , **__snake_case )
# each task is generated batch_size times
_lowercase =batch['''task_id'''].repeat(__snake_case )
_lowercase =accelerator.pad_across_processes(
__snake_case , dim=1 , pad_index=tokenizer.pad_token_id )
_lowercase , _lowercase =accelerator.gather((generated_tokens, generated_tasks) )
_lowercase =generated_tokens.cpu().numpy()
_lowercase =generated_tasks.cpu().numpy()
for task, generated_tokens in zip(__snake_case , __snake_case ):
gen_token_dict[task].append(__snake_case )
_lowercase =[[] for _ in range(__snake_case )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
_lowercase =tokenizer.decode(__snake_case , skip_special_tokens=__snake_case , clean_up_tokenization_spaces=__snake_case )
code_gens[task].append(remove_last_block(__snake_case ) )
return code_gens
def UpperCAmelCase_ ( ) -> str:
"""simple docstring"""
_lowercase =HfArgumentParser(__snake_case )
_lowercase =parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
_lowercase =args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
_lowercase ='''false'''
if args.num_workers is None:
_lowercase =multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
_lowercase =Accelerator()
set_seed(args.seed , device_specific=__snake_case )
# Load model and tokenizer
_lowercase =AutoTokenizer.from_pretrained(args.model_ckpt )
_lowercase =tokenizer.eos_token
_lowercase =AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
_lowercase ={
'''do_sample''': args.do_sample,
'''temperature''': args.temperature,
'''max_new_tokens''': args.max_new_tokens,
'''top_p''': args.top_p,
'''top_k''': args.top_k,
'''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0 , __snake_case , __snake_case )] ),
}
# Load evaluation dataset and metric
_lowercase =load_dataset('''openai_humaneval''' )
_lowercase =load_metric('''code_eval''' )
_lowercase =args.num_tasks if args.num_tasks is not None else len(human_eval['''test'''] )
_lowercase =args.n_samples // args.batch_size
_lowercase =TokenizedDataset(__snake_case , human_eval['''test'''] , n_copies=__snake_case , n_tasks=__snake_case )
# do not confuse args.batch_size, which is actually the num_return_sequences
_lowercase =DataLoader(__snake_case , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
_lowercase =code_eval_metric.compute(references=[''''''] , predictions=[['''''']] )
except ValueError as exception:
print(
'''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
''' flag to enable code evaluation.''' )
raise exception
_lowercase , _lowercase =accelerator.prepare(__snake_case , __snake_case )
_lowercase =complete_code(
__snake_case , __snake_case , __snake_case , __snake_case , n_tasks=__snake_case , batch_size=args.batch_size , **__snake_case , )
if accelerator.is_main_process:
_lowercase =[]
for task in tqdm(range(__snake_case ) ):
_lowercase =human_eval['''test'''][task]['''test''']
_lowercase =F"check({human_eval['test'][task]['entry_point']})"
references.append('''\n''' + test_func + '''\n''' + entry_point )
# Evaluate completions with "code_eval" metric
_lowercase , _lowercase =code_eval_metric.compute(
references=__snake_case , predictions=__snake_case , num_workers=args.num_workers )
print(F"Results: {pass_at_k}" )
# Save results to json file
with open(args.output_file , '''w''' ) as fp:
json.dump(__snake_case , __snake_case )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 5 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
__UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
__UpperCAmelCase : List[Any] = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
"self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
"self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
"self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
"self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
"self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
"self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
"self_attn.rotary_emb": "encoder.embed_positions",
"self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
"conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
"conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
"conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
"conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
"conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
"ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
"ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
"ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
"ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
"ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
"ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
__UpperCAmelCase : Any = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Dict:
for attribute in key.split("""."""):
__snake_case: str = getattr(__snake_case , __snake_case)
if weight_type is not None:
__snake_case: Tuple = getattr(__snake_case , __snake_case).shape
else:
__snake_case: List[Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}''')
if weight_type == "weight":
__snake_case: int = value
elif weight_type == "weight_g":
__snake_case: Optional[int] = value
elif weight_type == "weight_v":
__snake_case: List[Any] = value
elif weight_type == "bias":
__snake_case: str = value
elif weight_type == "running_mean":
__snake_case: int = value
elif weight_type == "running_var":
__snake_case: int = value
elif weight_type == "num_batches_tracked":
__snake_case: List[str] = value
elif weight_type == "inv_freq":
__snake_case: Any = value
else:
__snake_case: Tuple = value
logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''')
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> List[str]:
__snake_case: Dict = []
__snake_case: str = fairseq_model.state_dict()
__snake_case: int = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
__snake_case: str = False
if "conv_layers" in name:
load_conv_layer(
__snake_case , __snake_case , __snake_case , __snake_case , hf_model.config.feat_extract_norm == """group""" , )
__snake_case: Tuple = True
else:
for key, mapped_key in MAPPING.items():
__snake_case: Optional[Any] = """wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""")[-1] == name.split(""".""")[0]:
__snake_case: List[str] = True
if "*" in mapped_key:
__snake_case: Dict = name.split(__snake_case)[0].split(""".""")[-2]
__snake_case: Optional[Any] = mapped_key.replace("""*""" , __snake_case)
if "pos_bias_u" in name:
__snake_case: Any = None
elif "pos_bias_v" in name:
__snake_case: int = None
elif "weight_g" in name:
__snake_case: str = """weight_g"""
elif "weight_v" in name:
__snake_case: int = """weight_v"""
elif "bias" in name:
__snake_case: List[Any] = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__snake_case: int = """weight"""
elif "running_mean" in name:
__snake_case: List[str] = """running_mean"""
elif "inv_freq" in name:
__snake_case: List[Any] = """inv_freq"""
elif "running_var" in name:
__snake_case: Any = """running_var"""
elif "num_batches_tracked" in name:
__snake_case: List[Any] = """num_batches_tracked"""
else:
__snake_case: Optional[Any] = None
set_recursively(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case)
continue
if not is_used:
unused_weights.append(__snake_case)
logger.warning(F'''Unused weights: {unused_weights}''')
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> str:
__snake_case: Optional[Any] = full_name.split("""conv_layers.""")[-1]
__snake_case: Optional[Any] = name.split(""".""")
__snake_case: Tuple = int(items[0])
__snake_case: Union[str, Any] = int(items[1])
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''')
__snake_case: Dict = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''')
__snake_case: int = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''')
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''')
__snake_case: Union[str, Any] = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''')
__snake_case: Optional[int] = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''')
else:
unused_weights.append(__snake_case)
@torch.no_grad()
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=None , SCREAMING_SNAKE_CASE__=True) -> str:
if config_path is not None:
__snake_case: Optional[int] = WavaVecaConformerConfig.from_pretrained(__snake_case , hidden_act="""swish""")
else:
__snake_case: List[Any] = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
__snake_case: int = """rotary"""
if is_finetuned:
if dict_path:
__snake_case: List[str] = Dictionary.load(__snake_case)
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__snake_case: int = target_dict.pad_index
__snake_case: Optional[Any] = target_dict.bos_index
__snake_case: Dict = target_dict.eos_index
__snake_case: Optional[Any] = len(target_dict.symbols)
__snake_case: int = os.path.join(__snake_case , """vocab.json""")
if not os.path.isdir(__snake_case):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__snake_case))
return
os.makedirs(__snake_case , exist_ok=__snake_case)
__snake_case: int = target_dict.indices
# fairseq has the <pad> and <s> switched
__snake_case: Dict = 0
__snake_case: Dict = 1
with open(__snake_case , """w""" , encoding="""utf-8""") as vocab_handle:
json.dump(__snake_case , __snake_case)
__snake_case: Optional[Any] = WavaVecaCTCTokenizer(
__snake_case , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=__snake_case , )
__snake_case: List[str] = True if config.feat_extract_norm == """layer""" else False
__snake_case: List[str] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=__snake_case , return_attention_mask=__snake_case , )
__snake_case: str = WavaVecaProcessor(feature_extractor=__snake_case , tokenizer=__snake_case)
processor.save_pretrained(__snake_case)
__snake_case: List[str] = WavaVecaConformerForCTC(__snake_case)
else:
__snake_case: Union[str, Any] = WavaVecaConformerForPreTraining(__snake_case)
if is_finetuned:
__snake_case , __snake_case , __snake_case: Any = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""")[:-1])})
else:
__snake_case: Optional[Any] = argparse.Namespace(task="""audio_pretraining""")
__snake_case: Union[str, Any] = fairseq.tasks.setup_task(__snake_case)
__snake_case , __snake_case , __snake_case: Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=__snake_case)
__snake_case: str = model[0].eval()
recursively_load_weights(__snake_case , __snake_case , not is_finetuned)
hf_wavavec.save_pretrained(__snake_case)
if __name__ == "__main__":
__UpperCAmelCase : Dict = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
__UpperCAmelCase : int = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 111 |
UpperCAmelCase__ = 8.31_44_62 # Unit - J mol-1 K-1
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 5 | 0 |
import unittest
from transformers import MPNetConfig, is_torch_available
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 (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class __lowerCAmelCase :
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=1_3 , lowerCAmelCase__=7 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=False , lowerCAmelCase__=True , lowerCAmelCase__=9_9 , lowerCAmelCase__=6_4 , lowerCAmelCase__=5 , lowerCAmelCase__=4 , lowerCAmelCase__=6_4 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=5_1_2 , lowerCAmelCase__=1_6 , lowerCAmelCase__=2 , lowerCAmelCase__=0.02 , lowerCAmelCase__=3 , lowerCAmelCase__=4 , lowerCAmelCase__=None , ) -> Union[str, Any]:
'''simple docstring'''
a__ : int =parent
a__ : List[str] =batch_size
a__ : int =seq_length
a__ : int =is_training
a__ : List[str] =use_input_mask
a__ : List[str] =use_token_type_ids
a__ : str =use_labels
a__ : Union[str, Any] =vocab_size
a__ : Dict =hidden_size
a__ : Union[str, Any] =num_hidden_layers
a__ : Tuple =num_attention_heads
a__ : Any =intermediate_size
a__ : Tuple =hidden_act
a__ : str =hidden_dropout_prob
a__ : List[str] =attention_probs_dropout_prob
a__ : int =max_position_embeddings
a__ : List[Any] =type_vocab_size
a__ : List[str] =type_sequence_label_size
a__ : Dict =initializer_range
a__ : int =num_labels
a__ : Any =num_choices
a__ : str =scope
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
return MPNetConfig.from_pretrained("microsoft/mpnet-base" )
def _lowercase ( self ) -> Dict:
'''simple docstring'''
a__ : Tuple =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a__ : Optional[int] =None
if self.use_input_mask:
a__ : str =random_attention_mask([self.batch_size, self.seq_length] )
a__ : str =None
a__ : Tuple =None
a__ : int =None
if self.use_labels:
a__ : Any =ids_tensor([self.batch_size] , self.type_sequence_label_size )
a__ : Optional[Any] =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a__ : int =ids_tensor([self.batch_size] , self.num_choices )
a__ : Dict =self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowercase ( self ) -> str:
'''simple docstring'''
return MPNetConfig(
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 , initializer_range=self.initializer_range , )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
a__ : List[Any] =MPNetModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a__ : Dict =model(lowerCAmelCase__ , lowerCAmelCase__ )
a__ : int =model(lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]:
'''simple docstring'''
a__ : Any =MPNetForQuestionAnswering(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a__ : Dict =model(
lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , start_positions=lowerCAmelCase__ , end_positions=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 _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
a__ : int =self.num_labels
a__ : Optional[int] =MPNetForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a__ : int =model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Dict:
'''simple docstring'''
a__ : Any =self.num_choices
a__ : str =MPNetForMultipleChoice(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a__ : List[Any] =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : int =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a__ : int =model(
lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , labels=lowerCAmelCase__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
'''simple docstring'''
a__ : Optional[Any] =self.num_labels
a__ : List[Any] =MPNetForTokenClassification(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a__ : Tuple =model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
a__ : List[Any] =self.prepare_config_and_inputs()
((a__) , (a__) , (a__) , (a__) , (a__) , (a__)) : Any =config_and_inputs
a__ : Tuple ={"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , unittest.TestCase):
_lowercase : Optional[int] = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
_lowercase : Tuple = (
{
"""feature-extraction""": MPNetModel,
"""fill-mask""": MPNetForMaskedLM,
"""question-answering""": MPNetForQuestionAnswering,
"""text-classification""": MPNetForSequenceClassification,
"""token-classification""": MPNetForTokenClassification,
"""zero-shot""": MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
_lowercase : Dict = False
_lowercase : Optional[Any] = True
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ : Union[str, Any] =MPNetModelTester(self )
a__ : Any =ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=3_7 )
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def _lowercase ( self ) -> Dict:
'''simple docstring'''
a__ : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*lowerCAmelCase__ )
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
a__ : List[str] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowerCAmelCase__ )
def _lowercase ( self ) -> str:
'''simple docstring'''
a__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowerCAmelCase__ )
def _lowercase ( self ) -> Union[str, Any]:
'''simple docstring'''
a__ : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*lowerCAmelCase__ )
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
a__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*lowerCAmelCase__ )
@require_torch
class __lowerCAmelCase ( unittest.TestCase):
@slow
def _lowercase ( self ) -> str:
'''simple docstring'''
a__ : List[str] =MPNetModel.from_pretrained("microsoft/mpnet-base" )
a__ : List[Any] =torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
a__ : int =model(lowerCAmelCase__ )[0]
a__ : Tuple =torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , lowerCAmelCase__ )
a__ : Optional[Any] =torch.tensor(
[[[-0.05_50, 0.19_43, -0.07_40], [-0.05_62, 0.22_11, -0.05_79], [-0.04_37, 0.33_37, -0.06_41]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCAmelCase__ , atol=1E-4 ) )
| 95 |
from __future__ import annotations
from collections.abc import Callable
UpperCAmelCase__ = list[list[float | int]]
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> Matrix:
"""simple docstring"""
_lowercase =len(__snake_case )
_lowercase =[[0 for _ in range(size + 1 )] for _ in range(__snake_case )]
_lowercase =42
_lowercase =42
_lowercase =42
_lowercase =42
_lowercase =42
_lowercase =42
for row in range(__snake_case ):
for col in range(__snake_case ):
_lowercase =matrix[row][col]
_lowercase =vector[row][0]
_lowercase =0
_lowercase =0
while row < size and col < size:
# pivoting
_lowercase =max((abs(augmented[rowa][col] ), rowa) for rowa in range(__snake_case , __snake_case ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
_lowercase , _lowercase =augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , __snake_case ):
_lowercase =augmented[rowa][col] / augmented[row][col]
_lowercase =0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , __snake_case ):
for row in range(__snake_case ):
_lowercase =augmented[row][col] / augmented[col][col]
for cola in range(__snake_case , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 10 )] for row in range(__snake_case )
]
def UpperCAmelCase_ ( __snake_case ) -> Callable[[int], int]:
"""simple docstring"""
_lowercase =len(__snake_case )
_lowercase =[[0 for _ in range(__snake_case )] for _ in range(__snake_case )]
_lowercase =[[0] for _ in range(__snake_case )]
_lowercase =42
_lowercase =42
_lowercase =42
_lowercase =42
for x_val, y_val in enumerate(__snake_case ):
for col in range(__snake_case ):
_lowercase =(x_val + 1) ** (size - col - 1)
_lowercase =y_val
_lowercase =solve(__snake_case , __snake_case )
def interpolated_func(__snake_case ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(__snake_case ) )
return interpolated_func
def UpperCAmelCase_ ( __snake_case ) -> int:
"""simple docstring"""
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**10
)
def UpperCAmelCase_ ( __snake_case = question_function , __snake_case = 10 ) -> int:
"""simple docstring"""
_lowercase =[func(__snake_case ) for x_val in range(1 , order + 1 )]
_lowercase =[
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
_lowercase =0
_lowercase =42
_lowercase =42
for poly in polynomials:
_lowercase =1
while func(__snake_case ) == poly(__snake_case ):
x_val += 1
ret += poly(__snake_case )
return ret
if __name__ == "__main__":
print(f'''{solution() = }''')
| 5 | 0 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
_a = datasets.utils.logging.get_logger(__name__)
@dataclass
class A_ ( datasets.BuilderConfig ):
_lowercase : Optional[Any] = 1_0_0_0_0
_lowercase : Optional[Any] = None
_lowercase : Union[str, Any] = None
class A_ ( datasets.ArrowBasedBuilder ):
_lowercase : str = ParquetConfig
def UpperCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
return datasets.DatasetInfo(features=self.config.features )
def UpperCAmelCase ( self : List[str] , UpperCAmelCase : str ) -> List[str]:
if not self.config.data_files:
raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}''' )
__lowerCAmelCase: Dict = dl_manager.download_and_extract(self.config.data_files )
if isinstance(UpperCAmelCase , (str, list, tuple) ):
__lowerCAmelCase: Dict = data_files
if isinstance(UpperCAmelCase , UpperCAmelCase ):
__lowerCAmelCase: Optional[int] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
__lowerCAmelCase: int = [dl_manager.iter_files(UpperCAmelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'files': files} )]
__lowerCAmelCase: int = []
for split_name, files in data_files.items():
if isinstance(UpperCAmelCase , UpperCAmelCase ):
__lowerCAmelCase: str = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
__lowerCAmelCase: List[str] = [dl_manager.iter_files(UpperCAmelCase ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(UpperCAmelCase ):
with open(UpperCAmelCase , 'rb' ) as f:
__lowerCAmelCase: Union[str, Any] = datasets.Features.from_arrow_schema(pq.read_schema(UpperCAmelCase ) )
break
splits.append(datasets.SplitGenerator(name=UpperCAmelCase , gen_kwargs={'files': files} ) )
return splits
def UpperCAmelCase ( self : Optional[int] , UpperCAmelCase : Dict ) -> pa.Table:
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
__lowerCAmelCase: List[Any] = table_cast(UpperCAmelCase , self.info.features.arrow_schema )
return pa_table
def UpperCAmelCase ( self : Tuple , UpperCAmelCase : Any ) -> Tuple:
__lowerCAmelCase: Union[str, Any] = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
F'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\'''' )
for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase ) ):
with open(UpperCAmelCase , 'rb' ) as f:
__lowerCAmelCase: Optional[Any] = pq.ParquetFile(UpperCAmelCase )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size , columns=self.config.columns ) ):
__lowerCAmelCase: Dict = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield F'''{file_idx}_{batch_idx}''', self._cast_table(UpperCAmelCase )
except ValueError as e:
logger.error(F'''Failed to read file \'{file}\' with error {type(UpperCAmelCase )}: {e}''' )
raise
| 322 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase__ = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokenization_xlm''': ['''XLMTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
'''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLMForMultipleChoice''',
'''XLMForQuestionAnswering''',
'''XLMForQuestionAnsweringSimple''',
'''XLMForSequenceClassification''',
'''XLMForTokenClassification''',
'''XLMModel''',
'''XLMPreTrainedModel''',
'''XLMWithLMHeadModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
'''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLMForMultipleChoice''',
'''TFXLMForQuestionAnsweringSimple''',
'''TFXLMForSequenceClassification''',
'''TFXLMForTokenClassification''',
'''TFXLMMainLayer''',
'''TFXLMModel''',
'''TFXLMPreTrainedModel''',
'''TFXLMWithLMHeadModel''',
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 5 | 0 |
# Copyright 2023 The HuggingFace 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 typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__snake_case : Union[str, Any] = {
"""configuration_efficientnet""": [
"""EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EfficientNetConfig""",
"""EfficientNetOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Union[str, Any] = ["""EfficientNetImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Tuple = [
"""EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EfficientNetForImageClassification""",
"""EfficientNetModel""",
"""EfficientNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
__snake_case : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 248 |
# Copyright 2023 The HuggingFace 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 typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase__ = {
'''configuration_efficientnet''': [
'''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''EfficientNetConfig''',
'''EfficientNetOnnxConfig''',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = ['''EfficientNetImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
'''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''EfficientNetForImageClassification''',
'''EfficientNetModel''',
'''EfficientNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 5 | 0 |
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
A : Union[str, Any] = logging.get_logger(__name__)
A : List[str] = "▁"
A : Dict = {"vocab_file": "spiece.model"}
A : Tuple = {
"vocab_file": {
"google/reformer-crime-and-punishment": (
"https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
)
}
}
A : Optional[int] = {
"google/reformer-crime-and-punishment": 524288,
}
class _lowercase ( lowercase__):
"""simple docstring"""
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A__ = ["input_ids", "attention_mask"]
def __init__( self : int , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : str="</s>" , __lowerCamelCase : Dict="<unk>" , __lowerCamelCase : Optional[int]=[] , __lowerCamelCase : Optional[int] = None , **__lowerCamelCase : List[str] , ):
'''simple docstring'''
lowerCamelCase__ : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , additional_special_tokens=__lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCamelCase , )
lowerCamelCase__ : Tuple = vocab_file
lowerCamelCase__ : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__lowerCamelCase )
@property
def lowerCAmelCase ( self : Dict ):
'''simple docstring'''
return self.sp_model.get_piece_size()
def lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = {self.convert_ids_to_tokens(__lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[str] ):
'''simple docstring'''
lowerCamelCase__ : Any = self.__dict__.copy()
lowerCamelCase__ : Optional[Any] = None
return state
def __setstate__( self : Tuple , __lowerCamelCase : List[Any] ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowerCamelCase__ : Union[str, Any] = {}
lowerCamelCase__ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCAmelCase ( self : Tuple , __lowerCamelCase : List[str] ):
'''simple docstring'''
return self.sp_model.encode(__lowerCamelCase , out_type=__lowerCamelCase )
def lowerCAmelCase ( self : Optional[int] , __lowerCamelCase : List[Any] ):
'''simple docstring'''
return self.sp_model.piece_to_id(__lowerCamelCase )
def lowerCAmelCase ( self : Dict , __lowerCamelCase : Optional[int] ):
'''simple docstring'''
if index < self.sp_model.get_piece_size():
lowerCamelCase__ : Union[str, Any] = self.sp_model.IdToPiece(__lowerCamelCase )
return token
def lowerCAmelCase ( self : int , __lowerCamelCase : Any ):
'''simple docstring'''
lowerCamelCase__ : str = []
lowerCamelCase__ : List[str] = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(__lowerCamelCase ) + token
lowerCamelCase__ : List[str] = []
else:
current_sub_tokens.append(__lowerCamelCase )
out_string += self.sp_model.decode(__lowerCamelCase )
return out_string.strip()
def lowerCAmelCase ( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : List[Any] = None ):
'''simple docstring'''
if not os.path.isdir(__lowerCamelCase ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
lowerCamelCase__ : Dict = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowerCamelCase , "wb" ) as fi:
lowerCamelCase__ : Tuple = self.sp_model.serialized_model_proto()
fi.write(__lowerCamelCase )
return (out_vocab_file,)
| 184 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase__ = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase__ = [
'''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TimesformerModel''',
'''TimesformerForVideoClassification''',
'''TimesformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 5 | 0 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
a__ : Any = ['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']
class a_ ( a__ ):
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=None , _lowerCamelCase=1 ) ->Dict:
SCREAMING_SNAKE_CASE : int = tokenizer
SCREAMING_SNAKE_CASE : str = dataset
SCREAMING_SNAKE_CASE : int = len(_lowerCamelCase ) if n_tasks is None else n_tasks
SCREAMING_SNAKE_CASE : Union[str, Any] = n_copies
def __iter__( self ) ->Optional[Any]:
SCREAMING_SNAKE_CASE : str = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip() )
SCREAMING_SNAKE_CASE : List[str] = self.tokenizer(_lowerCamelCase , padding=_lowerCamelCase , return_tensors='''pt''' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class a_ ( a__ ):
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) ->Optional[Any]:
SCREAMING_SNAKE_CASE : Optional[Any] = start_length
SCREAMING_SNAKE_CASE : List[str] = eof_strings
SCREAMING_SNAKE_CASE : Dict = tokenizer
def __call__( self , _lowerCamelCase , _lowerCamelCase , **_lowerCamelCase ) ->Dict:
SCREAMING_SNAKE_CASE : Any = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
SCREAMING_SNAKE_CASE : Optional[int] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(_lowerCamelCase )
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = re.split('''(%s)''' % '''|'''.join(__snake_case ) , __snake_case )
# last string should be ""
return "".join(string_list[:-2] )
def UpperCAmelCase_( a__ , a__ , a__ , a__ , a__ , a__=20 , **a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = defaultdict(__snake_case ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(__snake_case ) ):
with torch.no_grad():
SCREAMING_SNAKE_CASE : Optional[int] = batch['''ids'''].shape[-1]
SCREAMING_SNAKE_CASE : Any = accelerator.unwrap_model(__snake_case ).generate(
input_ids=batch['''ids'''][:, : batch['''input_len''']] , num_return_sequences=__snake_case , **__snake_case )
# each task is generated batch_size times
SCREAMING_SNAKE_CASE : List[Any] = batch['''task_id'''].repeat(__snake_case )
SCREAMING_SNAKE_CASE : str = accelerator.pad_across_processes(
__snake_case , dim=1 , pad_index=tokenizer.pad_token_id )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = accelerator.gather((generated_tokens, generated_tasks) )
SCREAMING_SNAKE_CASE : Tuple = generated_tokens.cpu().numpy()
SCREAMING_SNAKE_CASE : int = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(__snake_case , __snake_case ):
gen_token_dict[task].append(__snake_case )
SCREAMING_SNAKE_CASE : Union[str, Any] = [[] for _ in range(__snake_case )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
SCREAMING_SNAKE_CASE : List[Any] = tokenizer.decode(__snake_case , skip_special_tokens=__snake_case , clean_up_tokenization_spaces=__snake_case )
code_gens[task].append(remove_last_block(__snake_case ) )
return code_gens
def UpperCAmelCase_( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = HfArgumentParser(__snake_case )
SCREAMING_SNAKE_CASE : Dict = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
SCREAMING_SNAKE_CASE : Any = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
SCREAMING_SNAKE_CASE : int = '''false'''
if args.num_workers is None:
SCREAMING_SNAKE_CASE : Optional[int] = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
SCREAMING_SNAKE_CASE : Union[str, Any] = Accelerator()
set_seed(args.seed , device_specific=__snake_case )
# Load model and tokenizer
SCREAMING_SNAKE_CASE : Any = AutoTokenizer.from_pretrained(args.model_ckpt )
SCREAMING_SNAKE_CASE : Dict = tokenizer.eos_token
SCREAMING_SNAKE_CASE : Dict = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
SCREAMING_SNAKE_CASE : str = {
'''do_sample''': args.do_sample,
'''temperature''': args.temperature,
'''max_new_tokens''': args.max_new_tokens,
'''top_p''': args.top_p,
'''top_k''': args.top_k,
'''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0 , __snake_case , __snake_case )] ),
}
# Load evaluation dataset and metric
SCREAMING_SNAKE_CASE : Optional[Any] = load_dataset('''openai_humaneval''' )
SCREAMING_SNAKE_CASE : List[Any] = load_metric('''code_eval''' )
SCREAMING_SNAKE_CASE : Any = args.num_tasks if args.num_tasks is not None else len(human_eval['''test'''] )
SCREAMING_SNAKE_CASE : List[str] = args.n_samples // args.batch_size
SCREAMING_SNAKE_CASE : Union[str, Any] = TokenizedDataset(__snake_case , human_eval['''test'''] , n_copies=__snake_case , n_tasks=__snake_case )
# do not confuse args.batch_size, which is actually the num_return_sequences
SCREAMING_SNAKE_CASE : Optional[int] = DataLoader(__snake_case , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
SCREAMING_SNAKE_CASE : str = code_eval_metric.compute(references=[''''''] , predictions=[['''''']] )
except ValueError as exception:
print(
'''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
''' flag to enable code evaluation.''' )
raise exception
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = accelerator.prepare(__snake_case , __snake_case )
SCREAMING_SNAKE_CASE : str = complete_code(
__snake_case , __snake_case , __snake_case , __snake_case , n_tasks=__snake_case , batch_size=args.batch_size , **__snake_case , )
if accelerator.is_main_process:
SCREAMING_SNAKE_CASE : Optional[Any] = []
for task in tqdm(range(__snake_case ) ):
SCREAMING_SNAKE_CASE : Any = human_eval['''test'''][task]['''test''']
SCREAMING_SNAKE_CASE : List[Any] = F"""check({human_eval["test"][task]["entry_point"]})"""
references.append('''\n''' + test_func + '''\n''' + entry_point )
# Evaluate completions with "code_eval" metric
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = code_eval_metric.compute(
references=__snake_case , predictions=__snake_case , num_workers=args.num_workers )
print(F"""Results: {pass_at_k}""" )
# Save results to json file
with open(args.output_file , '''w''' ) as fp:
json.dump(__snake_case , __snake_case )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 313 |
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> List[Any]:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__snake_case , int(b / 2 ) ) * actual_power(__snake_case , int(b / 2 ) )
else:
return a * actual_power(__snake_case , int(b / 2 ) ) * actual_power(__snake_case , int(b / 2 ) )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> float:
"""simple docstring"""
if b < 0:
return 1 / actual_power(__snake_case , __snake_case )
return actual_power(__snake_case , __snake_case )
if __name__ == "__main__":
print(power(-2, -3))
| 5 | 0 |
snake_case_ : Tuple = 8.314_462 # Unit - J mol-1 K-1
def A (__A : Any , __A : Optional[int] , __A : Any ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def A (__A : Dict , __A : List[str] , __A : int ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError('''Invalid inputs. Enter positive value.''' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 51 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCamelCase__ ( nn.Module):
def __init__(self , UpperCAmelCase = 1_6 , UpperCAmelCase = 8_8 , UpperCAmelCase = None , UpperCAmelCase = 1 , UpperCAmelCase = 0.0 , UpperCAmelCase = 3_2 , UpperCAmelCase = None , UpperCAmelCase = False , UpperCAmelCase = None , UpperCAmelCase = None , UpperCAmelCase = "geglu" , UpperCAmelCase = None , ) -> Any:
super().__init__()
_lowercase =nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=UpperCAmelCase , attention_head_dim=UpperCAmelCase , in_channels=UpperCAmelCase , num_layers=UpperCAmelCase , dropout=UpperCAmelCase , norm_num_groups=UpperCAmelCase , cross_attention_dim=UpperCAmelCase , attention_bias=UpperCAmelCase , sample_size=UpperCAmelCase , num_vector_embeds=UpperCAmelCase , activation_fn=UpperCAmelCase , num_embeds_ada_norm=UpperCAmelCase , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
_lowercase =0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
_lowercase =[7_7, 2_5_7]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
_lowercase =[1, 0]
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase=None , UpperCAmelCase = True , ) -> str:
_lowercase =hidden_states
_lowercase =[]
_lowercase =0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
_lowercase =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
_lowercase =self.transformer_index_for_condition[i]
_lowercase =self.transformers[transformer_index](
UpperCAmelCase , encoder_hidden_states=UpperCAmelCase , timestep=UpperCAmelCase , cross_attention_kwargs=UpperCAmelCase , return_dict=UpperCAmelCase , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
_lowercase =encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
_lowercase =output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=UpperCAmelCase )
| 5 | 0 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sqlalchemy
class A_ ( SCREAMING_SNAKE_CASE ):
def __init__( self : Tuple ,SCREAMING_SNAKE_CASE__ : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : List[Any] = None ,SCREAMING_SNAKE_CASE__ : List[Any] = None ,SCREAMING_SNAKE_CASE__ : Optional[int] = False ,**SCREAMING_SNAKE_CASE__ : List[str] ,):
super().__init__(features=SCREAMING_SNAKE_CASE__ ,cache_dir=SCREAMING_SNAKE_CASE__ ,keep_in_memory=SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[int] = Sql(
cache_dir=SCREAMING_SNAKE_CASE__ ,features=SCREAMING_SNAKE_CASE__ ,sql=SCREAMING_SNAKE_CASE__ ,con=SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ ,)
def lowerCAmelCase ( self : Union[str, Any]):
__lowerCamelCase : Any = None
__lowerCamelCase : int = None
__lowerCamelCase : Dict = None
__lowerCamelCase : Optional[int] = None
self.builder.download_and_prepare(
download_config=SCREAMING_SNAKE_CASE__ ,download_mode=SCREAMING_SNAKE_CASE__ ,verification_mode=SCREAMING_SNAKE_CASE__ ,base_path=SCREAMING_SNAKE_CASE__ ,)
# Build dataset for splits
__lowerCamelCase : Tuple = self.builder.as_dataset(
split='train' ,verification_mode=SCREAMING_SNAKE_CASE__ ,in_memory=self.keep_in_memory)
return dataset
class A_ :
def __init__( self : List[str] ,SCREAMING_SNAKE_CASE__ : str ,SCREAMING_SNAKE_CASE__ : Any ,SCREAMING_SNAKE_CASE__ : List[str] ,SCREAMING_SNAKE_CASE__ : List[Any] = None ,SCREAMING_SNAKE_CASE__ : int = None ,**SCREAMING_SNAKE_CASE__ : List[str] ,):
if num_proc is not None and num_proc <= 0:
raise ValueError(F"num_proc {num_proc} must be an integer > 0.")
__lowerCamelCase : Optional[Any] = dataset
__lowerCamelCase : Union[str, Any] = name
__lowerCamelCase : Optional[int] = con
__lowerCamelCase : Optional[int] = batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
__lowerCamelCase : Any = num_proc
__lowerCamelCase : List[str] = to_sql_kwargs
def lowerCAmelCase ( self : List[Any]):
__lowerCamelCase : str = self.to_sql_kwargs.pop('sql' ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[str] = self.to_sql_kwargs.pop('con' ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Union[str, Any] = self.to_sql_kwargs.pop('index' ,SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Dict = self._write(index=SCREAMING_SNAKE_CASE__ ,**self.to_sql_kwargs)
return written
def lowerCAmelCase ( self : List[Any] ,SCREAMING_SNAKE_CASE__ : List[str]):
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase : int = args
__lowerCamelCase : Optional[Any] = {**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
__lowerCamelCase : Union[str, Any] = query_table(
table=self.dataset.data ,key=slice(SCREAMING_SNAKE_CASE__ ,offset + self.batch_size) ,indices=self.dataset._indices ,)
__lowerCamelCase : Tuple = batch.to_pandas()
__lowerCamelCase : int = df.to_sql(self.name ,self.con ,index=SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__)
return num_rows or len(SCREAMING_SNAKE_CASE__)
def lowerCAmelCase ( self : Any ,SCREAMING_SNAKE_CASE__ : str ,**SCREAMING_SNAKE_CASE__ : List[Any]):
__lowerCamelCase : List[str] = 0
if self.num_proc is None or self.num_proc == 1:
for offset in logging.tqdm(
range(0 ,len(self.dataset) ,self.batch_size) ,unit='ba' ,disable=not logging.is_progress_bar_enabled() ,desc='Creating SQL from Arrow format' ,):
written += self._batch_sql((offset, index, to_sql_kwargs))
else:
__lowerCamelCase , __lowerCamelCase : Dict = len(self.dataset), self.batch_size
with multiprocessing.Pool(self.num_proc) as pool:
for num_rows in logging.tqdm(
pool.imap(
self._batch_sql ,[(offset, index, to_sql_kwargs) for offset in range(0 ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)] ,) ,total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size ,unit='ba' ,disable=not logging.is_progress_bar_enabled() ,desc='Creating SQL from Arrow format' ,):
written += num_rows
return written
| 73 |
import heapq as hq
import math
from collections.abc import Iterator
class lowerCamelCase__ :
def __init__(self , UpperCAmelCase ) -> Any:
_lowercase =str(id_ )
_lowercase =None
_lowercase =None
_lowercase =[]
_lowercase ={} # {vertex:distance}
def __lt__(self , UpperCAmelCase ) -> List[str]:
return self.key < other.key
def __repr__(self ) -> str:
return self.id
def __A (self , UpperCAmelCase ) -> Dict:
self.neighbors.append(UpperCAmelCase )
def __A (self , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
_lowercase =weight
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case , __snake_case ) -> List[str]:
"""simple docstring"""
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , __snake_case )
graph[b - 1].add_edge(graph[a - 1] , __snake_case )
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> list:
"""simple docstring"""
_lowercase =[]
for u in graph:
_lowercase =math.inf
_lowercase =None
_lowercase =0
_lowercase =graph[:]
while q:
_lowercase =min(__snake_case )
q.remove(__snake_case )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
_lowercase =u
_lowercase =u.edges[v.id]
for i in range(1 , len(__snake_case ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def UpperCAmelCase_ ( __snake_case , __snake_case ) -> Iterator[tuple]:
"""simple docstring"""
for u in graph:
_lowercase =math.inf
_lowercase =None
_lowercase =0
_lowercase =list(__snake_case )
hq.heapify(__snake_case )
while h:
_lowercase =hq.heappop(__snake_case )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
_lowercase =u
_lowercase =u.edges[v.id]
hq.heapify(__snake_case )
for i in range(1 , len(__snake_case ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def UpperCAmelCase_ ( ) -> None:
"""simple docstring"""
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
_A : Optional[int] = logging.get_logger(__name__) # pylint: disable=invalid-name
_A : str = '\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)["depth"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline("depth-estimation")\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to("cuda")\n\n >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to("cuda")\n\n\n >>> img = load_image(\n ... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n ... "/kandinsky/cat.png"\n ... ).resize((768, 768))\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n >>> prompt = "A robot, 4k photo"\n >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n >>> image_emb, zero_image_emb = pipe_prior(\n ... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n ... ).to_tuple()\n\n >>> images = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save("robot_cat.png")\n ```\n'
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=8 ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase__ : List[str] = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
lowerCamelCase__ : Optional[Any] = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
def __init__( self : int , A : List[Any] , A : int , A : List[Any] , ) ->Tuple:
super().__init__()
self.register_modules(
unet=A , scheduler=A , movq=A , )
lowerCamelCase__ : Optional[Any] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def __lowerCamelCase ( self : Dict , A : Any , A : Any , A : Dict , A : Dict , A : Union[str, Any] , A : List[str] ) ->Optional[Any]:
if latents is None:
lowerCamelCase__ : Dict = randn_tensor(A , generator=A , device=A , dtype=A )
else:
if latents.shape != shape:
raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}" )
lowerCamelCase__ : Optional[Any] = latents.to(A )
lowerCamelCase__ : Tuple = latents * scheduler.init_noise_sigma
return latents
def __lowerCamelCase ( self : List[Any] , A : int=0 ) ->Tuple:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
lowerCamelCase__ : int = torch.device(F"cuda:{gpu_id}" )
lowerCamelCase__ : Optional[int] = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(A , A )
def __lowerCamelCase ( self : Dict , A : Optional[Any]=0 ) ->str:
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
lowerCamelCase__ : Union[str, Any] = torch.device(F"cuda:{gpu_id}" )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=A )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
lowerCamelCase__ : int = None
for cpu_offloaded_model in [self.unet, self.movq]:
lowerCamelCase__ , lowerCamelCase__ : Tuple = cpu_offload_with_hook(A , A , prev_module_hook=A )
# We'll offload the last model manually.
lowerCamelCase__ : Optional[Any] = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __lowerCamelCase ( self : Tuple ) ->Union[str, Any]:
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(A , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(A )
def __call__( self : Dict , A : Optional[Any] , A : List[str] , A : List[str] , A : Tuple = 5_1_2 , A : Optional[int] = 5_1_2 , A : Tuple = 1_0_0 , A : int = 4.0 , A : List[str] = 1 , A : Dict = None , A : Tuple = None , A : Optional[Any] = "pil" , A : Dict = True , ) ->Any:
lowerCamelCase__ : List[str] = self._execution_device
lowerCamelCase__ : List[Any] = guidance_scale > 1.0
if isinstance(A , A ):
lowerCamelCase__ : int = torch.cat(A , dim=0 )
if isinstance(A , A ):
lowerCamelCase__ : str = torch.cat(A , dim=0 )
if isinstance(A , A ):
lowerCamelCase__ : int = torch.cat(A , dim=0 )
lowerCamelCase__ : Union[str, Any] = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
lowerCamelCase__ : Any = image_embeds.repeat_interleave(A , dim=0 )
lowerCamelCase__ : Union[str, Any] = negative_image_embeds.repeat_interleave(A , dim=0 )
lowerCamelCase__ : Tuple = hint.repeat_interleave(A , dim=0 )
lowerCamelCase__ : List[str] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=A )
lowerCamelCase__ : str = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=A )
self.scheduler.set_timesteps(A , device=A )
lowerCamelCase__ : List[Any] = self.scheduler.timesteps
lowerCamelCase__ : Union[str, Any] = self.movq.config.latent_channels
lowerCamelCase__ , lowerCamelCase__ : Tuple = downscale_height_and_width(A , A , self.movq_scale_factor )
# create initial latent
lowerCamelCase__ : Tuple = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , A , A , A , self.scheduler , )
for i, t in enumerate(self.progress_bar(A ) ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase__ : Union[str, Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase__ : int = {'''image_embeds''': image_embeds, '''hint''': hint}
lowerCamelCase__ : Tuple = self.unet(
sample=A , timestep=A , encoder_hidden_states=A , added_cond_kwargs=A , return_dict=A , )[0]
if do_classifier_free_guidance:
lowerCamelCase__ , lowerCamelCase__ : str = noise_pred.split(latents.shape[1] , dim=1 )
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = noise_pred.chunk(2 )
lowerCamelCase__ , lowerCamelCase__ : List[Any] = variance_pred.chunk(2 )
lowerCamelCase__ : Dict = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
lowerCamelCase__ : Optional[Any] = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase__ : List[str] = self.scheduler.step(
A , A , A , generator=A , )[0]
# post-processing
lowerCamelCase__ : Union[str, Any] = self.movq.decode(A , force_not_quantize=A )['''sample''']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}" )
if output_type in ["np", "pil"]:
lowerCamelCase__ : Tuple = image * 0.5 + 0.5
lowerCamelCase__ : Tuple = image.clamp(0 , 1 )
lowerCamelCase__ : Dict = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowerCamelCase__ : str = self.numpy_to_pil(A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A )
| 142 |
# flake8: noqa
# Lint as: python3
UpperCAmelCase__ = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
| 5 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__a = logging.get_logger(__name__)
class A__ ( UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Any = ['''input_features''', '''attention_mask''']
def __init__( self : Union[str, Any] , lowerCAmelCase__ : Dict=8_0 , lowerCAmelCase__ : List[str]=1_6_0_0_0 , lowerCAmelCase__ : Optional[int]=8_0 , lowerCAmelCase__ : Optional[int]=0.0 , lowerCAmelCase__ : Dict=True , lowerCAmelCase__ : Dict=True , lowerCAmelCase__ : Union[str, Any]=True , **lowerCAmelCase__ : int , ) -> Dict:
"""simple docstring"""
super().__init__(feature_size=lowerCAmelCase__ , sampling_rate=lowerCAmelCase__ , padding_value=lowerCAmelCase__ , **lowerCAmelCase__ )
_UpperCAmelCase : Any = num_mel_bins
_UpperCAmelCase : Any = do_ceptral_normalize
_UpperCAmelCase : Union[str, Any] = normalize_means
_UpperCAmelCase : int = normalize_vars
_UpperCAmelCase : Any = True
def _lowerCAmelCase ( self : List[str] , lowerCAmelCase__ : str , ) -> np.ndarray:
"""simple docstring"""
_UpperCAmelCase : List[Any] = waveform * (2**1_5) # Kaldi compliance: 16-bit signed integers
_UpperCAmelCase : List[str] = torch.from_numpy(lowerCAmelCase__ ).unsqueeze(0 )
_UpperCAmelCase : Any = ta_kaldi.fbank(lowerCAmelCase__ , num_mel_bins=self.num_mel_bins , sample_frequency=self.sampling_rate )
return features.numpy()
@staticmethod
def _lowerCAmelCase ( lowerCAmelCase__ : int , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : Any = True , lowerCAmelCase__ : Any = True , lowerCAmelCase__ : Any = 0.0 , ) -> np.ndarray:
"""simple docstring"""
if normalize_means:
_UpperCAmelCase : Union[str, Any] = x[:input_length].mean(axis=0 )
_UpperCAmelCase : Optional[int] = np.subtract(lowerCAmelCase__ , lowerCAmelCase__ )
if normalize_vars:
_UpperCAmelCase : Dict = x[:input_length].std(axis=0 )
_UpperCAmelCase : int = np.divide(lowerCAmelCase__ , lowerCAmelCase__ )
if input_length < x.shape[0]:
_UpperCAmelCase : Optional[int] = padding_value
# make sure array is in float32
_UpperCAmelCase : Any = x.astype(np.floataa )
return x
def _lowerCAmelCase ( self : Optional[Any] , lowerCAmelCase__ : str , lowerCAmelCase__ : List[Any] = None ) -> List[np.ndarray]:
"""simple docstring"""
_UpperCAmelCase : int = attention_mask.sum(-1 ) if attention_mask is not None else [x.shape[0] for x in input_features]
return [
self.utterance_cmvn(lowerCAmelCase__ , lowerCAmelCase__ , self.normalize_means , self.normalize_vars , self.padding_value )
for x, n in zip(lowerCAmelCase__ , lowerCAmelCase__ )
]
def __call__( self : Union[str, Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] = False , lowerCAmelCase__ : Dict = None , lowerCAmelCase__ : Union[str, Any] = False , lowerCAmelCase__ : Tuple = None , lowerCAmelCase__ : Union[str, Any] = None , lowerCAmelCase__ : int = None , lowerCAmelCase__ : Tuple = None , **lowerCAmelCase__ : int , ) -> BatchFeature:
"""simple docstring"""
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
F""" {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
_UpperCAmelCase : str = isinstance(lowerCAmelCase__ , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
_UpperCAmelCase : str = is_batched_numpy or (
isinstance(lowerCAmelCase__ , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_UpperCAmelCase : int = [np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(lowerCAmelCase__ , np.ndarray ):
_UpperCAmelCase : Union[str, Any] = np.asarray(lowerCAmelCase__ , dtype=np.floataa )
elif isinstance(lowerCAmelCase__ , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_UpperCAmelCase : str = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_UpperCAmelCase : Optional[int] = [raw_speech]
# extract fbank features
_UpperCAmelCase : str = [self._extract_fbank_features(lowerCAmelCase__ ) for waveform in raw_speech]
# convert into correct format for padding
_UpperCAmelCase : Dict = BatchFeature({"input_features": features} )
_UpperCAmelCase : List[Any] = self.pad(
lowerCAmelCase__ , padding=lowerCAmelCase__ , max_length=lowerCAmelCase__ , truncation=lowerCAmelCase__ , pad_to_multiple_of=lowerCAmelCase__ , return_attention_mask=lowerCAmelCase__ , **lowerCAmelCase__ , )
# make sure list is in array format
_UpperCAmelCase : str = padded_inputs.get("input_features" )
if isinstance(input_features[0] , lowerCAmelCase__ ):
_UpperCAmelCase : List[str] = [np.asarray(lowerCAmelCase__ , dtype=np.floataa ) for feature in input_features]
_UpperCAmelCase : Tuple = padded_inputs.get("attention_mask" )
if attention_mask is not None:
_UpperCAmelCase : Optional[int] = [np.asarray(lowerCAmelCase__ , dtype=np.intaa ) for array in attention_mask]
# Utterance-level cepstral mean and variance normalization
if self.do_ceptral_normalize:
_UpperCAmelCase : List[Any] = (
np.array(lowerCAmelCase__ , dtype=np.intaa )
if self._get_padding_strategies(lowerCAmelCase__ , max_length=lowerCAmelCase__ ) is not PaddingStrategy.DO_NOT_PAD
else None
)
_UpperCAmelCase : Tuple = self.normalize(
padded_inputs["input_features"] , attention_mask=lowerCAmelCase__ )
if return_tensors is not None:
_UpperCAmelCase : Optional[Any] = padded_inputs.convert_to_tensors(lowerCAmelCase__ )
return padded_inputs | 145 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class lowerCamelCase__ ( lowerCAmelCase):
SCREAMING_SNAKE_CASE__ = '''wavlm'''
def __init__(self , UpperCAmelCase=3_2 , UpperCAmelCase=7_6_8 , UpperCAmelCase=1_2 , UpperCAmelCase=1_2 , UpperCAmelCase=3_0_7_2 , UpperCAmelCase="gelu" , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=0.0 , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=0.02 , UpperCAmelCase=1e-5 , UpperCAmelCase="group" , UpperCAmelCase="gelu" , UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2) , UpperCAmelCase=(1_0, 3, 3, 3, 3, 2, 2) , UpperCAmelCase=False , UpperCAmelCase=1_2_8 , UpperCAmelCase=1_6 , UpperCAmelCase=3_2_0 , UpperCAmelCase=8_0_0 , UpperCAmelCase=False , UpperCAmelCase=True , UpperCAmelCase=0.05 , UpperCAmelCase=1_0 , UpperCAmelCase=2 , UpperCAmelCase=0.0 , UpperCAmelCase=1_0 , UpperCAmelCase=3_2_0 , UpperCAmelCase=2 , UpperCAmelCase=0.1 , UpperCAmelCase=1_0_0 , UpperCAmelCase=2_5_6 , UpperCAmelCase=2_5_6 , UpperCAmelCase=0.1 , UpperCAmelCase="mean" , UpperCAmelCase=False , UpperCAmelCase=False , UpperCAmelCase=2_5_6 , UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) , UpperCAmelCase=(5, 3, 3, 1, 1) , UpperCAmelCase=(1, 2, 3, 1, 1) , UpperCAmelCase=5_1_2 , UpperCAmelCase=8_0 , UpperCAmelCase=0 , UpperCAmelCase=1 , UpperCAmelCase=2 , UpperCAmelCase=False , UpperCAmelCase=3 , UpperCAmelCase=2 , UpperCAmelCase=3 , UpperCAmelCase=None , **UpperCAmelCase , ) -> Optional[Any]:
super().__init__(**UpperCAmelCase , pad_token_id=UpperCAmelCase , bos_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase )
_lowercase =hidden_size
_lowercase =feat_extract_norm
_lowercase =feat_extract_activation
_lowercase =list(UpperCAmelCase )
_lowercase =list(UpperCAmelCase )
_lowercase =list(UpperCAmelCase )
_lowercase =conv_bias
_lowercase =num_buckets
_lowercase =max_bucket_distance
_lowercase =num_conv_pos_embeddings
_lowercase =num_conv_pos_embedding_groups
_lowercase =len(self.conv_dim )
_lowercase =num_hidden_layers
_lowercase =intermediate_size
_lowercase =hidden_act
_lowercase =num_attention_heads
_lowercase =hidden_dropout
_lowercase =attention_dropout
_lowercase =activation_dropout
_lowercase =feat_proj_dropout
_lowercase =final_dropout
_lowercase =layerdrop
_lowercase =layer_norm_eps
_lowercase =initializer_range
_lowercase =num_ctc_classes
_lowercase =vocab_size
_lowercase =do_stable_layer_norm
_lowercase =use_weighted_layer_sum
_lowercase =classifier_proj_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)`, 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
_lowercase =apply_spec_augment
_lowercase =mask_time_prob
_lowercase =mask_time_length
_lowercase =mask_time_min_masks
_lowercase =mask_feature_prob
_lowercase =mask_feature_length
# parameters for pretraining with codevector quantized representations
_lowercase =num_codevectors_per_group
_lowercase =num_codevector_groups
_lowercase =contrastive_logits_temperature
_lowercase =num_negatives
_lowercase =codevector_dim
_lowercase =proj_codevector_dim
_lowercase =diversity_loss_weight
# ctc loss
_lowercase =ctc_loss_reduction
_lowercase =ctc_zero_infinity
# adapter
_lowercase =add_adapter
_lowercase =adapter_kernel_size
_lowercase =adapter_stride
_lowercase =num_adapter_layers
_lowercase =output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_lowercase =classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_lowercase =list(UpperCAmelCase )
_lowercase =list(UpperCAmelCase )
_lowercase =list(UpperCAmelCase )
_lowercase =xvector_output_dim
@property
def __A (self ) -> int:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 5 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__A : Any = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": ["export", "validate_model_outputs"],
"features": ["FeaturesManager"],
"utils": ["ParameterFormat", "compute_serialized_parameters_size"],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
__A : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 120 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class lowerCamelCase__ ( unittest.TestCase):
def __A (self ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def __A (self ) -> Optional[Any]:
_lowercase =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-inpaint/init_image.png''' )
_lowercase =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
_lowercase ='''xvjiarui/stable-diffusion-2-inpainting'''
_lowercase , _lowercase =FlaxStableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase , safety_checker=UpperCAmelCase )
_lowercase ='''Face of a yellow cat, high resolution, sitting on a park bench'''
_lowercase =jax.random.PRNGKey(0 )
_lowercase =5_0
_lowercase =jax.device_count()
_lowercase =num_samples * [prompt]
_lowercase =num_samples * [init_image]
_lowercase =num_samples * [mask_image]
_lowercase , _lowercase , _lowercase =pipeline.prepare_inputs(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# shard inputs and rng
_lowercase =replicate(UpperCAmelCase )
_lowercase =jax.random.split(UpperCAmelCase , jax.device_count() )
_lowercase =shard(UpperCAmelCase )
_lowercase =shard(UpperCAmelCase )
_lowercase =shard(UpperCAmelCase )
_lowercase =pipeline(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , jit=UpperCAmelCase )
_lowercase =output.images.reshape(UpperCAmelCase , 5_1_2 , 5_1_2 , 3 )
_lowercase =images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
_lowercase =jnp.asarray(jax.device_get(image_slice.flatten() ) )
_lowercase =jnp.array(
[0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084] )
print(f"output_slice: {output_slice}" )
assert jnp.abs(output_slice - expected_slice ).max() < 1e-2
| 5 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCAmelCase : Dict = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : Optional[int] = [
"XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMForMultipleChoice",
"XLMForQuestionAnswering",
"XLMForQuestionAnsweringSimple",
"XLMForSequenceClassification",
"XLMForTokenClassification",
"XLMModel",
"XLMPreTrainedModel",
"XLMWithLMHeadModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : Union[str, Any] = [
"TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMForMultipleChoice",
"TFXLMForQuestionAnsweringSimple",
"TFXLMForSequenceClassification",
"TFXLMForTokenClassification",
"TFXLMMainLayer",
"TFXLMModel",
"TFXLMPreTrainedModel",
"TFXLMWithLMHeadModel",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
__UpperCAmelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 111 |
import comet # From: unbabel-comet
import torch
import datasets
UpperCAmelCase__ = datasets.logging.get_logger(__name__)
UpperCAmelCase__ = '''\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon},
title = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},
booktitle = {Proceedings of the Fifth Conference on Machine Translation},
month = {November},
year = {2020},
address = {Online},
publisher = {Association for Computational Linguistics},
pages = {909--918},
}
@inproceedings{rei-etal-2020-comet,
title = "{COMET}: A Neural Framework for {MT} Evaluation",
author = "Rei, Ricardo and
Stewart, Craig and
Farinha, Ana C and
Lavie, Alon",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",
pages = "2685--2702",
}
'''
UpperCAmelCase__ = '''\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.
See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
'''
UpperCAmelCase__ = '''
COMET score.
Args:
`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.
Returns:
`samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
`scores`: List of scores.
Examples:
>>> comet_metric = datasets.load_metric(\'comet\')
>>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\') # you can also choose which model to use
>>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]
>>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]
>>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]
>>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
>>> print([round(v, 2) for v in results["scores"]])
[0.19, 0.92]
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class lowerCamelCase__ ( datasets.Metric):
def __A (self ) -> Optional[int]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='''https://unbabel.github.io/COMET/html/index.html''' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''sources''': datasets.Value('''string''' , id='''sequence''' ),
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , codebase_urls=['''https://github.com/Unbabel/COMET'''] , reference_urls=[
'''https://github.com/Unbabel/COMET''',
'''https://www.aclweb.org/anthology/2020.emnlp-main.213/''',
'''http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6''',
] , )
def __A (self , UpperCAmelCase ) -> Dict:
if self.config_name == "default":
_lowercase =comet.load_from_checkpoint(comet.download_model('''wmt20-comet-da''' ) )
else:
_lowercase =comet.load_from_checkpoint(comet.download_model(self.config_name ) )
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None , UpperCAmelCase=False ) -> int:
if gpus is None:
_lowercase =1 if torch.cuda.is_available() else 0
_lowercase ={'''src''': sources, '''mt''': predictions, '''ref''': references}
_lowercase =[dict(zip(UpperCAmelCase , UpperCAmelCase ) ) for t in zip(*data.values() )]
_lowercase , _lowercase =self.scorer.predict(UpperCAmelCase , gpus=UpperCAmelCase , progress_bar=UpperCAmelCase )
return {"mean_score": mean_score, "scores": scores}
| 5 | 0 |
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 : List[str] = pytest.mark.integration
@pytest.mark.parametrize("path" , ["paws", "csv"] )
def _A ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
inspect_dataset(__snake_case , __snake_case )
a__ : Optional[int] =path + ".py"
assert script_name in os.listdir(__snake_case )
assert "__pycache__" not in os.listdir(__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 _A ( SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : List[Any] ):
"""simple docstring"""
inspect_metric(__snake_case , __snake_case )
a__ : Dict =path + ".py"
assert script_name in os.listdir(__snake_case )
assert "__pycache__" not in os.listdir(__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 _A ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
a__ : int =get_dataset_config_info(__snake_case , config_name=__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 _A ( SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[int] ):
"""simple docstring"""
with pytest.raises(__snake_case ):
get_dataset_config_info(__snake_case , config_name=__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 _A ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
a__ : Optional[Any] =get_dataset_config_names(__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 _A ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
a__ : Optional[Any] =get_dataset_infos(__snake_case )
assert list(infos.keys() ) == expected_configs
a__ : Union[str, Any] =expected_configs[0]
assert expected_config in infos
a__ : str =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 _A ( SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
a__ : Optional[int] =get_dataset_infos(__snake_case )
assert expected_config in infos
a__ : Union[str, Any] =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 _A ( SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
with pytest.raises(__snake_case ):
get_dataset_split_names(__snake_case , config_name=__snake_case )
| 95 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class lowerCamelCase__ ( lowerCAmelCase , lowerCAmelCase):
SCREAMING_SNAKE_CASE__ = 1
@register_to_config
def __init__(self , UpperCAmelCase=2_0_0_0 , UpperCAmelCase=0.1 , UpperCAmelCase=2_0 , UpperCAmelCase=1e-3 ) -> List[str]:
_lowercase =None
_lowercase =None
_lowercase =None
def __A (self , UpperCAmelCase , UpperCAmelCase = None ) -> str:
_lowercase =torch.linspace(1 , self.config.sampling_eps , UpperCAmelCase , device=UpperCAmelCase )
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None ) -> Optional[int]:
if self.timesteps is None:
raise ValueError(
'''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
_lowercase =(
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
_lowercase =torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
_lowercase =std.flatten()
while len(std.shape ) < len(score.shape ):
_lowercase =std.unsqueeze(-1 )
_lowercase =-score / std
# compute
_lowercase =-1.0 / len(self.timesteps )
_lowercase =self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
_lowercase =beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
_lowercase =beta_t.unsqueeze(-1 )
_lowercase =-0.5 * beta_t * x
_lowercase =torch.sqrt(UpperCAmelCase )
_lowercase =drift - diffusion**2 * score
_lowercase =x + drift * dt
# add noise
_lowercase =randn_tensor(x.shape , layout=x.layout , generator=UpperCAmelCase , device=x.device , dtype=x.dtype )
_lowercase =x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__(self ) -> str:
return self.config.num_train_timesteps
| 5 | 0 |
from math import log
from scipy.constants import Boltzmann, physical_constants
A : str = 3_0_0 # TEMPERATURE (unit = K)
def __lowerCAmelCase ( a__ , a__ , a__ , ) -> float:
if donor_conc <= 0:
raise ValueError('''Donor concentration should be positive''' )
elif acceptor_conc <= 0:
raise ValueError('''Acceptor concentration should be positive''' )
elif intrinsic_conc <= 0:
raise ValueError('''Intrinsic concentration should be positive''' )
elif donor_conc <= intrinsic_conc:
raise ValueError(
'''Donor concentration should be greater than intrinsic concentration''' )
elif acceptor_conc <= intrinsic_conc:
raise ValueError(
'''Acceptor concentration should be greater than intrinsic concentration''' )
else:
return (
Boltzmann
* T
* log((donor_conc * acceptor_conc) / intrinsic_conc**2 )
/ physical_constants["electron volt"][0]
)
if __name__ == "__main__":
import doctest
doctest.testmod() | 6 |
def __lowerCAmelCase ( a__ ) -> str:
__a = []
__a = set({'''(''', '''[''', '''{'''} )
__a = set({''')''', ''']''', '''}'''} )
__a = {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}
for i in range(len(a__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(a__ ) == 0 or (len(a__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(a__ ) == 0
def __lowerCAmelCase ( ) -> Dict:
__a = input('''Enter sequence of brackets: ''' )
if is_balanced(a__ ):
print(a__ , '''is balanced''' )
else:
print(a__ , '''is not balanced''' )
if __name__ == "__main__":
main() | 6 | 1 |
from math import sqrt
def __lowerCAmelCase ( a__ ) -> bool:
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(sqrt(a__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def __lowerCAmelCase ( a__ = 1_0001 ) -> int:
__a = 0
__a = 1
while count != nth and number < 3:
number += 1
if is_prime(a__ ):
count += 1
while count != nth:
number += 2
if is_prime(a__ ):
count += 1
return number
if __name__ == "__main__":
print(F"{solution() = }") | 6 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : str = {
'configuration_blenderbot': [
'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlenderbotConfig',
'BlenderbotOnnxConfig',
],
'tokenization_blenderbot': ['BlenderbotTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = ['BlenderbotTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = [
'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlenderbotForCausalLM',
'BlenderbotForConditionalGeneration',
'BlenderbotModel',
'BlenderbotPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Dict = [
'TFBlenderbotForConditionalGeneration',
'TFBlenderbotModel',
'TFBlenderbotPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Dict = [
'FlaxBlenderbotForConditionalGeneration',
'FlaxBlenderbotModel',
'FlaxBlenderbotPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
A : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A : Dict = logging.get_logger(__name__)
A : List[Any] = {'tokenizer_file': 'tokenizer.json'}
A : Tuple = {
'tokenizer_file': {
'bigscience/tokenizer': 'https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json',
'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json',
'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json',
'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json',
'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json',
'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json',
'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json',
},
}
class __A( a ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = ['''input_ids''', '''attention_mask''']
snake_case_ = None
def __init__( self , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case="<unk>" , _snake_case="<s>" , _snake_case="</s>" , _snake_case="<pad>" , _snake_case=False , _snake_case=False , **_snake_case , ) -> Tuple:
'''simple docstring'''
super().__init__(
_snake_case , _snake_case , tokenizer_file=_snake_case , unk_token=_snake_case , bos_token=_snake_case , eos_token=_snake_case , pad_token=_snake_case , add_prefix_space=_snake_case , clean_up_tokenization_spaces=_snake_case , **_snake_case , )
__a = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , _snake_case ) != add_prefix_space:
__a = getattr(_snake_case , pre_tok_state.pop('''type''' ) )
__a = add_prefix_space
__a = pre_tok_class(**_snake_case )
__a = add_prefix_space
def SCREAMING_SNAKE_CASE_ ( self , *_snake_case , **_snake_case ) -> BatchEncoding:
'''simple docstring'''
__a = kwargs.get('''is_split_into_words''' , _snake_case )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
''' pretokenized inputs.''' )
return super()._batch_encode_plus(*_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , *_snake_case , **_snake_case ) -> BatchEncoding:
'''simple docstring'''
__a = kwargs.get('''is_split_into_words''' , _snake_case )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
''' pretokenized inputs.''' )
return super()._encode_plus(*_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None ) -> Tuple[str]:
'''simple docstring'''
__a = self._tokenizer.model.save(_snake_case , name=_snake_case )
return tuple(_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> List[int]:
'''simple docstring'''
__a = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(_snake_case , add_special_tokens=_snake_case ) + [self.eos_token_id] )
if len(_snake_case ) > self.model_max_length:
__a = input_ids[-self.model_max_length :]
return input_ids | 6 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Dict = {
'configuration_xlm_roberta': [
'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaConfig',
'XLMRobertaOnnxConfig',
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = ['XLMRobertaTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = ['XLMRobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[Any] = [
'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaForCausalLM',
'XLMRobertaForMaskedLM',
'XLMRobertaForMultipleChoice',
'XLMRobertaForQuestionAnswering',
'XLMRobertaForSequenceClassification',
'XLMRobertaForTokenClassification',
'XLMRobertaModel',
'XLMRobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = [
'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFXLMRobertaForCausalLM',
'TFXLMRobertaForMaskedLM',
'TFXLMRobertaForMultipleChoice',
'TFXLMRobertaForQuestionAnswering',
'TFXLMRobertaForSequenceClassification',
'TFXLMRobertaForTokenClassification',
'TFXLMRobertaModel',
'TFXLMRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'FlaxXLMRobertaForMaskedLM',
'FlaxXLMRobertaForCausalLM',
'FlaxXLMRobertaForMultipleChoice',
'FlaxXLMRobertaForQuestionAnswering',
'FlaxXLMRobertaForSequenceClassification',
'FlaxXLMRobertaForTokenClassification',
'FlaxXLMRobertaModel',
'FlaxXLMRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Tuple = logging.get_logger(__name__)
A : Dict = {
'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json',
'RWKV/rwkv-4-430m-pile': 'https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json',
'RWKV/rwkv-4-1b5-pile': 'https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json',
'RWKV/rwkv-4-3b-pile': 'https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json',
'RWKV/rwkv-4-7b-pile': 'https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json',
'RWKV/rwkv-4-14b-pile': 'https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json',
'RWKV/rwkv-raven-1b5': 'https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json',
'RWKV/rwkv-raven-3b': 'https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json',
'RWKV/rwkv-raven-7b': 'https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json',
'RWKV/rwkv-raven-14b': 'https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json',
}
class __A( a ):
snake_case_ = '''rwkv'''
snake_case_ = {'''max_position_embeddings''': '''context_length'''}
def __init__( self , _snake_case=50_277 , _snake_case=1_024 , _snake_case=4_096 , _snake_case=32 , _snake_case=None , _snake_case=None , _snake_case=1E-5 , _snake_case=0 , _snake_case=0 , _snake_case=6 , _snake_case=False , _snake_case=True , **_snake_case , ) -> List[str]:
'''simple docstring'''
__a = vocab_size
__a = context_length
__a = hidden_size
__a = num_hidden_layers
__a = attention_hidden_size if attention_hidden_size is not None else hidden_size
__a = intermediate_size if intermediate_size is not None else 4 * hidden_size
__a = layer_norm_epsilon
__a = rescale_every
__a = use_cache
__a = bos_token_id
__a = eos_token_id
super().__init__(
tie_word_embeddings=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case , **_snake_case ) | 6 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Optional[int] = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
'feature_extraction_whisper': ['WhisperFeatureExtractor'],
'processing_whisper': ['WhisperProcessor'],
'tokenization_whisper': ['WhisperTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = ['WhisperTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
'WhisperForConditionalGeneration',
'WhisperModel',
'WhisperPreTrainedModel',
'WhisperForAudioClassification',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFWhisperForConditionalGeneration',
'TFWhisperModel',
'TFWhisperPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = [
'FlaxWhisperForConditionalGeneration',
'FlaxWhisperModel',
'FlaxWhisperPreTrainedModel',
'FlaxWhisperForAudioClassification',
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
A : List[Any] = False
@skip_mps
class __A( a , a , a , unittest.TestCase ):
snake_case_ = StableDiffusionAttendAndExcitePipeline
snake_case_ = False
snake_case_ = TEXT_TO_IMAGE_PARAMS
snake_case_ = TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''} )
snake_case_ = TEXT_TO_IMAGE_IMAGE_PARAMS
snake_case_ = TEXT_TO_IMAGE_IMAGE_PARAMS
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls ) -> List[str]:
'''simple docstring'''
super().setUpClass()
torch.use_deterministic_algorithms(_snake_case )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls ) -> int:
'''simple docstring'''
super().tearDownClass()
torch.use_deterministic_algorithms(_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
__a = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=_snake_case , )
__a = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=_snake_case , set_alpha_to_one=_snake_case , )
torch.manual_seed(0 )
__a = 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 , sample_size=128 , )
torch.manual_seed(0 )
__a = 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=1_000 , hidden_act='''gelu''' , projection_dim=512 , )
__a = CLIPTextModel(_snake_case )
__a = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__a = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case=0 ) -> Any:
'''simple docstring'''
if str(_snake_case ).startswith('''mps''' ):
__a = torch.manual_seed(_snake_case )
else:
__a = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
__a = __a = {
'''prompt''': '''a cat and a frog''',
'''token_indices''': [2, 5],
'''generator''': generator,
'''num_inference_steps''': 1,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
'''max_iter_to_alter''': 2,
'''thresholds''': {0: 0.7},
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = '''cpu'''
__a = self.get_dummy_components()
__a = self.pipeline_class(**_snake_case )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
__a = self.get_dummy_inputs(_snake_case )
__a = pipe(**_snake_case ).images
__a = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 64, 64, 3) )
__a = np.array(
[0.6390_5364, 0.6289_7307, 0.4859_9017, 0.513_3624, 0.555_0048, 0.4576_9516, 0.5032_6973, 0.502_3139, 0.4538_4496] )
__a = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_snake_case , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
super().test_cpu_offload_forward_pass(expected_max_diff=5E-4 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
self._test_inference_batch_single_identical(batch_size=2 , expected_max_diff=7E-4 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
super().test_save_load_local(expected_max_difference=5E-4 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().test_save_load_optional_components(expected_max_difference=4E-4 )
@require_torch_gpu
@slow
class __A( unittest.TestCase ):
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls ) -> Dict:
'''simple docstring'''
super().setUpClass()
torch.use_deterministic_algorithms(_snake_case )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls ) -> Union[str, Any]:
'''simple docstring'''
super().tearDownClass()
torch.use_deterministic_algorithms(_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
__a = torch.manual_seed(51 )
__a = StableDiffusionAttendAndExcitePipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' , safety_checker=_snake_case , torch_dtype=torch.floataa )
pipe.to('''cuda''' )
__a = '''a painting of an elephant with glasses'''
__a = [5, 7]
__a = pipe(
prompt=_snake_case , token_indices=_snake_case , guidance_scale=7.5 , generator=_snake_case , num_inference_steps=5 , max_iter_to_alter=5 , output_type='''numpy''' , ).images[0]
__a = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy''' )
assert np.abs((expected_image - image).max() ) < 5E-1 | 6 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
'stable diffusion controlnet',
'0.22.0',
'Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.',
standard_warn=False,
stacklevel=3,
) | 6 | 1 |
import math
import flax.linen as nn
import jax.numpy as jnp
def __lowerCAmelCase ( a__ , a__ , a__ = 1 , a__ = 1 , a__ = 1.0e4 , a__ = False , a__ = 1.0 , ) -> jnp.ndarray:
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
__a = float(embedding_dim // 2 )
__a = math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
__a = min_timescale * jnp.exp(jnp.arange(a__ , dtype=jnp.floataa ) * -log_timescale_increment )
__a = jnp.expand_dims(a__ , 1 ) * jnp.expand_dims(a__ , 0 )
# scale embeddings
__a = scale * emb
if flip_sin_to_cos:
__a = jnp.concatenate([jnp.cos(a__ ), jnp.sin(a__ )] , axis=1 )
else:
__a = jnp.concatenate([jnp.sin(a__ ), jnp.cos(a__ )] , axis=1 )
__a = jnp.reshape(a__ , [jnp.shape(a__ )[0], embedding_dim] )
return signal
class __A( nn.Module ):
snake_case_ = 3_2
snake_case_ = jnp.floataa
@nn.compact
def __call__( self , _snake_case ) -> str:
'''simple docstring'''
__a = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_1''' )(_snake_case )
__a = nn.silu(_snake_case )
__a = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_2''' )(_snake_case )
return temb
class __A( nn.Module ):
snake_case_ = 3_2
snake_case_ = False
snake_case_ = 1
@nn.compact
def __call__( self , _snake_case ) -> Dict:
'''simple docstring'''
return get_sinusoidal_embeddings(
_snake_case , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift ) | 6 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a )
class __A( a ):
snake_case_ = field(default='''language-modeling''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
snake_case_ = Features({'''text''': Value('''string''' )} )
snake_case_ = Features({} )
snake_case_ = "text"
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict[str, str]:
'''simple docstring'''
return {self.text_column: "text"} | 6 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
A : Optional[int] = logging.get_logger(__name__)
A : List[Any] = {
'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}
class __A( a ):
snake_case_ = '''instructblip_vision_model'''
def __init__( self , _snake_case=1_408 , _snake_case=6_144 , _snake_case=39 , _snake_case=16 , _snake_case=224 , _snake_case=14 , _snake_case="gelu" , _snake_case=1E-6 , _snake_case=0.0 , _snake_case=1E-10 , _snake_case=True , **_snake_case , ) -> List[str]:
'''simple docstring'''
super().__init__(**_snake_case )
__a = hidden_size
__a = intermediate_size
__a = num_hidden_layers
__a = num_attention_heads
__a = patch_size
__a = image_size
__a = initializer_range
__a = attention_dropout
__a = layer_norm_eps
__a = hidden_act
__a = qkv_bias
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , _snake_case , **_snake_case ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(_snake_case )
__a , __a = cls.get_config_dict(_snake_case , **_snake_case )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get('''model_type''' ) == "instructblip":
__a = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(_snake_case , **_snake_case )
class __A( a ):
snake_case_ = '''instructblip_qformer'''
def __init__( self , _snake_case=30_522 , _snake_case=768 , _snake_case=12 , _snake_case=12 , _snake_case=3_072 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=0.02 , _snake_case=1E-12 , _snake_case=0 , _snake_case="absolute" , _snake_case=2 , _snake_case=1_408 , **_snake_case , ) -> Dict:
'''simple docstring'''
super().__init__(pad_token_id=_snake_case , **_snake_case )
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = hidden_act
__a = intermediate_size
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = initializer_range
__a = layer_norm_eps
__a = position_embedding_type
__a = cross_attention_frequency
__a = encoder_hidden_size
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , _snake_case , **_snake_case ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(_snake_case )
__a , __a = cls.get_config_dict(_snake_case , **_snake_case )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get('''model_type''' ) == "instructblip":
__a = config_dict['''qformer_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(_snake_case , **_snake_case )
class __A( a ):
snake_case_ = '''instructblip'''
snake_case_ = True
def __init__( self , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=32 , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**_snake_case )
if vision_config is None:
__a = {}
logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.''' )
if qformer_config is None:
__a = {}
logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.''' )
if text_config is None:
__a = {}
logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''' )
__a = InstructBlipVisionConfig(**_snake_case )
__a = InstructBlipQFormerConfig(**_snake_case )
__a = text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
__a = CONFIG_MAPPING[text_model_type](**_snake_case )
__a = self.text_config.tie_word_embeddings
__a = self.text_config.is_encoder_decoder
__a = num_query_tokens
__a = self.vision_config.hidden_size
__a = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__a = 1.0
__a = 0.02
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , _snake_case , _snake_case , _snake_case , **_snake_case , ) -> Optional[int]:
'''simple docstring'''
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **_snake_case , )
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
__a = copy.deepcopy(self.__dict__ )
__a = self.vision_config.to_dict()
__a = self.qformer_config.to_dict()
__a = self.text_config.to_dict()
__a = self.__class__.model_type
return output | 6 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __lowerCAmelCase ( a__ , a__ , a__=1024 , a__=1024 , a__=False , **a__ ) -> Optional[Any]:
__a = AutoTokenizer.from_pretrained(a__ )
__a = SeqaSeqDataset(a__ , a__ , a__ , a__ , type_path='''train''' , **a__ )
__a = tok.pad_token_id
def get_lens(a__ ):
__a = tqdm(
DataLoader(a__ , batch_size=512 , num_workers=8 , shuffle=a__ , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
__a = []
for batch in dl:
__a = batch['''input_ids'''].ne(a__ ).sum(1 ).tolist()
__a = batch['''labels'''].ne(a__ ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(a__ , a__ ):
max_lens.append(max(a__ , a__ ) )
else:
max_lens.extend(a__ )
return max_lens
__a = get_lens(a__ )
__a = SeqaSeqDataset(a__ , a__ , a__ , a__ , type_path='''val''' , **a__ )
__a = get_lens(a__ )
pickle_save(a__ , train_ds.len_file )
pickle_save(a__ , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file) | 6 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A : Dict = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processing_trocr': ['TrOCRProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Any = [
'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
'TrOCRForCausalLM',
'TrOCRPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
from .processing_trocr import TrOCRProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel
else:
import sys
A : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __lowerCAmelCase ( a__ , a__ , a__ = 1 / sqrt(2 ) ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = (1 - _cos) / 2
__a = 1 - _cos
__a = 1 + alpha
__a = -2 * _cos
__a = 1 - alpha
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ = 1 / sqrt(2 ) ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = (1 + _cos) / 2
__a = -1 - _cos
__a = 1 + alpha
__a = -2 * _cos
__a = 1 - alpha
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ = 1 / sqrt(2 ) ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = _sin / 2
__a = 0
__a = -ba
__a = 1 + alpha
__a = -2 * _cos
__a = 1 - alpha
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ = 1 / sqrt(2 ) ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = 1 - alpha
__a = -2 * _cos
__a = 1 + alpha
__a = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ , a__ = 1 / sqrt(2 ) , ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = 10 ** (gain_db / 40)
__a = 1 + alpha * big_a
__a = -2 * _cos
__a = 1 - alpha * big_a
__a = 1 + alpha / big_a
__a = -2 * _cos
__a = 1 - alpha / big_a
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ , a__ = 1 / sqrt(2 ) , ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = 10 ** (gain_db / 40)
__a = (big_a + 1) - (big_a - 1) * _cos
__a = (big_a + 1) + (big_a - 1) * _cos
__a = (big_a - 1) - (big_a + 1) * _cos
__a = (big_a - 1) + (big_a + 1) * _cos
__a = 2 * sqrt(a__ ) * alpha
__a = big_a * (pmc + aaa)
__a = 2 * big_a * mpc
__a = big_a * (pmc - aaa)
__a = ppmc + aaa
__a = -2 * pmpc
__a = ppmc - aaa
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ , a__ = 1 / sqrt(2 ) , ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = 10 ** (gain_db / 40)
__a = (big_a + 1) - (big_a - 1) * _cos
__a = (big_a + 1) + (big_a - 1) * _cos
__a = (big_a - 1) - (big_a + 1) * _cos
__a = (big_a - 1) + (big_a + 1) * _cos
__a = 2 * sqrt(a__ ) * alpha
__a = big_a * (ppmc + aaa)
__a = -2 * big_a * pmpc
__a = big_a * (ppmc - aaa)
__a = pmc + aaa
__a = 2 * mpc
__a = pmc - aaa
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt | 6 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : Union[str, Any] = {
'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
'xlm-roberta-large-finetuned-conll02-dutch': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll02-spanish': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll03-english': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
),
'xlm-roberta-large-finetuned-conll03-german': (
'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
),
}
class __A( a ):
snake_case_ = '''xlm-roberta'''
def __init__( self , _snake_case=30_522 , _snake_case=768 , _snake_case=12 , _snake_case=12 , _snake_case=3_072 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=2 , _snake_case=0.02 , _snake_case=1E-12 , _snake_case=1 , _snake_case=0 , _snake_case=2 , _snake_case="absolute" , _snake_case=True , _snake_case=None , **_snake_case , ) -> Dict:
'''simple docstring'''
super().__init__(pad_token_id=_snake_case , bos_token_id=_snake_case , eos_token_id=_snake_case , **_snake_case )
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = hidden_act
__a = intermediate_size
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = type_vocab_size
__a = initializer_range
__a = layer_norm_eps
__a = position_embedding_type
__a = use_cache
__a = classifier_dropout
class __A( a ):
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
__a = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
__a = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] ) | 6 |
def __lowerCAmelCase ( a__ , a__ , a__ ) -> list:
__a = len(a__ )
__a = [[0] * n for i in range(a__ )]
for i in range(a__ ):
__a = y_points[i]
for i in range(2 , a__ ):
for j in range(a__ , a__ ):
__a = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod() | 6 | 1 |
def __lowerCAmelCase ( a__ , a__ ) -> float:
def get_matched_characters(a__ , a__ ) -> str:
__a = []
__a = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
__a = int(max(0 , i - limit ) )
__a = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(a__ )
__a = F"""{_stra[0:_stra.index(a__ )]} {_stra[_stra.index(a__ ) + 1:]}"""
return "".join(a__ )
# matching characters
__a = get_matched_characters(a__ , a__ )
__a = get_matched_characters(a__ , a__ )
__a = len(a__ )
# transposition
__a = (
len([(ca, ca) for ca, ca in zip(a__ , a__ ) if ca != ca] ) // 2
)
if not match_count:
__a = 0.0
else:
__a = (
1
/ 3
* (
match_count / len(a__ )
+ match_count / len(a__ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
__a = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world')) | 6 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def __lowerCAmelCase ( a__ , a__ , a__ ) -> tuple[int | None, int | None, float]:
if not arr:
return None, None, 0
if low == high:
return low, high, arr[low]
__a = (low + high) // 2
__a , __a , __a = max_subarray(a__ , a__ , a__ )
__a , __a , __a = max_subarray(a__ , mid + 1 , a__ )
__a , __a , __a = max_cross_sum(a__ , a__ , a__ , a__ )
if left_sum >= right_sum and left_sum >= cross_sum:
return left_low, left_high, left_sum
elif right_sum >= left_sum and right_sum >= cross_sum:
return right_low, right_high, right_sum
return cross_left, cross_right, cross_sum
def __lowerCAmelCase ( a__ , a__ , a__ , a__ ) -> tuple[int, int, float]:
__a , __a = float('''-inf''' ), -1
__a , __a = float('''-inf''' ), -1
__a = 0
for i in range(a__ , low - 1 , -1 ):
summ += arr[i]
if summ > left_sum:
__a = summ
__a = i
__a = 0
for i in range(mid + 1 , high + 1 ):
summ += arr[i]
if summ > right_sum:
__a = summ
__a = i
return max_left, max_right, (left_sum + right_sum)
def __lowerCAmelCase ( a__ ) -> float:
__a = [randint(1 , a__ ) for _ in range(a__ )]
__a = time.time()
max_subarray(a__ , 0 , input_size - 1 )
__a = time.time()
return end - start
def __lowerCAmelCase ( ) -> None:
__a = [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
__a = [time_max_subarray(a__ ) for input_size in input_sizes]
print('''No of Inputs\t\tTime Taken''' )
for input_size, runtime in zip(a__ , a__ ):
print(a__ , '''\t\t''' , a__ )
plt.plot(a__ , a__ )
plt.xlabel('''Number of Inputs''' )
plt.ylabel('''Time taken in seconds''' )
plt.show()
if __name__ == "__main__":
from doctest import testmod
testmod() | 6 | 1 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
A : str = 1_0_0
A : Union[str, Any] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
continue
primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=100 )
def __lowerCAmelCase ( a__ ) -> set[int]:
if number_to_partition < 0:
return set()
elif number_to_partition == 0:
return {1}
__a = set()
__a = 42
__a = 42
for prime in primes:
if prime > number_to_partition:
continue
for sub in partition(number_to_partition - prime ):
ret.add(sub * prime )
return ret
def __lowerCAmelCase ( a__ = 5000 ) -> int | None:
for number_to_partition in range(1 , a__ ):
if len(partition(a__ ) ) > number_unique_partitions:
return number_to_partition
return None
if __name__ == "__main__":
print(F"{solution() = }") | 6 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __A( a , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __A( unittest.TestCase ):
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = ort.SessionOptions()
__a = False
return options
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
__a = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , safety_checker=_snake_case , feature_extractor=_snake_case , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_snake_case )
__a = '''A red cat sitting on a park bench'''
__a = np.random.RandomState(0 )
__a = pipe(
prompt=_snake_case , image=_snake_case , mask_image=_snake_case , guidance_scale=7.5 , num_inference_steps=10 , generator=_snake_case , output_type='''np''' , )
__a = output.images
__a = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
__a = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
__a = LMSDiscreteScheduler.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , subfolder='''scheduler''' , revision='''onnx''' )
__a = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , scheduler=_snake_case , safety_checker=_snake_case , feature_extractor=_snake_case , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_snake_case )
__a = '''A red cat sitting on a park bench'''
__a = np.random.RandomState(0 )
__a = pipe(
prompt=_snake_case , image=_snake_case , mask_image=_snake_case , guidance_scale=7.5 , num_inference_steps=20 , generator=_snake_case , output_type='''np''' , )
__a = output.images
__a = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
__a = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3 | 6 | 1 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
from .ta_film_transformer import TaFilmDecoder
from .transformer_ad import TransformeraDModel
from .unet_ad import UNetaDModel
from .unet_ad import UNetaDModel
from .unet_ad_condition import UNetaDConditionModel
from .unet_ad_condition import UNetaDConditionModel
from .vq_model import VQModel
if is_flax_available():
from .controlnet_flax import FlaxControlNetModel
from .unet_ad_condition_flax import FlaxUNetaDConditionModel
from .vae_flax import FlaxAutoencoderKL | 6 |
from math import ceil
def __lowerCAmelCase ( a__ = 1001 ) -> int:
__a = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__a = 2 * i + 1
__a = 2 * i
__a = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
A : List[Any] = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number') | 6 | 1 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __A:
snake_case_ = 42
snake_case_ = None
# Automatically constructed
snake_case_ = "dict"
snake_case_ = None
snake_case_ = field(default='''Translation''' , init=a , repr=a )
def __call__( self ) -> Tuple:
'''simple docstring'''
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
'''simple docstring'''
from .features import Value
return {k: Value('''string''' ) for k in sorted(self.languages )}
@dataclass
class __A:
snake_case_ = None
snake_case_ = None
snake_case_ = None
# Automatically constructed
snake_case_ = "dict"
snake_case_ = None
snake_case_ = field(default='''TranslationVariableLanguages''' , init=a , repr=a )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = sorted(set(self.languages ) ) if self.languages else None
__a = len(self.languages ) if self.languages else None
def __call__( self ) -> Any:
'''simple docstring'''
return pa.struct({'''language''': pa.list_(pa.string() ), '''translation''': pa.list_(pa.string() )} )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> str:
'''simple docstring'''
__a = set(self.languages )
if self.languages and set(_snake_case ) - lang_set:
raise ValueError(
F"""Some languages in example ({', '.join(sorted(set(_snake_case ) - lang_set ) )}) are not in valid set ({', '.join(_snake_case )}).""" )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
__a = []
for lang, text in translation_dict.items():
if isinstance(_snake_case , _snake_case ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
__a , __a = zip(*sorted(_snake_case ) )
return {"language": languages, "translation": translations}
def SCREAMING_SNAKE_CASE_ ( self ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
'''simple docstring'''
from .features import Sequence, Value
return {
"language": Sequence(Value('''string''' ) ),
"translation": Sequence(Value('''string''' ) ),
} | 6 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A( a ):
snake_case_ = ['''image_processor''', '''tokenizer''']
snake_case_ = '''ChineseCLIPImageProcessor'''
snake_case_ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self , _snake_case=None , _snake_case=None , **_snake_case ) -> Tuple:
'''simple docstring'''
__a = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , _snake_case , )
__a = kwargs.pop('''feature_extractor''' )
__a = 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__(_snake_case , _snake_case )
__a = self.image_processor
def __call__( self , _snake_case=None , _snake_case=None , _snake_case=None , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
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:
__a = self.tokenizer(_snake_case , return_tensors=_snake_case , **_snake_case )
if images is not None:
__a = self.image_processor(_snake_case , return_tensors=_snake_case , **_snake_case )
if text is not None and images is not None:
__a = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_snake_case ) , tensor_type=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , *_snake_case , **_snake_case ) -> str:
'''simple docstring'''
return self.tokenizer.batch_decode(*_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , *_snake_case , **_snake_case ) -> Dict:
'''simple docstring'''
return self.tokenizer.decode(*_snake_case , **_snake_case )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = self.tokenizer.model_input_names
__a = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , _snake_case , )
return self.image_processor_class | 6 | 1 |
from __future__ import annotations
def __lowerCAmelCase ( a__ , a__ ) -> float:
__a = sorted(numsa + numsa )
__a , __a = divmod(len(a__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
A : Optional[int] = [float(x) for x in input('Enter the elements of first array: ').split()]
A : Any = [float(x) for x in input('Enter the elements of second array: ').split()]
print(F"The median of two arrays is: {median_of_two_arrays(array_a, array_a)}") | 6 |
from __future__ import annotations
import typing
from collections import Counter
def __lowerCAmelCase ( a__ ) -> typing.Counter[int]:
__a = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(a__ , max_perimeter + 1 ):
__a = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(a__ ):
__a = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def __lowerCAmelCase ( a__ = 1000 ) -> int:
__a = pythagorean_triple(a__ )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(F"Perimeter {solution()} has maximum solutions") | 6 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
A : Optional[int] = logging.get_logger(__name__)
class __A( a ):
snake_case_ = ['''pixel_values''']
def __init__( self , _snake_case = True , _snake_case = None , _snake_case = PILImageResampling.BICUBIC , _snake_case = True , _snake_case = 1 / 255 , _snake_case = True , _snake_case = None , _snake_case = None , _snake_case = True , **_snake_case , ) -> None:
'''simple docstring'''
super().__init__(**_snake_case )
__a = size if size is not None else {'''height''': 384, '''width''': 384}
__a = get_size_dict(_snake_case , default_to_square=_snake_case )
__a = do_resize
__a = size
__a = resample
__a = do_rescale
__a = rescale_factor
__a = do_normalize
__a = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__a = image_std if image_std is not None else OPENAI_CLIP_STD
__a = do_convert_rgb
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = PILImageResampling.BICUBIC , _snake_case = None , **_snake_case , ) -> np.ndarray:
'''simple docstring'''
__a = get_size_dict(_snake_case , default_to_square=_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
__a = (size['''height'''], size['''width'''])
return resize(_snake_case , size=_snake_case , resample=_snake_case , data_format=_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = None , **_snake_case , ) -> Tuple:
'''simple docstring'''
return rescale(_snake_case , scale=_snake_case , data_format=_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case = None , **_snake_case , ) -> np.ndarray:
'''simple docstring'''
return normalize(_snake_case , mean=_snake_case , std=_snake_case , data_format=_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = ChannelDimension.FIRST , **_snake_case , ) -> PIL.Image.Image:
'''simple docstring'''
__a = do_resize if do_resize is not None else self.do_resize
__a = resample if resample is not None else self.resample
__a = do_rescale if do_rescale is not None else self.do_rescale
__a = rescale_factor if rescale_factor is not None else self.rescale_factor
__a = do_normalize if do_normalize is not None else self.do_normalize
__a = image_mean if image_mean is not None else self.image_mean
__a = image_std if image_std is not None else self.image_std
__a = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__a = size if size is not None else self.size
__a = get_size_dict(_snake_case , default_to_square=_snake_case )
__a = make_list_of_images(_snake_case )
if not valid_images(_snake_case ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__a = [convert_to_rgb(_snake_case ) for image in images]
# All transformations expect numpy arrays.
__a = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
__a = [self.resize(image=_snake_case , size=_snake_case , resample=_snake_case ) for image in images]
if do_rescale:
__a = [self.rescale(image=_snake_case , scale=_snake_case ) for image in images]
if do_normalize:
__a = [self.normalize(image=_snake_case , mean=_snake_case , std=_snake_case ) for image in images]
__a = [to_channel_dimension_format(_snake_case , _snake_case ) for image in images]
__a = BatchFeature(data={'''pixel_values''': images} , tensor_type=_snake_case )
return encoded_outputs | 6 |
# flake8: noqa
# Lint as: python3
A : Optional[Any] = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental | 6 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
A : int = '\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'
A : Dict = '\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'
A : Optional[int] = '\\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 ):
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
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 SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case=None ) -> Any:
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(_snake_case , _snake_case , sample_weight=_snake_case ) ),
} | 6 |
from typing import Dict
from .base import GenericTensor, Pipeline
class __A( a ):
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=None , _snake_case=None , _snake_case=None , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
if tokenize_kwargs is None:
__a = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)''' )
__a = truncation
__a = tokenize_kwargs
__a = {}
if return_tensors is not None:
__a = return_tensors
return preprocess_params, {}, postprocess_params
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , **_snake_case ) -> Dict[str, GenericTensor]:
'''simple docstring'''
__a = self.framework
__a = self.tokenizer(_snake_case , return_tensors=_snake_case , **_snake_case )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = self.model(**_snake_case )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case=False ) -> Optional[int]:
'''simple docstring'''
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self , *_snake_case , **_snake_case ) -> Any:
'''simple docstring'''
return super().__call__(*_snake_case , **_snake_case ) | 6 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 100 * 2**20, 900 * 2**20] )
def __lowerCAmelCase ( a__ , a__ , a__ ) -> Tuple:
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , '''IN_MEMORY_MAX_SIZE''' , a__ )
__a = datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
__a = dataset_size < in_memory_max_size
else:
__a = False
__a = is_small_dataset(a__ )
assert result == expected | 6 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Optional[int] = {
'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class __A( a ):
snake_case_ = '''levit'''
def __init__( self , _snake_case=224 , _snake_case=3 , _snake_case=3 , _snake_case=2 , _snake_case=1 , _snake_case=16 , _snake_case=[128, 256, 384] , _snake_case=[4, 8, 12] , _snake_case=[4, 4, 4] , _snake_case=[16, 16, 16] , _snake_case=0 , _snake_case=[2, 2, 2] , _snake_case=[2, 2, 2] , _snake_case=0.02 , **_snake_case , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**_snake_case )
__a = image_size
__a = num_channels
__a = kernel_size
__a = stride
__a = padding
__a = hidden_sizes
__a = num_attention_heads
__a = depths
__a = key_dim
__a = drop_path_rate
__a = patch_size
__a = attention_ratio
__a = mlp_ratio
__a = initializer_range
__a = [
['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class __A( a ):
snake_case_ = version.parse('''1.11''' )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> float:
'''simple docstring'''
return 1E-4 | 6 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : str = {
'configuration_blenderbot': [
'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlenderbotConfig',
'BlenderbotOnnxConfig',
],
'tokenization_blenderbot': ['BlenderbotTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = ['BlenderbotTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = [
'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlenderbotForCausalLM',
'BlenderbotForConditionalGeneration',
'BlenderbotModel',
'BlenderbotPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Dict = [
'TFBlenderbotForConditionalGeneration',
'TFBlenderbotModel',
'TFBlenderbotPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Dict = [
'FlaxBlenderbotForConditionalGeneration',
'FlaxBlenderbotModel',
'FlaxBlenderbotPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
A : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
A : int = '0.12' # assumed parallelism: 8
@require_flax
@is_staging_test
class __A( unittest.TestCase ):
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls ) -> Union[str, Any]:
'''simple docstring'''
__a = TOKEN
HfFolder.save_token(_snake_case )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls ) -> Union[str, Any]:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='''test-model-flax''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-model-flax-org''' )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
__a = FlaxBertModel(_snake_case )
model.push_to_hub('''test-model-flax''' , use_auth_token=self._token )
__a = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" )
__a = flatten_dict(unfreeze(model.params ) )
__a = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
__a = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_snake_case , 1E-3 , msg=F"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id='''test-model-flax''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(_snake_case , repo_id='''test-model-flax''' , push_to_hub=_snake_case , use_auth_token=self._token )
__a = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" )
__a = flatten_dict(unfreeze(model.params ) )
__a = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
__a = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_snake_case , 1E-3 , msg=F"""{key} not identical""" )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
__a = FlaxBertModel(_snake_case )
model.push_to_hub('''valid_org/test-model-flax-org''' , use_auth_token=self._token )
__a = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
__a = flatten_dict(unfreeze(model.params ) )
__a = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
__a = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_snake_case , 1E-3 , msg=F"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-model-flax-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
_snake_case , repo_id='''valid_org/test-model-flax-org''' , push_to_hub=_snake_case , use_auth_token=self._token )
__a = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
__a = flatten_dict(unfreeze(model.params ) )
__a = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
__a = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_snake_case , 1E-3 , msg=F"""{key} not identical""" )
def __lowerCAmelCase ( a__ , a__ ) -> str:
__a = True
__a = flatten_dict(modela.params )
__a = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1e-4:
__a = False
return models_are_equal
@require_flax
class __A( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
__a = FlaxBertModel(_snake_case )
__a = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_snake_case , _snake_case ) )
with self.assertRaises(_snake_case ):
__a = FlaxBertModel.from_pretrained(_snake_case )
__a = FlaxBertModel.from_pretrained(_snake_case , subfolder=_snake_case )
self.assertTrue(check_models_equal(_snake_case , _snake_case ) )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
__a = FlaxBertModel(_snake_case )
__a = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_snake_case , _snake_case ) , max_shard_size='''10KB''' )
with self.assertRaises(_snake_case ):
__a = FlaxBertModel.from_pretrained(_snake_case )
__a = FlaxBertModel.from_pretrained(_snake_case , subfolder=_snake_case )
self.assertTrue(check_models_equal(_snake_case , _snake_case ) )
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
__a = '''bert'''
__a = '''hf-internal-testing/tiny-random-bert-subfolder'''
with self.assertRaises(_snake_case ):
__a = FlaxBertModel.from_pretrained(_snake_case )
__a = FlaxBertModel.from_pretrained(_snake_case , subfolder=_snake_case )
self.assertIsNotNone(_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = '''bert'''
__a = '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
with self.assertRaises(_snake_case ):
__a = FlaxBertModel.from_pretrained(_snake_case )
__a = FlaxBertModel.from_pretrained(_snake_case , subfolder=_snake_case )
self.assertIsNotNone(_snake_case ) | 6 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def __lowerCAmelCase ( a__ , a__ , a__ ) -> Optional[int]:
# Initialise PyTorch model
__a = AlbertConfig.from_json_file(a__ )
print(F"""Building PyTorch model from configuration: {config}""" )
__a = AlbertForPreTraining(a__ )
# Load weights from tf checkpoint
load_tf_weights_in_albert(a__ , a__ , a__ )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
torch.save(model.state_dict() , a__ )
if __name__ == "__main__":
A : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--albert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained ALBERT 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.'
)
A : List[str] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path) | 6 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
A : Optional[Any] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(4_2)
A : List[str] = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}
A : Optional[int] = 'zero2'
A : str = 'zero3'
A : Tuple = [ZEROa, ZEROa]
def __lowerCAmelCase ( a__ , a__ , a__ ) -> Tuple:
# customize the test name generator function as we want both params to appear in the sub-test
# name, as by default it shows only the first param
__a = parameterized.to_safe_name('''_'''.join(str(a__ ) for x in param.args ) )
return F"""{func.__name__}_{param_based_name}"""
# Cartesian-product of zero stages with models to test
A : Union[str, Any] = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class __A( a ):
@parameterized.expand(_snake_case , name_func=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Any:
'''simple docstring'''
self.run_and_check(
stage=_snake_case , model=_snake_case , distributed=_snake_case , fpaa=_snake_case , )
@require_torch_multi_gpu
@parameterized.expand(_snake_case , name_func=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> int:
'''simple docstring'''
self.run_and_check(
stage=_snake_case , model=_snake_case , distributed=_snake_case , fpaa=_snake_case , )
@parameterized.expand(_snake_case , name_func=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> str:
'''simple docstring'''
self.run_and_check(
stage=_snake_case , model=_snake_case , distributed=_snake_case , fpaa=_snake_case , )
@require_torch_multi_gpu
@parameterized.expand(_snake_case , name_func=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
self.run_and_check(
stage=_snake_case , model=_snake_case , distributed=_snake_case , fpaa=_snake_case , )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = 10 , _snake_case = True , _snake_case = True , _snake_case = True , ) -> Any:
'''simple docstring'''
__a = models[model]
__a = self.run_trainer(
stage=_snake_case , model_name=_snake_case , eval_steps=_snake_case , num_train_epochs=1 , distributed=_snake_case , fpaa=_snake_case , )
self.do_checks(_snake_case )
return output_dir
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = 10 , _snake_case = 1 , _snake_case = True , _snake_case = True , ) -> Union[str, Any]:
'''simple docstring'''
__a = self.get_auto_remove_tmp_dir('''./xxx''' , after=_snake_case )
__a = F"""
--model_name_or_path {model_name}
--dataset_name hf-internal-testing/librispeech_asr_dummy
--dataset_config_name clean
--train_split_name validation
--validation_split_name validation
--output_dir {output_dir}
--num_train_epochs {str(_snake_case )}
--per_device_train_batch_size 2
--per_device_eval_batch_size 2
--evaluation_strategy steps
--learning_rate 5e-4
--warmup_steps 8
--orthography timit
--preprocessing_num_workers 1
--group_by_length
--freeze_feature_extractor
--report_to none
--save_steps 0
--eval_steps {eval_steps}
--report_to none
""".split()
if fpaa:
args.extend(['''--fp16'''] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
__a = F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
__a = [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
__a = self.get_launcher(_snake_case )
__a = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(_snake_case , env=self.get_env() )
return output_dir
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=False ) -> List[str]:
'''simple docstring'''
__a = min(2 , get_gpu_count() ) if distributed else 1
return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split() | 6 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A : List[Any] = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')
@dataclass
class __A:
snake_case_ = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
snake_case_ = field(
default=a , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
snake_case_ = field(
default=a , metadata={'''help''': '''The column name of the images in the files.'''} )
snake_case_ = field(default=a , metadata={'''help''': '''A folder containing the training data.'''} )
snake_case_ = field(default=a , metadata={'''help''': '''A folder containing the validation data.'''} )
snake_case_ = field(
default=0.15 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
snake_case_ = field(
default=a , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
snake_case_ = field(
default=a , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
__a = {}
if self.train_dir is not None:
__a = self.train_dir
if self.validation_dir is not None:
__a = self.validation_dir
__a = data_files if data_files else None
@dataclass
class __A:
snake_case_ = field(
default=a , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
snake_case_ = field(
default=a , metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''} )
snake_case_ = field(
default=a , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
snake_case_ = field(
default=a , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
snake_case_ = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
snake_case_ = field(default=a , metadata={'''help''': '''Name or path of preprocessor config.'''} )
snake_case_ = field(
default=a , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
snake_case_ = field(
default=0.75 , metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''} )
snake_case_ = field(
default=a , metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''} )
@dataclass
class __A( a ):
snake_case_ = field(
default=1E-3 , metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''} )
def __lowerCAmelCase ( a__ ) -> Tuple:
__a = torch.stack([example['''pixel_values'''] for example in examples] )
return {"pixel_values": pixel_values}
def __lowerCAmelCase ( ) -> int:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__a = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
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.
__a , __a , __a = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__a , __a , __a = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_mae''' , a__ , a__ )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
__a = training_args.get_process_log_level()
logger.setLevel(a__ )
transformers.utils.logging.set_verbosity(a__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ F"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(F"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
__a = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__a = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Initialize our dataset.
__a = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
__a = None if '''validation''' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , a__ ) and data_args.train_val_split > 0.0:
__a = ds['''train'''].train_test_split(data_args.train_val_split )
__a = split['''train''']
__a = split['''test''']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__a = {
'''cache_dir''': model_args.cache_dir,
'''revision''': model_args.model_revision,
'''use_auth_token''': True if model_args.use_auth_token else None,
}
if model_args.config_name:
__a = ViTMAEConfig.from_pretrained(model_args.config_name , **a__ )
elif model_args.model_name_or_path:
__a = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **a__ )
else:
__a = ViTMAEConfig()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.config_overrides is not None:
logger.info(F"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(F"""New config: {config}""" )
# adapt config
config.update(
{
'''mask_ratio''': model_args.mask_ratio,
'''norm_pix_loss''': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
__a = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **a__ )
elif model_args.model_name_or_path:
__a = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **a__ )
else:
__a = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
__a = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=a__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('''Training new model from scratch''' )
__a = ViTMAEForPreTraining(a__ )
if training_args.do_train:
__a = ds['''train'''].column_names
else:
__a = ds['''validation'''].column_names
if data_args.image_column_name is not None:
__a = data_args.image_column_name
elif "image" in column_names:
__a = '''image'''
elif "img" in column_names:
__a = '''img'''
else:
__a = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
__a = image_processor.size['''shortest_edge''']
else:
__a = (image_processor.size['''height'''], image_processor.size['''width'''])
__a = Compose(
[
Lambda(lambda a__ : img.convert('''RGB''' ) if img.mode != "RGB" else img ),
RandomResizedCrop(a__ , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(a__ ):
__a = [transforms(a__ ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('''--do_train requires a train dataset''' )
if data_args.max_train_samples is not None:
__a = ds['''train'''].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(a__ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('''--do_eval requires a validation dataset''' )
if data_args.max_eval_samples is not None:
__a = (
ds['''validation'''].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(a__ )
# Compute absolute learning rate
__a = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
__a = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
__a = Trainer(
model=a__ , args=a__ , train_dataset=ds['''train'''] if training_args.do_train else None , eval_dataset=ds['''validation'''] if training_args.do_eval else None , tokenizer=a__ , data_collator=a__ , )
# Training
if training_args.do_train:
__a = None
if training_args.resume_from_checkpoint is not None:
__a = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__a = last_checkpoint
__a = trainer.train(resume_from_checkpoint=a__ )
trainer.save_model()
trainer.log_metrics('''train''' , train_result.metrics )
trainer.save_metrics('''train''' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
__a = trainer.evaluate()
trainer.log_metrics('''eval''' , a__ )
trainer.save_metrics('''eval''' , a__ )
# Write model card and (optionally) push to hub
__a = {
'''tasks''': '''masked-auto-encoding''',
'''dataset''': data_args.dataset_name,
'''tags''': ['''masked-auto-encoding'''],
}
if training_args.push_to_hub:
trainer.push_to_hub(**a__ )
else:
trainer.create_model_card(**a__ )
def __lowerCAmelCase ( a__ ) -> Optional[int]:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main() | 6 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __A( a , a , unittest.TestCase ):
snake_case_ = AutoencoderKL
snake_case_ = '''sample'''
snake_case_ = 1E-2
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = 4
__a = 3
__a = (32, 32)
__a = floats_tensor((batch_size, num_channels) + sizes ).to(_snake_case )
return {"sample": image}
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
return (3, 32, 32)
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
return (3, 32, 32)
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = {
'''block_out_channels''': [32, 64],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 4,
}
__a = self.dummy_input
return init_dict, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skipIf(torch_device == '''mps''' , '''Gradient checkpointing skipped on MPS''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
__a , __a = self.prepare_init_args_and_inputs_for_common()
__a = self.model_class(**_snake_case )
model.to(_snake_case )
assert not model.is_gradient_checkpointing and model.training
__a = model(**_snake_case ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
__a = torch.randn_like(_snake_case )
__a = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
__a = self.model_class(**_snake_case )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(_snake_case )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
__a = model_a(**_snake_case ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
__a = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1E-5 )
__a = dict(model.named_parameters() )
__a = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5E-5 ) )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a , __a = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' , output_loading_info=_snake_case )
self.assertIsNotNone(_snake_case )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(_snake_case )
__a = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' )
__a = model.to(_snake_case )
model.eval()
if torch_device == "mps":
__a = torch.manual_seed(0 )
else:
__a = torch.Generator(device=_snake_case ).manual_seed(0 )
__a = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
__a = image.to(_snake_case )
with torch.no_grad():
__a = model(_snake_case , sample_posterior=_snake_case , generator=_snake_case ).sample
__a = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
__a = torch.tensor(
[
-4.0_078E-01,
-3.8_323E-04,
-1.2_681E-01,
-1.1_462E-01,
2.0_095E-01,
1.0_893E-01,
-8.8_247E-02,
-3.0_361E-01,
-9.8_644E-03,
] )
elif torch_device == "cpu":
__a = torch.tensor(
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] )
else:
__a = torch.tensor(
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485] )
self.assertTrue(torch_all_close(_snake_case , _snake_case , rtol=1E-2 ) )
@slow
class __A( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
return F"""gaussian_noise_s={seed}_shape={'_'.join([str(_snake_case ) for s in shape] )}.npy"""
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=0 , _snake_case=(4, 3, 512, 512) , _snake_case=False ) -> Any:
'''simple docstring'''
__a = torch.floataa if fpaa else torch.floataa
__a = torch.from_numpy(load_hf_numpy(self.get_file_format(_snake_case , _snake_case ) ) ).to(_snake_case ).to(_snake_case )
return image
def SCREAMING_SNAKE_CASE_ ( self , _snake_case="CompVis/stable-diffusion-v1-4" , _snake_case=False ) -> Optional[Any]:
'''simple docstring'''
__a = '''fp16''' if fpaa else None
__a = torch.floataa if fpaa else torch.floataa
__a = AutoencoderKL.from_pretrained(
_snake_case , subfolder='''vae''' , torch_dtype=_snake_case , revision=_snake_case , )
model.to(_snake_case ).eval()
return model
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=0 ) -> Tuple:
'''simple docstring'''
if torch_device == "mps":
return torch.manual_seed(_snake_case )
return torch.Generator(device=_snake_case ).manual_seed(_snake_case )
@parameterized.expand(
[
# fmt: off
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case ) -> List[Any]:
'''simple docstring'''
__a = self.get_sd_vae_model()
__a = self.get_sd_image(_snake_case )
__a = self.get_generator(_snake_case )
with torch.no_grad():
__a = model(_snake_case , generator=_snake_case , sample_posterior=_snake_case ).sample
assert sample.shape == image.shape
__a = sample[-1, -2:, -2:, :2].flatten().float().cpu()
__a = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(_snake_case , _snake_case , atol=3E-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
# fmt: on
] )
@require_torch_gpu
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Tuple:
'''simple docstring'''
__a = self.get_sd_vae_model(fpaa=_snake_case )
__a = self.get_sd_image(_snake_case , fpaa=_snake_case )
__a = self.get_generator(_snake_case )
with torch.no_grad():
__a = model(_snake_case , generator=_snake_case , sample_posterior=_snake_case ).sample
assert sample.shape == image.shape
__a = sample[-1, -2:, :2, -2:].flatten().float().cpu()
__a = torch.tensor(_snake_case )
assert torch_all_close(_snake_case , _snake_case , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = self.get_sd_vae_model()
__a = self.get_sd_image(_snake_case )
with torch.no_grad():
__a = model(_snake_case ).sample
assert sample.shape == image.shape
__a = sample[-1, -2:, -2:, :2].flatten().float().cpu()
__a = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(_snake_case , _snake_case , atol=3E-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
# fmt: on
] )
@require_torch_gpu
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = self.get_sd_vae_model()
__a = self.get_sd_image(_snake_case , shape=(3, 4, 64, 64) )
with torch.no_grad():
__a = model.decode(_snake_case ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
__a = sample[-1, -2:, :2, -2:].flatten().cpu()
__a = torch.tensor(_snake_case )
assert torch_all_close(_snake_case , _snake_case , atol=1E-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
# fmt: on
] )
@require_torch_gpu
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = self.get_sd_vae_model(fpaa=_snake_case )
__a = self.get_sd_image(_snake_case , shape=(3, 4, 64, 64) , fpaa=_snake_case )
with torch.no_grad():
__a = model.decode(_snake_case ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
__a = sample[-1, -2:, :2, -2:].flatten().float().cpu()
__a = torch.tensor(_snake_case )
assert torch_all_close(_snake_case , _snake_case , atol=5E-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Union[str, Any]:
'''simple docstring'''
__a = self.get_sd_vae_model(fpaa=_snake_case )
__a = self.get_sd_image(_snake_case , shape=(3, 4, 64, 64) , fpaa=_snake_case )
with torch.no_grad():
__a = model.decode(_snake_case ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
__a = model.decode(_snake_case ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(_snake_case , _snake_case , atol=1E-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> List[str]:
'''simple docstring'''
__a = self.get_sd_vae_model()
__a = self.get_sd_image(_snake_case , shape=(3, 4, 64, 64) )
with torch.no_grad():
__a = model.decode(_snake_case ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
__a = model.decode(_snake_case ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(_snake_case , _snake_case , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
# fmt: on
] )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = self.get_sd_vae_model()
__a = self.get_sd_image(_snake_case )
__a = self.get_generator(_snake_case )
with torch.no_grad():
__a = model.encode(_snake_case ).latent_dist
__a = dist.sample(generator=_snake_case )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
__a = sample[0, -1, -3:, -3:].flatten().cpu()
__a = torch.tensor(_snake_case )
__a = 3E-3 if torch_device != '''mps''' else 1E-2
assert torch_all_close(_snake_case , _snake_case , atol=_snake_case ) | 6 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
if is_tf_available():
import tensorflow as tf
from transformers import TFBlipTextModel
from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST
class __A:
def __init__( self , _snake_case , _snake_case=12 , _snake_case=7 , _snake_case=True , _snake_case=True , _snake_case=True , _snake_case=99 , _snake_case=32 , _snake_case=32 , _snake_case=2 , _snake_case=4 , _snake_case=37 , _snake_case=0.1 , _snake_case=0.1 , _snake_case=512 , _snake_case=0.02 , _snake_case=0 , _snake_case=None , ) -> Dict:
'''simple docstring'''
__a = parent
__a = batch_size
__a = seq_length
__a = is_training
__a = use_input_mask
__a = use_labels
__a = vocab_size
__a = hidden_size
__a = projection_dim
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = dropout
__a = attention_dropout
__a = max_position_embeddings
__a = initializer_range
__a = scope
__a = bos_token_id
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a = None
if self.use_input_mask:
__a = random_attention_mask([self.batch_size, self.seq_length] )
if input_mask is not None:
__a = input_mask.numpy()
__a , __a = input_mask.shape
__a = np.random.randint(1 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(_snake_case ):
__a = 1
__a = 0
__a = self.get_config()
return config, input_ids, tf.convert_to_tensor(_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
return BlipTextConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , projection_dim=self.projection_dim , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , dropout=self.dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , bos_token_id=self.bos_token_id , )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = TFBlipTextModel(config=_snake_case )
__a = model(_snake_case , attention_mask=_snake_case , training=_snake_case )
__a = model(_snake_case , training=_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = self.prepare_config_and_inputs()
__a , __a , __a = config_and_inputs
__a = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __A( a , unittest.TestCase ):
snake_case_ = (TFBlipTextModel,) if is_tf_available() else ()
snake_case_ = False
snake_case_ = False
snake_case_ = False
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = BlipTextModelTester(self )
__a = ConfigTester(self , config_class=_snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''Blip does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
pass
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a = TFBlipTextModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=True ) -> Optional[Any]:
'''simple docstring'''
super().test_pt_tf_model_equivalence(allow_missing_keys=_snake_case ) | 6 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
A : str = logging.get_logger(__name__)
class __A( a ):
def __init__( self , **_snake_case ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''bs4'''] )
super().__init__(**_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> int:
'''simple docstring'''
__a = []
__a = []
__a = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
__a = parent.find_all(child.name , recursive=_snake_case )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(_snake_case ) else next(i for i, s in enumerate(_snake_case , 1 ) if s is child ) )
__a = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = BeautifulSoup(_snake_case , '''html.parser''' )
__a = []
__a = []
__a = []
for element in html_code.descendants:
if type(_snake_case ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
__a = html.unescape(_snake_case ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(_snake_case )
__a , __a = self.xpath_soup(_snake_case )
stringaxtag_seq.append(_snake_case )
stringaxsubs_seq.append(_snake_case )
if len(_snake_case ) != len(_snake_case ):
raise ValueError('''Number of doc strings and xtags does not correspond''' )
if len(_snake_case ) != len(_snake_case ):
raise ValueError('''Number of doc strings and xsubs does not correspond''' )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = ''''''
for tagname, subs in zip(_snake_case , _snake_case ):
xpath += F"""/{tagname}"""
if subs != 0:
xpath += F"""[{subs}]"""
return xpath
def __call__( self , _snake_case ) -> BatchFeature:
'''simple docstring'''
__a = False
# Check that strings has a valid type
if isinstance(_snake_case , _snake_case ):
__a = True
elif isinstance(_snake_case , (list, tuple) ):
if len(_snake_case ) == 0 or isinstance(html_strings[0] , _snake_case ):
__a = True
if not valid_strings:
raise ValueError(
'''HTML strings must of type `str`, `List[str]` (batch of examples), '''
F"""but is of type {type(_snake_case )}.""" )
__a = bool(isinstance(_snake_case , (list, tuple) ) and (isinstance(html_strings[0] , _snake_case )) )
if not is_batched:
__a = [html_strings]
# Get nodes + xpaths
__a = []
__a = []
for html_string in html_strings:
__a , __a , __a = self.get_three_from_single(_snake_case )
nodes.append(_snake_case )
__a = []
for node, tag_list, sub_list in zip(_snake_case , _snake_case , _snake_case ):
__a = self.construct_xpath(_snake_case , _snake_case )
xpath_strings.append(_snake_case )
xpaths.append(_snake_case )
# return as Dict
__a = {'''nodes''': nodes, '''xpaths''': xpaths}
__a = BatchFeature(data=_snake_case , tensor_type=_snake_case )
return encoded_inputs | 6 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A : int = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfig']
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[Any] = ['VisionEncoderDecoderModel']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[str] = ['TFVisionEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = ['FlaxVisionEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
A : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 |
def __lowerCAmelCase ( a__ , a__ ) -> float:
def get_matched_characters(a__ , a__ ) -> str:
__a = []
__a = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
__a = int(max(0 , i - limit ) )
__a = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(a__ )
__a = F"""{_stra[0:_stra.index(a__ )]} {_stra[_stra.index(a__ ) + 1:]}"""
return "".join(a__ )
# matching characters
__a = get_matched_characters(a__ , a__ )
__a = get_matched_characters(a__ , a__ )
__a = len(a__ )
# transposition
__a = (
len([(ca, ca) for ca, ca in zip(a__ , a__ ) if ca != ca] ) // 2
)
if not match_count:
__a = 0.0
else:
__a = (
1
/ 3
* (
match_count / len(a__ )
+ match_count / len(a__ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
__a = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world')) | 6 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Tuple = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'GraphormerForGraphClassification',
'GraphormerModel',
'GraphormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_graphormer import (
GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
GraphormerForGraphClassification,
GraphormerModel,
GraphormerPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 |
def __lowerCAmelCase ( a__ ) -> str:
__a = []
__a = set({'''(''', '''[''', '''{'''} )
__a = set({''')''', ''']''', '''}'''} )
__a = {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}
for i in range(len(a__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(a__ ) == 0 or (len(a__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(a__ ) == 0
def __lowerCAmelCase ( ) -> Dict:
__a = input('''Enter sequence of brackets: ''' )
if is_balanced(a__ ):
print(a__ , '''is balanced''' )
else:
print(a__ , '''is not balanced''' )
if __name__ == "__main__":
main() | 6 | 1 |
import unittest
from knapsack import greedy_knapsack as kp
class __A( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = [10, 20, 30, 40, 50, 60]
__a = [2, 4, 6, 8, 10, 12]
__a = 100
self.assertEqual(kp.calc_profit(_snake_case , _snake_case , _snake_case ) , 210 )
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
self.assertRaisesRegex(_snake_case , '''max_weight must greater than zero.''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
self.assertRaisesRegex(_snake_case , '''Weight can not be negative.''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
self.assertRaisesRegex(_snake_case , '''Profit can not be negative.''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
self.assertRaisesRegex(_snake_case , '''max_weight must greater than zero.''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
self.assertRaisesRegex(
_snake_case , '''The length of profit and weight must be same.''' )
if __name__ == "__main__":
unittest.main() | 6 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : str = {
'configuration_blenderbot': [
'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlenderbotConfig',
'BlenderbotOnnxConfig',
],
'tokenization_blenderbot': ['BlenderbotTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = ['BlenderbotTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = [
'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlenderbotForCausalLM',
'BlenderbotForConditionalGeneration',
'BlenderbotModel',
'BlenderbotPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Dict = [
'TFBlenderbotForConditionalGeneration',
'TFBlenderbotModel',
'TFBlenderbotPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Dict = [
'FlaxBlenderbotForConditionalGeneration',
'FlaxBlenderbotModel',
'FlaxBlenderbotPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
A : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 | 1 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def __lowerCAmelCase ( a__ , a__ , a__=[] ) -> Any:
__a = size[0] - overlap_pixels * 2
__a = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
__a = np.ones((size_y, size_x) , dtype=np.uinta ) * 255
__a = np.pad(a__ , mode='''linear_ramp''' , pad_width=a__ , end_values=0 )
if "l" in remove_borders:
__a = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
__a = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
__a = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
__a = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def __lowerCAmelCase ( a__ , a__ , a__ ) -> str:
return max(a__ , min(a__ , a__ ) )
def __lowerCAmelCase ( a__ , a__ , a__ ) -> int:
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def __lowerCAmelCase ( a__ , a__ , a__ ) -> str:
__a = list(a__ )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
__a = clamp_rect(a__ , [0, 0] , [image_size[0], image_size[1]] )
return rect
def __lowerCAmelCase ( a__ , a__ , a__ , a__ ) -> List[str]:
__a = Image.new('''RGB''' , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(a__ , (original_slice, 0) )
return result
def __lowerCAmelCase ( a__ , a__ ) -> List[str]:
__a = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
__a = tile.crop(a__ )
return tile
def __lowerCAmelCase ( a__ , a__ ) -> Any:
__a = n % d
return n - divisor
class __A( a ):
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case = 350 , ) -> int:
'''simple docstring'''
super().__init__(
vae=_snake_case , text_encoder=_snake_case , tokenizer=_snake_case , unet=_snake_case , low_res_scheduler=_snake_case , scheduler=_snake_case , max_noise_level=_snake_case , )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , **_snake_case ) -> Tuple:
'''simple docstring'''
torch.manual_seed(0 )
__a = (
min(image.size[0] - (tile_size + original_image_slice) , x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice) , y * tile_size ),
min(image.size[0] , (x + 1) * tile_size ),
min(image.size[1] , (y + 1) * tile_size ),
)
__a = add_overlap_rect(_snake_case , _snake_case , image.size )
__a = image.crop(_snake_case )
__a = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
__a = translated_slice_x - (original_image_slice / 2)
__a = max(0 , _snake_case )
__a = squeeze_tile(_snake_case , _snake_case , _snake_case , _snake_case )
__a = to_input.size
__a = to_input.resize((tile_size, tile_size) , Image.BICUBIC )
__a = super(_snake_case , self ).__call__(image=_snake_case , **_snake_case ).images[0]
__a = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4) , Image.BICUBIC )
__a = unsqueeze_tile(_snake_case , _snake_case )
__a = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4) , Image.BICUBIC )
__a = []
if x == 0:
remove_borders.append('''l''' )
elif crop_rect[2] == image.size[0]:
remove_borders.append('''r''' )
if y == 0:
remove_borders.append('''t''' )
elif crop_rect[3] == image.size[1]:
remove_borders.append('''b''' )
__a = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]) , tile_border * 4 , remove_borders=_snake_case ) , mode='''L''' , )
final_image.paste(
_snake_case , (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4) , _snake_case )
@torch.no_grad()
def __call__( self , _snake_case , _snake_case , _snake_case = 75 , _snake_case = 9.0 , _snake_case = 50 , _snake_case = None , _snake_case = 1 , _snake_case = 0.0 , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = 1 , _snake_case = 128 , _snake_case = 32 , _snake_case = 32 , ) -> Optional[int]:
'''simple docstring'''
__a = Image.new('''RGB''' , (image.size[0] * 4, image.size[1] * 4) )
__a = math.ceil(image.size[0] / tile_size )
__a = math.ceil(image.size[1] / tile_size )
__a = tcx * tcy
__a = 0
for y in range(_snake_case ):
for x in range(_snake_case ):
self._process_tile(
_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , prompt=_snake_case , num_inference_steps=_snake_case , guidance_scale=_snake_case , noise_level=_snake_case , negative_prompt=_snake_case , num_images_per_prompt=_snake_case , eta=_snake_case , generator=_snake_case , latents=_snake_case , )
current_count += 1
if callback is not None:
callback({'''progress''': current_count / total_tile_count, '''image''': final_image} )
return final_image
def __lowerCAmelCase ( ) -> Optional[Any]:
# Run a demo
__a = '''stabilityai/stable-diffusion-x4-upscaler'''
__a = StableDiffusionTiledUpscalePipeline.from_pretrained(a__ , revision='''fp16''' , torch_dtype=torch.floataa )
__a = pipe.to('''cuda''' )
__a = Image.open('''../../docs/source/imgs/diffusers_library.jpg''' )
def callback(a__ ):
print(F"""progress: {obj['progress']:.4f}""" )
obj["image"].save('''diffusers_library_progress.jpg''' )
__a = pipe(image=a__ , prompt='''Black font, white background, vector''' , noise_level=40 , callback=a__ )
final_image.save('''diffusers_library.jpg''' )
if __name__ == "__main__":
main() | 6 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Dict = {
'configuration_xlm_roberta': [
'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaConfig',
'XLMRobertaOnnxConfig',
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = ['XLMRobertaTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = ['XLMRobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[Any] = [
'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaForCausalLM',
'XLMRobertaForMaskedLM',
'XLMRobertaForMultipleChoice',
'XLMRobertaForQuestionAnswering',
'XLMRobertaForSequenceClassification',
'XLMRobertaForTokenClassification',
'XLMRobertaModel',
'XLMRobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = [
'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFXLMRobertaForCausalLM',
'TFXLMRobertaForMaskedLM',
'TFXLMRobertaForMultipleChoice',
'TFXLMRobertaForQuestionAnswering',
'TFXLMRobertaForSequenceClassification',
'TFXLMRobertaForTokenClassification',
'TFXLMRobertaModel',
'TFXLMRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'FlaxXLMRobertaForMaskedLM',
'FlaxXLMRobertaForCausalLM',
'FlaxXLMRobertaForMultipleChoice',
'FlaxXLMRobertaForQuestionAnswering',
'FlaxXLMRobertaForSequenceClassification',
'FlaxXLMRobertaForTokenClassification',
'FlaxXLMRobertaModel',
'FlaxXLMRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 | 1 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
A : Dict = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.linear_1.weight'),
('time_embed.0.bias', 'time_embedding.linear_1.bias'),
('time_embed.2.weight', 'time_embedding.linear_2.weight'),
('time_embed.2.bias', 'time_embedding.linear_2.bias'),
('input_blocks.0.0.weight', 'conv_in.weight'),
('input_blocks.0.0.bias', 'conv_in.bias'),
('out.0.weight', 'conv_norm_out.weight'),
('out.0.bias', 'conv_norm_out.bias'),
('out.2.weight', 'conv_out.weight'),
('out.2.bias', 'conv_out.bias'),
]
A : List[Any] = [
# (stable-diffusion, HF Diffusers)
('in_layers.0', 'norm1'),
('in_layers.2', 'conv1'),
('out_layers.0', 'norm2'),
('out_layers.3', 'conv2'),
('emb_layers.1', 'time_emb_proj'),
('skip_connection', 'conv_shortcut'),
]
A : List[Any] = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
A : Dict = F"down_blocks.{i}.resnets.{j}."
A : int = F"input_blocks.{3*i + j + 1}.0."
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
A : List[Any] = F"down_blocks.{i}.attentions.{j}."
A : Optional[int] = F"input_blocks.{3*i + j + 1}.1."
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
A : Optional[int] = F"up_blocks.{i}.resnets.{j}."
A : List[Any] = F"output_blocks.{3*i + j}.0."
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
A : Optional[int] = F"up_blocks.{i}.attentions.{j}."
A : Dict = F"output_blocks.{3*i + j}.1."
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
A : Dict = F"down_blocks.{i}.downsamplers.0.conv."
A : Dict = F"input_blocks.{3*(i+1)}.0.op."
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
A : str = F"up_blocks.{i}.upsamplers.0."
A : int = F"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
A : Tuple = 'mid_block.attentions.0.'
A : List[str] = 'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
A : List[Any] = F"mid_block.resnets.{j}."
A : List[Any] = F"middle_block.{2*j}."
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def __lowerCAmelCase ( a__ ) -> Tuple:
# buyer beware: this is a *brittle* function,
# and correct output requires that all of these pieces interact in
# the exact order in which I have arranged them.
__a = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
__a = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
__a = v.replace(a__ , a__ )
__a = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
__a = v.replace(a__ , a__ )
__a = v
__a = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
A : str = [
# (stable-diffusion, HF Diffusers)
('nin_shortcut', 'conv_shortcut'),
('norm_out', 'conv_norm_out'),
('mid.attn_1.', 'mid_block.attentions.0.'),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
A : Union[str, Any] = F"encoder.down_blocks.{i}.resnets.{j}."
A : int = F"encoder.down.{i}.block.{j}."
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
A : List[str] = F"down_blocks.{i}.downsamplers.0."
A : List[Any] = F"down.{i}.downsample."
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
A : Any = F"up_blocks.{i}.upsamplers.0."
A : List[Any] = F"up.{3-i}.upsample."
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
A : str = F"decoder.up_blocks.{i}.resnets.{j}."
A : Union[str, Any] = F"decoder.up.{3-i}.block.{j}."
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
A : str = F"mid_block.resnets.{i}."
A : Tuple = F"mid.block_{i+1}."
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
A : Optional[Any] = [
# (stable-diffusion, HF Diffusers)
('norm.', 'group_norm.'),
('q.', 'query.'),
('k.', 'key.'),
('v.', 'value.'),
('proj_out.', 'proj_attn.'),
]
def __lowerCAmelCase ( a__ ) -> Dict:
# convert HF linear weights to SD conv2d weights
return w.reshape(*w.shape , 1 , 1 )
def __lowerCAmelCase ( a__ ) -> List[Any]:
__a = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
__a = v.replace(a__ , a__ )
__a = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
__a = v.replace(a__ , a__ )
__a = v
__a = {v: vae_state_dict[k] for k, v in mapping.items()}
__a = ['''q''', '''k''', '''v''', '''proj_out''']
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if F"""mid.attn_1.{weight_name}.weight""" in k:
print(F"""Reshaping {k} for SD format""" )
__a = reshape_weight_for_sd(a__ )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
A : Any = [
# (stable-diffusion, HF Diffusers)
('resblocks.', 'text_model.encoder.layers.'),
('ln_1', 'layer_norm1'),
('ln_2', 'layer_norm2'),
('.c_fc.', '.fc1.'),
('.c_proj.', '.fc2.'),
('.attn', '.self_attn'),
('ln_final.', 'transformer.text_model.final_layer_norm.'),
('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
A : Optional[int] = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A : Dict = re.compile('|'.join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
A : int = {'q': 0, 'k': 1, 'v': 2}
def __lowerCAmelCase ( a__ ) -> Tuple:
__a = {}
__a = {}
__a = {}
for k, v in text_enc_dict.items():
if (
k.endswith('''.self_attn.q_proj.weight''' )
or k.endswith('''.self_attn.k_proj.weight''' )
or k.endswith('''.self_attn.v_proj.weight''' )
):
__a = k[: -len('''.q_proj.weight''' )]
__a = k[-len('''q_proj.weight''' )]
if k_pre not in capture_qkv_weight:
__a = [None, None, None]
__a = v
continue
if (
k.endswith('''.self_attn.q_proj.bias''' )
or k.endswith('''.self_attn.k_proj.bias''' )
or k.endswith('''.self_attn.v_proj.bias''' )
):
__a = k[: -len('''.q_proj.bias''' )]
__a = k[-len('''q_proj.bias''' )]
if k_pre not in capture_qkv_bias:
__a = [None, None, None]
__a = v
continue
__a = textenc_pattern.sub(lambda a__ : protected[re.escape(m.group(0 ) )] , a__ )
__a = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing''' )
__a = textenc_pattern.sub(lambda a__ : protected[re.escape(m.group(0 ) )] , a__ )
__a = torch.cat(a__ )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing''' )
__a = textenc_pattern.sub(lambda a__ : protected[re.escape(m.group(0 ) )] , a__ )
__a = torch.cat(a__ )
return new_state_dict
def __lowerCAmelCase ( a__ ) -> Any:
return text_enc_dict
if __name__ == "__main__":
A : Tuple = argparse.ArgumentParser()
parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
)
A : List[str] = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
A : int = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
A : Union[str, Any] = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
A : Union[str, Any] = osp.join(args.model_path, 'text_encoder', 'model.safetensors')
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
A : Tuple = load_file(unet_path, device='cpu')
else:
A : List[Any] = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
A : List[str] = torch.load(unet_path, map_location='cpu')
if osp.exists(vae_path):
A : Optional[int] = load_file(vae_path, device='cpu')
else:
A : Dict = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
A : List[Any] = torch.load(vae_path, map_location='cpu')
if osp.exists(text_enc_path):
A : int = load_file(text_enc_path, device='cpu')
else:
A : Optional[Any] = osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
A : Optional[Any] = torch.load(text_enc_path, map_location='cpu')
# Convert the UNet model
A : Union[str, Any] = convert_unet_state_dict(unet_state_dict)
A : Optional[Any] = {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
A : Any = convert_vae_state_dict(vae_state_dict)
A : List[Any] = {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
A : List[str] = 'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
A : Dict = {'transformer.' + k: v for k, v in text_enc_dict.items()}
A : str = convert_text_enc_state_dict_vaa(text_enc_dict)
A : Union[str, Any] = {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
else:
A : int = convert_text_enc_state_dict(text_enc_dict)
A : Optional[Any] = {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
A : List[Any] = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
A : Dict = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
A : str = {'state_dict': state_dict}
torch.save(state_dict, args.checkpoint_path) | 6 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Optional[int] = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
'feature_extraction_whisper': ['WhisperFeatureExtractor'],
'processing_whisper': ['WhisperProcessor'],
'tokenization_whisper': ['WhisperTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = ['WhisperTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
'WhisperForConditionalGeneration',
'WhisperModel',
'WhisperPreTrainedModel',
'WhisperForAudioClassification',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFWhisperForConditionalGeneration',
'TFWhisperModel',
'TFWhisperPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = [
'FlaxWhisperForConditionalGeneration',
'FlaxWhisperModel',
'FlaxWhisperPreTrainedModel',
'FlaxWhisperForAudioClassification',
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : str = logging.get_logger(__name__)
A : Dict = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN models at https://huggingface.co/models?filter=glpn
}
class __A( a ):
snake_case_ = '''glpn'''
def __init__( self , _snake_case=3 , _snake_case=4 , _snake_case=[2, 2, 2, 2] , _snake_case=[8, 4, 2, 1] , _snake_case=[32, 64, 160, 256] , _snake_case=[7, 3, 3, 3] , _snake_case=[4, 2, 2, 2] , _snake_case=[1, 2, 5, 8] , _snake_case=[4, 4, 4, 4] , _snake_case="gelu" , _snake_case=0.0 , _snake_case=0.0 , _snake_case=0.02 , _snake_case=0.1 , _snake_case=1E-6 , _snake_case=64 , _snake_case=10 , _snake_case=-1 , **_snake_case , ) -> List[Any]:
'''simple docstring'''
super().__init__(**_snake_case )
__a = num_channels
__a = num_encoder_blocks
__a = depths
__a = sr_ratios
__a = hidden_sizes
__a = patch_sizes
__a = strides
__a = mlp_ratios
__a = num_attention_heads
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = initializer_range
__a = drop_path_rate
__a = layer_norm_eps
__a = decoder_hidden_size
__a = max_depth
__a = head_in_index | 6 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
'stable diffusion controlnet',
'0.22.0',
'Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.',
standard_warn=False,
stacklevel=3,
) | 6 | 1 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __A( a ):
snake_case_ = (PNDMScheduler,)
snake_case_ = (('''num_inference_steps''', 5_0),)
def SCREAMING_SNAKE_CASE_ ( self , **_snake_case ) -> str:
'''simple docstring'''
__a = {
'''num_train_timesteps''': 1_000,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
}
config.update(**_snake_case )
return config
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=0 , **_snake_case ) -> Any:
'''simple docstring'''
__a = dict(self.forward_default_kwargs )
__a = kwargs.pop('''num_inference_steps''' , _snake_case )
__a = self.dummy_sample
__a = 0.1 * sample
__a = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
__a = self.get_scheduler_config(**_snake_case )
__a = scheduler_class(**_snake_case )
scheduler.set_timesteps(_snake_case )
# copy over dummy past residuals
__a = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_snake_case )
__a = scheduler_class.from_pretrained(_snake_case )
new_scheduler.set_timesteps(_snake_case )
# copy over dummy past residuals
__a = dummy_past_residuals[:]
__a = scheduler.step_prk(_snake_case , _snake_case , _snake_case , **_snake_case ).prev_sample
__a = new_scheduler.step_prk(_snake_case , _snake_case , _snake_case , **_snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
__a = scheduler.step_plms(_snake_case , _snake_case , _snake_case , **_snake_case ).prev_sample
__a = new_scheduler.step_plms(_snake_case , _snake_case , _snake_case , **_snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=0 , **_snake_case ) -> Union[str, Any]:
'''simple docstring'''
__a = dict(self.forward_default_kwargs )
__a = kwargs.pop('''num_inference_steps''' , _snake_case )
__a = self.dummy_sample
__a = 0.1 * sample
__a = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
__a = self.get_scheduler_config()
__a = scheduler_class(**_snake_case )
scheduler.set_timesteps(_snake_case )
# copy over dummy past residuals (must be after setting timesteps)
__a = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(_snake_case )
__a = scheduler_class.from_pretrained(_snake_case )
# copy over dummy past residuals
new_scheduler.set_timesteps(_snake_case )
# copy over dummy past residual (must be after setting timesteps)
__a = dummy_past_residuals[:]
__a = scheduler.step_prk(_snake_case , _snake_case , _snake_case , **_snake_case ).prev_sample
__a = new_scheduler.step_prk(_snake_case , _snake_case , _snake_case , **_snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
__a = scheduler.step_plms(_snake_case , _snake_case , _snake_case , **_snake_case ).prev_sample
__a = new_scheduler.step_plms(_snake_case , _snake_case , _snake_case , **_snake_case ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def SCREAMING_SNAKE_CASE_ ( self , **_snake_case ) -> Tuple:
'''simple docstring'''
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config(**_snake_case )
__a = scheduler_class(**_snake_case )
__a = 10
__a = self.dummy_model()
__a = self.dummy_sample_deter
scheduler.set_timesteps(_snake_case )
for i, t in enumerate(scheduler.prk_timesteps ):
__a = model(_snake_case , _snake_case )
__a = scheduler.step_prk(_snake_case , _snake_case , _snake_case ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
__a = model(_snake_case , _snake_case )
__a = scheduler.step_plms(_snake_case , _snake_case , _snake_case ).prev_sample
return sample
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = dict(self.forward_default_kwargs )
__a = kwargs.pop('''num_inference_steps''' , _snake_case )
for scheduler_class in self.scheduler_classes:
__a = self.get_scheduler_config()
__a = scheduler_class(**_snake_case )
__a = self.dummy_sample
__a = 0.1 * sample
if num_inference_steps is not None and hasattr(_snake_case , '''set_timesteps''' ):
scheduler.set_timesteps(_snake_case )
elif num_inference_steps is not None and not hasattr(_snake_case , '''set_timesteps''' ):
__a = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
__a = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
__a = dummy_past_residuals[:]
__a = scheduler.step_prk(_snake_case , 0 , _snake_case , **_snake_case ).prev_sample
__a = scheduler.step_prk(_snake_case , 1 , _snake_case , **_snake_case ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
__a = scheduler.step_plms(_snake_case , 0 , _snake_case , **_snake_case ).prev_sample
__a = scheduler.step_plms(_snake_case , 1 , _snake_case , **_snake_case ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
for timesteps in [100, 1_000]:
self.check_over_configs(num_train_timesteps=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=_snake_case )
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config(steps_offset=1 )
__a = scheduler_class(**_snake_case )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) , )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
for beta_start, beta_end in zip([0.0001, 0.001] , [0.002, 0.02] ):
self.check_over_configs(beta_start=_snake_case , beta_end=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
for t in [1, 5, 10]:
self.check_over_forward(time_step=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = 27
for scheduler_class in self.scheduler_classes:
__a = self.dummy_sample
__a = 0.1 * sample
__a = self.get_scheduler_config()
__a = scheduler_class(**_snake_case )
scheduler.set_timesteps(_snake_case )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
__a = scheduler.step_prk(_snake_case , _snake_case , _snake_case ).prev_sample
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
with self.assertRaises(_snake_case ):
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config()
__a = scheduler_class(**_snake_case )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = self.full_loop()
__a = torch.sum(torch.abs(_snake_case ) )
__a = torch.mean(torch.abs(_snake_case ) )
assert abs(result_sum.item() - 198.1318 ) < 1E-2
assert abs(result_mean.item() - 0.2580 ) < 1E-3
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = self.full_loop(prediction_type='''v_prediction''' )
__a = torch.sum(torch.abs(_snake_case ) )
__a = torch.mean(torch.abs(_snake_case ) )
assert abs(result_sum.item() - 67.3986 ) < 1E-2
assert abs(result_mean.item() - 0.0878 ) < 1E-3
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = self.full_loop(set_alpha_to_one=_snake_case , beta_start=0.01 )
__a = torch.sum(torch.abs(_snake_case ) )
__a = torch.mean(torch.abs(_snake_case ) )
assert abs(result_sum.item() - 230.0399 ) < 1E-2
assert abs(result_mean.item() - 0.2995 ) < 1E-3
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = self.full_loop(set_alpha_to_one=_snake_case , beta_start=0.01 )
__a = torch.sum(torch.abs(_snake_case ) )
__a = torch.mean(torch.abs(_snake_case ) )
assert abs(result_sum.item() - 186.9482 ) < 1E-2
assert abs(result_mean.item() - 0.2434 ) < 1E-3 | 6 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a )
class __A( a ):
snake_case_ = field(default='''language-modeling''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
snake_case_ = Features({'''text''': Value('''string''' )} )
snake_case_ = Features({} )
snake_case_ = "text"
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict[str, str]:
'''simple docstring'''
return {self.text_column: "text"} | 6 | 1 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def __lowerCAmelCase ( a__ ) -> Union[str, Any]:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class __A( a ):
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _snake_case ) -> int:
'''simple docstring'''
__a = parser.add_parser('''download''' )
download_parser.add_argument(
'''--cache-dir''' , type=_snake_case , default=_snake_case , help='''Path to location to store the models''' )
download_parser.add_argument(
'''--force''' , action='''store_true''' , help='''Force the model to be download even if already in cache-dir''' )
download_parser.add_argument(
'''--trust-remote-code''' , action='''store_true''' , help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine''' , )
download_parser.add_argument('''model''' , type=_snake_case , help='''Name of the model to download''' )
download_parser.set_defaults(func=_snake_case )
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case ) -> Dict:
'''simple docstring'''
__a = model
__a = cache
__a = force
__a = trust_remote_code
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
from ..models.auto import AutoModel, AutoTokenizer
AutoModel.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code )
AutoTokenizer.from_pretrained(
self._model , cache_dir=self._cache , force_download=self._force , trust_remote_code=self._trust_remote_code ) | 6 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __lowerCAmelCase ( a__ , a__ , a__=1024 , a__=1024 , a__=False , **a__ ) -> Optional[Any]:
__a = AutoTokenizer.from_pretrained(a__ )
__a = SeqaSeqDataset(a__ , a__ , a__ , a__ , type_path='''train''' , **a__ )
__a = tok.pad_token_id
def get_lens(a__ ):
__a = tqdm(
DataLoader(a__ , batch_size=512 , num_workers=8 , shuffle=a__ , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
__a = []
for batch in dl:
__a = batch['''input_ids'''].ne(a__ ).sum(1 ).tolist()
__a = batch['''labels'''].ne(a__ ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(a__ , a__ ):
max_lens.append(max(a__ , a__ ) )
else:
max_lens.extend(a__ )
return max_lens
__a = get_lens(a__ )
__a = SeqaSeqDataset(a__ , a__ , a__ , a__ , type_path='''val''' , **a__ )
__a = get_lens(a__ )
pickle_save(a__ , train_ds.len_file )
pickle_save(a__ , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file) | 6 | 1 |
import random
class __A:
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _snake_case ) -> tuple[list[int], list[int]]:
'''simple docstring'''
__a = [ord(_snake_case ) for i in text]
__a = []
__a = []
for i in plain:
__a = random.randint(1 , 300 )
__a = (i + k) * k
cipher.append(_snake_case )
key.append(_snake_case )
return cipher, key
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _snake_case , _snake_case ) -> str:
'''simple docstring'''
__a = []
for i in range(len(_snake_case ) ):
__a = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(_snake_case ) )
return "".join(_snake_case )
if __name__ == "__main__":
A , A : Any = Onepad().encrypt('Hello')
print(c, k)
print(Onepad().decrypt(c, k)) | 6 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __lowerCAmelCase ( a__ , a__ , a__ = 1 / sqrt(2 ) ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = (1 - _cos) / 2
__a = 1 - _cos
__a = 1 + alpha
__a = -2 * _cos
__a = 1 - alpha
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ = 1 / sqrt(2 ) ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = (1 + _cos) / 2
__a = -1 - _cos
__a = 1 + alpha
__a = -2 * _cos
__a = 1 - alpha
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ = 1 / sqrt(2 ) ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = _sin / 2
__a = 0
__a = -ba
__a = 1 + alpha
__a = -2 * _cos
__a = 1 - alpha
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ = 1 / sqrt(2 ) ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = 1 - alpha
__a = -2 * _cos
__a = 1 + alpha
__a = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ , a__ = 1 / sqrt(2 ) , ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = 10 ** (gain_db / 40)
__a = 1 + alpha * big_a
__a = -2 * _cos
__a = 1 - alpha * big_a
__a = 1 + alpha / big_a
__a = -2 * _cos
__a = 1 - alpha / big_a
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ , a__ = 1 / sqrt(2 ) , ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = 10 ** (gain_db / 40)
__a = (big_a + 1) - (big_a - 1) * _cos
__a = (big_a + 1) + (big_a - 1) * _cos
__a = (big_a - 1) - (big_a + 1) * _cos
__a = (big_a - 1) + (big_a + 1) * _cos
__a = 2 * sqrt(a__ ) * alpha
__a = big_a * (pmc + aaa)
__a = 2 * big_a * mpc
__a = big_a * (pmc - aaa)
__a = ppmc + aaa
__a = -2 * pmpc
__a = ppmc - aaa
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ , a__ = 1 / sqrt(2 ) , ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = 10 ** (gain_db / 40)
__a = (big_a + 1) - (big_a - 1) * _cos
__a = (big_a + 1) + (big_a - 1) * _cos
__a = (big_a - 1) - (big_a + 1) * _cos
__a = (big_a - 1) + (big_a + 1) * _cos
__a = 2 * sqrt(a__ ) * alpha
__a = big_a * (ppmc + aaa)
__a = -2 * big_a * pmpc
__a = big_a * (ppmc - aaa)
__a = pmc + aaa
__a = 2 * mpc
__a = pmc - aaa
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt | 6 | 1 |
# Copyright 2021 The HuggingFace 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.
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def __lowerCAmelCase ( a__ ) -> Dict:
__a = botoa.client('''iam''' )
__a = {
'''Version''': '''2012-10-17''',
'''Statement''': [
{'''Effect''': '''Allow''', '''Principal''': {'''Service''': '''sagemaker.amazonaws.com'''}, '''Action''': '''sts:AssumeRole'''}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=a__ , AssumeRolePolicyDocument=json.dumps(a__ , indent=2 ) )
__a = {
'''Version''': '''2012-10-17''',
'''Statement''': [
{
'''Effect''': '''Allow''',
'''Action''': [
'''sagemaker:*''',
'''ecr:GetDownloadUrlForLayer''',
'''ecr:BatchGetImage''',
'''ecr:BatchCheckLayerAvailability''',
'''ecr:GetAuthorizationToken''',
'''cloudwatch:PutMetricData''',
'''cloudwatch:GetMetricData''',
'''cloudwatch:GetMetricStatistics''',
'''cloudwatch:ListMetrics''',
'''logs:CreateLogGroup''',
'''logs:CreateLogStream''',
'''logs:DescribeLogStreams''',
'''logs:PutLogEvents''',
'''logs:GetLogEvents''',
'''s3:CreateBucket''',
'''s3:ListBucket''',
'''s3:GetBucketLocation''',
'''s3:GetObject''',
'''s3:PutObject''',
],
'''Resource''': '''*''',
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=a__ , PolicyName=F"""{role_name}_policy_permission""" , PolicyDocument=json.dumps(a__ , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(F"""role {role_name} already exists. Using existing one""" )
def __lowerCAmelCase ( a__ ) -> Optional[int]:
__a = botoa.client('''iam''' )
return iam_client.get_role(RoleName=a__ )["Role"]["Arn"]
def __lowerCAmelCase ( ) -> Tuple:
__a = _ask_options(
'''How do you want to authorize?''' , ['''AWS Profile''', '''Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) '''] , a__ , )
__a = None
if credentials_configuration == 0:
__a = _ask_field('''Enter your AWS Profile name: [default] ''' , default='''default''' )
__a = aws_profile
else:
print(
'''Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'''
'''`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`''' )
__a = _ask_field('''AWS Access Key ID: ''' )
__a = aws_access_key_id
__a = _ask_field('''AWS Secret Access Key: ''' )
__a = aws_secret_access_key
__a = _ask_field('''Enter your AWS Region: [us-east-1]''' , default='''us-east-1''' )
__a = aws_region
__a = _ask_options(
'''Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?''' , ['''Provide IAM Role name''', '''Create new IAM role using credentials'''] , a__ , )
if role_management == 0:
__a = _ask_field('''Enter your IAM role name: ''' )
else:
__a = '''accelerate_sagemaker_execution_role'''
print(F"""Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials""" )
_create_iam_role_for_sagemaker(a__ )
__a = _ask_field(
'''Do you want to use custom Docker image? [yes/NO]: ''' , _convert_yes_no_to_bool , default=a__ , error_message='''Please enter yes or no.''' , )
__a = None
if is_custom_docker_image:
__a = _ask_field('''Enter your Docker image: ''' , lambda a__ : str(a__ ).lower() )
__a = _ask_field(
'''Do you want to provide SageMaker input channels with data locations? [yes/NO]: ''' , _convert_yes_no_to_bool , default=a__ , error_message='''Please enter yes or no.''' , )
__a = None
if is_sagemaker_inputs_enabled:
__a = _ask_field(
'''Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): ''' , lambda a__ : str(a__ ).lower() , )
__a = _ask_field(
'''Do you want to enable SageMaker metrics? [yes/NO]: ''' , _convert_yes_no_to_bool , default=a__ , error_message='''Please enter yes or no.''' , )
__a = None
if is_sagemaker_metrics_enabled:
__a = _ask_field(
'''Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): ''' , lambda a__ : str(a__ ).lower() , )
__a = _ask_options(
'''What is the distributed mode?''' , ['''No distributed training''', '''Data parallelism'''] , _convert_sagemaker_distributed_mode , )
__a = {}
__a = _ask_field(
'''Do you wish to optimize your script with torch dynamo?[yes/NO]:''' , _convert_yes_no_to_bool , default=a__ , error_message='''Please enter yes or no.''' , )
if use_dynamo:
__a = '''dynamo_'''
__a = _ask_options(
'''Which dynamo backend would you like to use?''' , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
__a = _ask_field(
'''Do you want to customize the defaults sent to torch.compile? [yes/NO]: ''' , _convert_yes_no_to_bool , default=a__ , error_message='''Please enter yes or no.''' , )
if use_custom_options:
__a = _ask_options(
'''Which mode do you want to use?''' , a__ , lambda a__ : TORCH_DYNAMO_MODES[int(a__ )] , default='''default''' , )
__a = _ask_field(
'''Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: ''' , _convert_yes_no_to_bool , default=a__ , error_message='''Please enter yes or no.''' , )
__a = _ask_field(
'''Do you want to enable dynamic shape tracing? [yes/NO]: ''' , _convert_yes_no_to_bool , default=a__ , error_message='''Please enter yes or no.''' , )
__a = '''Which EC2 instance type you want to use for your training?'''
if distributed_type != SageMakerDistributedType.NO:
__a = _ask_options(
a__ , a__ , lambda a__ : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(a__ )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
__a = _ask_field(a__ , lambda a__ : str(a__ ).lower() , default='''ml.p3.2xlarge''' )
__a = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
__a = _ask_field(
'''How many machines do you want use? [1]: ''' , a__ , default=1 , )
__a = _ask_options(
'''Do you wish to use FP16 or BF16 (mixed precision)?''' , ['''no''', '''fp16''', '''bf16''', '''fp8'''] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
'''Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.''' )
return SageMakerConfig(
image_uri=a__ , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=a__ , use_cpu=a__ , dynamo_config=a__ , eca_instance_type=a__ , profile=a__ , region=a__ , iam_role_name=a__ , mixed_precision=a__ , num_machines=a__ , sagemaker_inputs_file=a__ , sagemaker_metrics_file=a__ , ) | 6 |
def __lowerCAmelCase ( a__ , a__ , a__ ) -> list:
__a = len(a__ )
__a = [[0] * n for i in range(a__ )]
for i in range(a__ ):
__a = y_points[i]
for i in range(2 , a__ ):
for j in range(a__ , a__ ):
__a = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod() | 6 | 1 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
A : Dict = 2
# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
A : str = {
# fairseq:
'wmt19-ru-en': {'length_penalty': 1.1},
'wmt19-en-ru': {'length_penalty': 1.15},
'wmt19-en-de': {'length_penalty': 1.0},
'wmt19-de-en': {'length_penalty': 1.1},
# allenai:
'wmt16-en-de-dist-12-1': {'length_penalty': 0.6},
'wmt16-en-de-dist-6-1': {'length_penalty': 0.6},
'wmt16-en-de-12-1': {'length_penalty': 0.8},
'wmt19-de-en-6-6-base': {'length_penalty': 0.6},
'wmt19-de-en-6-6-big': {'length_penalty': 0.6},
}
# this remaps the different models to their organization names
A : Union[str, Any] = {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
A : Any = 'facebook'
for m in [
"wmt16-en-de-dist-12-1",
"wmt16-en-de-dist-6-1",
"wmt16-en-de-12-1",
"wmt19-de-en-6-6-base",
"wmt19-de-en-6-6-big",
]:
A : Dict = 'allenai'
def __lowerCAmelCase ( a__ ) -> int:
# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
__a = dict((re.sub(R'''@@$''' , '''''' , a__ ), v) if k.endswith('''@@''' ) else (re.sub(R'''$''' , '''</w>''' , a__ ), v) for k, v in d.items() )
__a = '''<s> <pad> </s> <unk>'''.split()
# restore the special tokens
for k in keep_keys:
del da[F"""{k}</w>"""]
__a = d[k] # restore
return da
def __lowerCAmelCase ( a__ , a__ ) -> Optional[int]:
# prep
assert os.path.exists(a__ )
os.makedirs(a__ , exist_ok=a__ )
print(F"""Writing results to {pytorch_dump_folder_path}""" )
# handle various types of models
__a = basename(a__ )
__a = dirname(a__ )
__a = fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
__a = cls.hub_models()
__a = {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
__a = '''.'''
# note: since the model dump is old, fairseq has upgraded its model some
# time later, and it does a whole lot of rewrites and splits on the saved
# weights, therefore we can't use torch.load() directly on the model file.
# see: upgrade_state_dict(state_dict) in fairseq_model.py
print(F"""using checkpoint {checkpoint_file}""" )
__a = hub_utils.from_pretrained(
a__ , a__ , a__ , archive_map=a__ , **a__ )
__a = vars(chkpt['''args''']['''model'''] )
__a = args['''source_lang''']
__a = args['''target_lang''']
__a = dirname(a__ )
__a = basename(a__ )
# dicts
__a = os.path.join(a__ , F"""dict.{src_lang}.txt""" )
__a = os.path.join(a__ , F"""dict.{tgt_lang}.txt""" )
__a = Dictionary.load(a__ )
__a = rewrite_dict_keys(src_dict.indices )
__a = len(a__ )
__a = os.path.join(a__ , '''vocab-src.json''' )
print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records""" )
with open(a__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(a__ , ensure_ascii=a__ , indent=a__ ) )
# detect whether this is a do_lower_case situation, which can be derived by checking whether we
# have at least one uppercase letter in the source vocab
__a = True
for k in src_vocab.keys():
if not k.islower():
__a = False
break
__a = Dictionary.load(a__ )
__a = rewrite_dict_keys(tgt_dict.indices )
__a = len(a__ )
__a = os.path.join(a__ , '''vocab-tgt.json''' )
print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records""" )
with open(a__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(a__ , ensure_ascii=a__ , indent=a__ ) )
# merges_file (bpecodes)
__a = os.path.join(a__ , VOCAB_FILES_NAMES['''merges_file'''] )
for fn in ["bpecodes", "code"]: # older fairseq called the merges file "code"
__a = os.path.join(a__ , a__ )
if os.path.exists(a__ ):
break
with open(a__ , encoding='''utf-8''' ) as fin:
__a = fin.read()
__a = re.sub(R''' \d+$''' , '''''' , a__ , 0 , re.M ) # remove frequency number
print(F"""Generating {merges_file}""" )
with open(a__ , '''w''' , encoding='''utf-8''' ) as fout:
fout.write(a__ )
# model config
__a = os.path.join(a__ , '''config.json''' )
# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
# may have to modify the tokenizer if a different type is used by a future model
assert args["bpe"] == "fastbpe", F"""need to extend tokenizer to support bpe={args['bpe']}"""
assert args["tokenizer"] == "moses", F"""need to extend tokenizer to support bpe={args['tokenizer']}"""
__a = {
'''architectures''': ['''FSMTForConditionalGeneration'''],
'''model_type''': '''fsmt''',
'''activation_dropout''': args['''activation_dropout'''],
'''activation_function''': '''relu''',
'''attention_dropout''': args['''attention_dropout'''],
'''d_model''': args['''decoder_embed_dim'''],
'''dropout''': args['''dropout'''],
'''init_std''': 0.02,
'''max_position_embeddings''': args['''max_source_positions'''],
'''num_hidden_layers''': args['''encoder_layers'''],
'''src_vocab_size''': src_vocab_size,
'''tgt_vocab_size''': tgt_vocab_size,
'''langs''': [src_lang, tgt_lang],
'''encoder_attention_heads''': args['''encoder_attention_heads'''],
'''encoder_ffn_dim''': args['''encoder_ffn_embed_dim'''],
'''encoder_layerdrop''': args['''encoder_layerdrop'''],
'''encoder_layers''': args['''encoder_layers'''],
'''decoder_attention_heads''': args['''decoder_attention_heads'''],
'''decoder_ffn_dim''': args['''decoder_ffn_embed_dim'''],
'''decoder_layerdrop''': args['''decoder_layerdrop'''],
'''decoder_layers''': args['''decoder_layers'''],
'''bos_token_id''': 0,
'''pad_token_id''': 1,
'''eos_token_id''': 2,
'''is_encoder_decoder''': True,
'''scale_embedding''': not args['''no_scale_embedding'''],
'''tie_word_embeddings''': args['''share_all_embeddings'''],
}
# good hparam defaults to start with
__a = 5
__a = False
if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
__a = best_score_hparams[model_dir]['''length_penalty''']
else:
__a = 1.0
print(F"""Generating {fsmt_model_config_file}""" )
with open(a__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(a__ , ensure_ascii=a__ , indent=a__ ) )
# tokenizer config
__a = os.path.join(a__ , a__ )
__a = {
'''langs''': [src_lang, tgt_lang],
'''model_max_length''': 1024,
'''do_lower_case''': do_lower_case,
}
print(F"""Generating {fsmt_tokenizer_config_file}""" )
with open(a__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(a__ , ensure_ascii=a__ , indent=a__ ) )
# model
__a = chkpt['''models'''][0]
__a = model.state_dict()
# rename keys to start with 'model.'
__a = OrderedDict(('''model.''' + k, v) for k, v in model_state_dict.items() )
# remove unneeded keys
__a = [
'''model.model''',
'''model.encoder.version''',
'''model.decoder.version''',
'''model.encoder_embed_tokens.weight''',
'''model.decoder_embed_tokens.weight''',
'''model.encoder.embed_positions._float_tensor''',
'''model.decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
model_state_dict.pop(a__ , a__ )
__a = FSMTConfig.from_pretrained(a__ )
__a = FSMTForConditionalGeneration(a__ )
# check that it loads ok
model_new.load_state_dict(a__ , strict=a__ )
# save
__a = os.path.join(a__ , a__ )
print(F"""Generating {pytorch_weights_dump_path}""" )
torch.save(a__ , a__ )
print('''Conversion is done!''' )
print('''\nLast step is to upload the files to s3''' )
print(F"""cd {data_root}""" )
print(F"""transformers-cli upload {model_dir}""" )
if __name__ == "__main__":
A : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--fsmt_checkpoint_path',
default=None,
type=str,
required=True,
help=(
'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
' bpecodes, etc.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
A : List[str] = parser.parse_args()
convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path) | 6 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def __lowerCAmelCase ( a__ , a__ , a__ ) -> tuple[int | None, int | None, float]:
if not arr:
return None, None, 0
if low == high:
return low, high, arr[low]
__a = (low + high) // 2
__a , __a , __a = max_subarray(a__ , a__ , a__ )
__a , __a , __a = max_subarray(a__ , mid + 1 , a__ )
__a , __a , __a = max_cross_sum(a__ , a__ , a__ , a__ )
if left_sum >= right_sum and left_sum >= cross_sum:
return left_low, left_high, left_sum
elif right_sum >= left_sum and right_sum >= cross_sum:
return right_low, right_high, right_sum
return cross_left, cross_right, cross_sum
def __lowerCAmelCase ( a__ , a__ , a__ , a__ ) -> tuple[int, int, float]:
__a , __a = float('''-inf''' ), -1
__a , __a = float('''-inf''' ), -1
__a = 0
for i in range(a__ , low - 1 , -1 ):
summ += arr[i]
if summ > left_sum:
__a = summ
__a = i
__a = 0
for i in range(mid + 1 , high + 1 ):
summ += arr[i]
if summ > right_sum:
__a = summ
__a = i
return max_left, max_right, (left_sum + right_sum)
def __lowerCAmelCase ( a__ ) -> float:
__a = [randint(1 , a__ ) for _ in range(a__ )]
__a = time.time()
max_subarray(a__ , 0 , input_size - 1 )
__a = time.time()
return end - start
def __lowerCAmelCase ( ) -> None:
__a = [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
__a = [time_max_subarray(a__ ) for input_size in input_sizes]
print('''No of Inputs\t\tTime Taken''' )
for input_size, runtime in zip(a__ , a__ ):
print(a__ , '''\t\t''' , a__ )
plt.plot(a__ , a__ )
plt.xlabel('''Number of Inputs''' )
plt.ylabel('''Time taken in seconds''' )
plt.show()
if __name__ == "__main__":
from doctest import testmod
testmod() | 6 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
A : Union[str, Any] = datasets.load_iris()
A : int = np.array(data['data'])
A : Optional[Any] = np.array(data['target'])
A : int = data['target_names']
A , A , A , A : Tuple = train_test_split(X, y)
def __lowerCAmelCase ( a__ , a__ ) -> Dict:
return np.linalg.norm(np.array(a__ ) - np.array(a__ ) )
def __lowerCAmelCase ( a__ , a__ , a__ , a__ , a__=5 ) -> Tuple:
__a = zip(a__ , a__ )
# List of distances of all points from the point to be classified
__a = []
for data_point in data:
__a = euclidean_distance(data_point[0] , a__ )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
__a = [i[1] for i in sorted(a__ )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
__a = Counter(a__ ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4])) | 6 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __A( a , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __A( unittest.TestCase ):
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = ort.SessionOptions()
__a = False
return options
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
__a = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , safety_checker=_snake_case , feature_extractor=_snake_case , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_snake_case )
__a = '''A red cat sitting on a park bench'''
__a = np.random.RandomState(0 )
__a = pipe(
prompt=_snake_case , image=_snake_case , mask_image=_snake_case , guidance_scale=7.5 , num_inference_steps=10 , generator=_snake_case , output_type='''np''' , )
__a = output.images
__a = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
__a = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
__a = LMSDiscreteScheduler.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , subfolder='''scheduler''' , revision='''onnx''' )
__a = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , scheduler=_snake_case , safety_checker=_snake_case , feature_extractor=_snake_case , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_snake_case )
__a = '''A red cat sitting on a park bench'''
__a = np.random.RandomState(0 )
__a = pipe(
prompt=_snake_case , image=_snake_case , mask_image=_snake_case , guidance_scale=7.5 , num_inference_steps=20 , generator=_snake_case , output_type='''np''' , )
__a = output.images
__a = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
__a = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3 | 6 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
A : Dict = logging.get_logger(__name__)
class __A( a ):
snake_case_ = ['''pixel_values''']
def __init__( self , _snake_case = True , _snake_case = None , _snake_case = PILImageResampling.BILINEAR , _snake_case = True , _snake_case = None , _snake_case = True , _snake_case = 1 / 255 , _snake_case = True , _snake_case = None , _snake_case = None , **_snake_case , ) -> None:
'''simple docstring'''
super().__init__(**_snake_case )
__a = size if size is not None else {'''shortest_edge''': 256}
__a = get_size_dict(_snake_case , default_to_square=_snake_case )
__a = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__a = get_size_dict(_snake_case , param_name='''crop_size''' )
__a = do_resize
__a = size
__a = resample
__a = do_center_crop
__a = crop_size
__a = do_rescale
__a = rescale_factor
__a = do_normalize
__a = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__a = image_std if image_std is not None else IMAGENET_STANDARD_STD
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = PILImageResampling.BICUBIC , _snake_case = None , **_snake_case , ) -> np.ndarray:
'''simple docstring'''
__a = get_size_dict(_snake_case , default_to_square=_snake_case )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
__a = get_resize_output_image_size(_snake_case , size=size['''shortest_edge'''] , default_to_square=_snake_case )
return resize(_snake_case , size=_snake_case , resample=_snake_case , data_format=_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = None , **_snake_case , ) -> np.ndarray:
'''simple docstring'''
__a = get_size_dict(_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}""" )
return center_crop(_snake_case , size=(size['''height'''], size['''width''']) , data_format=_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = None , **_snake_case ) -> np.ndarray:
'''simple docstring'''
return rescale(_snake_case , scale=_snake_case , data_format=_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case = None , **_snake_case , ) -> np.ndarray:
'''simple docstring'''
return normalize(_snake_case , mean=_snake_case , std=_snake_case , data_format=_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = ChannelDimension.FIRST , **_snake_case , ) -> Optional[int]:
'''simple docstring'''
__a = do_resize if do_resize is not None else self.do_resize
__a = size if size is not None else self.size
__a = get_size_dict(_snake_case , default_to_square=_snake_case )
__a = resample if resample is not None else self.resample
__a = do_center_crop if do_center_crop is not None else self.do_center_crop
__a = crop_size if crop_size is not None else self.crop_size
__a = get_size_dict(_snake_case , param_name='''crop_size''' )
__a = do_rescale if do_rescale is not None else self.do_rescale
__a = rescale_factor if rescale_factor is not None else self.rescale_factor
__a = do_normalize if do_normalize is not None else self.do_normalize
__a = image_mean if image_mean is not None else self.image_mean
__a = image_std if image_std is not None else self.image_std
__a = make_list_of_images(_snake_case )
if not valid_images(_snake_case ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
__a = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
__a = [self.resize(image=_snake_case , size=_snake_case , resample=_snake_case ) for image in images]
if do_center_crop:
__a = [self.center_crop(image=_snake_case , size=_snake_case ) for image in images]
if do_rescale:
__a = [self.rescale(image=_snake_case , scale=_snake_case ) for image in images]
if do_normalize:
__a = [self.normalize(image=_snake_case , mean=_snake_case , std=_snake_case ) for image in images]
__a = [to_channel_dimension_format(_snake_case , _snake_case ) for image in images]
__a = {'''pixel_values''': images}
return BatchFeature(data=_snake_case , tensor_type=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None ) -> Optional[int]:
'''simple docstring'''
__a = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(_snake_case ) != len(_snake_case ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(_snake_case ):
__a = target_sizes.numpy()
__a = []
for idx in range(len(_snake_case ) ):
__a = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=_snake_case )
__a = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(_snake_case )
else:
__a = logits.argmax(dim=1 )
__a = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation | 6 |
from math import ceil
def __lowerCAmelCase ( a__ = 1001 ) -> int:
__a = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__a = 2 * i + 1
__a = 2 * i
__a = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
A : List[Any] = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number') | 6 | 1 |
# flake8: noqa
# Lint as: python3
A : Optional[Any] = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental | 6 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A( a ):
snake_case_ = ['''image_processor''', '''tokenizer''']
snake_case_ = '''ChineseCLIPImageProcessor'''
snake_case_ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self , _snake_case=None , _snake_case=None , **_snake_case ) -> Tuple:
'''simple docstring'''
__a = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , _snake_case , )
__a = kwargs.pop('''feature_extractor''' )
__a = 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__(_snake_case , _snake_case )
__a = self.image_processor
def __call__( self , _snake_case=None , _snake_case=None , _snake_case=None , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
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:
__a = self.tokenizer(_snake_case , return_tensors=_snake_case , **_snake_case )
if images is not None:
__a = self.image_processor(_snake_case , return_tensors=_snake_case , **_snake_case )
if text is not None and images is not None:
__a = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_snake_case ) , tensor_type=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , *_snake_case , **_snake_case ) -> str:
'''simple docstring'''
return self.tokenizer.batch_decode(*_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , *_snake_case , **_snake_case ) -> Dict:
'''simple docstring'''
return self.tokenizer.decode(*_snake_case , **_snake_case )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = self.tokenizer.model_input_names
__a = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , _snake_case , )
return self.image_processor_class | 6 | 1 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class __A:
@staticmethod
def SCREAMING_SNAKE_CASE_ ( *_snake_case , **_snake_case ) -> Optional[int]:
'''simple docstring'''
pass
def __lowerCAmelCase ( a__ ) -> str:
__a = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __A( unittest.TestCase ):
snake_case_ = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case ) -> Tuple:
'''simple docstring'''
__a = DepthEstimationPipeline(model=_snake_case , image_processor=_snake_case )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
self.assertEqual({'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )} , _snake_case )
import datasets
__a = datasets.load_dataset('''hf-internal-testing/fixtures_image_utils''' , '''image''' , split='''test''' )
__a = depth_estimator(
[
Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ),
'''http://images.cocodataset.org/val2017/000000039769.jpg''',
# RGBA
dataset[0]['''file'''],
# LA
dataset[1]['''file'''],
# L
dataset[2]['''file'''],
] )
self.assertEqual(
[
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
{'''predicted_depth''': ANY(torch.Tensor ), '''depth''': ANY(Image.Image )},
] , _snake_case , )
@require_tf
@unittest.skip('''Depth estimation is not implemented in TF''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
pass
@slow
@require_torch
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = '''Intel/dpt-large'''
__a = pipeline('''depth-estimation''' , model=_snake_case )
__a = depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''' )
__a = hashimage(outputs['''depth'''] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item() ) , 2.662 )
@require_torch
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT''' ) | 6 |
from __future__ import annotations
import typing
from collections import Counter
def __lowerCAmelCase ( a__ ) -> typing.Counter[int]:
__a = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(a__ , max_perimeter + 1 ):
__a = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(a__ ):
__a = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def __lowerCAmelCase ( a__ = 1000 ) -> int:
__a = pythagorean_triple(a__ )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(F"Perimeter {solution()} has maximum solutions") | 6 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _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 (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class __A:
def __init__( self , _snake_case , _snake_case=100 , _snake_case=13 , _snake_case=30 , _snake_case=2 , _snake_case=3 , _snake_case=True , _snake_case=True , _snake_case=32 , _snake_case=4 , _snake_case=4 , _snake_case=37 , _snake_case="gelu" , _snake_case=0.1 , _snake_case=0.1 , _snake_case=10 , _snake_case=0.02 , _snake_case=3 , _snake_case=None , _snake_case=[0, 1, 2, 3] , ) -> List[str]:
'''simple docstring'''
__a = parent
__a = 100
__a = batch_size
__a = image_size
__a = patch_size
__a = num_channels
__a = is_training
__a = use_labels
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = intermediate_size
__a = hidden_act
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = type_sequence_label_size
__a = initializer_range
__a = scope
__a = out_indices
__a = num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
__a = (image_size // patch_size) ** 2
__a = num_patches + 1
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
__a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__a = None
__a = None
if self.use_labels:
__a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__a = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
__a = self.get_config()
return config, pixel_values, labels, pixel_labels
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
return BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_snake_case , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case ) -> List[Any]:
'''simple docstring'''
__a = BeitModel(config=_snake_case )
model.to(_snake_case )
model.eval()
__a = model(_snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = BeitForMaskedImageModeling(config=_snake_case )
model.to(_snake_case )
model.eval()
__a = model(_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case ) -> Union[str, Any]:
'''simple docstring'''
__a = self.type_sequence_label_size
__a = BeitForImageClassification(_snake_case )
model.to(_snake_case )
model.eval()
__a = model(_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__a = 1
__a = BeitForImageClassification(_snake_case )
model.to(_snake_case )
model.eval()
__a = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__a = model(_snake_case , labels=_snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case ) -> List[str]:
'''simple docstring'''
__a = self.num_labels
__a = BeitForSemanticSegmentation(_snake_case )
model.to(_snake_case )
model.eval()
__a = model(_snake_case )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
__a = model(_snake_case , labels=_snake_case )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = self.prepare_config_and_inputs()
__a , __a , __a , __a = config_and_inputs
__a = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __A( a , a , unittest.TestCase ):
snake_case_ = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
snake_case_ = (
{
'''feature-extraction''': BeitModel,
'''image-classification''': BeitForImageClassification,
'''image-segmentation''': BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
snake_case_ = False
snake_case_ = False
snake_case_ = False
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = BeitModelTester(self )
__a = ConfigTester(self , config_class=_snake_case , has_text_modality=_snake_case , hidden_size=37 )
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''BEiT does not use inputs_embeds''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = model_class(_snake_case )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_snake_case , nn.Linear ) )
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = model_class(_snake_case )
__a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a = [*signature.parameters.keys()]
__a = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
if not self.model_tester.is_training:
return
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
__a = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(_snake_case ), BeitForMaskedImageModeling]:
continue
__a = model_class(_snake_case )
model.to(_snake_case )
model.train()
__a = self._prepare_for_class(_snake_case , _snake_case , return_labels=_snake_case )
__a = model(**_snake_case ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
__a = False
__a = True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(_snake_case ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
__a = model_class(_snake_case )
model.gradient_checkpointing_enable()
model.to(_snake_case )
model.train()
__a = self._prepare_for_class(_snake_case , _snake_case , return_labels=_snake_case )
__a = model(**_snake_case ).loss
loss.backward()
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
__a = _config_zero_init(_snake_case )
for model_class in self.all_model_classes:
__a = model_class(config=_snake_case )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if 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""" , )
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a = BeitModel.from_pretrained(_snake_case )
self.assertIsNotNone(_snake_case )
def __lowerCAmelCase ( ) -> Optional[int]:
__a = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class __A( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224''' ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k''' ).to(_snake_case )
__a = self.default_image_processor
__a = prepare_img()
__a = image_processor(images=_snake_case , return_tensors='''pt''' ).pixel_values.to(_snake_case )
# prepare bool_masked_pos
__a = torch.ones((1, 196) , dtype=torch.bool ).to(_snake_case )
# forward pass
with torch.no_grad():
__a = model(pixel_values=_snake_case , bool_masked_pos=_snake_case )
__a = outputs.logits
# verify the logits
__a = torch.Size((1, 196, 8_192) )
self.assertEqual(logits.shape , _snake_case )
__a = torch.tensor(
[[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]] ).to(_snake_case )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , _snake_case , atol=1E-2 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
__a = BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224''' ).to(_snake_case )
__a = self.default_image_processor
__a = prepare_img()
__a = image_processor(images=_snake_case , return_tensors='''pt''' ).to(_snake_case )
# forward pass
with torch.no_grad():
__a = model(**_snake_case )
__a = outputs.logits
# verify the logits
__a = torch.Size((1, 1_000) )
self.assertEqual(logits.shape , _snake_case )
__a = torch.tensor([-1.2385, -1.0987, -1.0108] ).to(_snake_case )
self.assertTrue(torch.allclose(logits[0, :3] , _snake_case , atol=1E-4 ) )
__a = 281
self.assertEqual(logits.argmax(-1 ).item() , _snake_case )
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k''' ).to(
_snake_case )
__a = self.default_image_processor
__a = prepare_img()
__a = image_processor(images=_snake_case , return_tensors='''pt''' ).to(_snake_case )
# forward pass
with torch.no_grad():
__a = model(**_snake_case )
__a = outputs.logits
# verify the logits
__a = torch.Size((1, 21_841) )
self.assertEqual(logits.shape , _snake_case )
__a = torch.tensor([1.6881, -0.2787, 0.5901] ).to(_snake_case )
self.assertTrue(torch.allclose(logits[0, :3] , _snake_case , atol=1E-4 ) )
__a = 2_396
self.assertEqual(logits.argmax(-1 ).item() , _snake_case )
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''' )
__a = model.to(_snake_case )
__a = BeitImageProcessor(do_resize=_snake_case , size=640 , do_center_crop=_snake_case )
__a = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
__a = Image.open(ds[0]['''file'''] )
__a = image_processor(images=_snake_case , return_tensors='''pt''' ).to(_snake_case )
# forward pass
with torch.no_grad():
__a = model(**_snake_case )
__a = outputs.logits
# verify the logits
__a = torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape , _snake_case )
__a = version.parse(PIL.__version__ ) < version.parse('''9.0.0''' )
if is_pillow_less_than_a:
__a = torch.tensor(
[
[[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
[[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
[[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
] , device=_snake_case , )
else:
__a = torch.tensor(
[
[[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
[[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
[[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
] , device=_snake_case , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , _snake_case , atol=1E-4 ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
__a = BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''' )
__a = model.to(_snake_case )
__a = BeitImageProcessor(do_resize=_snake_case , size=640 , do_center_crop=_snake_case )
__a = load_dataset('''hf-internal-testing/fixtures_ade20k''' , split='''test''' )
__a = Image.open(ds[0]['''file'''] )
__a = image_processor(images=_snake_case , return_tensors='''pt''' ).to(_snake_case )
# forward pass
with torch.no_grad():
__a = model(**_snake_case )
__a = outputs.logits.detach().cpu()
__a = image_processor.post_process_semantic_segmentation(outputs=_snake_case , target_sizes=[(500, 300)] )
__a = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , _snake_case )
__a = image_processor.post_process_semantic_segmentation(outputs=_snake_case )
__a = torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape , _snake_case ) | 6 |
# flake8: noqa
# Lint as: python3
A : Optional[Any] = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental | 6 | 1 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
A : str = TypeVar('T')
A : Dict = Union[List[T], Tuple[T, ...]]
A : Union[str, Any] = Union[T, List[T], Dict[str, T]]
A : int = Union[str, bytes, os.PathLike] | 6 |
from typing import Dict
from .base import GenericTensor, Pipeline
class __A( a ):
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=None , _snake_case=None , _snake_case=None , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
if tokenize_kwargs is None:
__a = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)''' )
__a = truncation
__a = tokenize_kwargs
__a = {}
if return_tensors is not None:
__a = return_tensors
return preprocess_params, {}, postprocess_params
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , **_snake_case ) -> Dict[str, GenericTensor]:
'''simple docstring'''
__a = self.framework
__a = self.tokenizer(_snake_case , return_tensors=_snake_case , **_snake_case )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = self.model(**_snake_case )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case=False ) -> Optional[int]:
'''simple docstring'''
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self , *_snake_case , **_snake_case ) -> Any:
'''simple docstring'''
return super().__call__(*_snake_case , **_snake_case ) | 6 | 1 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __A( a , unittest.TestCase ):
snake_case_ = DDIMPipeline
snake_case_ = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
snake_case_ = PipelineTesterMixin.required_optional_params - {
'''num_images_per_prompt''',
'''latents''',
'''callback''',
'''callback_steps''',
}
snake_case_ = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
snake_case_ = False
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
torch.manual_seed(0 )
__a = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
__a = DDIMScheduler()
__a = {'''unet''': unet, '''scheduler''': scheduler}
return components
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case=0 ) -> List[str]:
'''simple docstring'''
if str(_snake_case ).startswith('''mps''' ):
__a = torch.manual_seed(_snake_case )
else:
__a = torch.Generator(device=_snake_case ).manual_seed(_snake_case )
__a = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = '''cpu'''
__a = self.get_dummy_components()
__a = self.pipeline_class(**_snake_case )
pipe.to(_snake_case )
pipe.set_progress_bar_config(disable=_snake_case )
__a = self.get_dummy_inputs(_snake_case )
__a = pipe(**_snake_case ).images
__a = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
__a = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
__a = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_snake_case , 1E-3 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
super().test_save_load_local(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __A( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = '''google/ddpm-cifar10-32'''
__a = UNetaDModel.from_pretrained(_snake_case )
__a = DDIMScheduler()
__a = DDIMPipeline(unet=_snake_case , scheduler=_snake_case )
ddim.to(_snake_case )
ddim.set_progress_bar_config(disable=_snake_case )
__a = torch.manual_seed(0 )
__a = ddim(generator=_snake_case , eta=0.0 , output_type='''numpy''' ).images
__a = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__a = np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = '''google/ddpm-ema-bedroom-256'''
__a = UNetaDModel.from_pretrained(_snake_case )
__a = DDIMScheduler.from_pretrained(_snake_case )
__a = DDIMPipeline(unet=_snake_case , scheduler=_snake_case )
ddpm.to(_snake_case )
ddpm.set_progress_bar_config(disable=_snake_case )
__a = torch.manual_seed(0 )
__a = ddpm(generator=_snake_case , output_type='''numpy''' ).images
__a = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
__a = np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 | 6 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Optional[int] = {
'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class __A( a ):
snake_case_ = '''levit'''
def __init__( self , _snake_case=224 , _snake_case=3 , _snake_case=3 , _snake_case=2 , _snake_case=1 , _snake_case=16 , _snake_case=[128, 256, 384] , _snake_case=[4, 8, 12] , _snake_case=[4, 4, 4] , _snake_case=[16, 16, 16] , _snake_case=0 , _snake_case=[2, 2, 2] , _snake_case=[2, 2, 2] , _snake_case=0.02 , **_snake_case , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(**_snake_case )
__a = image_size
__a = num_channels
__a = kernel_size
__a = stride
__a = padding
__a = hidden_sizes
__a = num_attention_heads
__a = depths
__a = key_dim
__a = drop_path_rate
__a = patch_size
__a = attention_ratio
__a = mlp_ratio
__a = initializer_range
__a = [
['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class __A( a ):
snake_case_ = version.parse('''1.11''' )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> float:
'''simple docstring'''
return 1E-4 | 6 | 1 |
def __lowerCAmelCase ( a__ = 1000 ) -> int:
__a = 3
__a = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(F"{solution() = }") | 6 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
A : int = '0.12' # assumed parallelism: 8
@require_flax
@is_staging_test
class __A( unittest.TestCase ):
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls ) -> Union[str, Any]:
'''simple docstring'''
__a = TOKEN
HfFolder.save_token(_snake_case )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls ) -> Union[str, Any]:
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='''test-model-flax''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-model-flax-org''' )
except HTTPError:
pass
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
__a = FlaxBertModel(_snake_case )
model.push_to_hub('''test-model-flax''' , use_auth_token=self._token )
__a = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" )
__a = flatten_dict(unfreeze(model.params ) )
__a = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
__a = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_snake_case , 1E-3 , msg=F"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id='''test-model-flax''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(_snake_case , repo_id='''test-model-flax''' , push_to_hub=_snake_case , use_auth_token=self._token )
__a = FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" )
__a = flatten_dict(unfreeze(model.params ) )
__a = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
__a = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_snake_case , 1E-3 , msg=F"""{key} not identical""" )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
__a = FlaxBertModel(_snake_case )
model.push_to_hub('''valid_org/test-model-flax-org''' , use_auth_token=self._token )
__a = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
__a = flatten_dict(unfreeze(model.params ) )
__a = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
__a = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_snake_case , 1E-3 , msg=F"""{key} not identical""" )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-model-flax-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
_snake_case , repo_id='''valid_org/test-model-flax-org''' , push_to_hub=_snake_case , use_auth_token=self._token )
__a = FlaxBertModel.from_pretrained('''valid_org/test-model-flax-org''' )
__a = flatten_dict(unfreeze(model.params ) )
__a = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
__a = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_snake_case , 1E-3 , msg=F"""{key} not identical""" )
def __lowerCAmelCase ( a__ , a__ ) -> str:
__a = True
__a = flatten_dict(modela.params )
__a = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1e-4:
__a = False
return models_are_equal
@require_flax
class __A( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
__a = FlaxBertModel(_snake_case )
__a = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_snake_case , _snake_case ) )
with self.assertRaises(_snake_case ):
__a = FlaxBertModel.from_pretrained(_snake_case )
__a = FlaxBertModel.from_pretrained(_snake_case , subfolder=_snake_case )
self.assertTrue(check_models_equal(_snake_case , _snake_case ) )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = BertConfig.from_pretrained('''hf-internal-testing/tiny-bert-flax-only''' )
__a = FlaxBertModel(_snake_case )
__a = '''bert'''
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_snake_case , _snake_case ) , max_shard_size='''10KB''' )
with self.assertRaises(_snake_case ):
__a = FlaxBertModel.from_pretrained(_snake_case )
__a = FlaxBertModel.from_pretrained(_snake_case , subfolder=_snake_case )
self.assertTrue(check_models_equal(_snake_case , _snake_case ) )
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
__a = '''bert'''
__a = '''hf-internal-testing/tiny-random-bert-subfolder'''
with self.assertRaises(_snake_case ):
__a = FlaxBertModel.from_pretrained(_snake_case )
__a = FlaxBertModel.from_pretrained(_snake_case , subfolder=_snake_case )
self.assertIsNotNone(_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = '''bert'''
__a = '''hf-internal-testing/tiny-random-bert-sharded-subfolder'''
with self.assertRaises(_snake_case ):
__a = FlaxBertModel.from_pretrained(_snake_case )
__a = FlaxBertModel.from_pretrained(_snake_case , subfolder=_snake_case )
self.assertIsNotNone(_snake_case ) | 6 | 1 |
from collections.abc import Generator
def __lowerCAmelCase ( ) -> Generator[int, None, None]:
__a , __a = 0, 1
while True:
__a , __a = b, a + b
yield b
def __lowerCAmelCase ( a__ = 1000 ) -> int:
__a = 1
__a = fibonacci_generator()
while len(str(next(a__ ) ) ) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip()))) | 6 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
A : Optional[Any] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from copy import deepcopy # noqa
from parameterized import parameterized # noqa
from transformers import TrainingArguments, is_torch_available # noqa
from transformers.deepspeed import is_deepspeed_available # noqa
from transformers.file_utils import WEIGHTS_NAME # noqa
from transformers.testing_utils import ( # noqa
CaptureLogger,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
mockenv_context,
require_deepspeed,
require_torch_gpu,
require_torch_multi_gpu,
slow,
)
from transformers.trainer_utils import set_seed # noqa
set_seed(4_2)
A : List[str] = {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}
A : Optional[int] = 'zero2'
A : str = 'zero3'
A : Tuple = [ZEROa, ZEROa]
def __lowerCAmelCase ( a__ , a__ , a__ ) -> Tuple:
# customize the test name generator function as we want both params to appear in the sub-test
# name, as by default it shows only the first param
__a = parameterized.to_safe_name('''_'''.join(str(a__ ) for x in param.args ) )
return F"""{func.__name__}_{param_based_name}"""
# Cartesian-product of zero stages with models to test
A : Union[str, Any] = list(itertools.product(stages, models.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class __A( a ):
@parameterized.expand(_snake_case , name_func=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Any:
'''simple docstring'''
self.run_and_check(
stage=_snake_case , model=_snake_case , distributed=_snake_case , fpaa=_snake_case , )
@require_torch_multi_gpu
@parameterized.expand(_snake_case , name_func=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> int:
'''simple docstring'''
self.run_and_check(
stage=_snake_case , model=_snake_case , distributed=_snake_case , fpaa=_snake_case , )
@parameterized.expand(_snake_case , name_func=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> str:
'''simple docstring'''
self.run_and_check(
stage=_snake_case , model=_snake_case , distributed=_snake_case , fpaa=_snake_case , )
@require_torch_multi_gpu
@parameterized.expand(_snake_case , name_func=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
self.run_and_check(
stage=_snake_case , model=_snake_case , distributed=_snake_case , fpaa=_snake_case , )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = 10 , _snake_case = True , _snake_case = True , _snake_case = True , ) -> Any:
'''simple docstring'''
__a = models[model]
__a = self.run_trainer(
stage=_snake_case , model_name=_snake_case , eval_steps=_snake_case , num_train_epochs=1 , distributed=_snake_case , fpaa=_snake_case , )
self.do_checks(_snake_case )
return output_dir
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = 10 , _snake_case = 1 , _snake_case = True , _snake_case = True , ) -> Union[str, Any]:
'''simple docstring'''
__a = self.get_auto_remove_tmp_dir('''./xxx''' , after=_snake_case )
__a = F"""
--model_name_or_path {model_name}
--dataset_name hf-internal-testing/librispeech_asr_dummy
--dataset_config_name clean
--train_split_name validation
--validation_split_name validation
--output_dir {output_dir}
--num_train_epochs {str(_snake_case )}
--per_device_train_batch_size 2
--per_device_eval_batch_size 2
--evaluation_strategy steps
--learning_rate 5e-4
--warmup_steps 8
--orthography timit
--preprocessing_num_workers 1
--group_by_length
--freeze_feature_extractor
--report_to none
--save_steps 0
--eval_steps {eval_steps}
--report_to none
""".split()
if fpaa:
args.extend(['''--fp16'''] )
# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
# hence the separate config files
__a = F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
__a = [F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
__a = self.get_launcher(_snake_case )
__a = launcher + script + args + ds_args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(_snake_case , env=self.get_env() )
return output_dir
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=False ) -> List[str]:
'''simple docstring'''
__a = min(2 , get_gpu_count() ) if distributed else 1
return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split() | 6 | 1 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
A : Dict = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n booktitle = {},\n year = {2002},\n pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n author = "Lin, Chin-Yew and\n Och, Franz Josef",\n booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n month = "aug 23{--}aug 27",\n year = "2004",\n address = "Geneva, Switzerland",\n publisher = "COLING",\n url = "https://www.aclweb.org/anthology/C04-1072",\n pages = "501--507",\n}\n'
A : Union[str, Any] = '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'
A : Dict = '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n predictions: list of translations to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\n max_order: Maximum n-gram order to use when computing BLEU score.\n smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n \'bleu\': bleu score,\n \'precisions\': geometric mean of n-gram precisions,\n \'brevity_penalty\': brevity penalty,\n \'length_ratio\': ratio of lengths,\n \'translation_length\': translation_length,\n \'reference_length\': reference_length\nExamples:\n\n >>> predictions = [\n ... ["hello", "there", "general", "kenobi"], # tokenized prediction of the first sample\n ... ["foo", "bar", "foobar"] # tokenized prediction of the second sample\n ... ]\n >>> references = [\n ... [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]], # tokenized references for the first sample (2 references)\n ... [["foo", "bar", "foobar"]] # tokenized references for the second sample (1 reference)\n ... ]\n >>> bleu = datasets.load_metric("bleu")\n >>> results = bleu.compute(predictions=predictions, references=references)\n >>> print(results["bleu"])\n 1.0\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A( datasets.Metric ):
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ),
'''references''': datasets.Sequence(
datasets.Sequence(datasets.Value('''string''' , id='''token''' ) , id='''sequence''' ) , id='''references''' ),
} ) , codebase_urls=['''https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py'''] , reference_urls=[
'''https://en.wikipedia.org/wiki/BLEU''',
'''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
] , )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case=4 , _snake_case=False ) -> Any:
'''simple docstring'''
__a = compute_bleu(
reference_corpus=_snake_case , translation_corpus=_snake_case , max_order=_snake_case , smooth=_snake_case )
((__a) , (__a) , (__a) , (__a) , (__a) , (__a)) = score
return {
"bleu": bleu,
"precisions": precisions,
"brevity_penalty": bp,
"length_ratio": ratio,
"translation_length": translation_length,
"reference_length": reference_length,
} | 6 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __A( a , a , unittest.TestCase ):
snake_case_ = AutoencoderKL
snake_case_ = '''sample'''
snake_case_ = 1E-2
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = 4
__a = 3
__a = (32, 32)
__a = floats_tensor((batch_size, num_channels) + sizes ).to(_snake_case )
return {"sample": image}
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
return (3, 32, 32)
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
return (3, 32, 32)
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a = {
'''block_out_channels''': [32, 64],
'''in_channels''': 3,
'''out_channels''': 3,
'''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
'''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
'''latent_channels''': 4,
}
__a = self.dummy_input
return init_dict, inputs_dict
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skipIf(torch_device == '''mps''' , '''Gradient checkpointing skipped on MPS''' )
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
__a , __a = self.prepare_init_args_and_inputs_for_common()
__a = self.model_class(**_snake_case )
model.to(_snake_case )
assert not model.is_gradient_checkpointing and model.training
__a = model(**_snake_case ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
__a = torch.randn_like(_snake_case )
__a = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
__a = self.model_class(**_snake_case )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(_snake_case )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
__a = model_a(**_snake_case ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
__a = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1E-5 )
__a = dict(model.named_parameters() )
__a = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data , named_params_a[name].grad.data , atol=5E-5 ) )
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
__a , __a = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' , output_loading_info=_snake_case )
self.assertIsNotNone(_snake_case )
self.assertEqual(len(loading_info['''missing_keys'''] ) , 0 )
model.to(_snake_case )
__a = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
__a = AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy''' )
__a = model.to(_snake_case )
model.eval()
if torch_device == "mps":
__a = torch.manual_seed(0 )
else:
__a = torch.Generator(device=_snake_case ).manual_seed(0 )
__a = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
__a = image.to(_snake_case )
with torch.no_grad():
__a = model(_snake_case , sample_posterior=_snake_case , generator=_snake_case ).sample
__a = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
__a = torch.tensor(
[
-4.0_078E-01,
-3.8_323E-04,
-1.2_681E-01,
-1.1_462E-01,
2.0_095E-01,
1.0_893E-01,
-8.8_247E-02,
-3.0_361E-01,
-9.8_644E-03,
] )
elif torch_device == "cpu":
__a = torch.tensor(
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026] )
else:
__a = torch.tensor(
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485] )
self.assertTrue(torch_all_close(_snake_case , _snake_case , rtol=1E-2 ) )
@slow
class __A( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
return F"""gaussian_noise_s={seed}_shape={'_'.join([str(_snake_case ) for s in shape] )}.npy"""
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=0 , _snake_case=(4, 3, 512, 512) , _snake_case=False ) -> Any:
'''simple docstring'''
__a = torch.floataa if fpaa else torch.floataa
__a = torch.from_numpy(load_hf_numpy(self.get_file_format(_snake_case , _snake_case ) ) ).to(_snake_case ).to(_snake_case )
return image
def SCREAMING_SNAKE_CASE_ ( self , _snake_case="CompVis/stable-diffusion-v1-4" , _snake_case=False ) -> Optional[Any]:
'''simple docstring'''
__a = '''fp16''' if fpaa else None
__a = torch.floataa if fpaa else torch.floataa
__a = AutoencoderKL.from_pretrained(
_snake_case , subfolder='''vae''' , torch_dtype=_snake_case , revision=_snake_case , )
model.to(_snake_case ).eval()
return model
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=0 ) -> Tuple:
'''simple docstring'''
if torch_device == "mps":
return torch.manual_seed(_snake_case )
return torch.Generator(device=_snake_case ).manual_seed(_snake_case )
@parameterized.expand(
[
# fmt: off
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case ) -> List[Any]:
'''simple docstring'''
__a = self.get_sd_vae_model()
__a = self.get_sd_image(_snake_case )
__a = self.get_generator(_snake_case )
with torch.no_grad():
__a = model(_snake_case , generator=_snake_case , sample_posterior=_snake_case ).sample
assert sample.shape == image.shape
__a = sample[-1, -2:, -2:, :2].flatten().float().cpu()
__a = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(_snake_case , _snake_case , atol=3E-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
# fmt: on
] )
@require_torch_gpu
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Tuple:
'''simple docstring'''
__a = self.get_sd_vae_model(fpaa=_snake_case )
__a = self.get_sd_image(_snake_case , fpaa=_snake_case )
__a = self.get_generator(_snake_case )
with torch.no_grad():
__a = model(_snake_case , generator=_snake_case , sample_posterior=_snake_case ).sample
assert sample.shape == image.shape
__a = sample[-1, -2:, :2, -2:].flatten().float().cpu()
__a = torch.tensor(_snake_case )
assert torch_all_close(_snake_case , _snake_case , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# fmt: on
] )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = self.get_sd_vae_model()
__a = self.get_sd_image(_snake_case )
with torch.no_grad():
__a = model(_snake_case ).sample
assert sample.shape == image.shape
__a = sample[-1, -2:, -2:, :2].flatten().float().cpu()
__a = torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice )
assert torch_all_close(_snake_case , _snake_case , atol=3E-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
# fmt: on
] )
@require_torch_gpu
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = self.get_sd_vae_model()
__a = self.get_sd_image(_snake_case , shape=(3, 4, 64, 64) )
with torch.no_grad():
__a = model.decode(_snake_case ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
__a = sample[-1, -2:, :2, -2:].flatten().cpu()
__a = torch.tensor(_snake_case )
assert torch_all_close(_snake_case , _snake_case , atol=1E-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
# fmt: on
] )
@require_torch_gpu
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = self.get_sd_vae_model(fpaa=_snake_case )
__a = self.get_sd_image(_snake_case , shape=(3, 4, 64, 64) , fpaa=_snake_case )
with torch.no_grad():
__a = model.decode(_snake_case ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
__a = sample[-1, -2:, :2, -2:].flatten().float().cpu()
__a = torch.tensor(_snake_case )
assert torch_all_close(_snake_case , _snake_case , atol=5E-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Union[str, Any]:
'''simple docstring'''
__a = self.get_sd_vae_model(fpaa=_snake_case )
__a = self.get_sd_image(_snake_case , shape=(3, 4, 64, 64) , fpaa=_snake_case )
with torch.no_grad():
__a = model.decode(_snake_case ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
__a = model.decode(_snake_case ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(_snake_case , _snake_case , atol=1E-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() , reason='''xformers is not required when using PyTorch 2.0.''' )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> List[str]:
'''simple docstring'''
__a = self.get_sd_vae_model()
__a = self.get_sd_image(_snake_case , shape=(3, 4, 64, 64) )
with torch.no_grad():
__a = model.decode(_snake_case ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
__a = model.decode(_snake_case ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(_snake_case , _snake_case , atol=1E-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
# fmt: on
] )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = self.get_sd_vae_model()
__a = self.get_sd_image(_snake_case )
__a = self.get_generator(_snake_case )
with torch.no_grad():
__a = model.encode(_snake_case ).latent_dist
__a = dist.sample(generator=_snake_case )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
__a = sample[0, -1, -3:, -3:].flatten().cpu()
__a = torch.tensor(_snake_case )
__a = 3E-3 if torch_device != '''mps''' else 1E-2
assert torch_all_close(_snake_case , _snake_case , atol=_snake_case ) | 6 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __A:
snake_case_ = 42
snake_case_ = None
snake_case_ = None
def __lowerCAmelCase ( ) -> Node | None:
__a = Node(1 )
__a = Node(2 )
__a = Node(3 )
__a = Node(4 )
__a = Node(5 )
return tree
def __lowerCAmelCase ( a__ ) -> list[int]:
return [root.data, *preorder(root.left ), *preorder(root.right )] if root else []
def __lowerCAmelCase ( a__ ) -> list[int]:
return postorder(root.left ) + postorder(root.right ) + [root.data] if root else []
def __lowerCAmelCase ( a__ ) -> list[int]:
return [*inorder(root.left ), root.data, *inorder(root.right )] if root else []
def __lowerCAmelCase ( a__ ) -> int:
return (max(height(root.left ) , height(root.right ) ) + 1) if root else 0
def __lowerCAmelCase ( a__ ) -> Sequence[Node | None]:
__a = []
if root is None:
return output
__a = deque([root] )
while process_queue:
__a = process_queue.popleft()
output.append(node.data )
if node.left:
process_queue.append(node.left )
if node.right:
process_queue.append(node.right )
return output
def __lowerCAmelCase ( a__ , a__ ) -> Sequence[Node | None]:
__a = []
def populate_output(a__ , a__ ) -> None:
if not root:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.left , level - 1 )
populate_output(root.right , level - 1 )
populate_output(a__ , a__ )
return output
def __lowerCAmelCase ( a__ , a__ ) -> Sequence[Node | None]:
__a = []
def populate_output(a__ , a__ ) -> None:
if root is None:
return
if level == 1:
output.append(root.data )
elif level > 1:
populate_output(root.right , level - 1 )
populate_output(root.left , level - 1 )
populate_output(a__ , a__ )
return output
def __lowerCAmelCase ( a__ ) -> Sequence[Node | None] | list[Any]:
if root is None:
return []
__a = []
__a = 0
__a = height(a__ )
for h in range(1 , height_tree + 1 ):
if not flag:
output.append(get_nodes_from_left_to_right(a__ , a__ ) )
__a = 1
else:
output.append(get_nodes_from_right_to_left(a__ , a__ ) )
__a = 0
return output
def __lowerCAmelCase ( ) -> None: # Main function for testing.
__a = make_tree()
print(F"""In-order Traversal: {inorder(a__ )}""" )
print(F"""Pre-order Traversal: {preorder(a__ )}""" )
print(F"""Post-order Traversal: {postorder(a__ )}""" , '''\n''' )
print(F"""Height of Tree: {height(a__ )}""" , '''\n''' )
print('''Complete Level Order Traversal: ''' )
print(level_order(a__ ) , '''\n''' )
print('''Level-wise order Traversal: ''' )
for level in range(1 , height(a__ ) + 1 ):
print(F"""Level {level}:""" , get_nodes_from_left_to_right(a__ , level=a__ ) )
print('''\nZigZag order Traversal: ''' )
print(zigzag(a__ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main() | 6 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
A : str = logging.get_logger(__name__)
class __A( a ):
def __init__( self , **_snake_case ) -> List[Any]:
'''simple docstring'''
requires_backends(self , ['''bs4'''] )
super().__init__(**_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> int:
'''simple docstring'''
__a = []
__a = []
__a = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
__a = parent.find_all(child.name , recursive=_snake_case )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(_snake_case ) else next(i for i, s in enumerate(_snake_case , 1 ) if s is child ) )
__a = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = BeautifulSoup(_snake_case , '''html.parser''' )
__a = []
__a = []
__a = []
for element in html_code.descendants:
if type(_snake_case ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
__a = html.unescape(_snake_case ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(_snake_case )
__a , __a = self.xpath_soup(_snake_case )
stringaxtag_seq.append(_snake_case )
stringaxsubs_seq.append(_snake_case )
if len(_snake_case ) != len(_snake_case ):
raise ValueError('''Number of doc strings and xtags does not correspond''' )
if len(_snake_case ) != len(_snake_case ):
raise ValueError('''Number of doc strings and xsubs does not correspond''' )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = ''''''
for tagname, subs in zip(_snake_case , _snake_case ):
xpath += F"""/{tagname}"""
if subs != 0:
xpath += F"""[{subs}]"""
return xpath
def __call__( self , _snake_case ) -> BatchFeature:
'''simple docstring'''
__a = False
# Check that strings has a valid type
if isinstance(_snake_case , _snake_case ):
__a = True
elif isinstance(_snake_case , (list, tuple) ):
if len(_snake_case ) == 0 or isinstance(html_strings[0] , _snake_case ):
__a = True
if not valid_strings:
raise ValueError(
'''HTML strings must of type `str`, `List[str]` (batch of examples), '''
F"""but is of type {type(_snake_case )}.""" )
__a = bool(isinstance(_snake_case , (list, tuple) ) and (isinstance(html_strings[0] , _snake_case )) )
if not is_batched:
__a = [html_strings]
# Get nodes + xpaths
__a = []
__a = []
for html_string in html_strings:
__a , __a , __a = self.get_three_from_single(_snake_case )
nodes.append(_snake_case )
__a = []
for node, tag_list, sub_list in zip(_snake_case , _snake_case , _snake_case ):
__a = self.construct_xpath(_snake_case , _snake_case )
xpath_strings.append(_snake_case )
xpaths.append(_snake_case )
# return as Dict
__a = {'''nodes''': nodes, '''xpaths''': xpaths}
__a = BatchFeature(data=_snake_case , tensor_type=_snake_case )
return encoded_inputs | 6 | 1 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('0.12.2'):
raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
raise Exception('requires fairseq < v2')
logging.set_verbosity_info()
A : str = logging.get_logger(__name__)
A : Union[str, Any] = 'Hello, World!'
A : Any = 'en_XX'
def __lowerCAmelCase ( a__ , a__ , a__ ) -> Any:
__a = Path('''data_bin''' )
__a = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(a__ ).parent ) , checkpoint_file=Path(a__ ).name , _name='''xmod_base''' , arch='''xmod_base''' , task='''multilingual_masked_lm''' , data_name_or_path=str(a__ ) , bpe='''sentencepiece''' , sentencepiece_model=str(Path(a__ ).parent / '''sentencepiece.bpe.model''' ) , src_dict=str(data_dir / '''dict.txt''' ) , )
xmod.eval() # disable dropout
print(a__ )
__a = xmod.model.encoder.sentence_encoder
__a = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , '''bottleneck''' , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
__a = xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
print('''Our X-MOD config:''' , a__ )
__a = XmodForSequenceClassification(a__ ) if classification_head else XmodForMaskedLM(a__ )
model.eval()
# Now let's copy all the weights.
# Embeddings
__a = xmod_sent_encoder.embed_tokens.weight
__a = xmod_sent_encoder.embed_positions.weight
__a = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
__a = xmod_sent_encoder.layernorm_embedding.weight
__a = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
__a = model.roberta.encoder.layer[i]
__a = xmod_sent_encoder.layers[i]
# self attention
__a = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError('''Dimensions of self-attention weights do not match.''' )
__a = xmod_layer.self_attn.q_proj.weight
__a = xmod_layer.self_attn.q_proj.bias
__a = xmod_layer.self_attn.k_proj.weight
__a = xmod_layer.self_attn.k_proj.bias
__a = xmod_layer.self_attn.v_proj.weight
__a = xmod_layer.self_attn.v_proj.bias
# self-attention output
__a = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError('''Dimensions of self-attention output weights do not match.''' )
__a = xmod_layer.self_attn.out_proj.weight
__a = xmod_layer.self_attn.out_proj.bias
__a = xmod_layer.self_attn_layer_norm.weight
__a = xmod_layer.self_attn_layer_norm.bias
# intermediate
__a = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('''Dimensions of intermediate weights do not match.''' )
__a = xmod_layer.fca.weight
__a = xmod_layer.fca.bias
# output
__a = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('''Dimensions of feed-forward weights do not match.''' )
__a = xmod_layer.fca.weight
__a = xmod_layer.fca.bias
__a = xmod_layer.final_layer_norm.weight
__a = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
__a = xmod_layer.adapter_layer_norm.weight
__a = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError('''Lists of language adapters do not match.''' )
for lang_code, adapter in xmod_layer.adapter_modules.items():
__a = bert_output.adapter_modules[lang_code]
__a = xmod_layer.adapter_modules[lang_code]
__a = from_adapter.fca.weight
__a = from_adapter.fca.bias
__a = from_adapter.fca.weight
__a = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
__a = xmod_sent_encoder.layer_norm.weight
__a = xmod_sent_encoder.layer_norm.bias
if classification_head:
__a = xmod.model.classification_heads['''mnli'''].dense.weight
__a = xmod.model.classification_heads['''mnli'''].dense.bias
__a = xmod.model.classification_heads['''mnli'''].out_proj.weight
__a = xmod.model.classification_heads['''mnli'''].out_proj.bias
else:
# LM Head
__a = xmod.model.encoder.lm_head.dense.weight
__a = xmod.model.encoder.lm_head.dense.bias
__a = xmod.model.encoder.lm_head.layer_norm.weight
__a = xmod.model.encoder.lm_head.layer_norm.bias
__a = xmod.model.encoder.lm_head.weight
__a = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
__a = xmod.encode(a__ ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(a__ )
__a = model(a__ )[0]
if classification_head:
__a = xmod.model.classification_heads['''mnli'''](xmod.extract_features(a__ ) )
else:
__a = xmod.model(a__ , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
__a = torch.max(torch.abs(our_output - their_output ) ).item()
print(F"""max_absolute_diff = {max_absolute_diff}""" ) # ~ 1e-7
__a = torch.allclose(a__ , a__ , atol=1e-3 )
print('''Do both models output the same tensors?''' , '''🔥''' if success else '''💩''' )
if not success:
raise Exception('''Something went wRoNg''' )
Path(a__ ).mkdir(parents=a__ , exist_ok=a__ )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(a__ )
if __name__ == "__main__":
A : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--classification_head', action='store_true', help='Whether to convert a final classification head.'
)
A : Optional[int] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
) | 6 |
def __lowerCAmelCase ( a__ , a__ ) -> float:
def get_matched_characters(a__ , a__ ) -> str:
__a = []
__a = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
__a = int(max(0 , i - limit ) )
__a = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(a__ )
__a = F"""{_stra[0:_stra.index(a__ )]} {_stra[_stra.index(a__ ) + 1:]}"""
return "".join(a__ )
# matching characters
__a = get_matched_characters(a__ , a__ )
__a = get_matched_characters(a__ , a__ )
__a = len(a__ )
# transposition
__a = (
len([(ca, ca) for ca, ca in zip(a__ , a__ ) if ca != ca] ) // 2
)
if not match_count:
__a = 0.0
else:
__a = (
1
/ 3
* (
match_count / len(a__ )
+ match_count / len(a__ )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
__a = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler('hello', 'world')) | 6 | 1 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowerCAmelCase ( a__ , a__ , a__ ) -> Optional[Any]:
# Construct model
if gpta_config_file == "":
__a = GPTaConfig()
else:
__a = GPTaConfig.from_json_file(a__ )
__a = GPTaModel(a__ )
# Load weights from numpy
load_tf_weights_in_gpta(a__ , a__ , a__ )
# Save pytorch-model
__a = pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
__a = pytorch_dump_folder_path + '''/''' + CONFIG_NAME
print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(model.state_dict() , a__ )
print(F"""Save configuration file to {pytorch_config_dump_path}""" )
with open(a__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
A : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--gpt2_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained OpenAI model. \n'
'This specifies the model architecture.'
),
)
A : Tuple = parser.parse_args()
convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path) | 6 |
def __lowerCAmelCase ( a__ ) -> str:
__a = []
__a = set({'''(''', '''[''', '''{'''} )
__a = set({''')''', ''']''', '''}'''} )
__a = {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}
for i in range(len(a__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(a__ ) == 0 or (len(a__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(a__ ) == 0
def __lowerCAmelCase ( ) -> Dict:
__a = input('''Enter sequence of brackets: ''' )
if is_balanced(a__ ):
print(a__ , '''is balanced''' )
else:
print(a__ , '''is not balanced''' )
if __name__ == "__main__":
main() | 6 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A : Dict = {'processing_layoutxlm': ['LayoutXLMProcessor']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[int] = ['LayoutXLMTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = ['LayoutXLMTokenizerFast']
if TYPE_CHECKING:
from .processing_layoutxlm import LayoutXLMProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm import LayoutXLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast
else:
import sys
A : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : str = {
'configuration_blenderbot': [
'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BlenderbotConfig',
'BlenderbotOnnxConfig',
],
'tokenization_blenderbot': ['BlenderbotTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = ['BlenderbotTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = [
'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
'BlenderbotForCausalLM',
'BlenderbotForConditionalGeneration',
'BlenderbotModel',
'BlenderbotPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Dict = [
'TFBlenderbotForConditionalGeneration',
'TFBlenderbotModel',
'TFBlenderbotPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Dict = [
'FlaxBlenderbotForConditionalGeneration',
'FlaxBlenderbotModel',
'FlaxBlenderbotPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
A : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : int = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json',
}
class __A( a ):
snake_case_ = '''falcon'''
snake_case_ = ['''past_key_values''']
def __init__( self , _snake_case=65_024 , _snake_case=4_544 , _snake_case=32 , _snake_case=71 , _snake_case=1E-5 , _snake_case=0.02 , _snake_case=True , _snake_case=0.0 , _snake_case=0.0 , _snake_case=None , _snake_case=False , _snake_case=False , _snake_case=True , _snake_case=True , _snake_case=False , _snake_case=11 , _snake_case=11 , **_snake_case , ) -> List[Any]:
'''simple docstring'''
__a = vocab_size
# Backward compatibility with n_embed kwarg
__a = kwargs.pop('''n_embed''' , _snake_case )
__a = hidden_size if n_embed is None else n_embed
__a = num_hidden_layers
__a = num_attention_heads
__a = layer_norm_epsilon
__a = initializer_range
__a = use_cache
__a = hidden_dropout
__a = attention_dropout
__a = bos_token_id
__a = eos_token_id
__a = num_attention_heads if num_kv_heads is None else num_kv_heads
__a = alibi
__a = new_decoder_architecture
__a = multi_query # Ignored when new_decoder_architecture is True
__a = parallel_attn
__a = bias
super().__init__(bos_token_id=_snake_case , eos_token_id=_snake_case , **_snake_case )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
return self.hidden_size // self.num_attention_heads
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
return not self.alibi | 6 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Dict = {
'configuration_xlm_roberta': [
'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaConfig',
'XLMRobertaOnnxConfig',
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Union[str, Any] = ['XLMRobertaTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = ['XLMRobertaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[Any] = [
'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'XLMRobertaForCausalLM',
'XLMRobertaForMaskedLM',
'XLMRobertaForMultipleChoice',
'XLMRobertaForQuestionAnswering',
'XLMRobertaForSequenceClassification',
'XLMRobertaForTokenClassification',
'XLMRobertaModel',
'XLMRobertaPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = [
'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFXLMRobertaForCausalLM',
'TFXLMRobertaForMaskedLM',
'TFXLMRobertaForMultipleChoice',
'TFXLMRobertaForQuestionAnswering',
'TFXLMRobertaForSequenceClassification',
'TFXLMRobertaForTokenClassification',
'TFXLMRobertaModel',
'TFXLMRobertaPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
'FlaxXLMRobertaForMaskedLM',
'FlaxXLMRobertaForCausalLM',
'FlaxXLMRobertaForMultipleChoice',
'FlaxXLMRobertaForQuestionAnswering',
'FlaxXLMRobertaForSequenceClassification',
'FlaxXLMRobertaForTokenClassification',
'FlaxXLMRobertaModel',
'FlaxXLMRobertaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
A : Optional[Any] = R'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n Args:\n title_sep (`str`, *optional*, defaults to `" / "`):\n Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n doc_sep (`str`, *optional*, defaults to `" // "`):\n Separator inserted between the text of the retrieved document and the original input when calling\n [`RagRetriever`].\n n_docs (`int`, *optional*, defaults to 5):\n Number of documents to retrieve.\n max_combined_length (`int`, *optional*, defaults to 300):\n Max length of contextualized input returned by [`~RagRetriever.__call__`].\n retrieval_vector_size (`int`, *optional*, defaults to 768):\n Dimensionality of the document embeddings indexed by [`RagRetriever`].\n retrieval_batch_size (`int`, *optional*, defaults to 8):\n Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n [`RagRetriever`].\n dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n using `datasets.list_datasets()`).\n dataset_split (`str`, *optional*, defaults to `"train"`)\n Which split of the `dataset` to load.\n index_name (`str`, *optional*, defaults to `"compressed"`)\n The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n `"compressed"`.\n index_path (`str`, *optional*)\n The path to the serialized faiss index on disk.\n passages_path (`str`, *optional*):\n A path to text passages compatible with the faiss index. Required if using\n [`~models.rag.retrieval_rag.LegacyIndex`]\n use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n Whether to load a "dummy" variant of the dataset specified by `dataset`.\n label_smoothing (`float`, *optional*, defaults to 0.0):\n Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n in the loss calculation. If set to 0, no label smoothing is performed.\n do_marginalize (`bool`, *optional*, defaults to `False`):\n If `True`, the logits are marginalized over all documents by making use of\n `torch.nn.functional.log_softmax`.\n reduce_loss (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n do_deduplication (`bool`, *optional*, defaults to `True`):\n Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n set to `False` if used while training with distributed backend.\n exclude_bos_score (`bool`, *optional*, defaults to `False`):\n Whether or not to disregard the BOS token when computing the loss.\n output_retrieved(`bool`, *optional*, defaults to `False`):\n If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n `context_attention_mask` are returned. See returned tensors for more detail.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models).\n forced_eos_token_id (`int`, *optional*):\n The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n `eos_token_id`.\n'
@add_start_docstrings(a )
class __A( a ):
snake_case_ = '''rag'''
snake_case_ = True
def __init__( self , _snake_case=None , _snake_case=True , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=None , _snake_case=" / " , _snake_case=" // " , _snake_case=5 , _snake_case=300 , _snake_case=768 , _snake_case=8 , _snake_case="wiki_dpr" , _snake_case="train" , _snake_case="compressed" , _snake_case=None , _snake_case=None , _snake_case=False , _snake_case=False , _snake_case=0.0 , _snake_case=True , _snake_case=False , _snake_case=False , _snake_case=False , _snake_case=True , _snake_case=None , **_snake_case , ) -> Optional[Any]:
'''simple docstring'''
super().__init__(
bos_token_id=_snake_case , pad_token_id=_snake_case , eos_token_id=_snake_case , decoder_start_token_id=_snake_case , forced_eos_token_id=_snake_case , is_encoder_decoder=_snake_case , prefix=_snake_case , vocab_size=_snake_case , **_snake_case , )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
__a = kwargs.pop('''question_encoder''' )
__a = question_encoder_config.pop('''model_type''' )
__a = kwargs.pop('''generator''' )
__a = decoder_config.pop('''model_type''' )
from ..auto.configuration_auto import AutoConfig
__a = AutoConfig.for_model(_snake_case , **_snake_case )
__a = AutoConfig.for_model(_snake_case , **_snake_case )
__a = reduce_loss
__a = label_smoothing
__a = exclude_bos_score
__a = do_marginalize
__a = title_sep
__a = doc_sep
__a = n_docs
__a = max_combined_length
__a = dataset
__a = dataset_split
__a = index_name
__a = retrieval_vector_size
__a = retrieval_batch_size
__a = passages_path
__a = index_path
__a = use_dummy_dataset
__a = output_retrieved
__a = do_deduplication
__a = use_cache
if self.forced_eos_token_id is None:
__a = getattr(self.generator , '''forced_eos_token_id''' , _snake_case )
@classmethod
def SCREAMING_SNAKE_CASE_ ( cls , _snake_case , _snake_case , **_snake_case ) -> PretrainedConfig:
'''simple docstring'''
return cls(question_encoder=question_encoder_config.to_dict() , generator=generator_config.to_dict() , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = copy.deepcopy(self.__dict__ )
__a = self.question_encoder.to_dict()
__a = self.generator.to_dict()
__a = self.__class__.model_type
return output | 6 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Optional[int] = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
'feature_extraction_whisper': ['WhisperFeatureExtractor'],
'processing_whisper': ['WhisperProcessor'],
'tokenization_whisper': ['WhisperTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = ['WhisperTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : str = [
'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
'WhisperForConditionalGeneration',
'WhisperModel',
'WhisperPreTrainedModel',
'WhisperForAudioClassification',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Tuple = [
'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFWhisperForConditionalGeneration',
'TFWhisperModel',
'TFWhisperPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : int = [
'FlaxWhisperForConditionalGeneration',
'FlaxWhisperModel',
'FlaxWhisperPreTrainedModel',
'FlaxWhisperForAudioClassification',
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
A : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 6 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCAmelCase ( a__ ) -> str:
return "".join(sorted(a__ ) )
def __lowerCAmelCase ( a__ ) -> list[str]:
return word_by_signature[signature(a__ )]
A : str = Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
A : List[str] = sorted({word.strip().lower() for word in data.splitlines()})
A : Tuple = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
A : Dict = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('anagrams.txt', 'w') as file:
file.write('all_anagrams = \n ')
file.write(pprint.pformat(all_anagrams)) | 6 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline # noqa: F401
deprecate(
'stable diffusion controlnet',
'0.22.0',
'Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.',
standard_warn=False,
stacklevel=3,
) | 6 | 1 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def __lowerCAmelCase ( a__ ) -> int:
return {key.lstrip('''-''' ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def __lowerCAmelCase ( ) -> Union[str, Any]:
__a = ArgumentParser(
'''HuggingFace Datasets CLI tool''' , usage='''datasets-cli <command> [<args>]''' , allow_abbrev=a__ )
__a = parser.add_subparsers(help='''datasets-cli command helpers''' )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(a__ )
EnvironmentCommand.register_subcommand(a__ )
TestCommand.register_subcommand(a__ )
RunBeamCommand.register_subcommand(a__ )
DummyDataCommand.register_subcommand(a__ )
# Parse args
__a , __a = parser.parse_known_args()
if not hasattr(a__ , '''func''' ):
parser.print_help()
exit(1 )
__a = parse_unknown_args(a__ )
# Run
__a = args.func(a__ , **a__ )
service.run()
if __name__ == "__main__":
main() | 6 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a )
class __A( a ):
snake_case_ = field(default='''language-modeling''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
snake_case_ = Features({'''text''': Value('''string''' )} )
snake_case_ = Features({} )
snake_case_ = "text"
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Dict[str, str]:
'''simple docstring'''
return {self.text_column: "text"} | 6 | 1 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
A : List[str] = 1_6
A : str = 3_2
def __lowerCAmelCase ( a__ , a__ = 16 ) -> Optional[Any]:
__a = AutoTokenizer.from_pretrained('''bert-base-cased''' )
__a = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(a__ ):
# max_length=None => use the model max length (it's actually the default)
__a = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=a__ , max_length=a__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__a = datasets.map(
a__ , batched=a__ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__a = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(a__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__a = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__a = 16
elif accelerator.mixed_precision != "no":
__a = 8
else:
__a = None
return tokenizer.pad(
a__ , padding='''longest''' , max_length=a__ , pad_to_multiple_of=a__ , return_tensors='''pt''' , )
# Instantiate dataloaders.
__a = DataLoader(
tokenized_datasets['''train'''] , shuffle=a__ , collate_fn=a__ , batch_size=a__ )
__a = DataLoader(
tokenized_datasets['''validation'''] , shuffle=a__ , collate_fn=a__ , batch_size=a__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
A : Union[str, Any] = mocked_dataloaders # noqa: F811
def __lowerCAmelCase ( a__ , a__ ) -> List[str]:
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , a__ ) == "1":
__a = 2
# Initialize accelerator
__a = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__a = config['''lr''']
__a = int(config['''num_epochs'''] )
__a = int(config['''seed'''] )
__a = int(config['''batch_size'''] )
__a = evaluate.load('''glue''' , '''mrpc''' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=a__ )
def inner_training_loop(a__ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(a__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__a = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=a__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__a = model.to(accelerator.device )
# Instantiate optimizer
__a = AdamW(params=model.parameters() , lr=a__ )
__a , __a = get_dataloaders(a__ , a__ )
# Instantiate scheduler
__a = get_linear_schedule_with_warmup(
optimizer=a__ , num_warmup_steps=100 , num_training_steps=(len(a__ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__a , __a , __a , __a , __a = accelerator.prepare(
a__ , a__ , a__ , a__ , a__ )
# Now we train the model
for epoch in range(a__ ):
model.train()
for step, batch in enumerate(a__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__a = model(**a__ )
__a = outputs.loss
accelerator.backward(a__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(a__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__a = model(**a__ )
__a = outputs.logits.argmax(dim=-1 )
__a , __a = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=a__ , references=a__ , )
__a = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , a__ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def __lowerCAmelCase ( ) -> Dict:
__a = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=a__ , default=a__ , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
__a = parser.parse_args()
__a = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(a__ , a__ )
if __name__ == "__main__":
main() | 6 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __lowerCAmelCase ( a__ , a__ , a__=1024 , a__=1024 , a__=False , **a__ ) -> Optional[Any]:
__a = AutoTokenizer.from_pretrained(a__ )
__a = SeqaSeqDataset(a__ , a__ , a__ , a__ , type_path='''train''' , **a__ )
__a = tok.pad_token_id
def get_lens(a__ ):
__a = tqdm(
DataLoader(a__ , batch_size=512 , num_workers=8 , shuffle=a__ , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
__a = []
for batch in dl:
__a = batch['''input_ids'''].ne(a__ ).sum(1 ).tolist()
__a = batch['''labels'''].ne(a__ ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(a__ , a__ ):
max_lens.append(max(a__ , a__ ) )
else:
max_lens.extend(a__ )
return max_lens
__a = get_lens(a__ )
__a = SeqaSeqDataset(a__ , a__ , a__ , a__ , type_path='''val''' , **a__ )
__a = get_lens(a__ )
pickle_save(a__ , train_ds.len_file )
pickle_save(a__ , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file) | 6 | 1 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
A : Any = {
# 1536-bit
5: {
'prime': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF',
base=1_6,
),
'generator': 2,
},
# 2048-bit
1_4: {
'prime': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AACAA68FFFFFFFFFFFFFFFF',
base=1_6,
),
'generator': 2,
},
# 3072-bit
1_5: {
'prime': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
+ 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
+ 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
+ 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
+ '43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF',
base=1_6,
),
'generator': 2,
},
# 4096-bit
1_6: {
'prime': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
+ 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
+ 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
+ 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
+ '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
+ '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
+ '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
+ '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
+ '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
+ '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'
+ 'FFFFFFFFFFFFFFFF',
base=1_6,
),
'generator': 2,
},
# 6144-bit
1_7: {
'prime': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'
+ '8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'
+ '302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'
+ 'A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'
+ '49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'
+ 'FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'
+ '180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'
+ '3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'
+ '04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'
+ 'B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'
+ '1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'
+ 'E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'
+ '99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'
+ '04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'
+ '233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'
+ 'D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
+ '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'
+ 'AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'
+ 'DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'
+ '2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'
+ 'F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'
+ 'BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
+ 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'
+ 'B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'
+ '387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'
+ '6DCC4024FFFFFFFFFFFFFFFF',
base=1_6,
),
'generator': 2,
},
# 8192-bit
1_8: {
'prime': int(
'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
+ '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
+ 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
+ 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
+ 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
+ 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
+ '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
+ '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
+ 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
+ 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
+ '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
+ 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
+ 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
+ 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
+ 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
+ '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
+ '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
+ '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
+ '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
+ '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
+ '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
+ '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'
+ 'F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'
+ '179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'
+ 'DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'
+ '5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'
+ 'D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'
+ '23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
+ 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'
+ '06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'
+ 'DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'
+ '12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'
+ '38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'
+ '741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'
+ '3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'
+ '22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'
+ '4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'
+ '062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'
+ '4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'
+ 'B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'
+ '4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'
+ '9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'
+ '60C980DD98EDD3DFFFFFFFFFFFFFFFFF',
base=1_6,
),
'generator': 2,
},
}
class __A:
def __init__( self , _snake_case = 14 ) -> None:
'''simple docstring'''
if group not in primes:
raise ValueError('''Unsupported Group''' )
__a = primes[group]['''prime''']
__a = primes[group]['''generator''']
__a = int(hexlify(urandom(32 ) ) , base=16 )
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
return hex(self.__private_key )[2:]
def SCREAMING_SNAKE_CASE_ ( self ) -> str:
'''simple docstring'''
__a = pow(self.generator , self.__private_key , self.prime )
return hex(_snake_case )[2:]
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> bool:
'''simple docstring'''
return (
2 <= key <= self.prime - 2
and pow(_snake_case , (self.prime - 1) // 2 , self.prime ) == 1
)
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> str:
'''simple docstring'''
__a = int(_snake_case , base=16 )
if not self.is_valid_public_key(_snake_case ):
raise ValueError('''Invalid public key''' )
__a = pow(_snake_case , self.__private_key , self.prime )
return shaaaa(str(_snake_case ).encode() ).hexdigest()
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _snake_case , _snake_case ) -> bool:
'''simple docstring'''
return (
2 <= remote_public_key_str <= prime - 2
and pow(_snake_case , (prime - 1) // 2 , _snake_case ) == 1
)
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _snake_case , _snake_case , _snake_case = 14 ) -> str:
'''simple docstring'''
__a = int(_snake_case , base=16 )
__a = int(_snake_case , base=16 )
__a = primes[group]['''prime''']
if not DiffieHellman.is_valid_public_key_static(_snake_case , _snake_case ):
raise ValueError('''Invalid public key''' )
__a = pow(_snake_case , _snake_case , _snake_case )
return shaaaa(str(_snake_case ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod() | 6 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def __lowerCAmelCase ( a__ , a__ , a__ = 1 / sqrt(2 ) ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = (1 - _cos) / 2
__a = 1 - _cos
__a = 1 + alpha
__a = -2 * _cos
__a = 1 - alpha
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ = 1 / sqrt(2 ) ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = (1 + _cos) / 2
__a = -1 - _cos
__a = 1 + alpha
__a = -2 * _cos
__a = 1 - alpha
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ = 1 / sqrt(2 ) ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = _sin / 2
__a = 0
__a = -ba
__a = 1 + alpha
__a = -2 * _cos
__a = 1 - alpha
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ = 1 / sqrt(2 ) ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = 1 - alpha
__a = -2 * _cos
__a = 1 + alpha
__a = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ , a__ = 1 / sqrt(2 ) , ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = 10 ** (gain_db / 40)
__a = 1 + alpha * big_a
__a = -2 * _cos
__a = 1 - alpha * big_a
__a = 1 + alpha / big_a
__a = -2 * _cos
__a = 1 - alpha / big_a
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ , a__ = 1 / sqrt(2 ) , ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = 10 ** (gain_db / 40)
__a = (big_a + 1) - (big_a - 1) * _cos
__a = (big_a + 1) + (big_a - 1) * _cos
__a = (big_a - 1) - (big_a + 1) * _cos
__a = (big_a - 1) + (big_a + 1) * _cos
__a = 2 * sqrt(a__ ) * alpha
__a = big_a * (pmc + aaa)
__a = 2 * big_a * mpc
__a = big_a * (pmc - aaa)
__a = ppmc + aaa
__a = -2 * pmpc
__a = ppmc - aaa
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def __lowerCAmelCase ( a__ , a__ , a__ , a__ = 1 / sqrt(2 ) , ) -> IIRFilter:
__a = tau * frequency / samplerate
__a = sin(a__ )
__a = cos(a__ )
__a = _sin / (2 * q_factor)
__a = 10 ** (gain_db / 40)
__a = (big_a + 1) - (big_a - 1) * _cos
__a = (big_a + 1) + (big_a - 1) * _cos
__a = (big_a - 1) - (big_a + 1) * _cos
__a = (big_a - 1) + (big_a + 1) * _cos
__a = 2 * sqrt(a__ ) * alpha
__a = big_a * (ppmc + aaa)
__a = -2 * big_a * pmpc
__a = big_a * (ppmc - aaa)
__a = pmc + aaa
__a = 2 * mpc
__a = pmc - aaa
__a = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt | 6 | 1 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __A( a ):
snake_case_ = ['''image_processor''', '''tokenizer''']
snake_case_ = '''AutoImageProcessor'''
snake_case_ = '''AutoTokenizer'''
def __init__( self , _snake_case=None , _snake_case=None , **_snake_case ) -> str:
'''simple docstring'''
__a = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , _snake_case , )
__a = kwargs.pop('''feature_extractor''' )
__a = 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__(_snake_case , _snake_case )
__a = self.image_processor
__a = False
def __call__( self , *_snake_case , **_snake_case ) -> Optional[int]:
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*_snake_case , **_snake_case )
__a = kwargs.pop('''images''' , _snake_case )
__a = kwargs.pop('''text''' , _snake_case )
if len(_snake_case ) > 0:
__a = args[0]
__a = args[1:]
if images is None and text is None:
raise ValueError('''You need to specify either an `images` or `text` input to process.''' )
if images is not None:
__a = self.image_processor(_snake_case , *_snake_case , **_snake_case )
if text is not None:
__a = self.tokenizer(_snake_case , **_snake_case )
if text is None:
return inputs
elif images is None:
return encodings
else:
__a = encodings['''input_ids''']
return inputs
def SCREAMING_SNAKE_CASE_ ( self , *_snake_case , **_snake_case ) -> List[str]:
'''simple docstring'''
return self.tokenizer.batch_decode(*_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , *_snake_case , **_snake_case ) -> int:
'''simple docstring'''
return self.tokenizer.decode(*_snake_case , **_snake_case )
@contextmanager
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(
'''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
'''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
'''your images inputs, or in a separate call.''' )
__a = True
__a = self.tokenizer
yield
__a = self.image_processor
__a = False
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case=False , _snake_case=None ) -> int:
'''simple docstring'''
if added_vocab is None:
__a = self.tokenizer.get_added_vocab()
__a = {}
while tokens:
__a = re.search(r'''<s_(.*?)>''' , _snake_case , re.IGNORECASE )
if start_token is None:
break
__a = start_token.group(1 )
__a = re.search(rF"""</s_{key}>""" , _snake_case , re.IGNORECASE )
__a = start_token.group()
if end_token is None:
__a = tokens.replace(_snake_case , '''''' )
else:
__a = end_token.group()
__a = re.escape(_snake_case )
__a = re.escape(_snake_case )
__a = re.search(F"""{start_token_escaped}(.*?){end_token_escaped}""" , _snake_case , re.IGNORECASE )
if content is not None:
__a = content.group(1 ).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
__a = self.tokenajson(_snake_case , is_inner_value=_snake_case , added_vocab=_snake_case )
if value:
if len(_snake_case ) == 1:
__a = value[0]
__a = value
else: # leaf nodes
__a = []
for leaf in content.split(r'''<sep/>''' ):
__a = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
__a = leaf[1:-2] # for categorical special tokens
output[key].append(_snake_case )
if len(output[key] ) == 1:
__a = output[key][0]
__a = tokens[tokens.find(_snake_case ) + len(_snake_case ) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=_snake_case , added_vocab=_snake_case )
if len(_snake_case ):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , _snake_case , )
return self.image_processor_class
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , _snake_case , )
return self.image_processor | 6 |
def __lowerCAmelCase ( a__ , a__ , a__ ) -> list:
__a = len(a__ )
__a = [[0] * n for i in range(a__ )]
for i in range(a__ ):
__a = y_points[i]
for i in range(2 , a__ ):
for j in range(a__ , a__ ):
__a = (
(xa - x_points[j - i + 1]) * q[j][i - 1]
- (xa - x_points[j]) * q[j - 1][i - 1]
) / (x_points[j] - x_points[j - i + 1])
return [q[n - 1][n - 1], q]
if __name__ == "__main__":
import doctest
doctest.testmod() | 6 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
A : Tuple = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __A( a ):
snake_case_ = ['''pixel_values''']
def __init__( self , _snake_case = True , _snake_case = None , _snake_case = PILImageResampling.BICUBIC , _snake_case = True , _snake_case = None , _snake_case = True , _snake_case = 1 / 255 , _snake_case = True , _snake_case = None , _snake_case = None , _snake_case = True , **_snake_case , ) -> None:
'''simple docstring'''
super().__init__(**_snake_case )
__a = size if size is not None else {'''shortest_edge''': 224}
__a = get_size_dict(_snake_case , default_to_square=_snake_case )
__a = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__a = get_size_dict(_snake_case , default_to_square=_snake_case , param_name='''crop_size''' )
__a = do_resize
__a = size
__a = resample
__a = do_center_crop
__a = crop_size
__a = do_rescale
__a = rescale_factor
__a = do_normalize
__a = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__a = image_std if image_std is not None else OPENAI_CLIP_STD
__a = do_convert_rgb
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = PILImageResampling.BICUBIC , _snake_case = None , **_snake_case , ) -> np.ndarray:
'''simple docstring'''
__a = get_size_dict(_snake_case , default_to_square=_snake_case )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
__a = get_resize_output_image_size(_snake_case , size=size['''shortest_edge'''] , default_to_square=_snake_case )
return resize(_snake_case , size=_snake_case , resample=_snake_case , data_format=_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = None , **_snake_case , ) -> np.ndarray:
'''simple docstring'''
__a = get_size_dict(_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(_snake_case , size=(size['''height'''], size['''width''']) , data_format=_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case = None , **_snake_case , ) -> Tuple:
'''simple docstring'''
return rescale(_snake_case , scale=_snake_case , data_format=_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case , _snake_case = None , **_snake_case , ) -> np.ndarray:
'''simple docstring'''
return normalize(_snake_case , mean=_snake_case , std=_snake_case , data_format=_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = None , _snake_case = ChannelDimension.FIRST , **_snake_case , ) -> PIL.Image.Image:
'''simple docstring'''
__a = do_resize if do_resize is not None else self.do_resize
__a = size if size is not None else self.size
__a = get_size_dict(_snake_case , param_name='''size''' , default_to_square=_snake_case )
__a = resample if resample is not None else self.resample
__a = do_center_crop if do_center_crop is not None else self.do_center_crop
__a = crop_size if crop_size is not None else self.crop_size
__a = get_size_dict(_snake_case , param_name='''crop_size''' , default_to_square=_snake_case )
__a = do_rescale if do_rescale is not None else self.do_rescale
__a = rescale_factor if rescale_factor is not None else self.rescale_factor
__a = do_normalize if do_normalize is not None else self.do_normalize
__a = image_mean if image_mean is not None else self.image_mean
__a = image_std if image_std is not None else self.image_std
__a = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__a = make_list_of_images(_snake_case )
if not valid_images(_snake_case ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__a = [convert_to_rgb(_snake_case ) for image in images]
# All transformations expect numpy arrays.
__a = [to_numpy_array(_snake_case ) for image in images]
if do_resize:
__a = [self.resize(image=_snake_case , size=_snake_case , resample=_snake_case ) for image in images]
if do_center_crop:
__a = [self.center_crop(image=_snake_case , size=_snake_case ) for image in images]
if do_rescale:
__a = [self.rescale(image=_snake_case , scale=_snake_case ) for image in images]
if do_normalize:
__a = [self.normalize(image=_snake_case , mean=_snake_case , std=_snake_case ) for image in images]
__a = [to_channel_dimension_format(_snake_case , _snake_case ) for image in images]
__a = {'''pixel_values''': images}
return BatchFeature(data=_snake_case , tensor_type=_snake_case ) | 6 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def __lowerCAmelCase ( a__ , a__ , a__ ) -> tuple[int | None, int | None, float]:
if not arr:
return None, None, 0
if low == high:
return low, high, arr[low]
__a = (low + high) // 2
__a , __a , __a = max_subarray(a__ , a__ , a__ )
__a , __a , __a = max_subarray(a__ , mid + 1 , a__ )
__a , __a , __a = max_cross_sum(a__ , a__ , a__ , a__ )
if left_sum >= right_sum and left_sum >= cross_sum:
return left_low, left_high, left_sum
elif right_sum >= left_sum and right_sum >= cross_sum:
return right_low, right_high, right_sum
return cross_left, cross_right, cross_sum
def __lowerCAmelCase ( a__ , a__ , a__ , a__ ) -> tuple[int, int, float]:
__a , __a = float('''-inf''' ), -1
__a , __a = float('''-inf''' ), -1
__a = 0
for i in range(a__ , low - 1 , -1 ):
summ += arr[i]
if summ > left_sum:
__a = summ
__a = i
__a = 0
for i in range(mid + 1 , high + 1 ):
summ += arr[i]
if summ > right_sum:
__a = summ
__a = i
return max_left, max_right, (left_sum + right_sum)
def __lowerCAmelCase ( a__ ) -> float:
__a = [randint(1 , a__ ) for _ in range(a__ )]
__a = time.time()
max_subarray(a__ , 0 , input_size - 1 )
__a = time.time()
return end - start
def __lowerCAmelCase ( ) -> None:
__a = [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
__a = [time_max_subarray(a__ ) for input_size in input_sizes]
print('''No of Inputs\t\tTime Taken''' )
for input_size, runtime in zip(a__ , a__ ):
print(a__ , '''\t\t''' , a__ )
plt.plot(a__ , a__ )
plt.xlabel('''Number of Inputs''' )
plt.ylabel('''Time taken in seconds''' )
plt.show()
if __name__ == "__main__":
from doctest import testmod
testmod() | 6 | 1 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class __A( a ):
snake_case_ = (DPMSolverSDEScheduler,)
snake_case_ = 1_0
def SCREAMING_SNAKE_CASE_ ( self , **_snake_case ) -> Union[str, Any]:
'''simple docstring'''
__a = {
'''num_train_timesteps''': 1_100,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''noise_sampler_seed''': 0,
}
config.update(**_snake_case )
return config
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
for timesteps in [10, 50, 100, 1_000]:
self.check_over_configs(num_train_timesteps=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001] , [0.0002, 0.002, 0.02] ):
self.check_over_configs(beta_start=_snake_case , beta_end=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[int]:
'''simple docstring'''
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config()
__a = scheduler_class(**_snake_case )
scheduler.set_timesteps(self.num_inference_steps )
__a = self.dummy_model()
__a = self.dummy_sample_deter * scheduler.init_noise_sigma
__a = sample.to(_snake_case )
for i, t in enumerate(scheduler.timesteps ):
__a = scheduler.scale_model_input(_snake_case , _snake_case )
__a = model(_snake_case , _snake_case )
__a = scheduler.step(_snake_case , _snake_case , _snake_case )
__a = output.prev_sample
__a = torch.sum(torch.abs(_snake_case ) )
__a = torch.mean(torch.abs(_snake_case ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.47_8210_4492_1875 ) < 1E-2
assert abs(result_mean.item() - 0.2178_7059_6456_5277 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3521_1181_6406 ) < 1E-2
assert abs(result_mean.item() - 0.2_2342_9068_9229_9652 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1E-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1E-3
def SCREAMING_SNAKE_CASE_ ( self ) -> int:
'''simple docstring'''
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config(prediction_type='''v_prediction''' )
__a = scheduler_class(**_snake_case )
scheduler.set_timesteps(self.num_inference_steps )
__a = self.dummy_model()
__a = self.dummy_sample_deter * scheduler.init_noise_sigma
__a = sample.to(_snake_case )
for i, t in enumerate(scheduler.timesteps ):
__a = scheduler.scale_model_input(_snake_case , _snake_case )
__a = model(_snake_case , _snake_case )
__a = scheduler.step(_snake_case , _snake_case , _snake_case )
__a = output.prev_sample
__a = torch.sum(torch.abs(_snake_case ) )
__a = torch.mean(torch.abs(_snake_case ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 124.77_1492_0043_9453 ) < 1E-2
assert abs(result_mean.item() - 0.1_6226_2890_1481_6284 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 128.1_6633_6059_5703 ) < 1E-2
assert abs(result_mean.item() - 0.1_6688_3260_0116_7297 ) < 1E-3
else:
assert abs(result_sum.item() - 119.8_4875_4882_8125 ) < 1E-2
assert abs(result_mean.item() - 0.1560_5306_6253_6621 ) < 1E-3
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config()
__a = scheduler_class(**_snake_case )
scheduler.set_timesteps(self.num_inference_steps , device=_snake_case )
__a = self.dummy_model()
__a = self.dummy_sample_deter.to(_snake_case ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
__a = scheduler.scale_model_input(_snake_case , _snake_case )
__a = model(_snake_case , _snake_case )
__a = scheduler.step(_snake_case , _snake_case , _snake_case )
__a = output.prev_sample
__a = torch.sum(torch.abs(_snake_case ) )
__a = torch.mean(torch.abs(_snake_case ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 167.46_9573_9746_0938 ) < 1E-2
assert abs(result_mean.item() - 0.2_1805_9346_0798_2635 ) < 1E-3
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 171.59_3536_3769_5312 ) < 1E-2
assert abs(result_mean.item() - 0.2_2342_9083_8241_5771 ) < 1E-3
else:
assert abs(result_sum.item() - 162.52_3834_2285_1562 ) < 1E-2
assert abs(result_mean.item() - 0.211_6195_7085_1326 ) < 1E-3
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
__a = self.scheduler_classes[0]
__a = self.get_scheduler_config()
__a = scheduler_class(**_snake_case , use_karras_sigmas=_snake_case )
scheduler.set_timesteps(self.num_inference_steps , device=_snake_case )
__a = self.dummy_model()
__a = self.dummy_sample_deter.to(_snake_case ) * scheduler.init_noise_sigma
__a = sample.to(_snake_case )
for t in scheduler.timesteps:
__a = scheduler.scale_model_input(_snake_case , _snake_case )
__a = model(_snake_case , _snake_case )
__a = scheduler.step(_snake_case , _snake_case , _snake_case )
__a = output.prev_sample
__a = torch.sum(torch.abs(_snake_case ) )
__a = torch.mean(torch.abs(_snake_case ) )
if torch_device in ["mps"]:
assert abs(result_sum.item() - 176.66_9741_3574_2188 ) < 1E-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2
elif torch_device in ["cuda"]:
assert abs(result_sum.item() - 177.63_6535_6445_3125 ) < 1E-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2
else:
assert abs(result_sum.item() - 170.3_1352_2338_8672 ) < 1E-2
assert abs(result_mean.item() - 0.2_3003_8727_3098_1811 ) < 1E-2 | 6 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __A( a , unittest.TestCase ):
# FIXME: add fast tests
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class __A( unittest.TestCase ):
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Union[str, Any]:
'''simple docstring'''
__a = ort.SessionOptions()
__a = False
return options
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
__a = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , safety_checker=_snake_case , feature_extractor=_snake_case , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_snake_case )
__a = '''A red cat sitting on a park bench'''
__a = np.random.RandomState(0 )
__a = pipe(
prompt=_snake_case , image=_snake_case , mask_image=_snake_case , guidance_scale=7.5 , num_inference_steps=10 , generator=_snake_case , output_type='''np''' , )
__a = output.images
__a = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
__a = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3
def SCREAMING_SNAKE_CASE_ ( self ) -> Tuple:
'''simple docstring'''
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo.png''' )
__a = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/in_paint/overture-creations-5sI6fQgYIuo_mask.png''' )
__a = LMSDiscreteScheduler.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , subfolder='''scheduler''' , revision='''onnx''' )
__a = OnnxStableDiffusionInpaintPipeline.from_pretrained(
'''runwayml/stable-diffusion-inpainting''' , revision='''onnx''' , scheduler=_snake_case , safety_checker=_snake_case , feature_extractor=_snake_case , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_snake_case )
__a = '''A red cat sitting on a park bench'''
__a = np.random.RandomState(0 )
__a = pipe(
prompt=_snake_case , image=_snake_case , mask_image=_snake_case , guidance_scale=7.5 , num_inference_steps=20 , generator=_snake_case , output_type='''np''' , )
__a = output.images
__a = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
__a = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-3 | 6 | 1 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
A : Optional[int] = True
except (ImportError, AttributeError):
A : Any = object
def __lowerCAmelCase ( *a__ , **a__ ) -> Any:
pass
A : Optional[Any] = False
A : Optional[int] = logging.get_logger('transformers-cli/serving')
def __lowerCAmelCase ( a__ ) -> Any:
__a = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
return ServeCommand(a__ , args.host , args.port , args.workers )
class __A( a ):
snake_case_ = 42
class __A( a ):
snake_case_ = 42
snake_case_ = 42
class __A( a ):
snake_case_ = 42
class __A( a ):
snake_case_ = 42
class __A( a ):
@staticmethod
def SCREAMING_SNAKE_CASE_ ( _snake_case ) -> Optional[int]:
'''simple docstring'''
__a = parser.add_parser(
'''serve''' , help='''CLI tool to run inference requests through REST and GraphQL endpoints.''' )
serve_parser.add_argument(
'''--task''' , type=_snake_case , choices=get_supported_tasks() , help='''The task to run the pipeline on''' , )
serve_parser.add_argument('''--host''' , type=_snake_case , default='''localhost''' , help='''Interface the server will listen on.''' )
serve_parser.add_argument('''--port''' , type=_snake_case , default=8_888 , help='''Port the serving will listen to.''' )
serve_parser.add_argument('''--workers''' , type=_snake_case , default=1 , help='''Number of http workers''' )
serve_parser.add_argument('''--model''' , type=_snake_case , help='''Model\'s name or path to stored model.''' )
serve_parser.add_argument('''--config''' , type=_snake_case , help='''Model\'s config name or path to stored model.''' )
serve_parser.add_argument('''--tokenizer''' , type=_snake_case , help='''Tokenizer name to use.''' )
serve_parser.add_argument(
'''--device''' , type=_snake_case , default=-1 , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' , )
serve_parser.set_defaults(func=_snake_case )
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case ) -> Any:
'''simple docstring'''
__a = pipeline
__a = host
__a = port
__a = workers
if not _serve_dependencies_installed:
raise RuntimeError(
'''Using serve command requires FastAPI and uvicorn. '''
'''Please install transformers with [serving]: pip install "transformers[serving]".'''
'''Or install FastAPI and uvicorn separately.''' )
else:
logger.info(F"""Serving model over {host}:{port}""" )
__a = FastAPI(
routes=[
APIRoute(
'''/''' , self.model_info , response_model=_snake_case , response_class=_snake_case , methods=['''GET'''] , ),
APIRoute(
'''/tokenize''' , self.tokenize , response_model=_snake_case , response_class=_snake_case , methods=['''POST'''] , ),
APIRoute(
'''/detokenize''' , self.detokenize , response_model=_snake_case , response_class=_snake_case , methods=['''POST'''] , ),
APIRoute(
'''/forward''' , self.forward , response_model=_snake_case , response_class=_snake_case , methods=['''POST'''] , ),
] , timeout=600 , )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[Any]:
'''simple docstring'''
run(self._app , host=self.host , port=self.port , workers=self.workers )
def SCREAMING_SNAKE_CASE_ ( self ) -> List[str]:
'''simple docstring'''
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case = Body(_snake_case , embed=_snake_case ) , _snake_case = Body(_snake_case , embed=_snake_case ) ) -> str:
'''simple docstring'''
try:
__a = self._pipeline.tokenizer.tokenize(_snake_case )
if return_ids:
__a = self._pipeline.tokenizer.convert_tokens_to_ids(_snake_case )
return ServeTokenizeResult(tokens=_snake_case , tokens_ids=_snake_case )
else:
return ServeTokenizeResult(tokens=_snake_case )
except Exception as e:
raise HTTPException(status_code=500 , detail={'''model''': '''''', '''error''': str(_snake_case )} )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case = Body(_snake_case , embed=_snake_case ) , _snake_case = Body(_snake_case , embed=_snake_case ) , _snake_case = Body(_snake_case , embed=_snake_case ) , ) -> str:
'''simple docstring'''
try:
__a = self._pipeline.tokenizer.decode(_snake_case , _snake_case , _snake_case )
return ServeDeTokenizeResult(model='''''' , text=_snake_case )
except Exception as e:
raise HTTPException(status_code=500 , detail={'''model''': '''''', '''error''': str(_snake_case )} )
async def SCREAMING_SNAKE_CASE_ ( self , _snake_case=Body(_snake_case , embed=_snake_case ) ) -> List[str]:
'''simple docstring'''
if len(_snake_case ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
__a = self._pipeline(_snake_case )
return ServeForwardResult(output=_snake_case )
except Exception as e:
raise HTTPException(500 , {'''error''': str(_snake_case )} ) | 6 |
from math import ceil
def __lowerCAmelCase ( a__ = 1001 ) -> int:
__a = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__a = 2 * i + 1
__a = 2 * i
__a = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
A : List[Any] = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number') | 6 | 1 |
from scipy.stats import pearsonr
import datasets
A : List[Any] = '\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'
A : int = '\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'
A : int = '\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 ):
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
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 SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case , _snake_case=False ) -> Dict:
'''simple docstring'''
if return_pvalue:
__a = pearsonr(_snake_case , _snake_case )
return {"pearsonr": results[0], "p-value": results[1]}
else:
return {"pearsonr": float(pearsonr(_snake_case , _snake_case )[0] )} | 6 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A( a ):
snake_case_ = ['''image_processor''', '''tokenizer''']
snake_case_ = '''ChineseCLIPImageProcessor'''
snake_case_ = ('''BertTokenizer''', '''BertTokenizerFast''')
def __init__( self , _snake_case=None , _snake_case=None , **_snake_case ) -> Tuple:
'''simple docstring'''
__a = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , _snake_case , )
__a = kwargs.pop('''feature_extractor''' )
__a = 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__(_snake_case , _snake_case )
__a = self.image_processor
def __call__( self , _snake_case=None , _snake_case=None , _snake_case=None , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
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:
__a = self.tokenizer(_snake_case , return_tensors=_snake_case , **_snake_case )
if images is not None:
__a = self.image_processor(_snake_case , return_tensors=_snake_case , **_snake_case )
if text is not None and images is not None:
__a = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_snake_case ) , tensor_type=_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , *_snake_case , **_snake_case ) -> str:
'''simple docstring'''
return self.tokenizer.batch_decode(*_snake_case , **_snake_case )
def SCREAMING_SNAKE_CASE_ ( self , *_snake_case , **_snake_case ) -> Dict:
'''simple docstring'''
return self.tokenizer.decode(*_snake_case , **_snake_case )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Any:
'''simple docstring'''
__a = self.tokenizer.model_input_names
__a = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def SCREAMING_SNAKE_CASE_ ( self ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , _snake_case , )
return self.image_processor_class | 6 | 1 |
class __A:
def __init__( self ) -> List[str]:
'''simple docstring'''
__a = ''''''
__a = ''''''
__a = []
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> int:
'''simple docstring'''
if m == -1:
return n + 1
elif n == -1:
return m + 1
elif self.dp[m][n] > -1:
return self.dp[m][n]
else:
if self.worda[m] == self.worda[n]:
__a = self.__min_dist_top_down_dp(m - 1 , n - 1 )
else:
__a = self.__min_dist_top_down_dp(_snake_case , n - 1 )
__a = self.__min_dist_top_down_dp(m - 1 , _snake_case )
__a = self.__min_dist_top_down_dp(m - 1 , n - 1 )
__a = 1 + min(_snake_case , _snake_case , _snake_case )
return self.dp[m][n]
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> int:
'''simple docstring'''
__a = worda
__a = worda
__a = [[-1 for _ in range(len(_snake_case ) )] for _ in range(len(_snake_case ) )]
return self.__min_dist_top_down_dp(len(_snake_case ) - 1 , len(_snake_case ) - 1 )
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case ) -> int:
'''simple docstring'''
__a = worda
__a = worda
__a = len(_snake_case )
__a = len(_snake_case )
__a = [[0 for _ in range(n + 1 )] for _ in range(m + 1 )]
for i in range(m + 1 ):
for j in range(n + 1 ):
if i == 0: # first string is empty
__a = j
elif j == 0: # second string is empty
__a = i
elif worda[i - 1] == worda[j - 1]: # last characters are equal
__a = self.dp[i - 1][j - 1]
else:
__a = self.dp[i][j - 1]
__a = self.dp[i - 1][j]
__a = self.dp[i - 1][j - 1]
__a = 1 + min(_snake_case , _snake_case , _snake_case )
return self.dp[m][n]
if __name__ == "__main__":
A : Dict = EditDistance()
print('****************** Testing Edit Distance DP Algorithm ******************')
print()
A : List[Any] = input('Enter the first string: ').strip()
A : Union[str, Any] = input('Enter the second string: ').strip()
print()
print(F"The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}")
print(F"The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}")
print()
print('*************** End of Testing Edit Distance DP Algorithm ***************') | 6 |
from __future__ import annotations
import typing
from collections import Counter
def __lowerCAmelCase ( a__ ) -> typing.Counter[int]:
__a = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in range(a__ , max_perimeter + 1 ):
__a = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(a__ ):
__a = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def __lowerCAmelCase ( a__ = 1000 ) -> int:
__a = pythagorean_triple(a__ )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(F"Perimeter {solution()} has maximum solutions") | 6 | 1 |
def __lowerCAmelCase ( a__ ) -> str:
__a = []
__a = set({'''(''', '''[''', '''{'''} )
__a = set({''')''', ''']''', '''}'''} )
__a = {'''{''': '''}''', '''[''': ''']''', '''(''': ''')'''}
for i in range(len(a__ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(a__ ) == 0 or (len(a__ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(a__ ) == 0
def __lowerCAmelCase ( ) -> Dict:
__a = input('''Enter sequence of brackets: ''' )
if is_balanced(a__ ):
print(a__ , '''is balanced''' )
else:
print(a__ , '''is not balanced''' )
if __name__ == "__main__":
main() | 6 |
# flake8: noqa
# Lint as: python3
A : Optional[Any] = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental | 6 | 1 |
from __future__ import annotations
import numpy as np
def __lowerCAmelCase ( a__ ) -> tuple[np.ndarray, np.ndarray]:
__a , __a = np.shape(a__ )
if rows != columns:
__a = (
'''\'table\' has to be of square shaped array but got a '''
F"""{rows}x{columns} array:\n{table}"""
)
raise ValueError(a__ )
__a = np.zeros((rows, columns) )
__a = np.zeros((rows, columns) )
for i in range(a__ ):
for j in range(a__ ):
__a = sum(lower[i][k] * upper[k][j] for k in range(a__ ) )
if upper[j][j] == 0:
raise ArithmeticError('''No LU decomposition exists''' )
__a = (table[i][j] - total) / upper[j][j]
__a = 1
for j in range(a__ , a__ ):
__a = sum(lower[i][k] * upper[k][j] for k in range(a__ ) )
__a = table[i][j] - total
return lower, upper
if __name__ == "__main__":
import doctest
doctest.testmod() | 6 |
from typing import Dict
from .base import GenericTensor, Pipeline
class __A( a ):
def SCREAMING_SNAKE_CASE_ ( self , _snake_case=None , _snake_case=None , _snake_case=None , **_snake_case ) -> Optional[Any]:
'''simple docstring'''
if tokenize_kwargs is None:
__a = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)''' )
__a = truncation
__a = tokenize_kwargs
__a = {}
if return_tensors is not None:
__a = return_tensors
return preprocess_params, {}, postprocess_params
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , **_snake_case ) -> Dict[str, GenericTensor]:
'''simple docstring'''
__a = self.framework
__a = self.tokenizer(_snake_case , return_tensors=_snake_case , **_snake_case )
return model_inputs
def SCREAMING_SNAKE_CASE_ ( self , _snake_case ) -> Optional[Any]:
'''simple docstring'''
__a = self.model(**_snake_case )
return model_outputs
def SCREAMING_SNAKE_CASE_ ( self , _snake_case , _snake_case=False ) -> Optional[int]:
'''simple docstring'''
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self , *_snake_case , **_snake_case ) -> Any:
'''simple docstring'''
return super().__call__(*_snake_case , **_snake_case ) | 6 | 1 |
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