code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 368 |
'''simple docstring'''
import functools
def UpperCamelCase ( a , a ) -> int:
'''simple docstring'''
__magic_name__ = len(a )
__magic_name__ = len(a )
@functools.cache
def min_distance(a , a ) -> int:
# i... | 98 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import ... | 369 |
'''simple docstring'''
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 : Optional[Any] , ... | 98 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIV... | 370 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] ):
__magic_name__ = []
__magic_name__ = 0
__magic_name__ ... | 98 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from ... | 371 |
'''simple docstring'''
from random import randint, random
def UpperCamelCase ( a , a , a , a = False , a = False , a = 5 , ) -> list:
'''simple docstring'''
__magic_name__ = [[-1] * number_of_cells] # Create a highway without a... | 98 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Union[str, Any] = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ... | 350 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 98 | 0 |
'''simple docstring'''
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,
t... | 351 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCamelCase ( a="ro" , a="en" , a="wmt16" , a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
... | 98 | 0 |
'''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
_lowerCAmelCase = 300 # TEMPERATURE (unit = K)
def UpperCamelCase ( a , a , a , ) -> Dict:
'''simple docstring'''
if donor_conc <= 0:
... | 352 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCamelCase ( a , a , a , a=1024 ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ , __magic_n... | 98 | 0 |
'''simple docstring'''
import functools
from typing import Any
def UpperCamelCase ( a , a ) -> Optional[Any]:
'''simple docstring'''
# Validation
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or len(UpperCAmelCase_ ) == 0:
raise Value... | 353 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCAmelCase = "\\n\n"
_lowerCAmelCase = "\nPerplexity (PPL) is one of the most co... | 98 | 0 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, 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 .... | 354 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _SCREAMING_SNAKE_CASE ( __a ,__a ):
__SCREAMI... | 98 | 0 |
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_dime... | 355 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase = object(... | 98 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class _SCREAMING_SNAKE_CASE ( _lowerCamelCase ):
def __init__( self : Dict ):
# test for the above condition
self.test()
def snake_case__ ( self : int ):
... | 356 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def Up... | 98 | 0 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__nam... | 357 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ver... | 98 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-si... | 358 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfo... | 98 | 0 |
'''simple docstring'''
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self : Tuple , a__ : Any ):
__magic_name__ = data
__magic_name__ = None
def __repr__( self : List[Any] ):
return F'''Node({se... | 359 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 98 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)... | 360 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threa... | 98 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class ... | 361 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMix... | 98 | 0 |
'''simple docstring'''
def UpperCamelCase ( a = 200 ) -> int:
'''simple docstring'''
__magic_name__ = [1, 2, 5, 10, 20, 50, 100, 200]
__magic_name__ = [0] * (pence + 1)
__magic_name__ = 1 # base case: 1 way to make 0 pence
... | 362 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, pre... | 98 | 0 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta im... | 363 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.... | 98 | 0 |
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
__SCREAMING_SNAKE_CASE :List[str] = (KDPMaDiscreteScheduler,... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapT... | 98 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_lowerCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( __lowerCAmelCase ):
def __init__( self : List[Any] , *a__ : Option... | 365 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extractio... | 98 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_lowerCAmelCase ... | 366 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurati... | 98 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_lowerCAmelCase = {
"configuration_trocr": ["TROCR_PRETRAINE... | 367 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():... | 98 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( a ) -> int:
'''simple docstring'''
__magic_name__ = len(lowerCamelCase_ ) // 2
# choose the middle 3 elements
__magic_name__ = lst[m - 1 : m + 2]
# i... | 368 |
'''simple docstring'''
import functools
def UpperCamelCase ( a , a ) -> int:
'''simple docstring'''
__magic_name__ = len(a )
__magic_name__ = len(a )
@functools.cache
def min_distance(a , a ) -> int:
# i... | 98 | 0 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def UpperCamelCase ( a ) -> Optional[int]:
'''simple docstring'''
if (
(cp >= 0x4_e00 and cp <= 0x9_fff)
or (cp >= 0x3... | 369 |
'''simple docstring'''
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 : Optional[Any] , ... | 98 | 0 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,... | 370 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] ):
__magic_name__ = []
__magic_name__ = 0
__magic_name__ ... | 98 | 0 |
'''simple docstring'''
from __future__ import annotations
import requests
_lowerCAmelCase = set(
"approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_... | 371 |
'''simple docstring'''
from random import randint, random
def UpperCamelCase ( a , a , a , a = False , a = False , a = 5 , ) -> list:
'''simple docstring'''
__magic_name__ = [[-1] * number_of_cells] # Create a highway without a... | 98 | 0 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCAmelCase : List[Any]... | 350 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 98 | 0 |
'''simple docstring'''
_lowerCAmelCase = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_lowerCAmelCase = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_lowerCAmelCase = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "Friday",
6: "Saturd... | 351 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCamelCase ( a="ro" , a="en" , a="wmt16" , a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
... | 98 | 0 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__nam... | 352 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCamelCase ( a , a , a , a=1024 ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ , __magic_n... | 98 | 0 |
'''simple docstring'''
def UpperCamelCase ( a , a ) -> List[Any]:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__magic_name__ = str(bin(a ) )[2:] # remove the leadi... | 353 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCAmelCase = "\\n\n"
_lowerCAmelCase = "\nPerplexity (PPL) is one of the most co... | 98 | 0 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class _SCREAMING_SNAKE_CASE :
def __init__( self : Tuple , a__ : Union[str, Any] ):
__magic_name__ = list_of_points
# Degree de... | 354 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _SCREAMING_SNAKE_CASE ( __a ,__a ):
__SCREAMI... | 98 | 0 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {"vocab_file": "vocab.txt"}
_lowerCAme... | 355 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase = object(... | 98 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolv... | 356 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def Up... | 98 | 0 |
'''simple docstring'''
def UpperCamelCase ( a , a ) -> list:
'''simple docstring'''
__magic_name__ = word.split()
def justify(a , a , a ) -> str:
__magic_name__ = max_width - width
__magic_name__ ... | 357 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ver... | 98 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.s... | 358 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfo... | 98 | 0 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Auto... | 359 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 98 | 0 |
'''simple docstring'''
from torch import nn
def UpperCamelCase ( a ) -> Dict:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
... | 360 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threa... | 98 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class ... | 361 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMix... | 98 | 0 |
'''simple docstring'''
def UpperCamelCase ( a , a ) -> float:
'''simple docstring'''
if digit_amount > 0:
return round(number - int(UpperCAmelCase__ ) , UpperCAmelCase__ )
return number - int(UpperCAmelCase__ )
if __name__ == "__main__":
pr... | 362 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, pre... | 98 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"huggingface/time-series-transformer-tourism-monthly": (
"ht... | 363 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.... | 98 | 0 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def UpperCamelCase ( a ) -> List[Any]:
'''simple docstring'''
__mag... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapT... | 98 | 0 |
'''simple docstring'''
import string
def UpperCamelCase ( a ) -> None:
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
__magic_name__ = ''''''
for symbol in message:
if symbol in string.ascii_uppe... | 365 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extractio... | 98 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transfor... | 366 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurati... | 98 | 0 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
_lowerCAmelCase = TypeVar("T")
class _SCREAMING_SNAKE_CASE ( Generic[T] ):
__SCREAMING_SNAKE_CASE :deque[T] # Cache store... | 367 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():... | 98 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
_lowerCAmelCase = TypeVar("T")
class _SCREAMING_SNAKE_CASE ( Generic[T] ):
def __init__( self : Any , a__ : list[T] , a__ : ... | 368 |
'''simple docstring'''
import functools
def UpperCamelCase ( a , a ) -> int:
'''simple docstring'''
__magic_name__ = len(a )
__magic_name__ = len(a )
@functools.cache
def min_distance(a , a ) -> int:
# i... | 98 | 0 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
# Se... | 369 |
'''simple docstring'''
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 : Optional[Any] , ... | 98 | 0 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,... | 370 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] ):
__magic_name__ = []
__magic_name__ = 0
__magic_name__ ... | 98 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
def __init__(... | 371 |
'''simple docstring'''
from random import randint, random
def UpperCamelCase ( a , a , a , a = False , a = False , a = 5 , ) -> list:
'''simple docstring'''
__magic_name__ = [[-1] * number_of_cells] # Create a highway without a... | 98 | 0 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_lowerCAmelCase : Optional[Any] = importlib.util.find_spec("s3fs") is not None
if _h... | 350 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 98 | 0 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as ... | 351 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCamelCase ( a="ro" , a="en" , a="wmt16" , a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
... | 98 | 0 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_... | 352 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCamelCase ( a , a , a , a=1024 ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ , __magic_n... | 98 | 0 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
_lowerCAmelCase = yaml.safe_load(
"\\nname: \"\"\nallow_empty: false\nallow_empty_text: true\nsubs... | 353 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCAmelCase = "\\n\n"
_lowerCAmelCase = "\nPerplexity (PPL) is one of the most co... | 98 | 0 |
'''simple docstring'''
import numpy as np
def UpperCamelCase ( a , a , a = 1e-12 , a = 100 , ) -> tuple[float, np.ndarray]:
'''simple docstring'''
assert np.shape(a )[0] == np.shape(a )[1]
# Ensure proper dimensionality.
... | 354 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _SCREAMING_SNAKE_CASE ( __a ,__a ):
__SCREAMI... | 98 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import ... | 355 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase = object(... | 98 | 0 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def ... | 356 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def Up... | 98 | 0 |
'''simple docstring'''
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def UpperCamelCase ( a ) -> Tuple:
'''simple docstring'''
return (torch.arange(state.num_processes ... | 357 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ver... | 98 | 0 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
_lowerCAmelCase = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any memb... | 358 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfo... | 98 | 0 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowerCAmelCase =... | 359 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 98 | 0 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
... | 360 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threa... | 98 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( a , a ) -> tuple[int, int]:
'''simple docstring'''
if b == 0:
return (1, 0)
((__magic_name__) , (__magic_name__)) = extended_euclid(a , a % b )
... | 361 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMix... | 98 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"facebook/nllb-moe-54B": "https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json",
}
clas... | 362 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, pre... | 98 | 0 |
'''simple docstring'''
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
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase ... | 363 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.... | 98 | 0 |
'''simple docstring'''
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 : Optional[Any] , ... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapT... | 98 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAKE_CASE :Optional[Any] = """Speech2TextFeatureExtractor"""
__SCREAMING_SNAKE_CASE :Optional[Any] ... | 365 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extractio... | 98 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def UpperCamelCase ( a ) -> Any:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.tf_checkpoint , ... | 366 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurati... | 98 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_lowerCAmelCase = logging.ge... | 367 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():... | 98 | 0 |
'''simple docstring'''
import math
def UpperCamelCase ( a , a ) -> float:
'''simple docstring'''
if (
not isinstance(a , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError('''power_factor must be ... | 368 |
'''simple docstring'''
import functools
def UpperCamelCase ( a , a ) -> int:
'''simple docstring'''
__magic_name__ = len(a )
__magic_name__ = len(a )
@functools.cache
def min_distance(a , a ) -> int:
# i... | 98 | 0 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from ... | 369 |
'''simple docstring'''
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 : Optional[Any] , ... | 98 | 0 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _SCREAMING_SNAKE_CASE ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
__SCREAMING_S... | 370 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] ):
__magic_name__ = []
__magic_name__ = 0
__magic_name__ ... | 98 | 0 |
'''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def UpperCamelCa... | 371 |
'''simple docstring'''
from random import randint, random
def UpperCamelCase ( a , a , a , a = False , a = False , a = 5 , ) -> list:
'''simple docstring'''
__magic_name__ = [[-1] * number_of_cells] # Create a highway without a... | 98 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( a , a ) -> set[str]:
'''simple docstring'''
__magic_name__ , __magic_name__ = set(a ), [start]
while stack:
__magic_name__ = stack.pop()
... | 350 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 98 | 0 |
'''simple docstring'''
def UpperCamelCase ( ) -> list[list[int]]:
'''simple docstring'''
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_lowerCAmelCase = generate_large_matrix()
_lowerCAmelCase = (
[[4, 3, 2,... | 351 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCamelCase ( a="ro" , a="en" , a="wmt16" , a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
... | 98 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_... | 352 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCamelCase ( a , a , a , a=1024 ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ , __magic_n... | 98 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
cla... | 353 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCAmelCase = "\\n\n"
_lowerCAmelCase = "\nPerplexity (PPL) is one of the most co... | 98 | 0 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase ... | 354 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _SCREAMING_SNAKE_CASE ( __a ,__a ):
__SCREAMI... | 98 | 0 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAKE_CASE ... | 355 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase = object(... | 98 | 0 |
'''simple docstring'''
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCAmelCase = logging.get_lo... | 356 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def Up... | 98 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( a , a ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
__magic_name__ = int(a )
__magic_name__ = int(a )
__magic... | 357 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ver... | 98 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
_lowerCAmelCase = logging.get_logger(... | 358 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfo... | 98 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_lowerCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
class _SCR... | 359 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils ... | 98 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCAmelCase = {
"configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
"to... | 360 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threa... | 98 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_lowerCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAKE_CASE :Li... | 361 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMix... | 98 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _SCREAMING_SNAKE_CASE ( __a ... | 362 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, pre... | 98 | 0 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
_lowerCAmelCase = [
# (stable-diffusion, HF Diffusers)
("time_embed.0.weight", "time_embeddi... | 363 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.... | 98 | 0 |
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'''kwargs, expected''' , [
({'''num_shards''': 0, '''max_num_jobs''': 1}, []),
({'''num_shards''': 10, '''max_num... | 364 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapT... | 98 | 0 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def UpperCamelCase ( a , a , a , a , ) -> list[float]:
'''simple docstring'''
__magic_name__ , __magic_name__ ... | 365 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extractio... | 98 | 0 |
'''simple docstring'''
import math
def UpperCamelCase ( a ) -> list:
'''simple docstring'''
__magic_name__ = [True] * n
__magic_name__ = False
__magic_name__ = False
__magic_name__ = True
for i in... | 366 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurati... | 98 | 0 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import dedup... | 367 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():... | 98 | 0 |
'''simple docstring'''
def UpperCamelCase ( a , a , a ) -> bool:
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(a ) )
def UpperCamelCase ( a , a , a , ... | 368 |
'''simple docstring'''
import functools
def UpperCamelCase ( a , a ) -> int:
'''simple docstring'''
__magic_name__ = len(a )
__magic_name__ = len(a )
@functools.cache
def min_distance(a , a ) -> int:
# i... | 98 | 0 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeMod... | 369 |
'''simple docstring'''
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 : Optional[Any] , ... | 98 | 0 |
'''simple docstring'''
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def UpperCamelCase ( a ) -> Optional[int]:
'''simple docstring'''
return getitem, k
def UpperCamelCase ( a , a ) ->... | 370 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] ):
__magic_name__ = []
__magic_name__ = 0
__magic_name__ ... | 98 | 0 |
'''simple docstring'''
import re
import subprocess
import sys
_lowerCAmelCase = subprocess.check_output("git merge-base main HEAD".split()).decode("utf-8")
_lowerCAmelCase = subprocess.check_output(F'''git diff --name-only {fork_point_sha}'''.split()).decode("utf-8").split()
_lowerCAmel... | 371 |
'''simple docstring'''
from random import randint, random
def UpperCamelCase ( a , a , a , a = False , a = False , a = 5 , ) -> list:
'''simple docstring'''
__magic_name__ = [[-1] * number_of_cells] # Create a highway without a... | 98 | 0 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tra... | 350 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils... | 98 | 0 |
'''simple docstring'''
import os
import numpy
import onnx
def UpperCamelCase ( a , a ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ =a.name
__magic_name__ =b.name
__magic_name__ =''''''
__magic_name__ ... | 351 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCamelCase ( a="ro" , a="en" , a="wmt16" , a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
... | 98 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCAmelCase = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of tha... | 352 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCamelCase ( a , a , a , a=1024 ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ , __magic_n... | 98 | 0 |
'''simple docstring'''
import os
import sys
import unittest
_lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_m... | 353 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCAmelCase = "\\n\n"
_lowerCAmelCase = "\nPerplexity (PPL) is one of the most co... | 98 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 354 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _SCREAMING_SNAKE_CASE ( __a ,__a ):
__SCREAMI... | 98 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
# See all XGLM models at https://huggingf... | 355 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase = object(... | 98 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__a )
class _SCREAMING_SNAKE_CASE ( __a ):
# `task` is not a ClassVar since we want it to be part of th... | 356 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def Up... | 98 | 0 |
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def UpperCamelCase ( a ) -> Optional[int]:
'''simple docstring'''
monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings''' , ... | 357 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import ver... | 98 | 0 |
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