code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
_lowerCamelCase =42
_lowerCamelCase =None
_lowerCamelCase =None
... | 51 |
'''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... | 51 | 1 |
'''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def __snake_case ( SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
UpperCAmelCase = prime_factors(SCREAMING_SNAKE_CASE_ )
if is_squar... | 51 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
fro... | 51 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.test... | 51 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : List[Any] = {'configuration_xlnet': ['XL... | 51 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
a__ : Optional[Any] = {'tokenization_herbert': ['HerbertTokenizer']}
try:
if not is_tokenizers_available():
raise OptionalDepen... | 51 |
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
f... | 51 | 1 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =(EulerDiscreteSch... | 51 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["image_processor", "tokenizer"]
_lowerCame... | 51 | 1 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
... | 51 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDepe... | 51 | 1 |
'''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_... | 51 |
'''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 t... | 51 | 1 |
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffu... | 51 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 51 | 1 |
'''simple docstring'''
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __snake_case ( SCREAMING_SNAKE_CASE_ : str = "" ) -> dict[str, float]:
"""simple docstring"""
UpperCAmelCase = url or '''https://www.imdb.com/chart/t... | 51 |
'''simple docstring'''
import torch
from transformers import AutoModel
class lowerCAmelCase__ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , a__ : List[str]="sayef/fsner-bert-base-uncased" ):
super(a__ , ... | 51 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : List[str] = logging.get_logger(__name__)
a__ : Union[str, Any] = {
'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/mai... | 51 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =(EulerDiscreteSch... | 51 | 1 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
fr... | 51 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __snake_case ( ... | 51 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a__ : Any = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xl... | 51 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassi... | 51 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 51 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[int] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
... | 51 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGenerat... | 51 |
'''simple docstring'''
from __future__ import annotations
a__ : List[str] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class lowerCAmelCase__ :
'''simple doc... | 51 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Paddin... | 51 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a__ : Tuple = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
a__ :... | 51 | 1 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_... | 51 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Any = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 51 | 1 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ) -> float:
"""simple docstring"""
if days_between_payments <= 0:
raise ValueError(... | 51 |
'''simple docstring'''
from math import factorial
def __snake_case ( SCREAMING_SNAKE_CASE_ : int = 100 ) -> int:
"""simple docstring"""
return sum(int(SCREAMING_SNAKE_CASE_ ) for x in str(factorial(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
print(solution(in... | 51 | 1 |
'''simple docstring'''
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 Dec... | 51 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionM... | 51 | 1 |
'''simple docstring'''
import gc
import threading
import time
import psutil
import torch
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Tuple ):
UpperCAmelCase = psutil.Process()
UpperCAmelCase = False
d... | 51 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : str ) -> Dict:
"""simple docstring"""
if not head:
return True
# split the list to two parts
UpperCAmelCase, UpperCAmelCase = head.next, head
while fast and fast.nex... | 51 | 1 |
'''simple docstring'''
import enum
import shutil
import sys
a__ , a__ : List[str] = shutil.get_terminal_size()
a__ : Any = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class lowerCAmelCase__ ( enum.Enum ):
'''simple docstring'... | 51 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["image_processor", "tokenizer"]
_lowerCame... | 51 | 1 |
'''simple docstring'''
# using dfs for finding eulerian path traversal
def __snake_case ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str]=None ) -> List[Any]:
"""simple docstring"""
... | 51 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
fr... | 51 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common im... | 51 |
'''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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_ver... | 51 | 1 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : int ) -> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
UpperCAmelCase = gray_cod... | 51 |
'''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... | 51 | 1 |
'''simple docstring'''
from math import loga
def __snake_case ( SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAK... | 51 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
fro... | 51 | 1 |
'''simple docstring'''
import string
def __snake_case ( SCREAMING_SNAKE_CASE_ : str ) -> str:
"""simple docstring"""
UpperCAmelCase = ''''''
for i in sequence:
UpperCAmelCase = ord(SCREAMING_SNAKE_CASE_ )
if 65 <= extract <= 90:
... | 51 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : List[Any] = {'configuration_xlnet': ['XL... | 51 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import C... | 51 |
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
f... | 51 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDepe... | 51 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["image_processor", "tokenizer"]
_lowerCame... | 51 | 1 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers imp... | 51 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDepe... | 51 | 1 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class lowerCAmelCase__ ( nn.Module ):
'''simple docstring'''
_lowerCamelCase =42
_lowerCamelCase =jnp.floataa
def __snake_case ( self : List[str] ):
... | 51 |
'''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 t... | 51 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_c... | 51 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 51 | 1 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_te... | 51 |
'''simple docstring'''
import torch
from transformers import AutoModel
class lowerCAmelCase__ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , a__ : List[str]="sayef/fsner-bert-base-uncased" ):
super(a__ , ... | 51 | 1 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
a__ : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
a__ : list[int] = [ord(letter) f... | 51 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =(EulerDiscreteSch... | 51 | 1 |
'''simple docstring'''
from math import factorial
def __snake_case ( SCREAMING_SNAKE_CASE_ : int = 100 ) -> int:
"""simple docstring"""
return sum(int(SCREAMING_SNAKE_CASE_ ) for x in str(factorial(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
print(solution(in... | 51 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __snake_case ( ... | 51 | 1 |
'''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 lowerCAmelCase__ ( UpperCAmelCase_ , unittest.TestCase ):
'''simp... | 51 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassi... | 51 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Tuple = {
'Intel/dpt-large': 'https://huggingface.co/In... | 51 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[int] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
... | 51 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a__ : Dict = {
'configuration_mobilenet_v2': [
'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileNetV... | 51 |
'''simple docstring'''
from __future__ import annotations
a__ : List[str] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class lowerCAmelCase__ :
'''simple doc... | 51 | 1 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( SCREAMING_SNAKE_CASE_ : list[int] ) -> bool:
"""simple docstring"""
return len(set(SCREAMING_SNAKE_CASE_ ) ) == len(SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
import doctest
doctes... | 51 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a__ : Tuple = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
a__ :... | 51 | 1 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename with blanks.csv'''] )
... | 51 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Any = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 51 | 1 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : list[list[int]] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : list[int] ) -> bool:
"""simple docstring"""
if graph[path[curr_ind - 1]][next_ver] == 0:
... | 51 |
'''simple docstring'''
from math import factorial
def __snake_case ( SCREAMING_SNAKE_CASE_ : int = 100 ) -> int:
"""simple docstring"""
return sum(int(SCREAMING_SNAKE_CASE_ ) for x in str(factorial(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
print(solution(in... | 51 | 1 |
'''simple docstring'''
import math
class lowerCAmelCase__ :
'''simple docstring'''
def __snake_case ( self : List[Any] , a__ : list[list[float]] , a__ : list[int] ):
UpperCAmelCase = 0.0
UpperCAmelC... | 51 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionM... | 51 | 1 |
'''simple docstring'''
# 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
#... | 51 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : str ) -> Dict:
"""simple docstring"""
if not head:
return True
# split the list to two parts
UpperCAmelCase, UpperCAmelCase = head.next, head
while fast and fast.nex... | 51 | 1 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( SCREAMING_SNAKE_CASE_ : list[int] ) -> int:
"""simple docstring"""
UpperCAmelCase = len(SCREAMING_SNAKE_CASE_ ) // 2
# choose the middle 3 elements
UpperCAmelCase = lst[m -... | 51 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["image_processor", "tokenizer"]
_lowerCame... | 51 | 1 |
'''simple docstring'''
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 ... | 51 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
fr... | 51 | 1 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __snake_case ( ... | 51 |
'''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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_ver... | 51 | 1 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 51 |
'''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... | 51 | 1 |
'''simple docstring'''
import unittest
import numpy as np
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, prepare_image_inputs
if is_torch_... | 51 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
fro... | 51 | 1 |
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
f... | 51 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : List[Any] = {'configuration_xlnet': ['XL... | 51 | 1 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
def __snake_case ( self : List[Any] , a__ : str ... | 51 |
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
f... | 51 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Optional[int] = logging.get_logger(__name__)
a__ : Optional[int] = {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook... | 51 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["image_processor", "tokenizer"]
_lowerCame... | 51 | 1 |
'''simple docstring'''
import qiskit
def __snake_case ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> qiskit.result.counts.Counts:
"""simple docstring"""
UpperCAmelCase = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circui... | 51 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDepe... | 51 | 1 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __snake_case ( self : Dict ):
UpperCAmelCase = [
... | 51 |
'''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 t... | 51 | 1 |
'''simple docstring'''
import random
from .binary_exp_mod import bin_exp_mod
def __snake_case ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Any=1_000 ) -> Any:
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
re... | 51 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 51 | 1 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
return int((input_a, input_a).count(0 ) != 0 )
def __snake_case ( ) -> None:
"""simple docstring"""
asser... | 51 |
'''simple docstring'''
import torch
from transformers import AutoModel
class lowerCAmelCase__ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , a__ : List[str]="sayef/fsner-bert-base-uncased" ):
super(a__ , ... | 51 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : Any , a__ : int , a__ : int , a__ : float = 0 ):
UpperCAme... | 51 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =(EulerDiscreteSch... | 51 | 1 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
a__ : Optional[Any] = TypeVar('KEY')
a__ : List[str] = TypeVar('VAL')
@dataclass(frozen=UpperCAmelCase_ , sl... | 51 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __snake_case ( ... | 51 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : int = logging.get_logger(__name__)
a__ : Dict = {
'jun... | 51 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassi... | 51 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Any = logging.get_logger(__name__)
a__ : Optional[Any] = {
'BAAI/AltCLIP': 'https://huggingface.co/BA... | 51 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[int] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
... | 51 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, Bert... | 51 |
'''simple docstring'''
from __future__ import annotations
a__ : List[str] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class lowerCAmelCase__ :
'''simple doc... | 51 | 1 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : list ) -> int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
... | 51 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a__ : Tuple = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
a__ :... | 51 | 1 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 51 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Any = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 51 | 1 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoin... | 0 |
'''simple docstring'''
from math import factorial
def __snake_case ( SCREAMING_SNAKE_CASE_ : int = 100 ) -> int:
"""simple docstring"""
return sum(int(SCREAMING_SNAKE_CASE_ ) for x in str(factorial(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
print(solution(in... | 51 | 0 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _A ( _lowercase ) -> Tuple:
"""simple docstring"""
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set() )
@pytest.... | 1 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionM... | 51 | 0 |
import logging
import os
from .state import PartialState
class lowerCamelCase__ ( logging.LoggerAdapter):
"""simple docstring"""
@staticmethod
def snake_case_ ( __lowerCAmelCase : Optional[int] ) -> Dict:
_A = PartialState()
return no... | 2 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : str ) -> Dict:
"""simple docstring"""
if not head:
return True
# split the list to two parts
UpperCAmelCase, UpperCAmelCase = head.next, head
while fast and fast.nex... | 51 | 0 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def A_( A : Any , A : List[Any]):
UpperCamelCase = int(A)
assert noofclusters < len(A)
# Find out the dimensionality
UpperCamelCase ... | 3 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["image_processor", "tokenizer"]
_lowerCame... | 51 | 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():
__UpperCamelCase : Tuple = yaml.safe_load(
'''\
name: ""
allow_empty: false
allow_empty_text... | 4 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
fr... | 51 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/resolve/main/... | 5 |
'''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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_ver... | 51 | 0 |
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: Union[str, Any] ):
return 1 / (1 + np.exp(-z ))
... | 6 |
'''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... | 51 | 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
a = logging.get_logger(__name__)
a = {
'''goo... | 7 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
fro... | 51 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE (a__ ):
lowerCAmelCase = ['''image_processor''', '''tokenizer''']
lowerCAmelCase = '''AutoIma... | 8 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : List[Any] = {'configuration_xlnet': ['XL... | 51 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}
try:... | 9 |
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
f... | 51 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
_lowerCAmelCase = logging.get_... | 10 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["image_processor", "tokenizer"]
_lowerCame... | 51 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "Dei... | 11 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDepe... | 51 | 0 |
def UpperCamelCase ( lowercase_ ) -> bool:
'''simple docstring'''
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6""") )
def UpperCamelCase ( lowercase_ ) -> bool:
'''simple docstring'''
lowercase__ ... | 12 |
'''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 t... | 51 | 0 |
'''simple docstring'''
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
A__ : Union[str, Any] = logging.get_logger(__name__)
A__ : Union[str, Any] = {name: getattr(tran... | 13 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 51 | 0 |
def __UpperCAmelCase ( __a : int ) -> str:
"""simple docstring"""
_a : List[str] = int(__a )
if decimal in (0, 1): # Exit cases for the recursion
return str(__a )
_a , _a : List[Any] = divmod(__a ,2 ... | 14 |
'''simple docstring'''
import torch
from transformers import AutoModel
class lowerCAmelCase__ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , a__ : List[str]="sayef/fsner-bert-base-uncased" ):
super(a__ , ... | 51 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderToke... | 15 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =(EulerDiscreteSch... | 51 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torc... | 16 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __snake_case ( ... | 51 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__n... | 17 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassi... | 51 | 0 |
'''simple docstring'''
import math
from collections import defaultdict
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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
de... | 18 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[int] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
... | 51 | 0 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCamelCase__ ( ) -> Dict:
"""simple docstring"""
_UpperCamelCase = HfArgumentParser(__snake_case )
_UpperCamel... | 19 |
'''simple docstring'''
from __future__ import annotations
a__ : List[str] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class lowerCAmelCase__ :
'''simple doc... | 51 | 0 |
def _lowercase( __a : int , __a : int ):
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
a__ =str(bin(__a ) )
binary_number += "0" * shift_amount
return binary_number
def ... | 20 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a__ : Tuple = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
a__ :... | 51 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Optional[Any] = {
"configuration_table_transformer": [
"TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TableTransformerConfi... | 21 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Any = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 51 | 0 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : list , UpperCamelCase : list ):
'''simple docstring'''
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
... | 22 |
'''simple docstring'''
from math import factorial
def __snake_case ( SCREAMING_SNAKE_CASE_ : int = 100 ) -> int:
"""simple docstring"""
return sum(int(SCREAMING_SNAKE_CASE_ ) for x in str(factorial(SCREAMING_SNAKE_CASE_ ) ) )
if __name__ == "__main__":
print(solution(in... | 51 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _snake_case (__lowercase , __lowercase):
UpperCamelCase_ = f"""{sampling_rate}"""
UpperCamelCase_ = '1'
UpperCamelCase_ = 'f32l... | 23 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionM... | 51 | 0 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class lowerCAmelCase ( nn.Module):
def __init__( self , __SCREAMING_SNAKE_CASE = 16 , __SCREAMING_SNAKE_CASE = 88 , __SCREAMING_SNAK... | 24 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : str ) -> Dict:
"""simple docstring"""
if not head:
return True
# split the list to two parts
UpperCAmelCase, UpperCAmelCase = head.next, head
while fast and fast.nex... | 51 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.tes... | 25 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["image_processor", "tokenizer"]
_lowerCame... | 51 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
"configuration_deberta": ["DEBERTA_PRET... | 26 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
fr... | 51 | 0 |
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
__A : Optional[Any] = object()
# For specifying empty leaf dict `{}`
__A : Union[str, Any] ... | 27 |
'''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 YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_ver... | 51 | 0 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: int ):
"""simple docstring"""
while a != 0:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = b % a, a
return b
d... | 28 |
'''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... | 51 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG... | 29 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
fro... | 51 | 0 |
from __future__ import annotations
from math import pow, sqrt
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
i... | 30 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : List[Any] = {'configuration_xlnet': ['XL... | 51 | 0 |
def UpperCAmelCase_ ( __UpperCAmelCase : list , __UpperCAmelCase : list ) -> float:
_validate_point(__UpperCAmelCase )
_validate_point(__UpperCAmelCase )
if len(__UpperCAmelCase ) != len(__UpperCAmelCase ):
raise ValueError('Both points m... | 31 |
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
f... | 51 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"microsoft/focalnet-tiny": "https://hug... | 32 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["image_processor", "tokenizer"]
_lowerCame... | 51 | 0 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
snake_case__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase = 100 ) -> int:
snake_case__ = 1
snake_case__ = 2
fo... | 33 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDepe... | 51 | 0 |
"""simple docstring"""
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVeca... | 34 |
'''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 t... | 51 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import T... | 35 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 51 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase : Dict = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''InstructBlipQFor... | 36 |
'''simple docstring'''
import torch
from transformers import AutoModel
class lowerCAmelCase__ ( torch.nn.Module ):
'''simple docstring'''
def __init__( self : Tuple , a__ : List[str]="sayef/fsner-bert-base-uncased" ):
super(a__ , ... | 51 | 0 |
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
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:... | 37 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =(EulerDiscreteSch... | 51 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Dict = logging.get_logger(__name__)
A_ : List[str] = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class ... | 38 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def __snake_case ( ... | 51 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCH... | 39 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassi... | 51 | 0 |
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