code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torc... | 626 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[list[str]] , SCREAMING_SNAKE_CASE_: int , ) -> ... | 626 | 1 |
import os
lowerCAmelCase__ = {"""I""": 1, """V""": 5, """X""": 1_0, """L""": 5_0, """C""": 1_0_0, """D""": 5_0_0, """M""": 1_0_0_0}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> int:
'''simple docstring'''
A__ = 0
A__ = 0
while i... | 626 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
... | 626 | 1 |
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 DPRContextEncoderTokenizer, DPR... | 626 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ = """src/transformers"""
# This is to make sure the transforme... | 626 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A__ = 1
A__ = 1
while repunit:
A__ = (1_0 * repunit + 1) % divisor
repunit_i... | 626 |
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_available():
import torch
i... | 626 | 1 |
from math import factorial
lowerCAmelCase__ = {str(digit): factorial(digit) for digit in range(1_0)}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
... | 626 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
"""
lowerCA... | 626 | 1 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""post_extract_proj""": """feature_projection.projection"""... | 626 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
fr... | 626 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
A__ = [[0 for _ in range(SCREAMING_SNAKE_CASE_ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
A__ = 1
for n in range(m + 1 ):
... | 626 |
from math import factorial
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0_0 ) -> int:
'''simple docstring'''
return sum(map(SCREAMING_SNAKE_CASE_ , str(factorial(SCREAMING_SNAKE_CASE_ ) ) ) )
if __name__ == "__main__":
print(soluti... | 626 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
... | 626 |
lowerCAmelCase__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: bytes ) -> bytes:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__... | 626 | 1 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 626 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 626 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 626 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A__ = 1
A__ = 1
while repunit:
A__ = (1_0 * repunit + 1) % divisor
repunit_i... | 626 | 1 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@requ... | 626 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> int:
'''simple docstring'''
A__ = data
A__ = None
class ... | 626 | 1 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
lowerCAmelCase__ = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
lowerCAmelCase__ = requests.get(url, headers={"""UserA... | 626 |
import math
lowerCAmelCase__ = 1_0
lowerCAmelCase__ = 7
lowerCAmelCase__ = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 2_0 ) -> str:
'''simple docstring'''
A__ = math.comb(SCREAMING_SNAKE_CASE_ , SCREAMING... | 626 | 1 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_p... | 626 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: str = "cpu" , SCREAMING_SNAKE_CASE_: Union[str, None] = None ) -> None:
'''simple docstring'''
A_... | 626 | 1 |
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_ARCHIVE_MAP""", """LlamaConfig"""],
... | 626 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = field(
metadata={'h... | 626 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0_0 ) -> int:
'''simple docstring'''
A__ = set()
A__ = 0
A__ = n + 1 # maximum limit
for a in range(2 , SCREAMING_SNAKE_CASE_ ):
for b in range(2 , SC... | 626 |
import collections
import importlib.util
import os
import re
from pathlib import Path
lowerCAmelCase__ = """src/transformers"""
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase__ = re.compile(R"""^_impo... | 626 | 1 |
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 WavaVecaPhonemeCTCTokenizerOutput
from tr... | 626 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> ... | 626 | 1 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
lowerCAmelCase__ = namedtuple(
"""_TestCommandArgs""",
[
"""dataset""",
... | 626 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
A__ = word_bank or []
# create a table
A__ = len(SCR... | 626 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("List is empty" )
return sum(SCREAMING_SNAKE_CASE_ ) / len(SCREAMING_SNAKE_CASE_ )
if __n... | 626 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_:... | 626 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: str ) -> list:
'''simple docstring'''
A__ = len(SCREAMING_SNAKE_CASE_ )
A__ = []
for i in range(len(SCREAMING_SNAKE_CASE_ ) - pat_len + 1 ):
... | 626 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Namespace ) -> Tuple:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.t... | 626 | 1 |
from math import pi
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int ) -> float:
'''simple docstring'''
return 2 * pi * radius * (angle / 3_6_0)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 626 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 626 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list , SCREAMING_SNAKE_CASE_: int | None = None , SCREAMING_SNAKE_CASE_: int | None = None ) -> None:
'''simple docstring'''
if start is None:
A__ = ... | 626 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf... | 626 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDCondi... | 626 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITION... | 626 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> ... | 626 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[list[str]] , SCREAMING_SNAKE_CASE_: int , ) -> ... | 626 | 1 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNA... | 626 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
... | 626 | 1 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@require_fl... | 626 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ = """src/transformers"""
# This is to make sure the transforme... | 626 | 1 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have a nice ... | 626 |
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_available():
import torch
i... | 626 | 1 |
from __future__ import annotations
import math
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> list[int]:
'''simple docstring'''
if num <= 0:
A__ = F'{num}: Invalid input, please enter a positive integer.'
raise ValueError(SCREAMING_SNA... | 626 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
"""
lowerCA... | 626 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dis... | 626 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
fr... | 626 | 1 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) ... | 626 |
from math import factorial
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0_0 ) -> int:
'''simple docstring'''
return sum(map(SCREAMING_SNAKE_CASE_ , str(factorial(SCREAMING_SNAKE_CASE_ ) ) ) )
if __name__ == "__main__":
print(soluti... | 626 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: float , SCREAMING_SNAKE_CASE_: float , SCREAMING_SNAKE_CASE_: float ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) !... | 626 |
lowerCAmelCase__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: bytes ) -> bytes:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__... | 626 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionT... | 626 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 626 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {
"""configuration_vision_text_dual_encoder""": ["""VisionTextDualEncoderConfig"""],
"""processing_vision_... | 626 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A__ = 1
A__ = 1
while repunit:
A__ = (1_0 * repunit + 1) % divisor
repunit_i... | 626 | 1 |
import os
# Precomputes a list of the 100 first triangular numbers
lowerCAmelCase__ = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def lowerCAmelCase__ ( ) -> List[str]:
'''simple docstring'''
A__ = os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE_ ) ... | 626 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> int:
'''simple docstring'''
A__ = data
A__ = None
class ... | 626 | 1 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.te... | 626 |
import math
lowerCAmelCase__ = 1_0
lowerCAmelCase__ = 7
lowerCAmelCase__ = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 2_0 ) -> str:
'''simple docstring'''
A__ = math.comb(SCREAMING_SNAKE_CASE_ , SCREAMING... | 626 | 1 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models... | 626 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: str = "cpu" , SCREAMING_SNAKE_CASE_: Union[str, None] = None ) -> None:
'''simple docstring'''
A_... | 626 | 1 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
lowerCAmelCase__ = logging.getLogger(__name__)
@dataclass
class a__ ... | 626 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = field(
metadata={'h... | 626 | 1 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class a__ ( snake_case ):
"""simple docstring"""
__... | 626 |
import collections
import importlib.util
import os
import re
from pathlib import Path
lowerCAmelCase__ = """src/transformers"""
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase__ = re.compile(R"""^_impo... | 626 | 1 |
from math import factorial
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0_0 ) -> int:
'''simple docstring'''
return sum(map(SCREAMING_SNAKE_CASE_ , str(factorial(SCREAMING_SNAKE_CASE_ ) ) ) )
if __name__ == "__main__":
print(soluti... | 626 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> ... | 626 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 626 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
A__ = word_bank or []
# create a table
A__ = len(SCR... | 626 | 1 |
from typing import Any
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> Tuple:
'''simple docstring'''
A__ = data
A__ = None
class a__ :
"""simple docstring"""
def __init__( se... | 626 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_:... | 626 | 1 |
import itertools
import math
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, al... | 626 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Namespace ) -> Tuple:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.t... | 626 | 1 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> str:
'''simple docstring'''
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ValueError("Empty s... | 626 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 626 | 1 |
from collections import defaultdict
class a__ :
"""simple docstring"""
def __init__( self , lowercase , lowercase ) -> Tuple:
'''simple docstring'''
A__ = total # total no of tasks (N)
# DP table will have a dimension of ... | 626 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf... | 626 | 1 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: dict ) -> str:
'''simple docstring'''
A__ = BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE_ , params=SCREAMING_SNAKE_CAS... | 626 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITION... | 626 | 1 |
from math import isclose, sqrt
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: float , SCREAMING_SNAKE_CASE_: float , SCREAMING_SNAKE_CASE_: float ) -> tuple[float, float, float]:
'''simple docstring'''
A__ = point_y / 4 / point_x
A__... | 626 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[list[str]] , SCREAMING_SNAKE_CASE_: int , ) -> ... | 626 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,... | 626 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
... | 626 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {"""vocab_file""": """vocab.json""", """merges_file""": "... | 626 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ = """src/transformers"""
# This is to make sure the transforme... | 626 | 1 |
from maths.prime_check import is_prime
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__ = F'Input value of [number={number}] must be an ... | 626 |
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_available():
import torch
i... | 626 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
requi... | 626 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
"""
lowerCA... | 626 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: Optional[int] ) -> Dict:
'''simple docst... | 700 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
fr... | 626 | 0 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCAmelCase__ = """src/diffusers"""
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(R"""is\_([a-z_]*)_available\(\)""")
#... | 701 |
from math import factorial
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0_0 ) -> int:
'''simple docstring'''
return sum(map(SCREAMING_SNAKE_CASE_ , str(factorial(SCREAMING_SNAKE_CASE_ ) ) ) )
if __name__ == "__main__":
print(soluti... | 626 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 702 |
lowerCAmelCase__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: bytes ) -> bytes:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__... | 626 | 0 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"snap-research/efficientformer-l1-300": (
"https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config... | 703 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 626 | 0 |
import argparse
from collections import defaultdict
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: Any ) -> ... | 704 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A__ = 1
A__ = 1
while repunit:
A__ = (1_0 * repunit + 1) % divisor
repunit_i... | 626 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ = {
"configuration_clip": [
"CLIP_PRETRAINED_CONFIG_... | 705 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> int:
'''simple docstring'''
A__ = data
A__ = None
class ... | 626 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ :
"""simple docstring"""
def __init__( self ... | 706 |
import math
lowerCAmelCase__ = 1_0
lowerCAmelCase__ = 7
lowerCAmelCase__ = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 2_0 ) -> str:
'''simple docstring'''
A__ = math.comb(SCREAMING_SNAKE_CASE_ , SCREAMING... | 626 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[list[float]] ) -> list[list[float]]:
'''simple docstring'''
A__ = []
for data in source_data:
for i, el in enumerate(SCREAMING_SNAKE_CASE_ ):
if len(SCREAMING_SNAKE_CA... | 707 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: str = "cpu" , SCREAMING_SNAKE_CASE_: Union[str, None] = None ) -> None:
'''simple docstring'''
A_... | 626 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
while b:
A__ = b, a % b
return a
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNA... | 708 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = field(
metadata={'h... | 626 | 0 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Union[str, Any] ,... | 709 |
import collections
import importlib.util
import os
import re
from pathlib import Path
lowerCAmelCase__ = """src/transformers"""
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase__ = re.compile(R"""^_impo... | 626 | 0 |
import math
class a__ :
"""simple docstring"""
def UpperCamelCase ( self , lowercase , lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = 0.0
A__ = 0.0
for i in range(l... | 710 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> ... | 626 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 4_0_0_0_0_0_0 ) -> List[str]:
'''simple docstring'''
A__ = []
A__ = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_lowerCAmelCase )
A__ = b, a + b
... | 711 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
A__ = word_bank or []
# create a table
A__ = len(SCR... | 626 | 0 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowerCAmelCase__ = 1_0
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: ... | 712 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_:... | 626 | 0 |
'''simple docstring'''
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[Any] ) -> bool:
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
A__ = F'Input value of [number={number}] must be an integer'
... | 713 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Namespace ) -> Tuple:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.t... | 626 | 0 |
from collections import deque
from math import floor
from random import random
from time import time
class a__ :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = {}
def UpperCamelCase ( self ... | 714 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 626 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceCl... | 715 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf... | 626 | 0 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( a__ ):
def __init__( self , *lowercase , **lowercase ) -> Union[str, Any]:
'''simple docstr... | 716 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITION... | 626 | 0 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase__ = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/mask2former-swin-small... | 717 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[list[str]] , SCREAMING_SNAKE_CASE_: int , ) -> ... | 626 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase__ = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
raise ... | 718 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
... | 626 | 0 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase... | 719 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ = """src/transformers"""
# This is to make sure the transforme... | 626 | 0 |
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
lowerCAmelCase__ = """Run commands across TPU VMs for initial setup before running `accelerate launch`."""
def lowerCAme... | 720 |
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_available():
import torch
i... | 626 | 0 |
import heapq
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: dict ) -> Optional[Any]:
'''simple docstring'''
A__ = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a ... | 721 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
"""
lowerCA... | 626 | 0 |
import math
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list , SCREAMING_SNAKE_CASE_: int ) -> Optional[Any]:
'''simple docstring'''
A__ = len(snake_case__ )
A__ = int(math.floor(math.sqrt(snake_case__ ) ) )
A_... | 700 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
fr... | 626 | 0 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {"""vocab_file""": """vocab.json""", """merges_file""": "... | 701 |
from math import factorial
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0_0 ) -> int:
'''simple docstring'''
return sum(map(SCREAMING_SNAKE_CASE_ , str(factorial(SCREAMING_SNAKE_CASE_ ) ) ) )
if __name__ == "__main__":
print(soluti... | 626 | 0 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str ) -> Tuple:
... | 702 |
lowerCAmelCase__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: bytes ) -> bytes:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__... | 626 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import ... | 703 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 626 | 0 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: Any , SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: Union[str, Any] ) -> str: # noqa: E741
'''simple docstring'''
wh... | 704 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A__ = 1
A__ = 1
while repunit:
A__ = (1_0 * repunit + 1) % divisor
repunit_i... | 626 | 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_dimens... | 705 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> int:
'''simple docstring'''
A__ = data
A__ = None
class ... | 626 | 0 |
from typing import Any
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> str:
'''simple docstring'''
A__ = data
A__ = None
class a__ :
"""simple docstring"""
def __init__( self... | 706 |
import math
lowerCAmelCase__ = 1_0
lowerCAmelCase__ = 7
lowerCAmelCase__ = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 2_0 ) -> str:
'''simple docstring'''
A__ = math.comb(SCREAMING_SNAKE_CASE_ , SCREAMING... | 626 | 0 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] ) -> List[Any]:
'''simple docstring'''
A__ = [
"encod... | 707 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: str = "cpu" , SCREAMING_SNAKE_CASE_: Union[str, None] = None ) -> None:
'''simple docstring'''
A_... | 626 | 0 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class a__ ( _UpperCAmelCase ):
"""simple docstring"""
__lowerCamelCase = (IPNDMScheduler,)
__lowerCamelCase = (('num_inference_steps', 50)... | 708 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = field(
metadata={'h... | 626 | 0 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class a__... | 709 |
import collections
import importlib.util
import os
import re
from pathlib import Path
lowerCAmelCase__ = """src/transformers"""
# Matches is_xxx_available()
lowerCAmelCase__ = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowerCAmelCase__ = re.compile(R"""^_impo... | 626 | 0 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> Optional[int]:
'''simple docstring'''
A__ = str(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) == 9 and set(UpperCAmelCase__ ) == s... | 710 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> ... | 626 | 0 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseTr... | 711 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str , SCREAMING_SNAKE_CASE_: list[str] | None = None ) -> list[list[str]]:
'''simple docstring'''
A__ = word_bank or []
# create a table
A__ = len(SCR... | 626 | 0 |
import pprint
import requests
lowerCAmelCase__ = 'https://zenquotes.io/api'
def lowerCAmelCase__ ( ) -> int:
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def lowerCAmelCase__ ( ) -> str:
'''simple docstring... | 712 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_:... | 626 | 0 |
'''simple docstring'''
import os
from pathlib import Path
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Tuple , SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: Optional[Any] , SCREAMING_SNAKE_CASE_: Tuple ) -> Any:
'''simpl... | 713 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Namespace ) -> Tuple:
'''simple docstring'''
return ConvertCommand(
args.model_type , args.t... | 626 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]}
... | 714 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 626 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] ... | 715 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf... | 626 | 0 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowerCAmelCase__ = TypeVar("""KT""")
lowerCAmelCase__ = TypeVar("""VT""")
class a__ ( Generic[KT, VT] ):
def __init__( self , lowercase = "root" , lowercase = None ... | 716 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITION... | 626 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {'''vocab_file''': '''sentencepiece.model'''}
low... | 717 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[int] , SCREAMING_SNAKE_CASE_: list[list[str]] , SCREAMING_SNAKE_CASE_: int , ) -> ... | 626 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
# See all S... | 718 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
RequestCounter,
... | 626 | 0 |
import heapq
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[Any] ) -> Optional[int]:
'''simple docstring'''
A__ = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the... | 719 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowerCAmelCase__ = """src/transformers"""
# This is to make sure the transforme... | 626 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environm... | 720 |
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_available():
import torch
i... | 626 | 0 |
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__ = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFileSystem #... | 721 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
"""
lowerCA... | 626 | 0 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import ... | 700 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import PriorTransformer
fr... | 626 | 0 |
from math import factorial
lowerCAmelCase__ = {str(d): factorial(d) for d in range(1_0)}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict ) -> int:
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(_SCREAMING_SNAKE_CASE ) )
def l... | 701 |
from math import factorial
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0_0 ) -> int:
'''simple docstring'''
return sum(map(SCREAMING_SNAKE_CASE_ , str(factorial(SCREAMING_SNAKE_CASE_ ) ) ) )
if __name__ == "__main__":
print(soluti... | 626 | 0 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@p... | 702 |
lowerCAmelCase__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: bytes ) -> bytes:
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
A__... | 626 | 0 |
import math
import tensorflow as tf
from packaging import version
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict ) -> List[Any]:
'''simple docstring'''
A__ = tf.convert_to_tensor(lowerCAmelCase_ )
A__ = 0.5 * (1.0 + tf.math.erf(x / tf.ca... | 703 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 626 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> Optional[int]:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 704 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A__ = 1
A__ = 1
while repunit:
A__ = (1_0 * repunit + 1) % divisor
repunit_i... | 626 | 0 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase )... | 705 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> int:
'''simple docstring'''
A__ = data
A__ = None
class ... | 626 | 0 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import Backb... | 706 |
import math
lowerCAmelCase__ = 1_0
lowerCAmelCase__ = 7
lowerCAmelCase__ = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 2_0 ) -> str:
'''simple docstring'''
A__ = math.comb(SCREAMING_SNAKE_CASE_ , SCREAMING... | 626 | 0 |
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