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 __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: str = "" ) -> dict[str, float]:
'''simple docstring'''
A__ = url or "https://www.imdb.com/chart/to... | 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_: Union[str, Any] , SCREAMING_SNAKE_CASE_: List[Any] ) -> tuple[float, float]:
'''simple docstring'''
if not len(__UpperCamelCase ) == len(__UpperCamelCase ) == 3:
raise ValueError("Please enter ... | 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 typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatena... | 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 argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
lowerCAmelCase__ = {
... | 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__ ( ) -> Optional[int]:
'''simple docstring'''
A__ = 0
for i in range(1 , 1_0_0_1 ):
total += i**i
return str(__UpperCamelCase )[-1_0:]
if __name__ == "__main__":
print(solution())
| 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 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 ... | 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
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertT... | 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 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pip... | 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 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""facebook/deit-base-distill... | 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 typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LxmertConfig'''],
'''t... | 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 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"""
),
# S... | 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 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... | 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 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,... | 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 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all BioGPT models at https://huggingface.co/models?filter... | 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 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTest... | 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 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int ) -> Dict:
'''simple docstring'''
if length <= 0 or not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in ra... | 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 json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_toke... | 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 copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCAmelCase__ = {
"""facebook/maskformer-swin-base-ade""": (
"""https://hugg... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""microsoft/focalnet-tiny""": """https://huggingface.c... | 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 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__ ( ) -> Dict:
'''simple docstring'''
A__ = os.path.dirname(os.path.realpath(__lowercase ) )
A__ ... | 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 |
lowerCAmelCase__ = 8.3_1_4_4_5_9_8
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[int] , SCREAMING_SNAKE_CASE_: List[Any] ) -> Tuple:
'''simple docstring'''
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
... | 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 json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''vocab_file''': '''vocab.json''',
'''tokenizer_config_file''': '''tokenizer_... | 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 |
# 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 ... | 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 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.checkpoint.default_... | 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'''
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 5_0 ) -> int:
'''simple docstring'''
A__ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile... | 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 inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def lowerCAmelCase__ ( *SCREAMING_SNAKE_CASE_: List[str] , SCREAMING_SNAKE_CASE_: Any = None , SCREAMING_SNAKE_CASE_: Dict=True , SCREAMING_SNAKE_CASE... | 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 inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
lowerCAmelCase__ = get_logger(__name__)
lowerCAmelCase__ = r'''
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
... | 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 gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImage... | 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 argparse
import hashlib # hashlib is only used inside the Test class
import struct
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> int:
'''simple docstring'''
A__ = data
A__ ... | 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 __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class a__ ( __lowerCamelCase ):
"""simple docstring"""
def UpperCamelCase ( self , lowercase ) -> List[Any]:
'''si... | 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 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Any ) -> str:
A__ = str(lowerCamelCase__ )
return n == n[::-1]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Optional[Any] = 1_0_0_0_0_0_0 ) -> List[st... | 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 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... | 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 |
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
lowerCAmelCase__ = logging.... | 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 Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, Flax... | 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 |
from __future__ import annotations
import pandas as pd
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: Dict , SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: List[str] ) -> list[int]:
'''simple docstring'''
A__ ... | 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 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig""",
"""ChineseCLI... | 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 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCAmelCase__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ["""B... | 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 warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( lowercase__ ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ) -> ... | 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 gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, lo... | 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 |
lowerCAmelCase__ = 8.3_1_4_4_5_9_8
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: float , SCREAMING_SNAKE_CASE_: float ) -> Tuple:
'''simple docstring'''
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar... | 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 warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self , *lowercase , **lowercase ... | 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 unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.... | 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 darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
lowerCAmelCase__ = {
"n_samples": 6_4,
"horizon": 3_2,
"num_inference_steps": 2_0,
"n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network
"scale_grad_by_std": True,
... | 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 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
return 1 if input_a == input_a else 0
def lowerCAmelCase__ ( ) -> None:
'''simple docstring'''
assert xnor_gat... | 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_: int ) -> List[str]:
'''simple docstring'''
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a num... | 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 os
import sys
import unittest
lowerCAmelCase__ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_mo... | 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 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__ = request... | 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 unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
... | 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 os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] ) -> str:
'''simple docstring'''
A__ = FileLock(str(tmpdir / "foo.lock" ) )
A__ = FileLoc... | 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 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": "merges.txt",... | 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'''
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chan... | 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 |
# 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... | 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 itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
lowerCAmelCase__ = """\
@misc{chen2021evaluating,
title={Evaluating Large Language Models Trai... | 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 typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCAmelCase__ = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals(... | 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 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNet... | 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 os
import time
import numpy as np
import onnxruntime as ort
lowerCAmelCase__ = "1"
lowerCAmelCase__ = "0"
lowerCAmelCase__ = "1"
lowerCAmelCase__ = ort.SessionOptions()
lowerCAmelCase__ = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("""Create inference session...""")
lowerCAmelCase__ ... | 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 inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDI... | 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 json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """... | 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 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class a__ ( snake_case ):
"""simple docstring"""
... | 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 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_to... | 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 os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class a__ ( snake_case ):
"""simple docstring"""
__lowerCamelCase = ''
__lowerCamelCase = (
None # pr... | 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 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: list ) -> list:
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE_ ) < 2:
return collection
def circle_sort_util(SCREAMING_SNAKE_CASE_: list , SCREAMING_SNAKE_CASE_: int , SCREAMING_S... | 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 __future__ import annotations
from math import pi, sqrt
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: float , SCREAMING_SNAKE_CASE_: float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negat... | 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 typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_availabl... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}
class a__ ( snake_case ):
"""simpl... | 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 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... | 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 |
# 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 requ... | 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 functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.co/microsoft/unispeech-large-1500h-cv/resol... | 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 typing import Any
class a__ :
"""simple docstring"""
def __init__( self , lowercase ) -> Tuple:
'''simple docstring'''
A__ = data
A__ = None
class a__ :
"""simple docstring"""
... | 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 abc import ABC, abstractmethod
from typing import List, Optional
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self ) -> Optional[Any]:
'''simple docstring'''
self.test()
def UpperCamelCase ( ... | 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 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... | 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 argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowerC... | 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_: int , SCREAMING_SNAKE_CASE_: int ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def lowerCAmelCase__ ( ) -> None:
... | 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 |
# 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... | 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
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,... | 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
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... | 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 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {... | 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 __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int ) -> list[str]:
'''simple docstring'''
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
... | 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 unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__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 |
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_... | 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 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... | 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 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__ ... | 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 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.checkpoint.default_pla... | 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 numpy as np
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: np.array ) -> np.array:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
lowerCAmelCase__ = R"""
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. Read the documen... | 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 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"""],
... | 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 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 tran... | 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 TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise Op... | 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 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 Acce... | 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 = 1_0 , SCREAMING_SNAKE_CASE_: int = 2_2 ) -> int:
'''simple docstring'''
A__ = range(1 , SCREAMING_SNAKE_CASE_ )
A__ = range(1 , SCREAMING_SNAKE_CASE_ )
retur... | 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 logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 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 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
@... | 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 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig""",
"""CLIPSegVisio... | 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 os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from da... | 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'''
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__ ... | 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 math import ceil, sqrt
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int = 1_0_0_0_0_0_0 ) -> int:
A__ = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
A__ = max(ceil(sqrt(outer_width**2 - lim... | 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
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCA... | 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 copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.p... | 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 numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase__ = version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
from nltk import word_tokenize
lowerCAmelCase__ = """... | 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 argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVec... | 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 |
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