code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase_ ( UpperCamelCase , ... | 54 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Tuple = {"configuration_plbart": ["PLBART_PRETRAI... | 136 | 0 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantiza... | 83 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class snake_case__ :
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase__ : int , UpperCamelCase__ : MutableSeque... | 83 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"facebook/data2... | 45 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A: Optional[Any] = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenize... | 109 | 0 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowerCAmelCase ( unitte... | 348 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-smal... | 348 | 1 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def __snake_case( _lowerCAmelCase ) -> Any:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
... | 35 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
... | 78 | 0 |
class lowercase :
def __init__( self ,A__):
lowercase = len(A__)
lowercase = [0] * len_array
if len_array > 0:
lowercase = array[0]
for i in range(1 ,A__):
low... | 97 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
lowercase__ :Union[str, Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if... | 97 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
A__ : Optional[int] ={
"""configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfi... | 70 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ : Union[str, Any] = {
"""configuration_funnel""": ["""FUNNEL_PRETRAIN... | 75 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_com... | 357 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __in... | 328 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def a ( snake_case__: str = "" ):
'''simple docstring'''
lowercase_ = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
lowercase_ = Beaut... | 30 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaToke... | 24 | 0 |
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
Bert... | 331 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class lowerCAmelCase__ ( unittest.Test... | 331 | 1 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
im... | 226 |
import numpy as np
class UpperCAmelCase__ :
'''simple docstring'''
def __init__( self : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = (0, 0)
__UpperCAmelCase : List[str] = None
... | 226 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
fro... | 215 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
c... | 215 | 1 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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 applic... | 345 | import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
... | 182 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class lowerCamelCase__ ( __magic_name__ ):
'''simple docstring'''
... | 341 |
"""simple docstring"""
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase ) -> int:
return int((input_a, input_a).count(1 ) != 0 )
def _lowerCamelCase() -> None:
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gat... | 341 | 1 |
import numpy as np
_lowerCAmelCase : Optional[Any] = [
["a", "b", "c", "d", "e"],
["f", "g", "h", "i", "k"],
["l", "m", "n", "o", "p"],
["q", "r", "s", "t", "u"],
["v", "w", "x", "y", "z"],
]
class __magic_name__ :
def __init__( self ) ... | 218 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __A ( )-> Tuple:
"""simple docstring"""
raise RuntimeError('CUDA out of me... | 39 | 0 |
from collections import deque
from math import floor
from random import random
from time import time
class __a :
def __init__( self ) -> Dict:
'''simple docstring'''
lowercase__: Dict = {}
def SCREAMING_SNAKE_CASE__ ( s... | 288 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeatur... | 288 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
snake_case_ : int = 1.0_54_57_18_17e-34 # unit of ℏ : J * s
snake_case_ : Optional[int] = 3e8 # unit of c : m * s^-1
... | 83 |
'''simple docstring'''
# 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
... | 83 | 1 |
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , ... | 121 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/m... | 121 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __snake_case ( unittest.TestCase ):
@proper... | 348 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
# See all SEW... | 348 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
__magic_name__ = [8, 5, 9, 7]
__magic_name__ = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__magic_name__ = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1... | 369 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
__magic_name__ = False
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"... | 255 | 0 |
import mpmath # for roots of unity
import numpy as np
class __magic_name__ :
def __init__( self , __snake_case=None , __snake_case=None ) -> Tuple:
'''simple docstring'''
# Input as list
__a =list(poly_a or [0] )[:]
__a... | 218 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(lowercase__ ) == 0:
raise ValueError('Input list must be a non ... | 96 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def SCREAMING_SNAKE_CASE__( ) -> Any:
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ = 9, 14 # noqa: ... | 31 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : list[int | str] ) -> None:
'''simple docstring'''
create_state_space_tree(_UpperCamelCase , [] , 0 , [0 for i in rang... | 31 | 1 |
import math
import flax.linen as nn
import jax.numpy as jnp
def _a ( SCREAMING_SNAKE_CASE_ : jnp.ndarray , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : float = 1 , SCREAMING_SNAKE_CASE_ : float = 1 , SCREAMING_S... | 92 | from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common im... | 343 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _snake_case ( a__ ):
@staticmethod
@abstractmethod
def lowerCamelCase__ ( UpperCAmelCase : ArgumentParser ):
raise NotImplementedError()
... | 64 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class _snake_case ... | 64 | 1 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class lowercase__( UpperCAmelCase , UpperCAmelCase ):
"""simple docstring"""
@register_to_config
def __init__( self : Tuple , ... | 30 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTe... | 277 | 0 |
'''simple docstring'''
def UpperCamelCase_ ( A__ : str ):
'''simple docstring'''
lowerCAmelCase_ : Optional[int] = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
lowerCAmelCase_ : An... | 89 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassificat... | 89 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_con... | 215 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Any = {
"""microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
"""micro... | 215 | 1 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE ( a_ , ... | 74 |
"""simple docstring"""
from manim import *
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ : List[str] = Rectangle(height=0.5 ,width=0.5 )
lowerCAmelCase__ : ... | 74 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__snake_case )
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCAmelC... | 341 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizer... | 341 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
SCREAMING_SNAKE_CASE_: Dict =['small', 'medium', 'large']
SCREAMING_SNAKE_CASE_: Dict ='lm_head.decoder.weight'
SCREAMING_SNAKE_CASE_: Dict ='lm_head.weight'
def lowerCAmelCase_ ... | 106 | '''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowerCAmelCase_ ( snake_case_ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase_ = FileLock(str(tmpdir / "foo.lock" ... | 106 | 1 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCAmelCase__ = logging.getLogger(__name__)
class lowerCAmelCase__ ( A_ ):
__a = """masked_bert"""
def __init__( self : Union[str... | 288 |
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : Tuple ) -> Any:
stooge(__lowerCamelCase , 0 , len(__lowerCamelCase ) - 1 )
return arr
def _UpperCAmelCase ( __lowerCamelCase : str , __lowerCamelCase : List[str] , __low... | 288 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
UpperCAmelCase: Any = generate_large_matrix()
UpperCAmelCase: Dict = (
[[4, 3, 2, -1], [3, 2, 1, -1],... | 336 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationSt... | 336 | 1 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers... | 121 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
# `task` is not a Cl... | 121 | 1 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowercase__ :
pass
| 351 | import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
... | 258 | 0 |
# Algorithm for the pigeonhole sorting
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: Optional[Any] = min(_UpperCAmelCase ) # min() finds the minimum value
SCREAMING_SNAKE_CASE_: Tuple = max(_UpperCAmelCase ) # max() finds the maximum value
SCREA... | 13 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase = None ) -> list[list[str]]:
'''simple docstring'''
lowercase : str = word_bank or []
# create a table
lowercase ... | 255 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
A : Dict = sorted(numsa + numsa )
A : Any = divmod(len(snake_case__ ) , ... | 357 |
'''simple docstring'''
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...t... | 311 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 31 | '''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE : str = loggin... | 31 | 1 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
lowerCamelCase_ : Any = False
class a__ ( unittest.TestCase ):
pas... | 365 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ : Dict = {"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}
try:
if not is_torch_ava... | 197 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMi... | 64 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : Optional[Any] ... | 64 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case__ : Any = logging.get_logger(__name__)
snake_case__ ... | 364 | '''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datas... | 274 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__lowerCAmelCase = logging.get_lo... | 89 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_se... | 89 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case : Any =get_tests_dir('fixtures/test_s... | 94 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__snake_case : Optional[int] ={
'configuration_vision_text_dual_encoder': ['VisionTextDualEncoderConfig'],
'processing_vi... | 94 | 1 |
"""simple docstring"""
import re
def _snake_case ( snake_case__ : str ):
A = re.compile(
r'^(?:0|94|\+94|0{2}94)' r'7(0|1|2|4|5|6|7|8)' r'(-| |)' r'\d{7}$' )
return bool(re.search(snake_case__ , snake_case__ ) )
if __name__ == "__main__":
_lowercase ... | 74 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}... | 74 | 1 |
"""simple docstring"""
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__ : Dict = ''
lowerCamelCas... | 316 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# -... | 316 | 1 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( A_ ):
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
lowerCAmelCase__ : Optional[Any] = len(A_ )
lowerCAmelCase__ : Tuple = max(A_ )
lowerCAmelCase__ : ... | 106 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 106 | 1 |
'''simple docstring'''
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a__ ( lowerCamelCase_ ):
def __init__( self ... | 199 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case ... | 199 | 1 |
def a__ ( ) -> list[list[int]]:
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
_lowerCamelCase : List[str] = generate_large_matrix()
_lowerCamelCase : Union[str, Any] = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-... | 336 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers... | 336 | 1 |
'''simple docstring'''
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowerCAmelCase : Dict = {
'<': operator.lt,
'<=': operator.le,
'==': operator.eq,
'!=': operator.ne,
'>=': operator.ge,
'... | 368 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE__ ( nn.Module):
lowerCAmelCase_ = 42
lowerCAmelCase_ = jnp.floataa
def UpperCAmelCase_ ( self )-> int:
... | 251 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 82 |
'''simple docstring'''
import os
import sys
import unittest
_lowerCamelCase : Optional[int] = 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
... | 258 | 0 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.... | 360 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin... | 102 | 0 |
import unittest
from typing import Dict, List, Optional, Union
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_inpu... | 270 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = "\\n@inproceedings{popovic-2015-chrf,\n title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n author = \"Popovi{\'c}, Maja\",\... | 270 | 1 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a , __a , __a ):
if depth < 0:
raise ValueError('Depth cannot be less than 0' )
if not scores:
raise ValueError('Scores cannot be empty' )
if depth == height:... | 88 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : List[Any] = k_size // 2
snake_case_ ,snake... | 88 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def A_ ( A__ ) -> Tuple:
# A local function to see if a dot lands in the circle.
def is_in_circle(A__ , A__ ) -> bool:
a__ : List[str] = sqrt... | 99 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class _A ( metaclass=lowerCAmelCase ):
snake_case__ : str = ['torch', 'transformers', 'onnx']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
... | 197 | 0 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
UpperCAmelCase_ = '%20'.join(argv[1:]) if len(argv) > 1 else quote(str(input('Search: ')))
print('Go... | 355 |
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
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'hustvl/yolos-smal... | 29 | 0 |
def _a ( SCREAMING_SNAKE_CASE_ : list[list[int]] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : list[int] ):
# 1. Validate that path exists between current and next vertices
if graph[p... | 92 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def __lowerCamelCase ( __a :Dict ) -> Any:
"""simple docstring"""
A__ = [
"""decoder.version"... | 274 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
lowerCAmelCase_ : Tuple = False
class ... | 360 |
'''simple docstring'''
import requests
lowerCAmelCase_ : List[Any] = 'YOUR API KEY'
def _lowerCamelCase ( lowercase : str , lowercase : str = giphy_api_key ) -> list:
_a = "+".join(query.split() )
_a = ... | 346 | 0 |
def __lowerCamelCase ( UpperCAmelCase_ : int ):
"""simple docstring"""
if p < 2:
raise ValueError('''p should not be less than 2!''' )
elif p == 2:
return True
a :int = 4
a :Union[str, Any] = ... | 94 |
from __future__ import annotations
def __lowerCamelCase ( UpperCAmelCase_ : dict , UpperCAmelCase_ : str ):
"""simple docstring"""
a , a :Optional[Any] = set(UpperCAmelCase_ ), [start]
while stack:
a :Optional[int... | 94 | 1 |
from __future__ import annotations
def __lowercase ( __lowerCAmelCase : list[int] ):
if len(__lowerCAmelCase ) == 0:
return array
a__ , a__ = min(__lowerCAmelCase ), max(__lowerCAmelCase )
# Compute the variables
... | 358 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case : Optional[int] = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig'... | 109 | 0 |
"""simple docstring"""
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
lowercase = ""
lowercase = (
None # pro... | 316 |
"""simple docstring"""
UpperCamelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_93_44,
"knot": 1.8_52,
}
UpperCamelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.2_77_77_77_78,
"mph": 0.6_21_37_11_92,
"knot": 0.5_39_95_68_03,
}
def A ( ... | 316 | 1 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils ... | 366 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 203 | 0 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAG... | 199 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaToken... | 199 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin... | 209 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
A__ : str = logging.get_logger(__name__)
class lowercase__ ( snake_case__ ):
def __init__( self : Optional[Any] , *snak... | 209 | 1 |
import unittest
from knapsack import knapsack as k
class UpperCAmelCase ( unittest.TestCase ):
def _SCREAMING_SNAKE_CASE (self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case : Optional[int] = 0
... | 59 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
"configuration_funnel": ["FUNNEL_PRETRAINED_... | 251 | 0 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1337 , num_examples=42 , dataset_name... | 360 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
UpperCamelCase_ = TypeVar('''T''')
UpperCamelCase_ = TypeVar('''U''')
class _snake_case ( Generic[T, U] ):
'''simple docstring'''
def __init__( ... | 59 | 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_environme... | 101 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _snake_case : int | str ) ->bool:
"""simple docstring"""
__snake_case : List[str] = str(_snake_case )
return n == n[::-1]
def lowercase ( _snake_case : in... | 102 | 0 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowercase :
@property
def a ( self ):
return self.get... | 200 |
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def __lowerCamelCase ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == ... | 200 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_A )
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
a__ = field(default="""summari... | 88 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
... | 88 | 1 |
from collections.abc import Callable
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> float:
"""simple docstring"""
A : float = a
A : float = b
if function(_lowerCAmelCase ) == 0: # one of the a... | 353 |
from random import randint, random
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = False , _lowerCAmelCase = False , _lowerCAmelCase = 5 , ) -> list:
"""simple docstring"""
A : Any = ... | 115 | 0 |
'''simple docstring'''
__snake_case ={
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalori... | 4 |
def lowercase__ ( __snake_case : Dict ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
UpperCAmelCase_ , UpperCAmelCase_ : Any = head.next, head
while fast and fast.next:... | 29 | 0 |
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 n... | 369 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipeli... | 293 | 0 |
"""simple docstring"""
def snake_case ( A__ = 1_00_00_00 ):
UpperCAmelCase_ : List[str] = 1
UpperCAmelCase_ : Union[str, Any] = 1
UpperCAmelCase_ : Tuple = {1: 1}
for inputa in range(2 ,A__ ):
UpperCAmelCase_ : Dict =... | 268 |
"""simple docstring"""
from torch import nn
def snake_case ( A__ ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"""Unsupported activation function: {ac... | 268 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _snake_case ( A__ ):
_l... | 355 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
a_ : Optional... | 327 | 0 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_com... | 91 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
A: Optional[Any] = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv p... | 109 | 0 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from trans... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
snake_case__ : List[Any] = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorte... | 44 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__lowerCAmelCase : Optional[int] = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'... | 107 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""facebook/xmod-base""": """ht... | 203 | 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 Accelerator, Distr... | 119 | from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class a_ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self : ... | 119 | 1 |
from __future__ import annotations
_a = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,) -> tuple[list[list[int]], list[list[int]]... | 209 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import tor... | 209 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
D... | 359 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimen... | 176 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridCo... | 19 |
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,
TFAutoMod... | 59 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : List[Any] = logging.get_logger(__name__)
lowerCamelCase : List[Any] = {
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.json",
}
... | 370 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from trans... | 208 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class lowercase__ ( unittest.TestCase ... | 200 |
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
UpperCAmelCase_ : List[Any] = 'https://api.github.com'
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCAmelCase_ : Optional[Any] ... | 200 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase = {
"""configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 351 |
'''simple docstring'''
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
fro... | 67 | 0 |
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0 ) -> list:
lowerCAmelCase__ : Optional[int] = length or len(_UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = False
for i in range(length - 1 ):
if list_... | 212 |
"""simple docstring"""
# 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
... | 115 | 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_v... | 356 |
a__: dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_93_44,
"knot": 1.8_52,
}
a__: dict[str, float] = {
"km/h": 1.0,
"m/s": 0.2_77_77_77_78,
"mph": 0.6_21_37_11_92,
"knot": 0.5_39_95_68_03,
}
def UpperCamelCase__( UpperCamelCas... | 39 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImga... | 135 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->float:
"""simple docstring"""
if discount_rate < 0:
raise ValueError('Discount rate cannot be negative' )
if not cash_flows:
raise ValueError('Cash flows list cannot be empty' )
lowerCA... | 293 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.ut... | 354 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , ) ->tuple[float | int, list[tuple[int, int]]]:
"""simple docstring"""
a_ , a_ = grid.shape
a_ = [-1, ... | 303 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import ... | 266 |
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import... | 327 | 0 |
def __UpperCamelCase ( lowercase__ : str , lowercase__ : str ) -> list:
'''simple docstring'''
lowerCAmelCase_ : str = len(lowercase__ )
lowerCAmelCase_ : Dict = []
for i in range(len(lowercase__ ) - pat_len + 1 ):
... | 28 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class __a ( __UpperCamelCase ):
def __init__( self : Union[str, Any] , *UpperCAmelCase : Optional[Any] , *... | 28 | 1 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequence... | 44 | """simple docstring"""
# 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
... | 44 | 1 |
'''simple docstring'''
import numpy
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ,__UpperCAmelCase ,__UpperCAmelCase ) -> None:
lowerCAmelCase__ : List[Any] = input_array
# Random initial weights are assigned where first argument is the
... | 361 |
'''simple docstring'''
from typing import Any
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ,__UpperCAmelCase ) -> Any:
lowerCAmelCase__ : Optional[Any] = data
lowerCAmelCase__ : str = None
def __repr__( self ) -> ... | 184 | 0 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import Co... | 119 |
def UpperCamelCase ( snake_case__ : int , snake_case__ : int ) -> int:
return int(input_a == input_a == 0 )
def UpperCamelCase ( ) -> None:
print('Truth Table of NOR Gate:' )
print('| Input 1 | Input 2 | Output |' )
print(F"""| 0 | 0 | ... | 119 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : int ) -> float:
"""simple docstring"""
if digit_amount > 0:
return round(number - int(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
return numb... | 67 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def a__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ) -> str | Literal[False]:
"""simple docstring"""
UpperCAmelCase... | 67 | 1 |
"""simple docstring"""
import re
def snake_case_ ( A_ : str ):
'''simple docstring'''
_lowerCamelCase : int = re.compile(
R'''^(?:0|94|\+94|0{2}94)''' R'''7(0|1|2|4|5|6|7|8)''' R'''(-| |)''' R'''\d{7}$''' )
return bool(re.... | 72 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configurati... | 176 | 0 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
__snake_case : Optional[int] = '__DUMMY_TRANSFORMERS_USER__'
__snake_case : List[Any] = 'Dumm... | 366 |
'''simple docstring'''
import torch
from torch import nn
class lowerCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase... | 136 | 0 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int ):
"""simple docstring"""
__UpperCAmelCase : str = 0
while len(_lowerCAmelCase ) > 1:
__UpperCAmelCase : Union[str, Any] = 0
# Consider two files with minim... | 254 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPText... | 208 | 0 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __SCREAMING_SNAKE_CASE ( *__UpperCAmelCase ):
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Tuple ... | 336 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase: Opt... | 336 | 1 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blender... | 182 | '''simple docstring'''
import logging
import os
from .state import PartialState
class a__ ( logging.LoggerAdapter ):
@staticmethod
def SCREAMING_SNAKE_CASE__ ( a : Optional[Any] ):
"""simple docstring"""
__lowerCamelCase = PartialState()
... | 67 | 0 |
import argparse
import struct
import unittest
class __lowercase :
"""simple docstring"""
def __init__( self , A ) -> str:
snake_case : Dict = data
# Initialize hash values
snake_case : Dict = [
0X6... | 360 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoRea... | 176 | 0 |
'''simple docstring'''
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 acceler... | 27 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_a = lo... | 39 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : int = logging.get_logger(__name__)
__UpperCamelCase : ... | 258 | import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
... | 258 | 1 |
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