code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _snake_case ( _snake_case : list[list[float]] ):
lowerCAmelCase : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implement... | 60 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (... | 60 | 1 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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_con... | 101 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
... | 101 | 1 |
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, TruncationStrategy
from ...utils import Te... | 330 |
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,
r... | 279 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ... | 325 |
from math import isqrt, loga
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[Any] = [True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2, __lowerCamelCase, __lo... | 325 | 1 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __UpperCAmelCase ( __a : int ,__a : int ,__a : str ) ... | 235 |
a__ = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_n... | 235 | 1 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from tra... | 320 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=A ):
"""simple docstring"""
__a = ["""keras_nlp"""]
def __init__( self : str , *UpperCamelCase : List[Any] , **UpperCamelCase ... | 320 | 1 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def __lowerCamelCase ( *_lowercase , _lowercase = None , _lowercase=True , _lowercase=2 ) -> Optional[Any]:
from .. import __version__
... | 265 |
'''simple docstring'''
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a : List[Any] = """__DUMMY_TRANSFORMERS_USER__"""
a : Tuple = """Dummy User"""
a : Optional[Any] ... | 265 | 1 |
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase ):
return int((input_a, input_a).count(0 ) != 0 )
def __lowerCAmelCase ():
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __na... | 370 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None ):
if version.parse(hfh.__version__ ).release < version.parse('0.11.0' ).rel... | 182 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : List[str] = logging.get_logger(__name__)
lowerCamelCase : Optional[Any] = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.j... | 124 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase : Tuple = {
'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
'tokenization_transfo_xl': ... | 124 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProces... | 359 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: str = logging.get_logger(__name__)
_UpperCamelCase: Union[str, Any] = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba... | 53 | 0 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArgument... | 36 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
UpperCAmelCase__ : Tuple = TypeVar("""T""")
class a__ ( Generic[T] ):... | 245 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''... | 244 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if... | 244 | 1 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowerCAmelCase_ : List[Any] = 10
def _lowerCamelCase ( lowercase : int... | 63 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : int = 100 ) -> int:
_a = 0
_a = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
... | 63 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_... | 244 |
"""simple docstring"""
from statistics import mean, stdev
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 3 ):
"""simple docstring"""
UpperCamelCase = min(_SCREAMING_SNAKE_CASE )
UpperCamelCase = max(_SCREAMING_SNAKE_CASE )
# normalize data
return ... | 244 | 1 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
return abs(_UpperCAmelCase ) if a == 0 else greatest_common_divisor(b % a , _UpperCAmelCase )
def lowerCAmelCase_ ( snake_case__ , ... | 3 |
"""simple docstring"""
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : List[str] = hex_num.strip()
if not hex_num:
raise ValueError('No value was passed to the function' )
A_ : Any = hex_num[0] == '-'
if is_ne... | 286 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTe... | 354 |
from sklearn.metrics import matthews_corrcoef
import datasets
a__ : Optional[Any] = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into accou... | 19 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_availa... | 235 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : str = ["image_processor", "tokenizer"]
UpperCAm... | 235 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sen... | 150 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
a : Dict = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 impl... | 150 | 1 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class snake_case_ (ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCAmelCase__ : Optional[An... | 240 |
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
cl... | 240 | 1 |
a ={"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
a =["""a""", """b""", """c""", """d""", """e"""]
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Tuple:
__lowerCamelCase : ... | 113 |
import argparse
import collections
import os
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_table.py
a ="""src/transformers"""
a ="""docs/source/en"""
... | 113 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : Optional[int] = {
"configuration_roformer": ["ROF... | 47 |
'''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
#
# Unl... | 47 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ : str = {
'co... | 369 |
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=_lowerCamelCase ):
lowerCAmelCase__ = ["""speech"""]
def __init__(self :Optional[int] , *_UpperCamelCase :int , **_UpperCamelCase :List[str] )-> List[str]:... | 250 | 0 |
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(lowerCAmelCase_ ), lowerCAmelCase_ )
return number - int(lowerCAmelCase_ )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0... | 334 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"vocab_file": "vocab.txt"}
_lowerCamelC... | 334 | 1 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCamelCase : List[str] = logging.get_logger(__name__)
UpperCamelCase : Tuple = {
"""google... | 356 | '''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase ( a_ , unittest.TestCase ):
"""simple docstring""... | 345 | 0 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a ... | 327 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
_SCREAMING_SNAKE_CASE = namedtuple("""covid_data""", """cases deaths recovered""")
def SCREAMING_SNAKE_CASE__ ( __a = "https://www.worldometers.info/coronavirus/" ):
snake_case_ ... | 327 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
),
'facebook/dpr-que... | 359 |
# 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 app... | 75 | 0 |
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : int = 50 ):
lowerCamelCase_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_leng... | 55 |
'''simple docstring'''
class _a :
def __init__( self : Any ):
'''simple docstring'''
UpperCAmelCase = {} # Mapping from char to TrieNode
UpperCAmelCase = False
def A ( self : int , lowercase : list[st... | 34 | 0 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''') | 358 |
'''simple docstring'''
import numpy as np
import datasets
__UpperCAmelCase = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Eucli... | 228 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCAmelCase_ = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
el... | 16 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( UpperC... | 142 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeli... | 244 |
"""simple docstring"""
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
lowerCAmelCase__ = logging.get_logger(_... | 244 | 1 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
lowercase__ ={
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
'C': 2.78,
'U': 2.76,
'M': 2.41,
... | 216 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ ={
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfi... | 216 | 1 |
import os
import string
import sys
lowerCamelCase : Dict = 1 << 8
lowerCamelCase : Tuple = {
'tab': ord('\t'),
'newline': ord('\r'),
'esc': 2_7,
'up': 6_5 + ARROW_KEY_FLAG,
'down': 6_6 + ARROW_KEY_FLAG,
'right': 6_7 + ARROW_KEY_FLAG,
'left': 6_8 + ARROW_KEY_FLAG,
'mod_i... | 176 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : str = {
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', '... | 176 | 1 |
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
__lowercase : Tuple = len(__lowerCAmelCase ) + 1
__lowercase : List[str] = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i ... | 156 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTes... | 156 | 1 |
import os
import time
import numpy as np
import onnxruntime as ort
A_ : int = '1'
A_ : Any = '0'
A_ : Optional[int] = '1'
A_ : str = ort.SessionOptions()
A_ : Any = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
A_ : Tuple = ['... | 359 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : int = OrderedDict(
[
# Ba... | 141 | 0 |
'''simple docstring'''
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class A ( __snake_case ):
__magi... | 3 |
'''simple docstring'''
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, s... | 3 | 1 |
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
from ...schedulers import HeunDiscreteScheduler
fr... | 75 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__A = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .safilesystem import SaFileSystem # no... | 75 | 1 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/... | 156 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase_ )
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
... | 156 | 1 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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 ...test_mod... | 162 |
import math
import unittest
def a__ ( A__ ):
assert isinstance(A__, A__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3... | 162 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transform... | 196 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> bool:
if len(snake_case ) == 0:
return False
lowercase__: Any = len(snake_case ) // 2
if a_list[midpoint] == item:
return True
... | 196 | 1 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils im... | 357 | import os
import pytest
from attr import dataclass
__SCREAMING_SNAKE_CASE : int = 'us-east-1' # defaults region
@dataclass
class lowercase_ :
_lowerCamelCase = 42
_lowerCamelCase = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
_lowerCamelCase... | 284 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
... | 2 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
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 import ConfigTester
from... | 244 | 0 |
'''simple docstring'''
def _snake_case ( A , A ) -> int:
return int((input_a, input_a).count(0 ) == 0 )
def _snake_case ( ) -> None:
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0... | 351 |
'''simple docstring'''
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_c... | 228 | 0 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _Uppe... | 264 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( __a : Dict[str, torch.Tensor] ):
'''simple docstring'''
UpperCamelCase__ = []
UpperCamelCase__... | 244 | 0 |
'''simple docstring'''
from PIL import Image
def lowerCamelCase ( UpperCAmelCase__ : Any , UpperCAmelCase__ : Tuple ) -> Image:
def brightness(UpperCAmelCase__ : Tuple ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
... | 361 | '''simple docstring'''
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
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 ... | 21 | 0 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git ... | 179 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ,snake_case_ : list ) ->Any:
'''simple docstring'''
_enforce_args(snake_case_ ,snake_case_ )
if n == 0:
return 0
__A : int = float('''-inf''' )
for i in range(1 ... | 179 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase : int = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRET... | 313 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def _UpperCAmelCase ( self : str):
... | 313 | 1 |
import argparse
import os
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_task_guides.py
lowerCamelCase = 'src/transformers'
lowerCamelCase = 'docs/sourc... | 131 | """simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditional... | 44 | 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 (
BnbQuantizationConfig... | 17 | '''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface... | 17 | 1 |
import string
def _A ( SCREAMING_SNAKE_CASE__ : int ):
UpperCamelCase :List[str] = """"""
for i in sequence:
UpperCamelCase :Tuple = ord(__lowerCAmelCase )
if 65 <= extract <= 90:
output += chr(155 - extract )
e... | 259 |
"""simple docstring"""
def _lowercase ( ) -> int:
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(__lowerCAmelCase , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
... | 132 | 0 |
"""simple docstring"""
import math
def lowercase ( _snake_case : float , _snake_case : float ) ->float:
"""simple docstring"""
return math.pow(_snake_case , 2 ) - a
def lowercase ( _snake_case : float ) ->float... | 363 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCAmelCase ( __snake_case ):
'''simple docstring'''
lowerCamelCase__ =['image_processor', 'tokenizer']
lowerCamelCase__ ... | 24 | 0 |
import random
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = num - 1
lowerCAmelCase_ = 0
while s % 2 == 0:
lowerCAmelCase_ = s // 2
t += 1
for _ in range(5 ):
lowerCAmelCase_ = random.randrange(2 , num... | 278 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
lowercase = logging.getLogger(__name__)
@dataclass
class UpperCamelCase_ ( snake_case_ ):
'''simple docstr... | 178 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 1000000 ):
lowerCAmelCase : Optional[int] = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , ... | 314 |
"""simple docstring"""
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _snake_case ( _snake_case ... | 314 | 1 |
"""simple docstring"""
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 .... | 84 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case ( lowercase__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = ... | 84 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A : Optional[int] = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
... | 8 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : int = {
'configuration_whisper': ['WHISPER_PRETRAINED... | 8 | 1 |
"""simple docstring"""
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 i... | 72 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics as compute_m... | 195 | 0 |
"""simple docstring"""
import random
def _A ( _a : list , _a : Any ):
"""simple docstring"""
A , A , A = [], [], []
for element in data:
if element < pivot:
less.append(... | 77 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
UpperCAmelCase =generate_large_matrix()
UpperCAmelCase =(
[[4, 3, 2, -1], [3, ... | 77 | 1 |
"""simple docstring"""
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : str ... | 54 |
"""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 UpperCamelCase_ ( unittest.TestCase):
"""s... | 54 | 1 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets... | 359 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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_docstring... | 7 | 0 |
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def lowercase ( a__ : List[str] , a__ : Optional[int] , a__ : Any ) -> Tuple:
_UpperCamelCase = OmegaConf.load(a__ )
_... | 256 | """simple docstring"""
import logging
from transformers import PretrainedConfig
UpperCAmelCase = logging.getLogger(__name__)
UpperCAmelCase = {
"""bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json""",
... | 256 | 1 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case, __snake_case ) -> str:
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
_UpperCamelCase = s... | 100 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common i... | 100 | 1 |
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,
AutoModelForSequenceClassification,
AutoT... | 334 | """simple docstring"""
from math import ceil
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] ,_lowerCamelCase : Union[str, Any] ) -> int:
_lowerCAmelCase : Dict = list(range(0 ,_lowerCamelCase ) )
_lowerCAmelCase : ... | 44 | 0 |
'''simple docstring'''
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,
PreTrainedTokeniz... | 352 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase_ = {
'''configuration_efficientformer''': [
'''EFFICIENTFORMER_PRETR... | 174 | 0 |
import torch
from torch import nn
class UpperCAmelCase_ ( nn.Module ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=1 , SCR... | 259 | '''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFea... | 31 | 0 |
from __future__ import annotations
from PIL import Image
# Define glider example
lowercase__ : Any = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0,... | 180 |
from math import isqrt
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(__UpperCamelCase) + 1))
def SCREAMING_SNAKE_CASE ( __UpperCamelCase = 10**6) -> int:
a = 0
a = 1
... | 180 | 1 |
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from tr... | 47 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCAmelCase ( ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =ArgumentPar... | 47 | 1 |
class lowercase__ :
def __init__( self : Optional[Any] , UpperCamelCase__ : List[str] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = ... | 258 | 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 | 1 |
def _a ( SCREAMING_SNAKE_CASE_ : list , SCREAMING_SNAKE_CASE_ : list , SCREAMING_SNAKE_CASE_ : int ):
__lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ )
__lowerCAmelCase = [[0] * n for i in range(SCREAMING_SNAKE_CASE_ ... | 92 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, ... | 92 | 1 |
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_common import TokenizerTesterMix... | 354 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = '''ClapFeatureExtractor'''
UpperCamelCase_ : Any = ... | 319 | 0 |
'''simple docstring'''
def UpperCamelCase_( snake_case : Dict ):
'''simple docstring'''
snake_case_ = 0
snake_case_ = len(snake_case )
for i in range(n - 1 ):
for j in range(i + 1 , snake_case ):
... | 85 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase : str = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_rag': ['RagTokeniz... | 2 | 0 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowercase_ = logging.get_logger(__name__)
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = r"\w+[.]\d+"
low... | 224 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase__ = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
lowercase__ = n - k
# Calculate C(n,k)
for i in range(SCREAMING_SNAKE_CASE_ ):
result *=... | 224 | 1 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase : Union[str, Any] = ... | 95 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 209 | 0 |
from collections import defaultdict
from math import gcd
def lowerCAmelCase_ ( __UpperCAmelCase: int = 150_0000 ) -> Dict:
UpperCamelCase__ : List[str] = defaultdict(__lowerCAmelCase )
UpperCamelCase__ : str = 2
while 2 * ... | 371 |
import unittest
import numpy as np
from transformers import BertConfig, 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 t... | 247 | 0 |
# 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
#
# Unless required by a... | 182 | import itertools
import string
from collections.abc import Generator, Iterable
def A ( _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = iter(_lowercase )
while True:
SCREAMING_SNAKE_CASE : Optional[Any] = tup... | 182 | 1 |
'''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.... | 98 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def UpperCamelCase ( a="ro" , a="en" , a="wmt16" , a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
... | 98 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class a__ ( a_ ):
__lowerCAmelCase = """MCTCTFeatureExtractor"""
__lowerCAmelCase = """AutoTokenizer"""
def __init__( ... | 202 |
"""simple docstring"""
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( __snake_case : Optional[Any] , __snake_case : ... | 202 | 1 |
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 : Tuple = """\
@misc{chen2021evaluating,
title={Evaluating Large... | 140 |
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 tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 140 | 1 |
"""simple docstring"""
import random
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
lowerCAmelCase = num - 1
lowerCAmelCase = 0
while s % 2 == 0:
lowerCAmelCase = s // 2
... | 46 |
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..tab... | 46 | 1 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowercase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase_ ( self : List[Any] ):
"""simple docstring"""
_SCREAMING... | 111 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase_ = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
... | 111 | 1 |
def lowercase_ ( _A : str , _A : Any , _A : Union[str, Any] ):
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(UpperCAmelCase_ , n - 1 , UpperCAmelCase_ ... | 184 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import ... | 185 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : List[Any] = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 360 |
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention... | 92 | 0 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin,... | 31 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 50 ) -> int:
_lowerCAmelCase : int = [1] * (length + 1)
for row_length in range(3 ,length + 1 ):
for block_length in range(3 ,row_length + 1 ):
for block_start in range(... | 44 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( _UpperCamelCase : int , _UpperCamelCase : int ) -> list[str]:
"""simple docstring"""
if partitions <= 0:
raise ValueError('partitions must be a p... | 149 | """simple docstring"""
SCREAMING_SNAKE_CASE__ = {str(digit): digit**5 for digit in range(10)}
def lowerCAmelCase__ ( _UpperCamelCase : int ) -> int:
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_UpperCamelCase ... | 149 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
__UpperCAmelCase : Tuple = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json... | 111 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
__UpperCAmelCase : Optional[int] ... | 111 | 1 |
"""simple docstring"""
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
snake_case_ = """src/transformers"""... | 360 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ = {
"""configuration_rember... | 181 | 0 |
def __snake_case ( __UpperCamelCase : list ):
"""simple docstring"""
if len(__UpperCamelCase ) < 2:
return collection
def circle_sort_util(__UpperCamelCase : list ,__UpperCamelCase : int ,__UpperCamelCase : int ) -... | 312 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class _a ( snake_case_ ):
"""simple doc... | 312 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
A__ : str ... | 359 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a_ ( _UpperCAmelCase : List[Any] ) -> ... | 0 | 0 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logge... | 37 |
'''simple docstring'''
import math
def UpperCAmelCase_ ( __lowercase : int ) -> bool:
'''simple docstring'''
return math.sqrt(__lowercase ) * math.sqrt(__lowercase ) == num
def UpperCAmelCase_ ( __lowercase : int ) -> ... | 22 | 0 |
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 _a ( _lowercase... | 211 |
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 lowerCamelCase_ ( _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : List[... | 211 | 1 |
'''simple docstring'''
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_... | 97 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class A_ :
'''simple docstring'''
pass
| 61 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''shi-labs/... | 312 | """simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import Mvp... | 312 | 1 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def lowercase ( lowerCAmelCase__ : Callable ) -> Tuple:
@wraps(lowerCAmelCase__ )
def _inner_fn(*lowerCAmelCase__ : Optional[Any] , **lowerCAmelCase__ : st... | 45 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowercase_ (A : List[str] ):
snake_case_... | 277 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 253 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ = logging.get_logger... | 253 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__: str = logging.get_logger(__name__)
a__: Optional[int] = {
'bert-base-uncased': 'h... | 193 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase__( UpperCamelCase__ : Any , UpperCamelCase__ : int , UpperCamel... | 193 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Generic, TypeVar
_SCREAMING_SNAKE_CASE : Tuple = TypeVar("T")
class _snake_case ( Generic[T] ):
def __init__( self , a__ ) -> None:
'''simple docstring'''
... | 92 |
'''simple docstring'''
def UpperCamelCase_( snake_case : int , snake_case : int ):
'''simple docstring'''
while b:
snake_case_ , snake_case_ = b, a % b
return a
def UpperCamelCase_( snake_case : int ,... | 92 | 1 |
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : dict ) -> set:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = set()
# edges = list of graph's edges
SCREAMING_SNAKE_CASE__ = get_edges(__UpperCamelCase )
# While there are still... | 219 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __SCREAMING_SNAKE_CASE ( ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = Argu... | 219 | 1 |
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_to... | 369 |
def __lowercase ( _UpperCamelCase = 50 ) ->int:
"""simple docstring"""
lowercase : str = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for... | 173 | 0 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_bac... | 310 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__snake_case = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. Read... | 310 | 1 |
"""simple docstring"""
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
... | 203 | """simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
lowercase__ = None
try:
import msvcrt
except ImportError:
lowercase__ = None
try:
import fcntl
except ImportError:
lowercase__ = None
# Backward compatibility
# --------------... | 203 | 1 |
"""simple docstring"""
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _snake_case ( snake_case__ : Optional[int] ):
A = []
A = []
A = []
for rt in... | 74 |
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, Dist... | 19 | 0 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowerCamelCase__ = l... | 182 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 182 | 1 |
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