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"""
def _A ( lowercase ):
"""simple docstring"""
a =int(lowercase )
if n_element < 1:
a =ValueError('''a should be a positive number''' )
raise my_error
a =[1]
a , a , a =(0, 0, 0)
a ... | 81 |
"""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 ( _SCREAMING_SNA... | 81 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers im... | 108 |
"""simple docstring"""
import numpy as np
def a__ ( __SCREAMING_SNAKE_CASE ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 108 | 1 |
from collections.abc import Iterable
from typing import Any
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCamelCase__ : int | None = None ) -> List[Any]:
"""simple docstring"""
__... | 88 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDi... | 88 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
return [ord(__UpperCAmelCase ) - 96 for elem in plain]
def lowercase ( _SCREAMING_SNAKE_CASE :... | 356 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def ... | 326 | 0 |
import math
snake_case_ = 10
snake_case_ = 7
snake_case_ = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCamelCase__ ( snake_case_ : int = 20 ) -> str:
__snake_case = math.comb(snake_case_ , snake_case_ )
__snake_case... | 24 |
import unittest
from transformers import LiltConfig, 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_modeling_common import ModelTester... | 32 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAIN... | 359 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ) -> float:
UpperCAmelCase__ : Tuple = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
... | 299 | 0 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 274 |
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 BatchFeature
from ...utils import ... | 299 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve... | 367 |
'''simple docstring'''
from __future__ import annotations
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[int] , _lowerCamelCase : int ):
"""simple docstring"""
... | 4 | 0 |
'''simple docstring'''
__lowerCAmelCase : dict[str, float] ={
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609344,
"knot": 1.852,
}
__lowerCAmelCase : dict[str, float] ={
"km/h": 1.0,
"m/s": 0.277777778,
"mph": 0.621371192,
"knot": 0.539956803,
}
def UpperCamelCas... | 237 |
'''simple docstring'''
import functools
def UpperCamelCase ( _lowerCamelCase : str , _lowerCamelCase : str ):
A__ = len(_lowerCamelCase )
A__ = len(_lowerCamelCase )
@functools.cache
def min_distance(_lowerCamelCase :... | 237 | 1 |
def _a ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multiplicative_persis... | 51 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
def _a ( SCREAMING_SNAKE_CASE : Union[tf.Tensor, np.ndarray] ):
"""simple docstring"""
if isinst... | 51 | 1 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowerCAmelCase__ ( a):
'''simp... | 11 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase__ = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvai... | 11 | 1 |
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:
__lowerCa... | 10 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def UpperCamelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCa... | 10 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import ... | 153 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, ... | 110 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = ["""image_processor""", """tokenizer"""]
_lowerCAmelCase = """AutoImageProcessor"""
_lowerCAmelCase = """AutoTokenize... | 362 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUE... | 104 | 0 |
"""simple docstring"""
from math import ceil
def _a ( _SCREAMING_SNAKE_CASE = 1_001 ) -> Optional[int]:
snake_case_ = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
snake_case_ = 2 * i + 1
snake_case_ = 2 * i
... | 347 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = int(number**0.5 )
return number == sq * sq
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCa... | 299 | 0 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ )... | 284 | import logging
from transformers import PretrainedConfig
__SCREAMING_SNAKE_CASE : Any = logging.getLogger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/mai... | 284 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : str = logging.get_logger(__name__)
snake_case_ : Tuple = {}
class lowercase__ ( lowercase ):
lowercase__ = """llama"""
lowercase__ = ["""pas... | 83 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
snake_case_ : Any = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def A__ ( ):
... | 83 | 1 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class UpperCamelCase :
def __init__( self) -> Dict:
snake_case_ = {}
def a_ ( self, lowerCAmelCase__, lowerCAmelCase_... | 312 | """simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
... | 312 | 1 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__lowerCamelCase : str = 0
__lowerCamelCase : Tuple = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, ... | 52 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
__lowerCamelCase : str = logging.get_logger(__name__) # pylint: disable=invalid-name
class A__ ( __snake_... | 52 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCAmelCase__ : int = 1_0_0_0_0_0_0 ) ->int:
A__ : int = set(range(3, UpperCAmelCase__, 2 ) )
primes.add(2 )
for p in range(3, UpperCAmelCase__, 2 ):
if p not in primes:
... | 353 |
"""simple docstring"""
import os
from distutils.util import strtobool
def _lowerCAmelCase ( UpperCAmelCase__ : List[Any], UpperCAmelCase__ : Optional[Any] ) ->List[str]:
for e in env_keys:
A__ : List[Any] = int(os.environ.get(UpperCAme... | 296 | 0 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
fr... | 245 |
import os
# Precomputes a list of the 100 first triangular numbers
a : Optional[Any] = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowerCAmelCase_ ():
"""simple docstring"""
UpperCAmelCase_: Any = os.path.dirname(os.path.real... | 147 | 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 SequenceFeatu... | 213 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : int = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_availab... | 213 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( lowercase_ ):
UpperCAmelCase = []
UpperCAmelCase = []
UpperCAmelCase = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
... | 78 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def _lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ ):
UpperCAmelCase = x
UpperCAmelCase = y
for step in range(lowercase_ ): # noqa: B007
... | 78 | 1 |
import cva
import numpy as np
class A__ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
if k in (0.04, 0.06):
A_ = k
A_ = window_size
else:
... | 358 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ = 1_00 ) -> int:
A_ = n * (n + 1) * (2 * n + 1) / 6
A_ = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 101 | 0 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='''session''' )
def UpperCamelCase ( ):
'''simple docst... | 101 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ :Dict = logging.get_logger(__name__)
lowercase__ :Optional[int] = {
"BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
# See a... | 101 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
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 Config... | 362 |
'''simple docstring'''
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 i... | 199 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.co/microsoft/unispeech-large-1500h-cv... | 66 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_i... | 248 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modelin... | 352 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise OptionalDepend... | 48 | 0 |
from maths.prime_factors import prime_factors
def a__ ( A_ ):
'''simple docstring'''
if not isinstance(A_, A_ ):
__magic_name__ = f'''Input value of [number={number}] must be an integer'''
raise TypeError(A_ )
if number < 1:
raise... | 88 |
import argparse
import datetime
def lowerCAmelCase__( lowercase : str ) -> str:
__snake_case : int = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Saturday",
}
__snake_... | 326 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = []
UpperCAmelCase = []
UpperCAmelCase = {
"""^""": 3,
"""*""": 2,
... | 353 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( lowerCAmelCase = 4 ):
'''simple docstring'''
UpperCAmelCase = abs(lowerCAmelCase ) or 4
return [[1 + x + y * row_size for x in range(lowerCAmelCase )] for y in ra... | 248 | 0 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
... | 50 |
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
if len(a_ ) <= 1:
return [tuple(a_ )]
__A = []
def generate(a_ , a_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , ... | 15 | 0 |
'''simple docstring'''
import logging
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_DOC... | 107 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 107 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__A = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else... | 164 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger(__name__)
def _A ( lowercase__ ):
lowercase__ ... | 164 | 1 |
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_roberta import RobertaTokenizer
... | 355 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 167 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 309 |
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''-''': 1,
} # Priority of each operator
SCREAMING_SNAKE_CASE_ ... | 299 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
lowerCAmelCase_ = 6_3_7_8_1_3_7.0
lowerCAmelCase_ = 6_3_5_6_7_5_2.3_1_4_2_4_5
lowerCAmelCase_ = 6_37_81_37
def snake_case( __magic_name__ , __magic_name__ , __ma... | 116 |
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 BatchFe... | 116 | 1 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
... | 11 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from... | 36 | 0 |
'''simple docstring'''
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import... | 174 |
'''simple docstring'''
import os
lowerCamelCase_ = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}
def __lowercase ( __lowercase ) -> int:
'''simple docstring'''
_A = 0
_A = 0
whi... | 174 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : str = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitPro... | 3 | def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> bool:
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(UpperCamelCase_ ) )
def lowercase( UpperCamelCase_ ,... | 343 | 0 |
import os
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Tuple ) -> int:
UpperCAmelCase_ = len(grid[0] )
UpperCAmelCase_ = len(__UpperCamelCase )
UpperCAmelCase_ = 0
UpperCAmelCase_ = 0
UpperC... | 177 |
from __future__ import annotations
_lowerCamelCase = list[list[int]]
# assigning initial values to the grid
_lowerCamelCase = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0... | 177 | 1 |
"""simple docstring"""
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager imp... | 179 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_util... | 179 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config",
"Mob... | 371 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PA... | 43 | 0 |
'''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 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, 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 Config... | 257 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenizat... | 348 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
... | 348 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to tak... | 244 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Optional[Any] = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https... | 333 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/beit-base-patch16-224-pt22k"... | 362 |
_snake_case = 8.3144598
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than ... | 300 | 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
__lowercase : Dict = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'te... | 27 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVeca... | 27 | 1 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _A (... | 131 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaa... | 131 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
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 ..pi... | 120 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecu... | 120 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def _lowerCAmelCase ( __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Optional[Any] , __snake_case : ... | 365 |
'''simple docstring'''
import itertools
import math
def _lowerCAmelCase ( __snake_case : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 190 | 0 |
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : Tuple = [0 for i in range(r + 1 )]
# nc0 = 1
UpperCAmelCase__ : Tuple = 1
for i in range(1 , n + 1 ):
# to... | 163 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :str , a_ :str) -> bool:
__a : Optional[Any] = get_failure_array(a_)
# 2) Step through text searching for pattern
__a , __a : Union[str, Any] = ... | 160 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_A = logging.get_l... | 366 |
"""simple docstring"""
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
_A = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
_A = [file for file in filepaths i... | 205 | 0 |
lowerCAmelCase__ = [
'Audio',
'Array2D',
'Array3D',
'Array4D',
'Array5D',
'ClassLabel',
'Features',
'Sequence',
'Value',
'Image',
'Translation',
'TranslationVariableLanguages',
]
from .audio import Audio
from .features import ArrayaD, ArrayaD, ArrayaD, ArrayaD, ... | 11 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .... | 231 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .t... | 76 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : Tuple , UpperCamelCase : Tuple , UpperCamelCase : Tuple=False ):
if isinstance(UpperCamelCase , UpperCamelCase ) and isinstance(UpperCamelCase , UpperCamelCase ):
UpperCAmelCase :... | 76 | 1 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
snake_case : int = loggin... | 94 |
import math
lowerCAmelCase__ :Optional[int] = 1_0
lowerCAmelCase__ :Optional[Any] = 7
lowerCAmelCase__ :Union[str, Any] = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCAmelCase__ ( a__: int = 2_0 ) -> str:
'''simple docstring'''
_UpperCAmelCase ... | 329 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params impo... | 366 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/confi... | 211 | 0 |
import operator as op
def A ( a_ ) -> List[Any]:
__UpperCamelCase : List[str] =[]
__UpperCamelCase : Union[str, Any] =lambda a_ ,a_ : int(x / y ) # noqa: E731 integer division operation
__UpperCam... | 71 |
a_ = 8.314_4598
def lowerCamelCase__ ( _a , _a):
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K")
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol")
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_ma... | 76 | 0 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class __lowerCAme... | 158 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( lowerCAmelCase):
_a = (DDIMParallelScheduler,)
_a = (('''eta''', 0.0), ('''num_inference_steps''', 50))
def SCREAMING_SNAKE_CAS... | 158 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
... | 48 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREA... | 48 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsC... | 215 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransfor... | 215 | 1 |
"""simple docstring"""
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class ... | 64 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : list ):
"""simple docstring"""
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
_snake_case : List[Any] = []
def generate(snake_case__ : int , snake_case__... | 64 | 1 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCamelCase_ ( __lowercase ):
lowerCAmelCase_ = CustomTokenizer
pass
| 352 |
from cva import destroyAllWindows, imread, imshow, waitKey
def lowerCamelCase__ ( UpperCamelCase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
_snake_case , _snake_case = img.shape[0], img.shape[1]
# converting each pixel's colo... | 295 | 0 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTo... | 225 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
lowerCamelCase__ : List[str] = logging.get_logger(__name__)
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( sel... | 225 | 1 |
"""simple docstring"""
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
a :int = HfArgumentParser(InitializationArguments)
a :Union[str, Any] = parser.parse_args()
# Load codeparrot tokenizer tr... | 56 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a :Dict = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenization_rag": ["RagTokenizer"],
}
try:
if no... | 56 | 1 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ : Any = logging.get_logger(__nam... | 192 |
"""simple docstring"""
def A ( snake_case__ = 50 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in r... | 165 | 0 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
UpperCAmelCase__ = logging.get_logger(__name__)
class __lowerCAmelCase ( A ):
def __init__( self : int , *A : List[Any] , **A : int) ->... | 290 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase__ = {
# 1536-bit
5: {
"prime": int(
"FFFFFFFFFFFF... | 290 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 6 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __lowerCAmelCase ( a__ , a__ , a__=1024 , a__=1024 , a__=False , **a__ ) -> Optional[Any]:
__... | 6 | 1 |
"""simple docstring"""
def _lowerCamelCase( a , a = False ):
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 1_0 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_3_1_7_0_4_4_0_6... | 268 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( snake_case_ ... | 268 | 1 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class a_ ( a__ , a__ ... | 313 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('''ignore''', category=UserWarning, module='''torch.optim.lr_scheduler''')
class a_ :
"""simple d... | 313 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__SCREAMING_SNAKE_CASE : str = logging.get_logge... | 284 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Optional[int] = {
'configuration_distilbert': [
'DISTIL... | 284 | 1 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def UpperCamelCase ( _A ):
"""simple docstring"""
for param in module.parameters():
__magic_name__ : List[Any] = False
def UpperCamelCase ( ):
... | 342 |
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
__magic_name__: str = logging.get_logger(__name__)
__magic_name__: ... | 342 | 1 |
import sys
_lowerCAmelCase : List[str] = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648... | 364 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCAmelCase : Dict = {
'''configuration_blip''': [
'''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 70 | 0 |
from typing import Dict
from .base import GenericTensor, Pipeline
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
def SCREAMING_SNAKE_CASE_( self , lowercase=None , lowercase=None , lowercase=None , **lowercase ) -> str:
if tokenize_kwargs is None:
... | 19 |
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.te... | 9 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision... | 61 |
'''simple docstring'''
import os
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Any ):
'''simple docstring'''
UpperCAmelCase__ = len(grid[0] )
UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = 0
UpperCAmelCase__ ... | 61 | 1 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def snake_case ( A__ ,A__=0 ):
return sorted(A__ ,key=lambda A__ : x[column] )
def snake_case ( A__ ,A__ ,A__=float("inf" ) ... | 268 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCamelCase_ = lo... | 268 | 1 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase ) ->list[int]:
"""simple docstring"""
return [ord(UpperCAmelCase ) - 96 for elem in plain]
def __a ( UpperCAmelCase ) ->str:
"""simple docstring"""
return "".join... | 370 |
'''simple docstring'''
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
... | 337 | 0 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as Prophet... | 176 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {"""vocab_file""": """vocab.txt"""}
__snake_case ... | 176 | 1 |
def __lowercase ( _UpperCamelCase ) ->int:
"""simple docstring"""
lowercase : list[list[int]] = [[0 for _ in range(_UpperCamelCase )] for _ in range(m + 1 )]
for i in range(m + 1 ):
lowercase : Union[str, Any] = ... | 357 |
# flake8: noqa
# Lint as: python3
__a = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progre... | 173 | 0 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppToken... | 50 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 50 | 1 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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_con... | 356 | from __future__ import annotations
lowerCAmelCase__ = tuple[int, int, int]
lowerCAmelCase__ = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
lowerCAmelCase__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# -------------------------- default selecti... | 119 | 0 |
"""simple docstring"""
import argparse
import os
import re
__lowercase = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowercase = re.com... | 40 |
"""simple docstring"""
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
return x + 2
class UpperCamelCase_ ( unittest.TestCase):
... | 54 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
impor... | 356 |
'''simple docstring'''
import os
def lowerCamelCase ():
with open(os.path.dirname(_SCREAMING_SNAKE_CASE ) + '/p022_names.txt' ) as file:
__a : List[Any] = str(file.readlines()[0] )
__a : str = names.replace('"' , '' ).split('... | 294 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_... | 2 |
"""simple docstring"""
import faiss # 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 requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklea... | 266 | 0 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class A_ :
def __init__( self : Dict , snake_case_ : int | None = None ):
_UpperCAmelCase = value
_UpperCAmelCase = None # Added in order to delete... | 156 |
'''simple docstring'''
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... | 156 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 97 |
"""simple docstring"""
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,
)
UpperCamelCase_ = {'configuration_xgl... | 243 | 0 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...... | 352 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__UpperCamelCase : List[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets b... | 309 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case ( l... | 55 |
'''simple docstring'''
a_ : str = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
a_ : Any... | 55 | 1 |
from typing import 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 BatchFeature
from ....file_utils import Padd... | 365 | from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
if is_torc... | 165 | 0 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_snake_case = 10
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__, snake_case__ ) -... | 157 | def _UpperCamelCase ( snake_case__ ) -> int:
__UpperCAmelCase : Union[str, Any] = abs(snake_case__ )
__UpperCAmelCase : Dict = 0
while n > 0:
res += n % 10
n //= 10
return res
def _UpperCamelCas... | 157 | 1 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
_a = datasets.utils.logging.get_logger(__name__)
@dataclass
class _lowerCAmelCas... | 144 |
"""simple docstring"""
import numpy
# List of input, output pairs
_a = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
_a = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
_a = [2, 4, 1, 5]
_a = len(... | 144 | 1 |
"""simple docstring"""
from __future__ import annotations
import bisect
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = 0 , _UpperCamelCase = -1 ):
if hi < 0:
__lowerCAmelCase : Tuple = len(_UpperCamelCase )
while lo < hi:
__l... | 86 |
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _a ( unittest.TestCase ... | 251 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 257 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def A__ ( ):
SCREAMING_SNAKE_CASE_ = 9
SCREAMING_SNAKE_CASE_ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
... | 257 | 1 |
"""simple docstring"""
from __future__ import annotations
a :Optional[int] = "Muhammad Umer Farooq"
a :Any = "MIT"
a :Optional[int] = "1.0.0"
a :Dict = "Muhammad Umer Farooq"
a :Dict = "contact@muhammadumerfarooq.me"
a :List[str] = "Alpha"
import r... | 132 |
"""simple docstring"""
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoencoder... | 132 | 1 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_... | 124 |
import numpy
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[str] ,A : numpy.ndarray ,A : numpy.ndarray ):
__A = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previou... | 124 | 1 |
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase:
def __init__( self , __a=None) -> List[Any]:
'''simple docstring'''
_UpperCamelCase = data
_UpperCamelCase = None
def... | 194 |
# Lint as: python3
import itertools
import os
import re
lowerCamelCase = re.compile(R'([A-Z]+)([A-Z][a-z])')
lowerCamelCase = re.compile(R'([a-z\d])([A-Z])')
lowerCamelCase = re.compile(R'(?<!_)_(?!_)')
lowerCamelCase = re.compile(R'(_{2,})')
lowerC... | 199 | 0 |
from __future__ import annotations
def UpperCamelCase ( snake_case__ : Union[str, Any] , snake_case__ : List[str] = None , snake_case__ : Any = None , snake_case__ : str = False , ) -> tuple[int, float, str]:
UpperCamelCase : Dict = cipher_alphabet or ... | 364 |
import re
def UpperCamelCase ( snake_case__ : str ) -> str:
if len(re.findall('[ATCG]' , snake_case__ ) ) != len(snake_case__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main... | 103 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils... | 275 |
def _lowercase ( lowercase__ , lowercase__ ):
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__":... | 275 | 1 |
'''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.... | 357 |
'''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... | 217 | 0 |
import argparse
UpperCamelCase = """docs/source/_static/js/custom.js"""
def __lowerCamelCase ( snake_case__ ) -> Optional[int]:
"""simple docstring"""
with open(UpperCAmelCase_ ,encoding="""utf-8""" ,newline="""\n""" ) as f:
... | 306 |
'''simple docstring'''
from __future__ import annotations
from typing import Generic, TypeVar
a_ : List[str] = TypeVar("""T""")
class snake_case ( Generic[T] ):
"""simple docstring"""
def __init__( self , UpperCamelCase ):
... | 55 | 0 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# C... | 64 | """simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
raise Optiona... | 64 | 1 |
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