code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceCl... | 205 |
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
... | 205 | 1 |
def __lowerCamelCase (UpperCAmelCase__ : list[list[int]] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : set ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = len(snake_case_ ), len(grid[0] )
... | 719 | import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 647 | 0 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class a__ ( lowerCamelCase_ , unittest.TestCase ):
... | 216 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCAmelCase ( metaclass=lowerCamelCase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Dict = ["""torch""", """torchsde"""]
def __init__( ... | 247 | 0 |
import socket
def __magic_name__( ) -> Union[str, Any]:
'''simple docstring'''
_lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_lowerCamelCase = socket.gethostname()
_lowerCamelCase = 1_2312
sock.connect(... | 710 | from typing import List
import numpy as np
def __magic_name__( __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = {key: len(__UpperCAmelCase ) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase , __UpperCAmelCase )}
if le... | 638 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
... | 39 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffuse... | 574 | 0 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOC... | 262 |
from __future__ import annotations
def A__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = None , SCREAMING_SNAKE_CASE_ = False , ) -> tuple[int, float, str]:
lowerCamelCase : Union[str, Any] =... | 262 | 1 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from tran... | 321 | '''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase = 400_0000 ):
lowercase__ : List[Any] = [0, 1]
lowercase__ : Union[str, Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
lowercase__ : ... | 152 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = OrderedDict(
[
... | 387 | import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 387 | 1 |
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
ge... | 354 |
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
lowercase_ = logging.get_logge... | 354 | 1 |
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.table import table_cast
from datasets.utils.py_util... | 712 | '''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_: Dict ={
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Au... | 415 | 0 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __UpperCamelCase ( snake_case__ ):
if not is_accelerate_available():
return method
A_ : int = version.pars... | 180 |
"""simple docstring"""
import math
import os
import sys
def __UpperCamelCase ( snake_case__ ):
A_ : Optional[Any] = """"""
try:
with open(snake_case__ , """rb""" ) as binary_file:
A_ : Union[str, Any] = binary_file.read()
for dat in data:
A_ ... | 180 | 1 |
"""simple docstring"""
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... | 716 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMSch... | 615 | 0 |
'''simple docstring'''
import numpy as np
def A (__lowerCamelCase :np.array ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 5 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase : List[Any] =logging.get_logger(__name__)
_UpperCamelCase : Dict ={
'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resol... | 206 | 0 |
"""simple docstring"""
lowerCAmelCase__ = [
(1_000, '''M'''),
(900, '''CM'''),
(500, '''D'''),
(400, '''CD'''),
(100, '''C'''),
(90, '''XC'''),
(50, '''L'''),
(40, '''XL'''),
(10, '''X'''),
(9, '''IX'''),
(5, '''V'''),
(4, '''... | 707 |
"""simple docstring"""
from typing import Any
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Dict = data
lowerCAmelCase : Any = None
... | 681 | 0 |
'''simple docstring'''
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMas... | 262 | '''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowerCAmelCase ( UpperCamelCase__ : BertModel , UpperCamelCase__ : str , UpperCamelCase__ : str ):
... | 262 | 1 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def a_ ( lowerCamelCase ):
UpperCAmelCase__ = test_file.split(os.path.sep )
if compo... | 632 | """simple docstring"""
from __future__ import annotations
class snake_case :
"""simple docstring"""
def __init__( self : Dict ,lowerCamelCase__ : list[list[int]] ):
UpperCAmelCase__ = TypeError(
'Matrices must be formed from a list of z... | 632 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def lowercase ( __A : Union[str, Any] ) -> Any:
'''simple docstring'''
if "cls_token" in name:
snake_case : ... | 36 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
... | 549 | 0 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class _a ( UpperCAmel... | 618 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTo... | 618 | 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.ut... | 44 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher... | 372 | 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 impor... | 708 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIP... | 448 | 0 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class __magic_name__ ( unittest.TestCas... | 348 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 109 | 0 |
from __future__ import annotations
from collections.abc import Callable
__lowerCAmelCase : Optional[Any] = list[list[float | int]]
def __magic_name__ ( A : Optional[Any], A : int ):
'''simple docstring'''
a = len(lowerCamelCase__ )
a =... | 721 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acce... | 662 | 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
A_: int = logging.get_logger(__name__)
A_: Any = {"""vocab_file"""... | 398 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow... | 0 | 0 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_to... | 704 |
'''simple docstring'''
def lowerCAmelCase__ ( a_ : float , a_ : list[float] ) -> float:
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be emp... | 599 | 0 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert... | 129 |
"""simple docstring"""
def _A ( __lowercase , __lowercase ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 129 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( __lowerCamelCase:int = 1_0_0 ):
'''simple docstring'''
__magic_name__ = n * (n + 1) * (2 * n + 1) / 6
__magic_name__ = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ =... | 707 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffuser... | 468 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https://huggingf... | 518 |
# 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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges ... | 518 | 1 |
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
... | 714 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class _UpperCamelCase ( lowerCAmelCase ):
# to overwrite at feature extractactor specific tests
... | 364 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}
try:
if not is_torch_available():
... | 322 |
def UpperCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ) -> int:
'''simple docstring'''
def count_of_possible_combinations(UpperCAmelCase_ ) -> int:
if target < 0:
return 0
if target == 0:
... | 322 | 1 |
import sys
__lowercase : List[Any] = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""668966489504452... | 702 |
"""simple docstring"""
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ... | 66 | 0 |
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
__SCREAMING_SNAKE_CASE : Optional[int] = 1.0_54_57_18_17E-34 # unit of ℏ : J * s
__SCREAMING_SNAKE_CASE : Dict = 3E8 # unit of c : ... | 670 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case : Optional[Any] ... | 545 | 0 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __magic_name__ ( __UpperCAmelCase = "isbn/0140328726" ) -> Any:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = olid.... | 721 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a = logging.get_logger(__name__)
a = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class __a ( ... | 13 | 0 |
def lowerCAmelCase_ ( __a , __a ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase__: Union[str, Any] =len(__a )
lowerCamelCase__: Dict =len(__a )
lowerCamelCase__: Any =(
first_str_length if first_str_length > second_s... | 59 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Dict = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
... | 121 | 0 |
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : bool = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
ret... | 707 | '''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : List[Any] = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
try:
... | 30 | 0 |
"""simple docstring"""
def A ( snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = []
SCREAMING_SNAKE_CASE__ = set({"""(""", """[""", """{"""} )
SCREAMING_SNAKE_CASE__ = set({""")""", """]""", """}"""} )
S... | 196 |
"""simple docstring"""
from __future__ import annotations
def A ( snake_case__ ):
'''simple docstring'''
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(... | 196 | 1 |
import gc
import unittest
from transformers import CTRLConfig, 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 ModelTeste... | 447 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobe... | 447 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfig''',
]... | 40 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transforme... | 96 | 0 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_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_commo... | 230 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowercas... | 230 | 1 |
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
_lowercase : List[Any] =logging.get_logger(__name__)
class UpperCamelCase_ ( snake_case__ ):
def __init__( self : Optional[Any] , ... | 364 |
import argparse
import json
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 a... | 364 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by... | 720 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( lowercase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ["image_processor", "tokenizer"]
SCREAMING_SNAKE_CASE = "AutoImageProcessor... | 199 | 0 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_proc... | 478 |
def lowercase_ (A : int , A : int ):
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
snake_case__ : List[str] = str(bin(A ) )[2:] # remove the leading "0b"
snake_case__ : int = ... | 478 | 1 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
lowerCamelCase = False
lowerCamelCase = True
lowerCamelCase = False
if __name__ == "__main__":
lowerCamelCase ... | 102 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_... | 102 | 1 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Dict:
if not is_accelerate_available():
return method
snake_case__ = version.parse(... | 33 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''tanreinama/GPTSAN-2.8B-spout_is_uniform''': (
''... | 452 | 0 |
'''simple docstring'''
from collections.abc import Callable
class __UpperCAmelCase :
'''simple docstring'''
def __init__( self , _SCREAMING_SNAKE_CASE = None ) -> None:
# Stores actual heap items.
A_ = []
# Stores indexes o... | 721 | '''simple docstring'''
from collections import defaultdict
from math import gcd
def _UpperCAmelCase ( _UpperCamelCase : int = 1_50_00_00 ) -> int:
A_ = defaultdict(_UpperCamelCase )
A_ = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
... | 174 | 0 |
import unittest
from transformers import MobileBertConfig, 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 ConfigTester
from ...test_mode... | 105 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smarta... | 253 | 0 |
'''simple docstring'''
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... | 713 |
from collections import deque
from .hash_table import HashTable
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def __init__( self : Optional[int] , *__lowerCamelCase : List[Any] , **__lowerCamelCase : Optional[Any] ):
... | 698 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
lowercase : int = True
except (ImportError, ModuleNotFoundError):
lowercase : List[str] = False
if NLTK_AVAILABLE:
with FileLock(""".lock... | 116 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A (__UpperCAmelCase ):
_SCREAMI... | 326 | 0 |
"""simple docstring"""
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common impo... | 707 |
"""simple docstring"""
import logging
from transformers import PretrainedConfig
_lowerCAmelCase = logging.getLogger(__name__)
_lowerCAmelCase = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/con... | 348 | 0 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
lowerCAmelCase_ ... | 414 |
# 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... | 333 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""vocab_file""": """vocab.json""",
"""merges... | 675 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""bert-base-uncased""": """https://huggingface... | 675 | 1 |
"""simple docstring"""
from collections import deque
from .hash_table import HashTable
class __a ( __a ):
'''simple docstring'''
def __init__( self , *_lowerCamelCase , **_lowerCamelCase ) -> Any:
'''sim... | 118 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.p... | 118 | 1 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attent... | 370 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = 0 # The first color of the flag.
SCREAMING_SNAKE_CASE_ = 1 # The second color of the flag.
SCREAMING_SNAKE_CASE_ = 2 # The third color of the flag.
SCREAMING_SNAKE_CASE_ = (red, white, blue)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE... | 370 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( A : float ) -> float:
return 1_0 - x * x
def __UpperCAmelCase ( A : float , A : float ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(__UpperCamelCase ) * eq... | 541 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowerCamelCase = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
_lowerCamelCase ... | 144 | 0 |
"""simple docstring"""
from math import isqrt
def __a ( A ):
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 , isqrt(A ) + 1 ) )
def __a ( A = 10**6 ):
'''simp... | 704 | """simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class a__ ( _a ):
snake_case_ = (IPNDMScheduler,)
snake_case_ = (("num_inference_steps", 50),)
def snake_case__ ( self, **... | 668 | 0 |
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
if not isinstance(a_ , a_ ):
__A = F'''Input value of [number={number}] must be an integer'''
raise TypeError(a_ )
if number < 0:
return False
__A = number * num... | 55 |
"""simple docstring"""
class lowerCAmelCase__ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None ):
'''simple docstring'''
A__ = data
A__ = previous
A__ = next_node
def __str__( s... | 337 | 0 |
"""simple docstring"""
import cmath
import math
def lowerCAmelCase (__UpperCamelCase : float , __UpperCamelCase : float , __UpperCamelCase : float , __UpperCamelCase : float ):
"""simple docstring"""
__UpperCamelCa... | 296 | """simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__lowercase = 299_792_458
# Symbols
__lowercase , __lowercase , __lowercase , __lowercase = symbols('''ct x y z''')
def low... | 296 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = '▁'
__a = {'vocab_file':... | 97 | '''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational... | 396 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE : Optional[Any] = {"configuration_vit": ["VIT_PRETRAINED... | 721 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_... | 441 | 0 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, 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():
imp... | 202 |
"""simple docstring"""
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 transfor... | 223 | 0 |
'''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/... | 377 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
neste... | 377 | 1 |
from ...configuration_utils import PretrainedConfig
lowerCamelCase : List[Any] = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https:... | 70 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_... | 180 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __lowerCAmelCase ( unittes... | 714 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ : Tuple = logging.get_logger(__name__)
UpperCamelCase_ : Optional[int] = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve... | 482 | 0 |
def __snake_case ( lowerCAmelCase_ = 1_0_0 ) -> int:
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 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__ ... | 100 | 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_ima... | 85 | 0 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
lowercase = logging.getLogger(__name__)
lowercase = 50 # max ... | 714 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 150 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_doc... | 645 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils... | 428 | 0 |
'''simple docstring'''
from itertools import count
def A_ ( _lowerCAmelCase : Optional[int] = 50 ):
"""simple docstring"""
_lowerCamelCase : int = [1] * min_block_length
for n in count(__lowerCAmelCase ):
fill_count_functions... | 717 |
'''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_av... | 11 | 0 |
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int = 10**12 ):
"""simple docstring"""
__a = 1
__a = 0
__a = 1
__a = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev... | 225 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowercase ( __snake_case : List[str] , __snake_case : Any=False ):
lowercase_ : List[str... | 231 | 0 |
"""simple docstring"""
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_UpperCamelCase : List[str] = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n ... | 134 |
"""simple docstring"""
from PIL import Image
def _SCREAMING_SNAKE_CASE ( __snake_case : Image ):
'''simple docstring'''
lowercase , lowercase = image.size
lowercase = 0
lowercase = image.load()
for i in range(__snake_case ):
... | 134 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case : int = {
'configuration_clip': [
... | 53 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def a_ ( lowerCAmelCase_ : Optional[Any], lowerCAmelCase_ : List[Any], lowerCAmelCase_ : str ... | 53 | 1 |
'''simple docstring'''
import numpy as np
def lowerCamelCase_ ( A_ , A_ ):
return np.where(vector > 0 , A_ , (alpha * (np.exp(A_ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 575 |
'''simple docstring'''
from statistics import mean
import numpy as np
def lowerCamelCase_ ( A_ , A_ , A_ , A_ ):
__lowerCamelCase = 0
# Number of processes finished
__lowerCamelCase = 0
# Displays the finished process.
# If it is 0, the pe... | 575 | 1 |
"""simple docstring"""
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from ... | 227 |
"""simple docstring"""
import argparse
import datetime
def UpperCAmelCase ( snake_case : str ):
_lowerCAmelCase:Dict = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
'''4''': ''... | 227 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCAmelCase_ : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError('List is empty' )
return sum(UpperCAmelCase_ ) / len(UpperCAmelCase_ )
if __name__ == "__main__":
import doctest... | 648 |
import functools
def lowerCamelCase_ ( UpperCAmelCase_ : list[int] , UpperCAmelCase_ : list[int] ) -> int:
'''simple docstring'''
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or not all(isinstance(UpperCAmelCase_ , ... | 648 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
im... | 298 |
'''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowercase : List[str] = """."""
if __name__ == "__main__":
lowercase : List... | 116 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 444 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_atte... | 444 | 1 |
import numpy as np
from PIL import Image
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
lowerCamelCase_ : str = np.array(lowerCAmelCase__ )
if arr.shape[0] != arr.shape[1]:
raise ValueError('The input array is n... | 364 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_lowercase : int =logging.get_logger(__name__)
class UpperCamelCase_ ( snake_case__ ):
def __init__( self : Tuple , *lowerCamelC... | 364 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : Optional[Any] = {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.jso... | 720 |
import random
class SCREAMING_SNAKE_CASE__ :
@staticmethod
def SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE__ : str ) -> tuple[list[int], list[int]]:
a_ : int = [ord(SCREAMING_SNAKE_CASE__ ) for i in text]
a_ : Any = ... | 443 | 0 |
"""simple docstring"""
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def lowercase__ ( snake_case_ :List[str] , snake_case_ :... | 49 |
"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey
def _lowerCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
_lowercase , _lowercase: Union[str, Any] = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for ... | 353 | 0 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_ = '''.'''
# Internal TensorFlow ops that can be safely ign... | 115 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def _a ( Upper... | 115 | 1 |
"""simple docstring"""
from collections.abc import Sequence
def lowercase ( __snake_case : Tuple , __snake_case : Dict ):
return sum(c * (x**i) for i, c in enumerate(SCREAMING_SNAKE_CASE__ ) )
def lowercase ( __s... | 231 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
lowercase__ = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={a... | 638 | 0 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__A : int = 0
__A : 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, 0, 0],
[0, 0... | 267 |
'''simple docstring'''
import torch
from transformers import AutoModel
class __UpperCamelCase ( torch.nn.Module ):
def __init__( self :Union[str, Any] ,_UpperCamelCase :Tuple="sayef/fsner-bert-base-uncased" ):
super(_UpperCamelCase ,self ).__init__()
sn... | 267 | 1 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio... | 259 |
def SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(snake_case ) )
def SCREAMING_SNAKE_CASE ( snake_case , snake_case ... | 375 | 0 |
'''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : Any ) -> int:
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
raise ValueError('Input must be an integer' )
if input_num <= 0:
raise ValueError... | 704 | '''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''roberta-base''': '''https:/... | 320 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosi... | 37 |
'''simple docstring'''
from timeit import timeit
__UpperCamelCase : int = {
"""MALAYALAM""": True,
"""String""": False,
"""rotor""": True,
"""level""": True,
"""A""": True,
"""BB""": True,
"""ABC""": False,
"""amanaplanacanalpanama""": Tr... | 448 | 0 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_lowercase : Tuple =2_9_9_7_9_2_4_5_8
# Symbols
_lowercase : Any =symbols('''ct x y z''')
def A__ ( lowercase: float ) -> float:
... | 711 | import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_lowercase : Any =logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_ ( lowerCAmelCa... | 661 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/ma... | 585 |
'''simple docstring'''
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__lowerCAmelCase = logging.getLogger(__name__)
def __SCREAMING_SNAKE_CASE ( ):
_snake_case = argparse.ArgumentParser(
description=""... | 585 | 1 |
import copy
import re
class A__ :
UpperCAmelCase = "hp"
UpperCAmelCase = {}
UpperCAmelCase = None
@classmethod
def __UpperCamelCase ( cls : Optional[int] , _a : Optional[int] ... | 703 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_to... | 191 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
lowerCamelCase_ : int = {
"""google/pix2struct-textcaps-base""": (
"""https:... | 559 | import math
import sys
def lowerCAmelCase( __lowerCamelCase ):
if number != int(__lowerCamelCase ):
raise ValueError('the value of input must be a natural number' )
if number < 0:
raise ValueError('the value of input must not be a negative number' )
if number == 0:... | 559 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin... | 717 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
fr... | 598 | 0 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered... | 47 | """simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__lowerCAmelCase : Optional[int] = None
try:
import msvcrt
except ImportError:
__lowerCAmelCase : List[Any] = None
try:
import fcntl
except Impor... | 644 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> str:
__lowerCamelCase : Optional[int] = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) ... | 703 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a ={
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": ["""XLMTokenizer"""],
}
... | 337 | 0 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCamelCase__ ( _a):
return getitem, k
def lowerCamelCase__ ( _a , _a):
return setitem, k, v
def lowerCamelCase__ ( _a):
return delitem, k
def l... | 25 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
... | 25 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torc... | 713 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
A : str = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
A : Optional[int] = requests.get(url... | 5 | 0 |
from __future__ import annotations
def a__ ( _UpperCamelCase : list[int] ):
if not nums:
return 0
__lowerCamelCase = nums[0]
__lowerCamelCase = 0
for num in nums[1:]:
__lowerCamelCase ,__lowerCamelCase = (
max_excluding + n... | 175 |
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_ = (((515, 22, 13), 555), ((61, 35, 49), 150))
a_ = [2, 4, 1, 5]
a_ = len(train_data)
a_ = ... | 175 | 1 |
"""simple docstring"""
def UpperCamelCase ( _A , _A ) -> float:
if digit_amount > 0:
return round(number - int(_A ) , _A )
return number - int(_A )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345... | 348 |
"""simple docstring"""
def UpperCamelCase ( _A , _A ) -> str:
lowercase : list[list[str]] = [[] for _ in range(_A )]
lowercase : Any = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
i... | 348 | 1 |
def _UpperCAmelCase ( UpperCAmelCase : int | float | str ):
"""simple docstring"""
try:
__lowerCamelCase : Dict = float(UpperCAmelCase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowe... | 519 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
... | 519 | 1 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __lowerCAmelCase ( __magic_name__ , __magic_name__ , __magic_name__ ):
_lowercase: Dict = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так... | 206 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
_SCREAMING_SNAKE_C... | 206 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterM... | 541 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from... | 541 | 1 |
'''simple docstring'''
import numpy as np
UpperCAmelCase_ : List[Any] = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v"""... | 705 |
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_ : Optional[Any] = logging.get_logger(__name__)
... | 440 | 0 |
"""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,
)
lowerCAmelCase_ : List[Any] ... | 673 |
"""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/LI... | 673 | 1 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from... | 712 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
__magic_name__ : Optional[Any] = logging.get_logger(__name__)
__magic_name__ : Tuple = {
'google/umt5-small': 'h... | 608 | 0 |
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