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 |
|---|---|---|---|---|
from __future__ import annotations
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> dict[str, float]:
"""simple docstring"""
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('''One and only one argument must... | 371 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
A__ = False
class a ( unittest.TestCase ):
... | 44 | 0 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
... | 350 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 44 | 0 |
from math import factorial
A__ = {str(d): factorial(d) for d in range(10)}
def _lowerCAmelCase ( __lowerCAmelCase ) -> Dict:
"""simple docstring"""
return sum(DIGIT_FACTORIAL[d] for d in str(__lowerCamelCase ) )
def _lowerCAmelCase ( ) -> List[str]:
... | 351 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 44 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
fr... | 352 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[in... | 44 | 0 |
from __future__ import annotations
from typing import Any
def _lowerCAmelCase ( __lowerCAmelCase ) -> Any:
"""simple docstring"""
create_state_space_tree(_A , [] , 0 )
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ... | 353 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
A__ = logging.get_logger(__name__)
A__ = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
# See a... | 354 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case__ : str = sorted(string.lower() )
r... | 44 | 0 |
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_atten... | 355 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 44 | 0 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> float:
"""simple docstring"""
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowercase__ , lo... | 356 |
A__ = 0 # The first color of the flag.
A__ = 1 # The second color of the flag.
A__ = 2 # The third color of the flag.
A__ = (red, white, blue)
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if n... | 44 | 0 |
from __future__ import annotations
A__ = list[list[int]]
# assigning initial values to the grid
A__ = [
[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, 8, 6, 3, 0, 0, 5],
[0... | 357 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case__ : List[str] = 1
snake_case__ : int = 1
while repunit:
snake_case__ : Dic... | 44 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {
'''configuration_rembert''': ['''REMBERT_PRETRAINED_CO... | 358 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 | 0 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logg... | 359 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import C... | 44 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 100 , ) -> float:
"""simple docstring"""
snake_case... | 360 |
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Dict = ... | 44 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
... | 361 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
snake_case__ : List[Any] = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorte... | 44 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a ( __snake_case ):
@staticmethod
@abstractmethod
def __lowerCamelCase ( __lowercase :List[Any] ):
raise NotImplementedError()
@abstractmethod
def ... | 362 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transfor... | 44 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
# See all SEW-D m... | 363 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A__ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be sm... | 44 | 0 |
import random
from .binary_exp_mod import bin_exp_mod
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase=1000 ) -> Optional[Any]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
snake_case__ : str ... | 364 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 44 | 0 |
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, TableTransformerConfig, TableTransformerForObject... | 365 |
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__ = logging.ge... | 44 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def _lowerCAmelCase ( __lowerCAmelCase ) -> List[str]:
"""simple docstring"""
return 1 / (1 + np.exp(-z ))
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase )... | 366 |
from sklearn.metrics import mean_squared_error
import datasets
A__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenho... | 44 | 0 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMix... | 367 |
from manim import *
class a ( __lowerCamelCase ):
def __lowerCamelCase ( self :Union[str, Any] ):
snake_case__ : Optional[Any] = Rectangle(height=0.5 ,width=0.5 )
snake_case__ : Optional[int] = Rectangle(he... | 44 | 0 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case__ : Union[str, Any] = sorted(string.lower()... | 368 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 44 | 0 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
A__ = logging.get_logger(__name__)
class a ( __lowerCamelCase ):
def __init__( self :Tuple ,*__lowercase :Any ,**__lowercase :in... | 369 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase ) + 1
snake_case__ : Tuple = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where d... | 44 | 0 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _lowerCAmelCase ( __lowerCAmelCase = 3 ) -> Tuple:
"""simple docstring"""
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
... | 370 |
def _lowerCAmelCase ( __lowerCAmelCase = 50 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for bloc... | 44 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassif... | 371 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
A__ = False
class a ( unittest.TestCase ):
... | 44 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteS... | 350 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 44 | 0 |
A__ = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> int:
"""simpl... | 351 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 44 | 0 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
A__ = collections.namedtuple('''_Datasets''', ['''train''... | 352 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[in... | 44 | 0 |
import random
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> Optional[int]:
"""simple docstring"""
snake_case__ , snake_case__ , snake_case__ : Optional[Any] = [], [], []
for element in data:
if element < pivot:
... | 353 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 0 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from tra... | 354 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case__ : str = sorted(string.lower() )
r... | 44 | 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 (
ProphetNetForConditionalGeneratio... | 355 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 44 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixi... | 356 |
A__ = 0 # The first color of the flag.
A__ = 1 # The second color of the flag.
A__ = 2 # The third color of the flag.
A__ = (red, white, blue)
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if n... | 44 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
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 impor... | 357 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case__ : List[str] = 1
snake_case__ : int = 1
while repunit:
snake_case__ : Dic... | 44 | 0 |
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A__ = abspath(join(dirname(dirname(dirname(__file__))), '''s... | 358 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 | 0 |
import os
import sys
import transformers
A__ = '''3'''
print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_ava... | 359 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import C... | 44 | 0 |
"""simple docstring"""
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 MarkupLMFeatureExtrac... | 360 |
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Dict = ... | 44 | 0 |
def _lowerCAmelCase ( ) -> Dict:
"""simple docstring"""
snake_case__ : Any = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
snake_case__ : str = 6
snake_case__ : str = 1
snake_case__ : List[str] = ... | 361 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
snake_case__ : List[Any] = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorte... | 44 | 0 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
A__ = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\n Dorr, Bonnie and\n ... | 362 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transfor... | 44 | 0 |
from __future__ import annotations
import math
def _lowerCAmelCase ( __lowerCAmelCase ) -> list[int]:
"""simple docstring"""
if num <= 0:
snake_case__ : str = f"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(__snake_case ... | 363 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A__ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be sm... | 44 | 0 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
A__ = '''\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu ... | 364 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 44 | 0 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> str:
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 365 |
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__ = logging.ge... | 44 | 0 |
A__ = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def ... | 366 |
from sklearn.metrics import mean_squared_error
import datasets
A__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenho... | 44 | 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
__lowercase = '''.'''
# Internal TensorFlow op... | 367 |
from manim import *
class a ( __lowerCamelCase ):
def __lowerCamelCase ( self :Union[str, Any] ):
snake_case__ : Optional[Any] = Rectangle(height=0.5 ,width=0.5 )
snake_case__ : Optional[int] = Rectangle(he... | 44 | 0 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALU... | 368 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 44 | 0 |
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
# Copied... | 369 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase ) + 1
snake_case__ : Tuple = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where d... | 44 | 0 |
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
UpperCAmelCase = loggi... | 370 |
def _lowerCAmelCase ( __lowerCAmelCase = 50 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for bloc... | 44 | 0 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common imp... | 371 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
A__ = False
class a ( unittest.TestCase ):
... | 44 | 0 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionP... | 350 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 44 | 0 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> Dict:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(UpperCamelCase__ , (list, tuple) ) or not all(
isinstance(UpperCamelCase__ , UpperCamelCase__ ) for number in numbers ):
raise Value... | 351 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 44 | 0 |
import pytest
import datasets
# Import fixture modules as plugins
A__ = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
for item in it... | 352 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[in... | 44 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
lowercase_ = ''''''
lowercase_ = ''''''
lowercase_ = ''''''
lowercase_ = 1 # (0 is vertical, 1 is horizontal)
def _lowerCAmelCase ( ) -> None:
... | 353 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 0 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimeste... | 354 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case__ : str = sorted(string.lower() )
r... | 44 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
A__ = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def _lowerCAmelCase ( ) -> Dict:
"""simple docstring"""
snake_case__ : Any = os.path.dirname(os.path.realpath(__UpperCAmelCas... | 355 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 44 | 0 |
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 a ( data... | 356 |
A__ = 0 # The first color of the flag.
A__ = 1 # The second color of the flag.
A__ = 2 # The third color of the flag.
A__ = (red, white, blue)
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if n... | 44 | 0 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin... | 357 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case__ : List[str] = 1
snake_case__ : int = 1
while repunit:
snake_case__ : Dic... | 44 | 0 |
A__ = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''',
15: '''f''',
}
def... | 358 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 | 0 |
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_utils import Audio... | 359 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import C... | 44 | 0 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt=''... | 360 |
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Dict = ... | 44 | 0 |
from __future__ import annotations
import pandas as pd
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
"""simple docstring"""
snake_case__ : Optional[Any] = [0] * no_of_processes
snake_case__ : ... | 361 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
snake_case__ : List[Any] = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorte... | 44 | 0 |
from __future__ import annotations
A__ = '''#'''
class a :
def __init__( self :Optional[Any] ):
snake_case__ : dict = {}
def __lowerCamelCase ( self :List[str] ,__lowercase :Dict ):
snake_case_... | 362 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transfor... | 44 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, D... | 363 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A__ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be sm... | 44 | 0 |
import numpy as np
def _lowerCAmelCase ( __lowerCAmelCase ) -> np.array:
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 364 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 44 | 0 |
def _lowerCAmelCase ( __lowerCAmelCase = 600851475143 ) -> str:
"""simple docstring"""
try:
snake_case__ = int(lowerCAmelCase__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <=... | 365 |
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__ = logging.ge... | 44 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
'''kssteven/ibert-roberta-base''': '''https://huggi... | 366 |
from sklearn.metrics import mean_squared_error
import datasets
A__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenho... | 44 | 0 |
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
from transfo... | 367 |
from manim import *
class a ( __lowerCamelCase ):
def __lowerCamelCase ( self :Union[str, Any] ):
snake_case__ : Optional[Any] = Rectangle(height=0.5 ,width=0.5 )
snake_case__ : Optional[int] = Rectangle(he... | 44 | 0 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbone_common import... | 368 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 44 | 0 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 369 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase ) + 1
snake_case__ : Tuple = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where d... | 44 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/confi... | 370 |
def _lowerCAmelCase ( __lowerCAmelCase = 50 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for bloc... | 44 | 0 |
from __future__ import annotations
from math import pi, sqrt
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
if inductance <= 0:
raise ValueError('''Inductance cannot be 0 or negative''' )
elif c... | 371 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
A__ = False
class a ( unittest.TestCase ):
... | 44 | 0 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class a (... | 350 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 44 | 0 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
def merge(__lowerCAmelCase , __lowerCAmelCase ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from ... | 351 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 44 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 352 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[in... | 44 | 0 |
import logging
import os
from .state import PartialState
class a ( logging.LoggerAdapter ):
@staticmethod
def __lowerCamelCase ( __lowercase :Union[str, Any] ):
snake_case__ : Optional[Any] = PartialState()
return not main_... | 353 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 0 |
A__ = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def _lowerCAmelCase ( __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Union[str, Any] = 0
while number:
# Increased Speed Slightly... | 354 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case__ : str = sorted(string.lower() )
r... | 44 | 0 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _lowerCAmelCase ( __lowerCAmelCase ) -> List[Any]:
"""simple docstring"""
return "".join(sorted(lowerCamelCase_ ) )
def _lowerCAmelCase ( __lowerCAmelCase ... | 355 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 44 | 0 |
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 ..pipeline_params import (
TEXT_G... | 356 |
A__ = 0 # The first color of the flag.
A__ = 1 # The second color of the flag.
A__ = 2 # The third color of the flag.
A__ = (red, white, blue)
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if n... | 44 | 0 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfAr... | 357 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case__ : List[str] = 1
snake_case__ : int = 1
while repunit:
snake_case__ : Dic... | 44 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {"vocab_file": ... | 358 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 | 0 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=1024 ) -> Union[str, Any]:
"""simple docstring"""
... | 359 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import C... | 44 | 0 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@requi... | 360 |
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Dict = ... | 44 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
'''huggingface/informer-tourism-monthly''': (
'''https://huggingface.co/huggingface/informer-tour... | 361 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
snake_case__ : List[Any] = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorte... | 44 | 0 |
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... | 362 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transfor... | 44 | 0 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM ... | 363 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A__ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be sm... | 44 | 0 |
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
f... | 364 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 44 | 0 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowerCAmelCase ( __lowerCAmelCase = "isbn/0140328726" ) -> dict:
"""simple docstring"""
snake_case__ = olid.strip().strip('''/''' ) # Remove leading/... | 365 |
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__ = logging.ge... | 44 | 0 |
A__ = '''
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
A__ = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
A__ = {
'... | 366 |
from sklearn.metrics import mean_squared_error
import datasets
A__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenho... | 44 | 0 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatur... | 367 |
from manim import *
class a ( __lowerCamelCase ):
def __lowerCamelCase ( self :Union[str, Any] ):
snake_case__ : Optional[Any] = Rectangle(height=0.5 ,width=0.5 )
snake_case__ : Optional[int] = Rectangle(he... | 44 | 0 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
A__ = {
'''facebook/maskformer-swin-base-ade''': (
'... | 368 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 44 | 0 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase = "cpu" , __lowerCAmelCase = None ):
"""simple docstring"""
snake_case__ : Union[str, Any] = torch.load(__SCREAMING_SNAKE_CA... | 369 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase ) + 1
snake_case__ : Tuple = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where d... | 44 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {
'''configuration_blenderbot''': [
'''BLENDER... | 370 |
def _lowerCAmelCase ( __lowerCAmelCase = 50 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for bloc... | 44 | 0 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
snake_case__ : Optional[int] = [0] * (upper_limit + 1)
# Base case: C(... | 371 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
A__ = False
class a ( unittest.TestCase ):
... | 44 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {'''vocab_file''':... | 350 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 44 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils impo... | 351 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 44 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requi... | 352 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[in... | 44 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .... | 353 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json... | 354 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case__ : str = sorted(string.lower() )
r... | 44 | 0 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
f... | 355 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
... | 44 | 0 |
from sklearn.metrics import mean_squared_error
import datasets
A__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenho... | 356 |
A__ = 0 # The first color of the flag.
A__ = 1 # The second color of the flag.
A__ = 2 # The third color of the flag.
A__ = (red, white, blue)
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if n... | 44 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 357 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case__ : List[str] = 1
snake_case__ : int = 1
while repunit:
snake_case__ : Dic... | 44 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
A__ = logging.get_logger(__name__)
def _lowerCAmelCase ( __lowerCAmelCase=None , __lowerCAmelCase=None ) ... | 358 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCAmelCase ( ) -> List[Any]:
"""simple docstring"""
snake_case__ : Any = ArgumentParser(
... | 359 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import C... | 44 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( __lowerCAmelCase = 50 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length... | 360 |
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Dict = ... | 44 | 0 |
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