code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from collections import deque
class __UpperCamelCase :
def __init__( self : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> None:
"""simple docstring"""
__lowercase = pr... | 80 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 670 | 0 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_snake_case : Optional[Any] = False
class a (unittest.TestCase ):
"""simple docstrin... | 81 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (... | 670 | 0 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
lowerCamelCase = pd.read_csv("""sample_data.csv""", hea... | 82 | import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = loggin... | 670 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( _lowercase , unittest.TestCase):
snake_case__ : L... | 83 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __snake_case ):
_lowerCamelCase = ['image_processor', 'tokenizer']
_lowerCamelCase = 'CLIPImageProcessor'
_lowerCamelCase = ('XLM... | 670 | 0 |
from __future__ import annotations
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = order
# a_{0} ... a_{k}
lowercase = [1.0] + [0.0] * order
# b_{0} ... b_{k}
lowercase = [1.0] + [0.0] * order
# x[n-1] ... x[... | 84 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 670 | 0 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"linear": PIL.Image.Resampling.BILINEAR,
"bili... | 85 | import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case (*__lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = list(__lowercase )... | 670 | 0 |
def __snake_case ( __UpperCamelCase : int ,__UpperCamelCase : int ):
"""simple docstring"""
return abs(__UpperCamelCase ) if a == 0 else greatest_common_divisor(b % a ,__UpperCamelCase )
def __snake_case ( __UpperCamelCase : int ,__UpperCamelCase : in... | 86 | __SCREAMING_SNAKE_CASE : Union[str, Any] = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o'... | 670 | 0 |
import math
def SCREAMING_SNAKE_CASE ( lowercase_ = 100 ) -> int:
"""simple docstring"""
A__ = sum(i * i for i in range(1 , n + 1 ) )
A__ = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum ... | 87 | import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import rep... | 670 | 0 |
"""simple docstring"""
import math
def _snake_case ( __snake_case : float , __snake_case : float ):
"""simple docstring"""
if (
not isinstance(__snake_case , (int, float) )
or power_factor < -1
or power_factor... | 88 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 0 |
from collections import defaultdict
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> bool:
_lowercase : Any = first_str.lower().strip()
_lowercase : List[str] = second_str.lower().strip()
# Remove whitespace
_lowercase : str... | 89 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
_lower... | 670 | 0 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
__UpperCAmelCase = '''src/transformers'''
# Matches is_xxx_available()
__UpperCAmelCase = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches... | 90 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/c... | 670 | 0 |
"""simple docstring"""
import socket
def _snake_case ( ):
A = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
A = socket.gethostname()
A = 1_2312
sock.connect((host, port) )
sock.send(B'Hello server!' )
with open('Received_file' , 'wb' ) as out_file:
print('F... | 91 | from cva import destroyAllWindows, imread, imshow, waitKey
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
_snake_case ,_snake_case : int = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(__lowercase ):
... | 670 | 0 |
'''simple docstring'''
import math
UpperCamelCase_ = 10
UpperCamelCase_ = 7
UpperCamelCase_ = BALLS_PER_COLOUR * NUM_COLOURS
def _lowerCAmelCase ( __magic_name__ : int = 20 ) -> str:
lowercase : Any =math.comb(__magic_... | 92 | import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__SCREAMING_SNAKE_CASE : List[str] = Mapping[str, np.ndarray]
__SCREAMING_SNAKE_CASE : List[Any] = Mapping[st... | 670 | 0 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils impor... | 93 | from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 670 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
SCREAMING_SNAKE_CASE = True
except (ImportError, ModuleNotFoundError):
SCREAMING_SNAKE_CASE = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('p... | 94 | def snake_case (__lowercase ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_snake... | 670 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface.co/google/realm-cc-news-pretra... | 95 | import random
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
_snake_case ,_snake_case ,_snake_case : List[Any] = [], [], []
for element in data:
if element < pivot:
less.append(__lowercase )
... | 670 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
__lowerCamelCase ... | 96 | from math import pow, sqrt
def snake_case (*__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case (__lowercase , __lowercase ) -> float | ValueError:
... | 670 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 97 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 0 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import... | 98 | from __future__ import annotations
from typing import TypedDict
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
_lowerCamelCase = 42
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(__lowercase , __... | 670 | 0 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
... | 99 | # NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import ... | 670 | 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,... | 100 | from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin import... | 670 | 0 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAut... | 101 | import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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 TokenizerTeste... | 670 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
... | 102 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 670 | 0 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
snake_case = datasets.load_iris()
snake_case = np.array(data['''data'''])
snake_case = np.array(data[... | 103 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (... | 670 | 0 |
"""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.... | 104 | import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = loggin... | 670 | 0 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCamelCase__ : Dict = logging.getLogger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def ... | 105 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __snake_case ):
_lowerCamelCase = ['image_processor', 'tokenizer']
_lowerCamelCase = 'CLIPImageProcessor'
_lowerCamelCase = ('XLM... | 670 | 0 |
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
__snake_case :str =False
class lowerCAmelCase__ ( unittest.TestCas... | 106 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 670 | 0 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
_UpperCAmelCase : Any = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])
_... | 107 | import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case (*__lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = list(__lowercase )... | 670 | 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
__a: Union[str, Any] = loggin... | 108 | __SCREAMING_SNAKE_CASE : Union[str, Any] = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o'... | 670 | 0 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''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... | 109 | import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import rep... | 670 | 0 |
'''simple docstring'''
import string
import numpy
def snake_case__ ( UpperCamelCase ,UpperCamelCase ) -> int:
return b if a == 0 else greatest_common_divisor(b % a ,__lowercase )
class UpperCAmelCase :
"""simple docstring"""
A__ : List[Any] = ... | 683 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
A : int ... | 636 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
_lower... | 670 | 0 |
import warnings
from ..trainer import Trainer
from ..utils import logging
lowerCAmelCase = logging.get_logger(__name__)
class lowerCamelCase ( __snake_case ):
def __init__( self , lowercase__=None , **lowercase__):
warnings.warn(
'''`SageMakerTrainer`... | 462 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/c... | 670 | 0 |
"""simple docstring"""
a : str = [
[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 lowercase__(A , A , A , A ) ->List... | 218 | from cva import destroyAllWindows, imread, imshow, waitKey
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
_snake_case ,_snake_case : int = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(__lowercase ):
... | 670 | 0 |
'''simple docstring'''
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
lowerCamelCase_ = HUGGINGFACE_HUB_CACHE
lowerCamelCase_ = 'config.json'
lowerCamelCase_ = 'diffusion_pytorch_model.bin'
lowerCamelCase_ = 'diffusion_flax_model.msg... | 418 | import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__SCREAMING_SNAKE_CASE : List[str] = Mapping[str, np.ndarray]
__SCREAMING_SNAKE_CASE : List[Any] = Mapping[st... | 670 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image... | 593 | from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 670 | 0 |
A_ = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
"cross_attention_kwargs",
]
)
A_ = frozenset(["prompt", "negative_prompt"])
A_ = frozense... | 393 | def snake_case (__lowercase ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_snake... | 670 | 0 |
'''simple docstring'''
import math
import random
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase = False )-> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_A: int = 0.02
def _lowerCAmelCase... | 126 | import random
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
_snake_case ,_snake_case ,_snake_case : List[Any] = [], [], []
for element in data:
if element < pivot:
less.append(__lowercase )
... | 670 | 0 |
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAcc... | 163 | from math import pow, sqrt
def snake_case (*__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case (__lowercase , __lowercase ) -> float | ValueError:
... | 670 | 0 |
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
return 1_0 - x * x
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
if equation(__lowercase ) * equation(__lowercase ) >= 0:
raise ValueError("""Wrong space!""" )
A_ : ... | 569 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : int ={'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if not is_torch_avai... | 483 | from __future__ import annotations
from typing import TypedDict
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
_lowerCamelCase = 42
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(__lowercase , __... | 670 | 0 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
_UpperCAmelCase : List[str] = 3
def snake_case__ ( UpperCamelCase ) -> int:
print('''Generating primitive root of p''' )
while True:
_Upp... | 683 | # NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import ... | 670 | 0 |
"""simple docstring"""
import re
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase ... | 636 | from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin import... | 670 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def __SCREAMING_SNAKE_CASE ( ) -> int:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = 9
__UpperCAmelCase : Optional[int] = [
[0, 1, 4],
[0, 7, 8],
... | 462 | import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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 TokenizerTeste... | 670 | 0 |
"""simple docstring"""
def lowercase__(A ) ->list:
"""simple docstring"""
def merge(A , A ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop... | 218 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 670 | 0 |
'''simple docstring'''
import operator as op
def SCREAMING_SNAKE_CASE_ ( __A : List[str] ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = lambda __A , __A : int(x / y ) # noqa: E731 integer division operation
_SCREAMING_SNAKE_CASE = ... | 418 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (... | 670 | 0 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__A = logging.get_logger(__name__)
... | 593 | import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = loggin... | 670 | 0 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_... | 393 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __snake_case ):
_lowerCamelCase = ['image_processor', 'tokenizer']
_lowerCamelCase = 'CLIPImageProcessor'
_lowerCamelCase = ('XLM... | 670 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
_A: Lis... | 126 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 670 | 0 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False ) -> list[float]:
"""simp... | 163 | import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case (*__lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = list(__lowercase )... | 670 | 0 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_lowerCAmelCase = logging.get_logger("""transformers.models.speecht5""")
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCas... | 569 | __SCREAMING_SNAKE_CASE : Union[str, Any] = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o'... | 670 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseMo... | 483 | import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import rep... | 670 | 0 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
_UpperCAmelCase : Union[str, Any] = logging.... | 683 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Optional[int] = logging.get_logger(__name__)
A : Any = {
'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}
class _... | 636 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
_lower... | 670 | 0 |
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = len(__lowercase )
for i in range(1 , __lowercase ):
__UpperCAmelCase : Tuple = collection[i]
... | 462 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/c... | 670 | 0 |
"""simple docstring"""
a : List[str] = 'Alexander Joslin'
import operator as op
from .stack import Stack
def lowercase__(A ) ->int:
"""simple docstring"""
lowercase__ : Optional[Any]= {"*": op.mul, "/": op.trued... | 218 | from cva import destroyAllWindows, imread, imshow, waitKey
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
_snake_case ,_snake_case : int = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(__lowercase ):
... | 670 | 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/LICENSE-2.0
#... | 418 | import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__SCREAMING_SNAKE_CASE : List[str] = Mapping[str, np.ndarray]
__SCREAMING_SNAKE_CASE : List[Any] = Mapping[st... | 670 | 0 |
def __a ( lowerCAmelCase_ : Tuple ,lowerCAmelCase_ : Optional[int] ,lowerCAmelCase_ : Tuple ,lowerCAmelCase_ : str ,lowerCAmelCase_ : str ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
UpperCAmelCase_= ... | 593 | from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 670 | 0 |
import numpy
class snake_case :
'''simple docstring'''
def __init__( self : List[Any] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : List[str] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = input_... | 393 | def snake_case (__lowercase ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_snake... | 670 | 0 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from ... | 126 | import random
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
_snake_case ,_snake_case ,_snake_case : List[Any] = [], [], []
for element in data:
if element < pivot:
less.append(__lowercase )
... | 670 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d... | 163 | from math import pow, sqrt
def snake_case (*__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case (__lowercase , __lowercase ) -> float | ValueError:
... | 670 | 0 |
import torch
from transformers import AutoModel
class _UpperCAmelCase ( torch.nn.Module ):
def __init__( self , a__="sayef/fsner-bert-base-uncased" ):
super(lowercase_ , self ).__init__()
A_ : List[Any] = AutoModel.from_pretrained(low... | 569 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 0 |
from manim import *
class lowercase_ ( __snake_case):
"""simple docstring"""
def lowercase__ ( self ):
"""simple docstring"""
a_ = Rectangle(height=0.5 , width=0.5 )
... | 483 | from __future__ import annotations
from typing import TypedDict
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
_lowerCamelCase = 42
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(__lowercase , __... | 670 | 0 |
'''simple docstring'''
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 UpperCAmelCase ( __snake_case ... | 683 | # NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import ... | 670 | 0 |
"""simple docstring"""
A : Any = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
A : int ... | 636 | from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin import... | 670 | 0 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 462 | import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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 TokenizerTeste... | 670 | 0 |
"""simple docstring"""
def lowercase__(A ) ->int:
"""simple docstring"""
lowercase__ : Optional[int]= len(__lowercase )
lowercase__ : int= sum(__lowercase )
lowercase__ : List[str]= [[False for x in range(s + 1 ... | 218 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 670 | 0 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from tran... | 418 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A = {
'configuration_efficientformer': [
'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Effic... | 593 | import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = loggin... | 670 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ = logging.get_logger(__name__)
A_ = {
'shi-labs/dinat-mini-in1k-224': 'https://huggingface.co/shi-labs/dinat... | 393 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __snake_case ):
_lowerCamelCase = ['image_processor', 'tokenizer']
_lowerCamelCase = 'CLIPImageProcessor'
_lowerCamelCase = ('XLM... | 670 | 0 |
'''simple docstring'''
import numpy as np
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1e-1_2 , _lowerCAmelCase = 1_00 , )-> tuple[float, np.ndarray]:
assert np.shape(__lowercase )[0] == np.shape(__lowercase )[1]
# Ens... | 126 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 670 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'SenseTime/deformable-detr': 'https://huggingfa... | 163 | import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case (*__lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = list(__lowercase )... | 670 | 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
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {'vocab_file': 'vo... | 569 | __SCREAMING_SNAKE_CASE : Union[str, Any] = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o'... | 670 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[Any] =logging.get_logger(__name__)
A_ : Union[str, Any] ={
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class... | 483 | import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import rep... | 670 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class UpperCAmelCase ( __snake_case ):
"""simple docstring"""
A__ : Optional[int] = ['image_processor', 'feature_extractor']
A__ : Any = 'TvltImageProcessor'
A__ : Optional[Any] = 'TvltFeatu... | 683 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 0 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerC... | 636 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
_lower... | 670 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
lowerCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
def __SCREAMING_SNAKE_CASE ... | 462 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/c... | 670 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltF... | 218 | from cva import destroyAllWindows, imread, imshow, waitKey
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
_snake_case ,_snake_case : int = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(__lowercase ):
... | 670 | 0 |
'''simple docstring'''
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 ..u... | 418 | import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__SCREAMING_SNAKE_CASE : List[str] = Mapping[str, np.ndarray]
__SCREAMING_SNAKE_CASE : List[Any] = Mapping[st... | 670 | 0 |
import re
def __a ( lowerCAmelCase_ : str ) -> bool:
'''simple docstring'''
UpperCAmelCase_= re.compile(r"""^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$""" )
if match := re.search(__lowercase ,__lowercase ):
return match.string == phone
r... | 593 | from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 670 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 393 | def snake_case (__lowercase ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_snake... | 670 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: List[Any] = logging.get_logger(__name__)
_A: Union[str, Any] = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.jso... | 126 | import random
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
_snake_case ,_snake_case ,_snake_case : List[Any] = [], [], []
for element in data:
if element < pivot:
less.append(__lowercase )
... | 670 | 0 |
"""simple docstring"""
import argparse
import os
import re
_SCREAMING_SNAKE_CASE = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_SCREAMING_SNAKE_CASE = re.compile(... | 163 | from math import pow, sqrt
def snake_case (*__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case (__lowercase , __lowercase ) -> float | ValueError:
... | 670 | 0 |
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,
)
_lowerCAmelCase = {
'configuration_clip': [
'CLIP_PRETRAIN... | 569 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 0 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = "x" , UpperCAmelCase__ = 10**-10 , UpperCAmelCase__ = 1 , ):
""... | 483 | from __future__ import annotations
from typing import TypedDict
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
_lowerCamelCase = 42
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(__lowercase , __... | 670 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
Auto... | 683 | # NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import ... | 670 | 0 |
"""simple docstring"""
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transfor... | 636 | from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin import... | 670 | 0 |
import itertools
import string
from collections.abc import Generator, Iterable
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Generator[tuple[str, ...], None, None]:
'''simple docstring'''
__UpperCAmelCase : int = iter(__lowercase... | 462 | import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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 TokenizerTeste... | 670 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCAmelCase( __snake_case ):
"""simple docstring"""
__lowerCamelCase = ["image_p... | 218 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 670 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase_ = {
'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHI... | 418 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (... | 670 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
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 checko... | 593 | import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = loggin... | 670 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A_ = {
'configuration_layoutlmv3': [
'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 393 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __snake_case ):
_lowerCamelCase = ['image_processor', 'tokenizer']
_lowerCamelCase = 'CLIPImageProcessor'
_lowerCamelCase = ('XLM... | 670 | 0 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@... | 126 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 670 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
_SCREAMING_SNAKE_CASE = logging.get_lo... | 163 | import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case (*__lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = list(__lowercase )... | 670 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedu... | 569 | __SCREAMING_SNAKE_CASE : Union[str, Any] = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o'... | 670 | 0 |
def lowerCamelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
a_ = [[] for _ in range(__lowercase )]
a_ = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
... | 483 | import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import rep... | 670 | 0 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( __snake_case ):
... | 683 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 0 |
"""simple docstring"""
import requests
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = {"Content-Type": "application/json"}
__lowerCAmelCase = requests.post(__lowercase , json={"text... | 636 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
_lower... | 670 | 0 |
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
'''simple docstring'''
__UpperCAmelCase : str = int(__lowercase )
if n_element < 1:
__UpperCAmelCase : List[str] = ValueError('''a should be a positive number''' )
... | 462 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/c... | 670 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
a : int = 200
# Number of elements selected in every generation of evolution. The selection takes
# plac... | 218 | from cva import destroyAllWindows, imread, imshow, waitKey
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
_snake_case ,_snake_case : int = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(__lowercase ):
... | 670 | 0 |
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