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 |
|---|---|---|---|---|
"""simple docstring"""
from math import isqrt
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(snake_case ) + 1 ) )
def SCREAMING_SNAKE_CASE_ ( snake_case : int = 10**6 )-> i... | 650 |
"""simple docstring"""
# Imports
import numpy as np
class __a :
def __init__( self , a__=None , a__=None , a__=None , a__=None , a__=None ):
self.set_matricies(red=a__ , green=a__ , blue=a__ , red_edge=a__ , nir=a__ )
def snake_case_ ( self... | 650 | 1 |
"""simple docstring"""
A_ : Union[str, Any] ={
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottam... | 650 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Union[str, Any] =logging.get_logger(__name__)
A_ : Optional[Any] ={
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.c... | 650 | 1 |
"""simple docstring"""
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_ : List[str] =collections.namedtuple("""_Data... | 650 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class __a ( lowerCAmelCase__ ):
def __init__( self , a__ , a__=None , a__=True , a__=None , ... | 650 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
A_ : int =logging.get_logger(__name__)
A_ : Optional[Any] ={
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/con... | 650 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __a ( metaclass=lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] = ["flax"]
def __init__( self , *a__ , **a__ ):
requires_backends(self , ['flax'] )
... | 650 | 1 |
"""simple docstring"""
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 ):
SCREAMING_... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> bool:
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
_lowerCamelCase = 4
_lowerCamelCase = (1 <... | 650 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 650 |
"""simple docstring"""
# Copyright 2022 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
#... | 650 | 1 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta ... | 650 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common i... | 650 | 1 |
"""simple docstring"""
import numpy as np
def SCREAMING_SNAKE_CASE_ ( snake_case : np.ndarray , snake_case : float )-> np.ndarray:
return np.where(vector > 0 , snake_case , (alpha * (np.exp(snake_case ) - 1)) )
if __name__ == "__main__":
import doctes... | 650 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
A_ : int =np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership f... | 650 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docst... | 650 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
A_ : str =logging.get_logger(__name__)
A_ : Any ="""... | 650 | 1 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
... | 650 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A_ : int =logging.get_... | 650 | 1 |
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class __a ( lowerCAmelCase__ ):
def __init__( self , *a__ , **a__ ):
super().__init__(*a__ , **a__ )
_lowerCamelCase = {}
def snake_cas... | 650 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] =logging.get_logger(__name__)
A_ : List[str] ={
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT mod... | 650 | 1 |
"""simple docstring"""
from string import ascii_uppercase
A_ : str ={char: i for i, char in enumerate(ascii_uppercase)}
A_ : List[str] =dict(enumerate(ascii_uppercase))
def SCREAMING_SNAKE_CASE_ ( snake_case : str , snake_case : str )-> str:
_lowerCamelCase ... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( )-> Union[str, Any]:
_lowerCamelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_lowerCamelCase = 6
_lowerCamelCase = 1
_lowerCamelCase = 1_901
_lowerCa... | 650 | 1 |
"""simple docstring"""
A_ : Dict ="""ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def SCREAMING_SNAKE_CASE_ ( snake_case : bytes )-> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(snake_case , snake_case )... | 650 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
A_ : Union[str, Any] ={
"""User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
""" (KHTML, like Gecko) Chrome/70.0.3538.102 Safar... | 650 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] =logging.get_logger(__name__)
A_ : Any ={
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
"""google/fnet-large""": "... | 650 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Union[str, Any] ={"""configuration_xlnet""": ["""XLNET_... | 650 | 1 |
"""simple docstring"""
import socket
def SCREAMING_SNAKE_CASE_ ( )-> Optional[int]:
_lowerCamelCase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_lowerCamelCase = socket.gethostname()
_lowerCamelCase = 12_312
s... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_lowerCamelCase = 1
_lowerCamelCase = 1
while repunit:
_lowerCamelCase = ... | 650 | 1 |
"""simple docstring"""
from __future__ import annotations
A_ : int =[True] * 1_0_0_0_0_0_1
A_ : Dict =2
while i * i <= 1_0_0_0_0_0_0:
if seive[i]:
for j in range(i * i, 1_0_0_0_0_0_1, i):
A_ : Dict =False
i += 1
def SCREAMING_SNAKE_CASE_... | 650 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def SCREAMING_SNAKE_CASE_ ( snake_case : int , snake_case : int )-> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
... | 650 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : List[str] ={
"""configuration_distilbert""": [
"""DI... | 650 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 650 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> list[int]:
if length <= 0 or not isinstance(snake_case , snake_case ):
raise ValueError('Length must be a positive integer.' )
return [n * (2 * n - 1) for n in range(snake_case ... | 650 |
"""simple docstring"""
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE_ ( snake_case : int = 1_000_000 )-> int:
_lowerCamelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_lowe... | 650 | 1 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int , snake_case : Tuple )-> Dict:
_lowerCamelCase = [1]
for i in range(2 , snake_case ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * ... | 650 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A_ : Union[str, Any] =logging.get_logger(__name_... | 650 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docst... | 650 | 1 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_im... | 650 |
"""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
A_ : Optional[int] =logging.get_logger(__na... | 650 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A_ : int =logging.get_... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : float , snake_case : float )-> float:
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
... | 650 | 1 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vis... | 650 |
"""simple docstring"""
# Imports
import numpy as np
class __a :
def __init__( self , a__=None , a__=None , a__=None , a__=None , a__=None ):
self.set_matricies(red=a__ , green=a__ , blue=a__ , red_edge=a__ , nir=a__ )
def snake_case_ ( self... | 650 | 1 |
"""simple docstring"""
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
A_ : List[Any] =[
"""word_embed... | 650 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Union[str, Any] =logging.get_logger(__name__)
A_ : Optional[Any] ={
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.c... | 650 | 1 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preproce... | 650 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class __a ( lowerCAmelCase__ ):
def __init__( self , a__ , a__=None , a__=True , a__=None , ... | 650 | 1 |
"""simple docstring"""
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE_ ( snake_case : int = 1_000_000 )-> int:
_lowerCamelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_lowe... | 650 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __a ( metaclass=lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] = ["flax"]
def __init__( self , *a__ , **a__ ):
requires_backends(self , ['flax'] )
... | 650 | 1 |
"""simple docstring"""
import math
import sys
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> int:
if number != int(snake_case ):
raise ValueError('the value of input must be a natural number' )
if number < 0:
raise ValueError('the value ... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> bool:
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
_lowerCamelCase = 4
_lowerCamelCase = (1 <... | 650 | 1 |
"""simple docstring"""
from math import factorial
A_ : dict[str, int] ={str(digit): factorial(digit) for digit in range(1_0)}
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> int:
if not isinstance(snake_case , snake_case ):
raise TypeError('Parameter... | 650 |
"""simple docstring"""
# Copyright 2022 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
#... | 650 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is... | 650 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common i... | 650 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
... | 650 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
A_ : int =np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership f... | 650 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
... | 650 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
A_ : str =logging.get_logger(__name__)
A_ : Any ="""... | 650 | 1 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class __a ( lowerCAmelCase__ ):
def __init__( self , a__ , a__=None , a__=True , a__=None , ... | 650 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A_ : int =logging.get_... | 650 | 1 |
"""simple docstring"""
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE_ ( snake_case : Sequence[int] | None = None )-> int:
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
_lowerCamelCase = nu... | 650 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] =logging.get_logger(__name__)
A_ : List[str] ={
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT mod... | 650 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_confi... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( )-> Union[str, Any]:
_lowerCamelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_lowerCamelCase = 6
_lowerCamelCase = 1
_lowerCamelCase = 1_901
_lowerCa... | 650 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import Patch... | 650 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
A_ : Union[str, Any] ={
"""User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
""" (KHTML, like Gecko) Chrome/70.0.3538.102 Safar... | 650 | 1 |
"""simple docstring"""
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( snake_case : list[float] )-> float:
_lowerCamelCase = 0.0_0
_lowerCamelCase = 0
for resistor in resistors:
if resistor <= 0:
... | 650 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Union[str, Any] ={"""configuration_xlnet""": ["""XLNET_... | 650 | 1 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
A_ : Union[str, Any] =TypeVar("""_T""")
class __a ( Generic[_T] ):
def __init__( self , a__ = None ):
_lowerCamelCase = list(iterable or [] )
... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_lowerCamelCase = 1
_lowerCamelCase = 1
while repunit:
_lowerCamelCase = ... | 650 | 1 |
"""simple docstring"""
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"""):
A_ : Dict ={
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear"... | 650 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def SCREAMING_SNAKE_CASE_ ( snake_case : int , snake_case : int )-> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
... | 650 | 1 |
"""simple docstring"""
A_ : List[Any] =8.31_4462 # Unit - J mol-1 K-1
def SCREAMING_SNAKE_CASE_ ( snake_case : float , snake_case : float , snake_case : float )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('Invalid inputs. ... | 650 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 650 | 1 |
"""simple docstring"""
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to... | 650 |
"""simple docstring"""
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE_ ( snake_case : int = 1_000_000 )-> int:
_lowerCamelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_lowe... | 650 | 1 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __a ( low... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int , snake_case : Tuple )-> Dict:
_lowerCamelCase = [1]
for i in range(2 , snake_case ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * ... | 650 | 1 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 650 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docst... | 650 | 1 |
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHEC... | 650 |
"""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
A_ : Optional[int] =logging.get_logger(__na... | 650 | 1 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
A_ : str ="""\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
auth... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : float , snake_case : float )-> float:
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
... | 650 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, tor... | 650 |
"""simple docstring"""
# Imports
import numpy as np
class __a :
def __init__( self , a__=None , a__=None , a__=None , a__=None , a__=None ):
self.set_matricies(red=a__ , green=a__ , blue=a__ , red_edge=a__ , nir=a__ )
def snake_case_ ( self... | 650 | 1 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common... | 650 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Union[str, Any] =logging.get_logger(__name__)
A_ : Optional[Any] ={
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.c... | 650 | 1 |
"""simple docstring"""
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
fr... | 650 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class __a ( lowerCAmelCase__ ):
def __init__( self , a__ , a__=None , a__=True , a__=None , ... | 650 | 1 |
"""simple docstring"""
import math
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> list:
_lowerCamelCase = [True] * n
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = True
for i in ... | 650 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __a ( metaclass=lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] = ["flax"]
def __init__( self , *a__ , **a__ ):
requires_backends(self , ['flax'] )
... | 650 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf,... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> bool:
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
_lowerCamelCase = 4
_lowerCamelCase = (1 <... | 650 | 1 |
"""simple docstring"""
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipelin... | 650 |
"""simple docstring"""
# Copyright 2022 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
#... | 650 | 1 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git w... | 650 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common i... | 650 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils impo... | 650 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
A_ : int =np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership f... | 650 | 1 |
"""simple docstring"""
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> list[int]:
_lowerCamelCase = 2
_lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
... | 650 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
A_ : str =logging.get_logger(__name__)
A_ : Any ="""... | 650 | 1 |
"""simple docstring"""
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_ : List[str] ={
"""facebook/maskformer-swin-base-a... | 650 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A_ : int =logging.get_... | 650 | 1 |
"""simple docstring"""
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,
Stab... | 650 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] =logging.get_logger(__name__)
A_ : List[str] ={
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT mod... | 650 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : str )-> str:
return " ".join(
''.join(word[::-1] ) if len(snake_case ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
pr... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( )-> Union[str, Any]:
_lowerCamelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_lowerCamelCase = 6
_lowerCamelCase = 1
_lowerCamelCase = 1_901
_lowerCa... | 650 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
imp... | 650 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
A_ : Union[str, Any] ={
"""User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
""" (KHTML, like Gecko) Chrome/70.0.3538.102 Safar... | 650 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import GenerationT... | 650 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Union[str, Any] ={"""configuration_xlnet""": ["""XLNET_... | 650 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
A_ : Union[str, Any] ={
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnn... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_lowerCamelCase = 1
_lowerCamelCase = 1
while repunit:
_lowerCamelCase = ... | 650 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : str , snake_case : str )-> bool:
_lowerCamelCase = len(snake_case ) + 1
_lowerCamelCase = len(snake_case ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether pre... | 650 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def SCREAMING_SNAKE_CASE_ ( snake_case : int , snake_case : int )-> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
... | 650 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWi... | 650 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 650 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Union[str, Any] =logging.get_logger(__name__)
A_ : Optional[Any] ={
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.c... | 650 |
"""simple docstring"""
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE_ ( snake_case : int = 1_000_000 )-> int:
_lowerCamelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_lowe... | 650 | 1 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTes... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int , snake_case : Tuple )-> Dict:
_lowerCamelCase = [1]
for i in range(2 , snake_case ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * ... | 650 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : str )-> list:
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(snake_case ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("""doctest... | 650 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docst... | 650 | 1 |
"""simple docstring"""
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : Union[str, Any] =logging.get_logger(__name__)
A_ ... | 650 |
"""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
A_ : Optional[int] =logging.get_logger(__na... | 650 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : list )-> bool:
if not isinstance(snake_case , snake_case ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(snake_case ) == 0:
raise ValueE... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : float , snake_case : float )-> float:
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
... | 650 | 1 |
"""simple docstring"""
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
A_ : List[str] =[
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),... | 650 |
"""simple docstring"""
# Imports
import numpy as np
class __a :
def __init__( self , a__=None , a__=None , a__=None , a__=None , a__=None ):
self.set_matricies(red=a__ , green=a__ , blue=a__ , red_edge=a__ , nir=a__ )
def snake_case_ ( self... | 650 | 1 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __a ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (UnCLIPScheduler,)
def snake_case_ ( self , **a__ ... | 650 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Union[str, Any] =logging.get_logger(__name__)
A_ : Optional[Any] ={
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.c... | 650 | 1 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Ac... | 650 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class __a ( lowerCAmelCase__ ):
def __init__( self , a__ , a__=None , a__=True , a__=None , ... | 650 | 1 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __a ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
SCREAMING_SNAKE... | 650 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __a ( metaclass=lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] = ["flax"]
def __init__( self , *a__ , **a__ ):
requires_backends(self , ['flax'] )
... | 650 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ : List[Any] ={"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> bool:
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
_lowerCamelCase = 4
_lowerCamelCase = (1 <... | 650 | 1 |
"""simple docstring"""
class __a :
def __init__( self , a__ ):
_lowerCamelCase = set_counts
_lowerCamelCase = max(a__ )
_lowerCamelCase = len(a__ )
_lowerCamelCase = [1] * num_sets
... | 650 |
"""simple docstring"""
# Copyright 2022 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
#... | 650 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,... | 650 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common i... | 650 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> int:
if n == 1 or not isinstance(snake_case , snake_case ):
return 0
elif n == 2:
return 1
else:
_lowerCamelCase = [0, 1]
fo... | 650 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
A_ : int =np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership f... | 650 | 1 |
"""simple docstring"""
import math
def SCREAMING_SNAKE_CASE_ ( snake_case : Optional[Any] , snake_case : int )-> List[Any]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(snake_case )
... | 650 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
A_ : str =logging.get_logger(__name__)
A_ : Any ="""... | 650 | 1 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE_ ( snake_case : List[str] )-> int:
_lowerCamelCase = args.pruning_method
_lowe... | 650 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A_ : int =logging.get_... | 650 | 1 |
"""simple docstring"""
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_... | 650 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] =logging.get_logger(__name__)
A_ : List[str] ={
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT mod... | 650 | 1 |
"""simple docstring"""
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."""
)
| 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( )-> Union[str, Any]:
_lowerCamelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_lowerCamelCase = 6
_lowerCamelCase = 1
_lowerCamelCase = 1_901
_lowerCa... | 650 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : float )-> float:
if edge <= 0 or not isinstance(snake_case , snake_case ):
raise ValueError('Length must be a positive.' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
... | 650 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
A_ : Union[str, Any] ={
"""User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
""" (KHTML, like Gecko) Chrome/70.0.3538.102 Safar... | 650 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Dict ={
"""configuration_electra""": ["""ELECTRA_PRETRAINED_... | 650 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Union[str, Any] ={"""configuration_xlnet""": ["""XLNET_... | 650 | 1 |
"""simple docstring"""
import unittest
from transformers import DonutProcessor
A_ : Union[str, Any] ="""naver-clova-ix/donut-base"""
class __a ( unittest.TestCase ):
def snake_case_ ( self ):
_lowerCamelCase = DonutProcessor.from_pretr... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_lowerCamelCase = 1
_lowerCamelCase = 1
while repunit:
_lowerCamelCase = ... | 650 | 1 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
A_ : str =1_0
def SCREAMING_SNAKE_CASE_ ( snake_case : int , snake_case : int , ... | 650 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def SCREAMING_SNAKE_CASE_ ( snake_case : int , snake_case : int )-> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
... | 650 | 1 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependen... | 650 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 650 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Tuple =logging.get_logger(__name__)
A_ : int ={
"""Salesforce/blip-vqa-base""": """https://huggingface.co/Salesforce/blip-vqa-base... | 650 |
"""simple docstring"""
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE_ ( snake_case : int = 1_000_000 )-> int:
_lowerCamelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
_lowe... | 650 | 1 |
"""simple docstring"""
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils im... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int , snake_case : Tuple )-> Dict:
_lowerCamelCase = [1]
for i in range(2 , snake_case ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * ... | 650 | 1 |
"""simple docstring"""
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def SCREAMING_SNA... | 650 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docst... | 650 | 1 |
"""simple docstring"""
# Algorithm for the pigeonhole sorting
def SCREAMING_SNAKE_CASE_ ( snake_case : Union[str, Any] )-> List[Any]:
_lowerCamelCase = min(snake_case ) # min() finds the minimum value
_lowerCamelCase = max(snake_case ) ... | 650 |
"""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
A_ : Optional[int] =logging.get_logger(__na... | 650 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.uti... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : float , snake_case : float )-> float:
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
... | 650 | 1 |
"""simple docstring"""
import gc
import threading
import time
import psutil
import torch
class __a :
def __init__( self ):
_lowerCamelCase = psutil.Process()
_lowerCamelCase = False
def snake_case_ ( self ):
... | 650 |
"""simple docstring"""
# Imports
import numpy as np
class __a :
def __init__( self , a__=None , a__=None , a__=None , a__=None , a__=None ):
self.set_matricies(red=a__ , green=a__ , blue=a__ , red_edge=a__ , nir=a__ )
def snake_case_ ( self... | 650 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A_ : List[Any] =logging.get_logger(__name__)
class __a ( lowerCAmelCase__ ,... | 650 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Union[str, Any] =logging.get_logger(__name__)
A_ : Optional[Any] ={
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.c... | 650 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A_ : Any ={
"""configuration_mask2former""": [
"""MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Mask2FormerConfig""... | 650 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class __a ( lowerCAmelCase__ ):
def __init__( self , a__ , a__=None , a__=True , a__=None , ... | 650 | 1 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def SCREAMING_SNAKE_CASE_ ( snake_case : int = 8 )-> str:
_lowerCamelCase = ascii_letters + digits + punctuation
... | 650 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __a ( metaclass=lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : List[str] = ["flax"]
def __init__( self , *a__ , **a__ ):
requires_backends(self , ['flax'] )
... | 650 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : str )-> str:
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> bool:
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
_lowerCamelCase = 4
_lowerCamelCase = (1 <... | 650 | 1 |
"""simple docstring"""
import numpy as np
import datasets
A_ : List[str] ="""
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was ... | 650 |
"""simple docstring"""
# Copyright 2022 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
#... | 650 | 1 |
"""simple docstring"""
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
A_ : str =TypeVar("""T""")
class __a ( Generic[T] ):
d... | 650 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common i... | 650 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : List[Any] )-> Optional[int]:
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
... | 650 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
A_ : int =np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership f... | 650 | 1 |
"""simple docstring"""
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTe... | 650 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
A_ : str =logging.get_logger(__name__)
A_ : Any ="""... | 650 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __a ( uni... | 650 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A_ : int =logging.get_... | 650 | 1 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMSchedul... | 650 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] =logging.get_logger(__name__)
A_ : List[str] ={
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT mod... | 650 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
A_ : int =[8, 5, 9, 7]
A_ : str =[
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
A_ : List[str] =[
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( )-> Union[str, Any]:
_lowerCamelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_lowerCamelCase = 6
_lowerCamelCase = 1
_lowerCamelCase = 1_901
_lowerCa... | 650 | 1 |
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