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"""
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 |
"""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"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> int:
if not isinstance(snake_case , snake_case ):
raise TypeError('Input value must be an \'int\' type' )
_lowerCamelCase = 0
while number:
positi... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] =logging.get_logger(__name__)
A_ : Dict ={
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
# See all ViT M... | 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 typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelera... | 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 Any
import numpy as np
def SCREAMING_SNAKE_CASE_ ( snake_case : np.ndarray )-> bool:
return np.array_equal(snake_case , matrix.conjugate().T )
def SCREAMING_SNAKE_CASE_ ( snake_case : np.ndarray , snake_case : np.n... | 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 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
f... | 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 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 |
"""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 doctest
from collections import deque
import numpy as np
class __a :
def __init__( self ):
_lowerCamelCase = [2, 1, 2, -1]
_lowerCamelCase = [1, 2, 3, 4]
def snake_case_ ( self ):
... | 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 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 |
"""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_ ( )-> 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 |
"""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"""
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE_ ( snake_case : list , snake_case : list )-> list:
if len(snake_case ) != 2 or len(a[0] ) != 2 or len(snake_case ) != 2 or len(b[0] ) != 2:
raise E... | 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 shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def SCREAMING_SNAKE_CASE_ ( snake_case : Union[str, Any] , snake_case : Dict , snake_case : List[str] , snake_case : List[str]=1_024 )... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Union[str, Any] =logging.get_logger(__name__)
A_ : int ={
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/m... | 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 __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_c... | 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 argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
i... | 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 json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@re... | 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"""
from collections.abc import Callable
class __a :
def __init__( self , a__ = None ):
# Stores actual heap items.
_lowerCamelCase = []
# Stores indexes of each item for supporting updates and deletion.
... | 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 sys
A_ : str =(
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66896... | 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 TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A_ : str ={}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable... | 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
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def SCREAMING_SNAKE_CASE_ ( )-> int:
raise RuntimeError('CUDA out of memory.' )
... | 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 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int = {
"""asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-10... | 0 |
"""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 | 0 |
def _A ( ) -> List[str]:
"""simple docstring"""
__UpperCamelCase = 0
for i in range(1 , 10_01 ):
total += i**i
return str(_lowercase )[-10:]
if __name__ == "__main__":
print(solution())
| 1 |
"""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 | 0 |
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.preprocessing import Po... | 2 |
"""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 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 3 |
"""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 | 0 |
"""simple docstring"""
__UpperCamelCase : str = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''... | 4 |
"""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 | 0 |
'''simple docstring'''
def A (__lowerCamelCase :int , __lowerCamelCase :int ):
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
_lowerCAmelCase = str(bin(__lowerCamelCase ) )[2:] # remove the leading "0b"
_low... | 5 |
"""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 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
... | 6 |
"""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 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''facebook/xmod-base''': '''https://... | 7 |
"""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 | 0 |
'''simple docstring'''
class SCREAMING_SNAKE_CASE : # Public class to implement a graph
def __init__( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : str = row
... | 8 |
"""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 | 0 |
from collections import defaultdict
def A ( __UpperCamelCase , __UpperCamelCase ) -> bool:
A__ = first_str.lower().strip()
A__ = second_str.lower().strip()
# Remove whitespace
A__ = first_str.replace(' ' , '' )
A__ = second_str.replac... | 9 |
"""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 | 0 |
from __future__ import annotations
_lowerCAmelCase = []
def _snake_case ( __snake_case , __snake_case , __snake_case ):
for i in range(len(__snake_case ) ):
if board[row][i] == 1:
return False
for i in range(len(__snake_case ) ):
... | 10 |
"""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 | 0 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_lo... | 11 |
"""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 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _snake_case ( UpperCAmelCase_ ):
__lowerCAmelCase ... | 12 |
"""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 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase__ ( UpperCAmelCase_ : str = "AAPL" ) -> str:
__lowerCamelCase : str = F'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
__lowerCamelCase : Opti... | 13 |
"""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 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
a__ = logging.get_logger(__name__)
... | 14 |
"""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 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def UpperCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
lowercase__ = ArgumentParser(
... | 15 |
"""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 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from... | 16 |
"""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 | 0 |
def __SCREAMING_SNAKE_CASE ( a__ : list ,a__ : int = 0 ) -> list:
__A : Optional[Any] = length or len(a__ )
__A : Dict = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
__A , __A : Dict = list_data[i + 1]... | 17 |
"""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 | 0 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowerCAmelCase_ ( nn.Module ):
__lowerCamelCase : int
__lowerCamelCase : int
__lowerCa... | 18 |
"""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 | 0 |
"""simple docstring"""
from typing import Any
class _UpperCAmelCase:
def __init__( self , __a) -> List[str]:
'''simple docstring'''
_UpperCamelCase = data
_UpperCamelCase = None
class ... | 19 |
"""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 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
_lowerCAmelCase: int = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path... | 20 |
"""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 | 0 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=False ):
__magic_name__ : Optional[int] =OmegaConf.load(lowerCamelCase )
if display:
... | 21 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A_ : int =logging.get_... | 650 | 0 |
'''simple docstring'''
from __future__ import annotations
import bisect
def snake_case_ (UpperCamelCase : list[int] , UpperCamelCase : int , UpperCamelCase : int = 0 , UpperCamelCase : int = -1 ):
'''simple docstring'''
if h... | 22 |
"""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 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMSchedule... | 23 |
"""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 | 0 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _UpperCamelCase (_lowerCamelCase : Optional[Any] , _lowerCamelCase : Any , _lowerCamelCase : Optional[int] , _lowerCamelCase ... | 24 |
"""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 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCamelCase__ ( _a):
return (data["data"], data["target"])
def ... | 25 |
"""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 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct":... | 26 |
"""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 | 0 |
from random import shuffle
import tensorflow as tf
from numpy import array
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
_A = int(_SCREAMING_SNAKE_CASE )
a... | 27 |
"""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 | 0 |
'''simple docstring'''
UpperCamelCase_ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def lowercase__( __UpperCamelCase: dict ,__UpperCamelCase: Dict ... | 28 |
"""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 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
# Initialise PyTorch... | 29 |
"""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 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils... | 30 |
"""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 | 0 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCamelCase__ : Dict = version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk import word_tokenize
lowe... | 31 |
"""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 | 0 |
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,
require_tokenizers,
require_torch,... | 32 |
"""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 | 0 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py... | 33 |
"""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 | 0 |
"""simple docstring"""
import random
class snake_case_ :
"""simple docstring"""
@staticmethod
def UpperCAmelCase__ ( lowerCamelCase_) -> tuple[list[int], list[int]]:
UpperCamelCase = [ord(lowerCamelCase_) for i in text]
UpperCamelCase ... | 34 |
"""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 | 0 |
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
a_ :Optional[Any] = logging.get_logger(__name__)
a_ :Optional[A... | 35 |
"""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 | 0 |
from math import factorial
__lowercase : Optional[Any] = {str(d): factorial(d) for d in range(10)}
def lowercase ( __A : int ) -> int:
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(__A ) )
def lowercase ( ) -> int:... | 36 |
"""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 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def UpperCamelCase_ ( __a ) -> List[Tuple[int, ...]]:
a__ : Any = []
... | 37 |
"""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 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
A_ : Union[str, Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
... | 38 |
"""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 | 0 |
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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImag... | 39 |
"""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 | 0 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_p... | 40 |
"""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 | 0 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def _A ( A__ , A__ ):
"""simple docstring"""
__lowercase = int(A__ )
assert noofclusters < len(A__ )
# Find out the dimensionality
__lowercase = len(vectors... | 41 |
"""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 | 0 |
'''simple docstring'''
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ) -> int:
while second != 0:
lowerCamelCase_ = first & second
first ^= second
lowerCamelCase_ = c << 1
return first
if __name__ == "__main__":
import doc... | 42 |
"""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 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiua... | 43 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A_ : int =logging.get_... | 650 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor,... | 44 |
"""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 | 0 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
"nielsr/canine-s": 2_048,
}
# Unicode defines 1,114,112 total “codepoints”
UpperCam... | 45 |
"""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 | 0 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> int:
'''simple docstring'''
while second != 0:
_lowerCamelCase : int = first & second
first ^= second
_lowerCamelCase : str = c << 1
return fi... | 46 |
"""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 | 0 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
SCREAMING_SNAKE_CASE__ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5... | 47 |
"""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 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def A ( UpperCamelCase_ : List[Any] ) -> Tuple:
'''simple docstring'''
if "img_encoder.pos_embed" in name:
... | 48 |
"""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 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def lowercase__ (... | 49 |
"""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 | 0 |
'''simple docstring'''
def A__ ( __lowerCAmelCase : str , __lowerCAmelCase : bool = False ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowerCamelCase__ = F'''Expected string as input, found {type(__lowerCAmelCase )}'''
... | 50 |
"""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 | 0 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassi... | 51 |
"""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 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def __A ( a_ :Optional[Any]="ro" , a_ :List[str]="en" , a_ :str="wmt16" , a_ :str=None) -> None:
try:
import datasets
except (ModuleNotFoundErr... | 52 |
"""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 | 0 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to... | 53 |
"""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 | 0 |
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =[int(lowercase__ ) for i in ip_va_address.split("." ) if i.isdigit()]
return len(lowercase__ ) == 4 and all(0 <= int(lowercase__ ) <= 2_5_4 for octet in octets )
if __name__ == "_... | 54 |
"""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 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
lo... | 55 |
"""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 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Dict = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class ... | 56 |
"""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 | 0 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attenti... | 57 |
"""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 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin... | 58 |
"""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 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A = "▁"
__A = {"vocab_file": "spiece.model"}
__A = {
"vo... | 59 |
"""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 | 0 |
import math
import tensorflow as tf
from packaging import version
def lowerCamelCase_ ( _UpperCamelCase ) -> Dict:
"""simple docstring"""
snake_case_ : List[Any] = tf.convert_to_tensor(_UpperCamelCase )
snake_case_ : Union[str, Any] ... | 60 |
"""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 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase = get_tests_dir('fi... | 61 |
"""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 | 0 |
snake_case = 8.314462 # Unit - J mol-1 K-1
def lowerCamelCase__ ( lowercase , lowercase , lowercase ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL... | 62 |
"""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 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a : ... | 63 |
"""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 | 0 |
from itertools import permutations
def A__ ( snake_case_ : tuple ):
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
SCREAMING_SNAKE_CASE__: Dict= [7, 11, 13, 17]
for i, test in enumerate(snake_case_ ):
if... | 64 |
"""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 | 0 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerati... | 65 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
A_ : int =logging.get_... | 650 | 0 |
import os
import sys
import unittest
UpperCamelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, r... | 66 |
"""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 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {
"""configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}
try:
if not is_torch_availa... | 67 |
"""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 | 0 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_datase... | 68 |
"""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 | 0 |
'''simple docstring'''
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
... | 69 |
"""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 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
... | 70 |
"""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 | 0 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def a__ ( _SCREAMING_SNAKE_CASE : Optional[int] ... | 71 |
"""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 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ... | 72 |
"""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 | 0 |
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase):
SCREAMING_SNAKE_CASE = x
SCREAMING_SNAKE_CASE = y
for step in range(_UpperCAmelCase): # noqa: B007
SCREAMING_SNAKE_CA... | 73 |
"""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 | 0 |
def a__ ( snake_case = 10 , snake_case = 22 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = range(1 , snake_case )
__SCREAMING_SNAKE_CASE : List[Any] = range(1 , snake_case )
return sum(
1 for power in powe... | 74 |
"""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 | 0 |
'''simple docstring'''
from __future__ import annotations
UpperCamelCase__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
... | 75 |
"""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 | 0 |
"""simple docstring"""
import unittest
from typing import Dict, List, Optional, Union
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 I... | 76 |
"""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 | 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.org/licenses/LICENS... | 77 |
"""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 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def lowerCAmelCase_ ( snake_case_ : float , snake_case_ : float , snake_case_ : float ) -> tuple:
'''simple docstring'''
UpperCAmelCase_ ... | 78 |
"""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 | 0 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
SCREAMING_SNAKE_CASE__ : Dict = get_logger(__name__)
class UpperCAmelCase_ ( enum.Enum ):
__lowerCamelCase = 'a... | 79 |
"""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 | 0 |
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