code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = k_size // 2
... | 101 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a__( nn.Module ):
def __init__( self : Any , __snake_case : int = 16 , __snake_case : int = 88 , __snake_... | 297 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _snake_case : float , _snake_case : float , _snake_case : float , ) ->tuple:
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
... | 102 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase: Union[str, Any] = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED... | 297 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, D... | 103 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 297 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if ... | 104 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import Paddin... | 297 | 0 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _SCREAMING_SNAKE_CASE ( _lowercase : str ) ->Any:
'''simple docstring'''
def decorator(_lowercase : List[str] ):
a : Optio... | 105 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a__:
def __init__( self : Tuple ):
a : Optional[int] = ''
a : Optional[Any] = ''
a : str = ... | 297 | 0 |
"""simple docstring"""
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class SCREAMING_SNAKE_CASE ( a_... | 106 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class a__:
def __init__( self : List[Any] , __snake_case : Union[str, Any] ):
if isin... | 297 | 0 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class snake_case__ :
"""simple docstring"""
def __init__( self : Any , __lowerCamelCase : list[tuple[float, float]] ) -> Tuple:
a = list_of_points
... | 107 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accel... | 297 | 0 |
"""simple docstring"""
def a__ ( SCREAMING_SNAKE_CASE : list[int] ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError("List is empty" )
lowerCAmelCase : str = sum(SCREAMING_SNAKE_CASE ) / len(SCREAMI... | 108 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data... | 297 | 0 |
"""simple docstring"""
from timeit import timeit
def _snake_case ( UpperCamelCase : int ):
if number < 0:
raise ValueError("""the value of input must not be negative""" )
UpperCAmelCase : Tuple = 0
while number:
number &= number - 1
result += 1
return result
def ... | 109 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoe... | 297 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
... | 4 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A , _A , _A , _A , ):
a : Dict = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError('All input parameters must be positive' )
if any(... | 297 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
a ={
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_A... | 73 |
'''simple docstring'''
from __future__ import annotations
import math
class a__:
def __init__( self : List[str] , __snake_case : int ):
a : str = size
# approximate the overall size of segment tree with given value
a : Optional[i... | 297 | 0 |
SCREAMING_SNAKE_CASE_ = [0, 2, 4, 6, 8]
SCREAMING_SNAKE_CASE_ = [1, 3, 5, 7, 9]
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
... | 296 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A ):
while second != 0:
a : Union[str, Any] = first & second
first ^= second
a : Tuple = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod(... | 297 | 0 |
"""simple docstring"""
import re
import subprocess
import sys
_UpperCamelCase: Optional[Any] = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
_UpperCamelCase: Tuple = (
subprocess.check_output(f'''git diff --diff-filter=d --name-only {... | 255 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def ... | 297 | 0 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class _UpperCamelCase ( lowerCamelCase__ ,lowerCamelCase__ ):
"""simple... | 210 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCamelCase__ ( _A = "laptop" ):
a : Any = f"""https://www.amazon.in/laptop/s?k={product}"""
a : Tuple = {... | 297 | 0 |
"""simple docstring"""
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
... | 33 |
'''simple docstring'''
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, DDIMSche... | 297 | 0 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
... | 28 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCAmelCase: Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase: List[str] = ... | 297 | 0 |
class _lowercase :
"""simple docstring"""
def __init__( self : Optional[int] ):
'''simple docstring'''
lowerCamelCase__ : int = ''
lowerCamelCase__ : List[Any] = ''
lowerCamelCase__ : ... | 184 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase__ ( _A , _A ):
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance <= 0:
raise ValueError('Capacitance cannot be 0 or negative... | 297 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_form... | 225 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase: ... | 297 | 0 |
class lowercase :
def __init__( self ,A__):
lowercase = len(__snake_case)
lowercase = [0] * len_array
if len_array > 0:
lowercase = array[0]
for i in range(1 ,__snake_case):
lower... | 101 |
'''simple docstring'''
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 (
... | 297 | 0 |
"""simple docstring"""
from __future__ import annotations
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = str(_A )
return len(_A ) == 9 and set(_A ) == set("123456789" )
def a__ ( ):
"""simple docstring"""
for base_num... | 153 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase: List[Any] = logging.get_logger(__name__)
lowerCAmelCase: List[Any] ... | 297 | 0 |
'''simple docstring'''
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
__snake_case ='Usage of script: script_name <size_of_canvas:int>'
__snake_case =[0] * 100 + [1] * 10
random.shuffle(choice)
... | 4 |
'''simple docstring'''
def lowerCamelCase__ ( _A ):
return 10 - x * x
def lowerCamelCase__ ( _A , _A ):
# Bolzano theory in order to find if there is a root between a and b
if equation(_A ) * equation(_A ) >= 0:
raise ValueError('Wrong space!' )
... | 297 | 0 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ) -> Optional[Any]:
if isinstance(_A , _A ) and isinstance(_A , _A ):
__lowerCamelCase : List[Any] = len(set_a.intersection(_A ) )
if alternative_union:
__lowerCamelCas... | 73 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_... | 297 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BigBirdPegasusConfig',
'B... | 296 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a__( nn.Module ):
def __init__( self : Any , __snake_case : int = 16 , __snake_case : int = 88 , __snake_... | 297 | 0 |
"""simple docstring"""
_UpperCamelCase: Optional[Any] = '0.18.2'
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_availabl... | 255 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase: Union[str, Any] = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED... | 297 | 0 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
cl... | 210 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 297 | 0 |
"""simple docstring"""
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_... | 33 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import Paddin... | 297 | 0 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ ) -> Optional[Any]:
"""simple docstring"""
UpperCamelCase = ''
for i in table:
res += inp[i - 1]
return res
def __lowerCamelCase ( A__ ) -> Any:
... | 28 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a__:
def __init__( self : Tuple ):
a : Optional[int] = ''
a : Optional[Any] = ''
a : str = ... | 297 | 0 |
def lowercase_ ( _A : str ):
"""simple docstring"""
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 184 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class a__:
def __init__( self : List[Any] , __snake_case : Union[str, Any] ):
if isin... | 297 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase__ : str = {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}
try:
if not is_torch_available():
... | 225 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accel... | 297 | 0 |
import sys
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
lowercase = len(_A )
lowercase = [[0 for x in range(_A )] for x in range(_A )]
lowercase = [[0 for x in range(_A )] for x in range(_A )]
for chain_length in ran... | 101 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data... | 297 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolv... | 153 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoe... | 297 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case =logging.get_logger(__name__)
__snake_case ={
'roberta-ba... | 4 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A , _A , _A , _A , ):
a : Dict = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError('All input parameters must be positive' )
if any(... | 297 | 0 |
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=lowerCamelCase__ ):
_UpperCAmelCase : Optional[Any] = ['''flax''']
def __init__( self : Tuple ,*SCREAMING_SNAKE_CASE__ : Any ,**SCREAMING_SNAKE_CASE__ : List[Any]):... | 73 |
'''simple docstring'''
from __future__ import annotations
import math
class a__:
def __init__( self : List[str] , __snake_case : int ):
a : str = size
# approximate the overall size of segment tree with given value
a : Optional[i... | 297 | 0 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from ... | 296 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A ):
while second != 0:
a : Union[str, Any] = first & second
first ^= second
a : Tuple = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod(... | 297 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowercase : List[Any] = u
for i in range(1 , _A ):
... | 255 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def ... | 297 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ra... | 210 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCamelCase__ ( _A = "laptop" ):
a : Any = f"""https://www.amazon.in/laptop/s?k={product}"""
a : Tuple = {... | 297 | 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 ( __snake_case : int , __snake_case : Dict... | 33 |
'''simple docstring'''
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, DDIMSche... | 297 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embedd... | 28 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCAmelCase: Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase: List[str] = ... | 297 | 0 |
import math
def lowercase_ ( _A : List[str] , _A : Optional[Any] ):
"""simple docstring"""
return math.pow(_A , 2 ) - a
def lowercase_ ( _A : str ):
"""simple docstring"""
return 2 * x
def lo... | 184 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase__ ( _A , _A ):
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance <= 0:
raise ValueError('Capacitance cannot be 0 or negative... | 297 | 0 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
... | 225 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase: ... | 297 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
lowercase = f'{sampling_rate}'
lowercase = '1'
lowercase ... | 101 |
'''simple docstring'''
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 (
... | 297 | 0 |
"""simple docstring"""
lowerCAmelCase__ = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses':... | 153 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase: List[Any] = logging.get_logger(__name__)
lowerCAmelCase: List[Any] ... | 297 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
... | 4 |
'''simple docstring'''
def lowerCamelCase__ ( _A ):
return 10 - x * x
def lowerCamelCase__ ( _A , _A ):
# Bolzano theory in order to find if there is a root between a and b
if equation(_A ) * equation(_A ) >= 0:
raise ValueError('Wrong space!' )
... | 297 | 0 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
fro... | 73 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_... | 297 | 0 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {'voc... | 296 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a__( nn.Module ):
def __init__( self : Any , __snake_case : int = 16 , __snake_case : int = 88 , __snake_... | 297 | 0 |
"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils... | 255 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase: Union[str, Any] = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED... | 297 | 0 |
from __future__ import annotations
def UpperCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase , ):
"""simple docstring"""
__lowercase = len(_A )
# If row is equal to the size of the board it means there ar... | 210 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 297 | 0 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowercase ( __snake_case : Optional[int] = "" ):
lowercase_ : Tuple = url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
lowe... | 33 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import Paddin... | 297 | 0 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __lowerCamelCase ( A__ = "laptop" ) -> List[Any]:
"""simple docstring"""
UpperCamelCase = F"""https://www.am... | 28 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a__:
def __init__( self : Tuple ):
a : Optional[int] = ''
a : Optional[Any] = ''
a : str = ... | 297 | 0 |
def lowercase_ ( _A : Tuple , _A : Tuple ):
"""simple docstring"""
while second != 0:
lowerCamelCase__ : Union[str, Any] = first & second
first ^= second
lowerCamelCase__ : Tuple = c << 1
return fir... | 184 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class a__:
def __init__( self : List[Any] , __snake_case : Union[str, Any] ):
if isin... | 297 | 0 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from... | 225 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accel... | 297 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=lowerCamelCase__ )
class lowercase ( lowerCamelCase__ ):
lowercase_ : Optional[int] ... | 101 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data... | 297 | 0 |
"""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()
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__... | 153 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoe... | 297 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class UpperCAmelCase_ ( lowerCamelCase__ ):
def __UpperCAmelCase ( self : Optional[int] ) -> Dict:
re... | 4 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A , _A , _A , _A , ):
a : Dict = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError('All input parameters must be positive' )
if any(... | 297 | 0 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class A_ ( lowerCamelCase__ ):
def lowerCAmelCase ( self : Any ,SCREAMING_SNAKE_CASE__ : float):
return 0.0
... | 73 |
'''simple docstring'''
from __future__ import annotations
import math
class a__:
def __init__( self : List[str] , __snake_case : int ):
a : str = size
# approximate the overall size of segment tree with given value
a : Optional[i... | 297 | 0 |
from timeit import timeit
SCREAMING_SNAKE_CASE_ = {
'MALAYALAM': True,
'String': False,
'rotor': True,
'level': True,
'A': True,
'BB': True,
'ABC': False,
'amanaplanacanalpanama': True, # "a man a plan a canal panama"
}
# Ensure our test data is va... | 296 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A ):
while second != 0:
a : Union[str, Any] = first & second
first ^= second
a : Tuple = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod(... | 297 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase: Optional[int] = {'configuration_beit': ['BEIT_PRETRAI... | 255 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def ... | 297 | 0 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _UpperCamelCase :
"""simple docstring"""
__a : Optional[Any] = 42
__a : List[str] = None
__a : Tuple = None
__a : Optional[Any] ... | 210 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCamelCase__ ( _A = "laptop" ):
a : Any = f"""https://www.amazon.in/laptop/s?k={product}"""
a : Tuple = {... | 297 | 0 |
"""simple docstring"""
import os
from distutils.util import strtobool
def lowercase ( __snake_case : List[Any] , __snake_case : List[str] ):
for e in env_keys:
lowercase_ : Optional[int] = int(os.environ.get(_A , -1 ) )
if val >= 0:
... | 33 |
'''simple docstring'''
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, DDIMSche... | 297 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : int = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available... | 28 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCAmelCase: Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase: List[str] = ... | 297 | 0 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
A : str = get_logger(__name__)
A : Any = r'\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, seque... | 184 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase__ ( _A , _A ):
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance <= 0:
raise ValueError('Capacitance cannot be 0 or negative... | 297 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutput... | 225 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase: ... | 297 | 0 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ :Union[str, Any] = logging.get_logger(__name__)
# TODO Update this
lowercase__ :Union[str, Any] = {
'facebook/esm-1b': 'https://huggingf... | 101 |
'''simple docstring'''
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 (
... | 297 | 0 |
"""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, ... | 153 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase: List[Any] = logging.get_logger(__name__)
lowerCAmelCase: List[Any] ... | 297 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_token... | 4 |
'''simple docstring'''
def lowerCamelCase__ ( _A ):
return 10 - x * x
def lowerCamelCase__ ( _A , _A ):
# Bolzano theory in order to find if there is a root between a and b
if equation(_A ) * equation(_A ) >= 0:
raise ValueError('Wrong space!' )
... | 297 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def SCREAMING_SNAKE_CASE__ ( lowe... | 73 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_... | 297 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)... | 296 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a__( nn.Module ):
def __init__( self : Any , __snake_case : int = 16 , __snake_case : int = 88 , __snake_... | 297 | 0 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
_UpperCamelCase: Any = 'docs/source/en/_toctree.yml'
def lowercase__ ( _UpperCAmelCase ) -> str:
'''simple docstring'''
lowercase : Optional[A... | 255 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase: Union[str, Any] = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED... | 297 | 0 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class _UpperCamelCase :
"""simple docstring"""
def __init__( self , lowerCAmelCase__ ) -> Optional[Any]:
... | 210 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 297 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
Ro... | 33 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import Paddin... | 297 | 0 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models ... | 28 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a__:
def __init__( self : Tuple ):
a : Optional[int] = ''
a : Optional[Any] = ''
a : str = ... | 297 | 0 |
import os
import numpy
import onnx
def lowercase_ ( _A : Any , _A : List[Any] ):
"""simple docstring"""
lowerCamelCase__ : Any = a.name
lowerCamelCase__ : Tuple = b.name
lowerCamelCase__ : List[Any] = ''
lo... | 184 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class a__:
def __init__( self : List[Any] , __snake_case : Union[str, Any] ):
if isin... | 297 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase_ ( lowerCamelCase__ ):
'''simple docstring'''
lowercase_ = ["image_processor", "tokenizer"]
lowercase_ = "AutoImageProcessor"
lowercas... | 225 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accel... | 297 | 0 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interl... | 101 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data... | 297 | 0 |
"""simple docstring"""
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCa... | 153 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoe... | 297 | 0 |
'''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 (
... | 4 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A , _A , _A , _A , ):
a : Dict = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError('All input parameters must be positive' )
if any(... | 297 | 0 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # 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 six # noqa: F401 # Here to have a ... | 73 |
'''simple docstring'''
from __future__ import annotations
import math
class a__:
def __init__( self : List[str] , __snake_case : int ):
a : str = size
# approximate the overall size of segment tree with given value
a : Optional[i... | 297 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils ... | 296 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A ):
while second != 0:
a : Union[str, Any] = first & second
first ^= second
a : Tuple = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod(... | 297 | 0 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_UpperCamelCase: List[str] = '\\n@misc{c... | 255 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def ... | 297 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__a : str = logging.get_logger(__name__) # pylint: disable=invalid-name
def UpperCAmelCase ( lower... | 210 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCamelCase__ ( _A = "laptop" ):
a : Any = f"""https://www.amazon.in/laptop/s?k={product}"""
a : Tuple = {... | 297 | 0 |
"""simple docstring"""
def lowercase ( __snake_case : Dict ):
lowercase_ : str = [0] * len(_A )
lowercase_ : Optional[Any] = []
lowercase_ : Union[str, Any] = []
lowercase_ : List[Any] = 0
for values in grap... | 33 |
'''simple docstring'''
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, DDIMSche... | 297 | 0 |
'''simple docstring'''
import os
from pathlib import Path
def __lowerCamelCase ( A__ , A__ , A__ , A__ ) -> Dict:
"""simple docstring"""
UpperCamelCase = {
'en': 'Machine learning is great, isn\'t it?',
'ru':... | 28 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCAmelCase: Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase: List[str] = ... | 297 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Optional[int] = logging.get_logger(__name__)
class _lowercase ( lowerCamelCase__):
"""simple docstring"""
A__ = "timm_backbone"
... | 184 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def lowerCamelCase__ ( _A , _A ):
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance <= 0:
raise ValueError('Capacitance cannot be 0 or negative... | 297 | 0 |
from collections import namedtuple
lowerCamelCase__ : Dict = namedtuple('from_to', 'from_ to')
lowerCamelCase__ : Optional[int] = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.001, 1_000),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_0454, 264.172),
'cubicyard... | 225 |
'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase: ... | 297 | 0 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("To use the rich extension, install rich with `pip install rich`")
| 101 |
'''simple docstring'''
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 (
... | 297 | 0 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random... | 153 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase: List[Any] = logging.get_logger(__name__)
lowerCAmelCase: List[Any] ... | 297 | 0 |
'''simple docstring'''
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
__snake_case =logging.get_logger(__name__)
__snake_ca... | 4 |
'''simple docstring'''
def lowerCamelCase__ ( _A ):
return 10 - x * x
def lowerCamelCase__ ( _A , _A ):
# Bolzano theory in order to find if there is a root between a and b
if equation(_A ) * equation(_A ) >= 0:
raise ValueError('Wrong space!' )
... | 297 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a =logging.get_logger(__name__)
a ={
'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'
... | 73 |
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_... | 297 | 0 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
fr... | 296 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a__( nn.Module ):
def __init__( self : Any , __snake_case : int = 16 , __snake_case : int = 88 , __snake_... | 297 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
_UpperCamelCase: Optional[int] =... | 255 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase: Union[str, Any] = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED... | 297 | 0 |
from __future__ import annotations
__a : Any = 'Muhammad Umer Farooq'
__a : Optional[Any] = 'MIT'
__a : Optional[int] = '1.0.0'
__a : Optional[Any] = 'Muhammad Umer Farooq'
__a : Tuple = 'contact@muhammadumerfarooq.me'
__a : List[str] = 'Alpha'
i... | 210 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 297 | 0 |
"""simple docstring"""
def lowercase ( __snake_case : Tuple ):
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(_A , _A ):
raise TypeError('''Input value must be a \'int\' type''' )
return bin(_A ).count('''1''' )
... | 33 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import Paddin... | 297 | 0 |
'''simple docstring'''
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
_lowerCamelCase : List[Any] = 'Run commands across TPU VMs for initial setup... | 28 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a__:
def __init__( self : Tuple ):
a : Optional[int] = ''
a : Optional[Any] = ''
a : str = ... | 297 | 0 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datas... | 184 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class a__:
def __init__( self : List[Any] , __snake_case : Union[str, Any] ):
if isin... | 297 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ ( lowerCamelCase__ ):
'''simple ... | 225 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accel... | 297 | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase__ :Union[str, Any] = logging.get_logger(__name__)
lowercase__ :List[str] = {
't5-small': 'https://huggingface.co/t5-small/resolve/main/... | 101 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data... | 297 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_ide... | 153 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoe... | 297 | 0 |
'''simple docstring'''
from __future__ import annotations
__snake_case =tuple[int, int, int]
__snake_case =tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
__snake_case ='ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# ------------------... | 4 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A , _A , _A , _A , ):
a : Dict = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError('All input parameters must be positive' )
if any(... | 297 | 0 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> Optional[int]:... | 73 |
'''simple docstring'''
from __future__ import annotations
import math
class a__:
def __init__( self : List[str] , __snake_case : int ):
a : str = size
# approximate the overall size of segment tree with given value
a : Optional[i... | 297 | 0 |
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = len(_A )
SCREAMING_SNAKE_CASE = sum(_A )
SCREAMING_SNAKE_CASE = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i... | 296 |
'''simple docstring'''
def lowerCamelCase__ ( _A , _A ):
while second != 0:
a : Union[str, Any] = first & second
first ^= second
a : Tuple = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod(... | 297 | 0 |
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