code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__A : Any = logging.get_logger(__name__)
# TODO: upload to AWS
__A : int = {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-... | 656 | """simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from... | 656 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_... | 656 | """simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_ava... | 656 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : Tuple , UpperCamelCase : List[str] ):
"""simple docstring"""
A__ : Optional[Any] =[1]
for i in range(2 , UpperCamelCase ):
factorials.append(factorials[-1] * i )
assert ... | 656 | """simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.... | 656 | 1 |
"""simple docstring"""
def lowercase ( ):
"""simple docstring"""
return 1
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def lowercase ( UpperCamelCase : int ):
... | 656 | """simple docstring"""
__A : Union[str, Any] = {str(digit): digit**5 for digit in range(10)}
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(UpperCamelCase ) )
def lowercase ( ):
... | 656 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, P... | 656 | """simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention,... | 656 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformer... | 656 | """simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_para... | 656 | 1 |
"""simple docstring"""
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
f... | 656 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : Any = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONF... | 656 | 1 |
"""simple docstring"""
import fire
from utils import calculate_rouge, save_json
def lowercase ( UpperCamelCase : Optional[Any] , UpperCamelCase : Dict , UpperCamelCase : Dict=None , **UpperCamelCase : List[Any] ):
"""simple docstring"""
... | 656 | """simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 656 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : dict ):
"""simple docstring"""
A__ : Tuple =set()
# edges = list of graph's edges
A__ : List[Any] =get_edges(UpperCamelCase )
# While there are still elements in edges list, take an arbit... | 656 | """simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils... | 656 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : str = "The quick brown fox jumps over the lazy dog" , ):
"""simple docstring"""
A__ : Optional[int] =set()
# Replace all the whitespace in our sentence
A__ : Optional[Any] =input_str.repla... | 656 | """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 import ModelMixin
... | 656 | 1 |
"""simple docstring"""
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
__A : List[Any] = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n auth... | 656 | """simple docstring"""
import os
def lowercase ( ):
"""simple docstring"""
A__ : List[Any] =os.path.dirname(os.path.realpath(UpperCamelCase ) )
A__ : str =os.path.join(UpperCamelCase , "triangle.txt" )
with open(UpperCamelCase ) as f:
... | 656 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : Tuple = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class __lowe... | 656 | """simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A : int ... | 656 | 1 |
"""simple docstring"""
import argparse
import collections
import os
import re
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_table.py
__A : List[Any] = "src/tr... | 656 | """simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__A : Any =... | 656 | 1 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, ... | 656 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Any = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __lowerCAmelCa... | 656 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta impor... | 656 | """simple docstring"""
from __future__ import annotations
def lowercase ( UpperCamelCase : list[float] ):
"""simple docstring"""
if len(UpperCamelCase ) < 2:
raise ValueError("Monogons and Digons are not polygons in the Euclidean space" )
if any(i <= 0 for i i... | 656 | 1 |
"""simple docstring"""
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=No... | 656 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : Optional[Any] = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not ... | 656 | 1 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowercase ( UpperCamelCase : Any , UpperCamelCase : Optional[Any]=False ):
"""simple docstring"""
A__ : Any =Omeg... | 656 | """simple docstring"""
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
if num <= 0:
raise ValueError("Input must be a positive integer" )
A__ : Union[str, Any] =[True] * (num + 1)
A__ : Union[str, Any] =2
while p *... | 656 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_ava... | 656 | """simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowerCAmelCase ( unittest.TestCase):
'''simple docstring'''
def ... | 656 | 1 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_m... | 656 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 656 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[str] = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 656 | """simple docstring"""
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 : Optional[int] = l... | 656 | 1 |
"""simple docstring"""
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def lowercase ( UpperCa... | 656 | """simple docstring"""
def lowercase ( UpperCamelCase : int , UpperCamelCase : list[int] , UpperCamelCase : int ):
"""simple docstring"""
def count_of_possible_combinations(UpperCamelCase : int ) -> int:
if target < 0:
... | 656 | 1 |
"""simple docstring"""
from collections import deque
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Any , UpperCamelCase__ : str , UpperCamelCase__ : int , UpperCamelCase__ : int ):
A__ : Optional[Any] =... | 656 | """simple docstring"""
import math
import tensorflow as tf
from packaging import version
def lowercase ( UpperCamelCase : Optional[Any] ):
"""simple docstring"""
A__ : List[Any] =tf.convert_to_tensor(UpperCamelCase )
A__ : List[Any] =0.5 * (1.0 + tf.ma... | 656 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_comm... | 656 | """simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from... | 656 | 1 |
"""simple docstring"""
from sklearn.metrics import mean_squared_error
import datasets
__A : List[Any] = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, ... | 656 | """simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_ava... | 656 | 1 |
"""simple docstring"""
__A : Union[str, Any] = {str(digit): digit**5 for digit in range(10)}
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(UpperCamelCase ) )
def lowercase ( ):
... | 656 | """simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.... | 656 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( UpperCamelCase : str , UpperCamelCase : str ):
"""simple docstring"""
A__ : Any =get_failure_array(UpperCamelCase )
# 2) Step through text searching for pattern
A__ ... | 656 | """simple docstring"""
__A : Union[str, Any] = {str(digit): digit**5 for digit in range(10)}
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(UpperCamelCase ) )
def lowercase ( ):
... | 656 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__A : List[str] = logging.get_logger(__name__)
class __lowerCAmelCase ( _UpperCamelCase):
'''simple docstring'''
def __init__( ... | 656 | """simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention,... | 656 | 1 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
__A : List[str] = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlat... | 656 | """simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_para... | 656 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : str ):
"""simple docstring"""
return credit_card_number.startswith(("34", "35", "37", "4", "5", "6") )
def lowercase ( UpperCamelCase : str ):
"""simple docstring"""
A__ : Dict =c... | 656 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : Any = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONF... | 656 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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 Config... | 656 | """simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 656 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorT... | 656 | """simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils... | 656 | 1 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def lowercase ( UpperCamelCase : str ):
"""simple docstring"""
return "".join(sorted(UpperCamelCase ) )
def lowercase ( UpperCamelCase : str ... | 656 | """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 import ModelMixin
... | 656 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : List[str] = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig",... | 656 | """simple docstring"""
import os
def lowercase ( ):
"""simple docstring"""
A__ : List[Any] =os.path.dirname(os.path.realpath(UpperCamelCase ) )
A__ : str =os.path.join(UpperCamelCase , "triangle.txt" )
with open(UpperCamelCase ) as f:
... | 656 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... | 656 | """simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A : int ... | 656 | 1 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare... | 656 | """simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__A : Any =... | 656 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : int , UpperCamelCase : int ):
"""simple docstring"""
return int(input_a == input_a == 0 )
def lowercase ( ):
"""simple docstring"""
print("Truth Table of NOR Gate:" )
print("| ... | 656 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Any = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __lowerCAmelCa... | 656 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 656 | """simple docstring"""
from __future__ import annotations
def lowercase ( UpperCamelCase : list[float] ):
"""simple docstring"""
if len(UpperCamelCase ) < 2:
raise ValueError("Monogons and Digons are not polygons in the Euclidean space" )
if any(i <= 0 for i i... | 656 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : int = 10**12 ):
"""simple docstring"""
A__ : Optional[int] =1
A__ : Any =0
A__ : Any =1
A__ : Union[str, Any] =1
while numerator <= 2 * min_total - 1:
... | 656 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : Optional[Any] = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not ... | 656 | 1 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__A : List[Any] = models.Sequential()... | 656 | """simple docstring"""
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
if num <= 0:
raise ValueError("Input must be a positive integer" )
A__ : Union[str, Any] =[True] * (num + 1)
A__ : Union[str, Any] =2
while p *... | 656 | 1 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from util... | 656 | """simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowerCAmelCase ( unittest.TestCase):
'''simple docstring'''
def ... | 656 | 1 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowercase ( ):
"""simple docstring"""
A__ : Optional[Any] =HfArgumentParser(UpperCamelCase )
A__ : List[str] =parser.parse_args_into_dataclass... | 656 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 656 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
__A : Optional[Any] = logging.get_logger(__name__)
class __lowerCAmelCase ( _UpperCamelCase):
'''simple docstring'''
def __init__... | 656 | """simple docstring"""
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 : Optional[int] = l... | 656 | 1 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionC... | 656 | """simple docstring"""
def lowercase ( UpperCamelCase : int , UpperCamelCase : list[int] , UpperCamelCase : int ):
"""simple docstring"""
def count_of_possible_combinations(UpperCamelCase : int ) -> int:
if target < 0:
... | 656 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowerCAmelCase ( _UpperCamelCase):
'''simple docstring'''... | 656 | """simple docstring"""
import math
import tensorflow as tf
from packaging import version
def lowercase ( UpperCamelCase : Optional[Any] ):
"""simple docstring"""
A__ : List[Any] =tf.convert_to_tensor(UpperCamelCase )
A__ : List[Any] =0.5 * (1.0 + tf.ma... | 656 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
if num <= 0:
raise ValueError("Input must be a positive integer" )
A__ : Union[str, Any] =[True] * (num + 1)
A__ : Union[str, Any] =2
while p *... | 656 | """simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from... | 656 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from .... | 656 | """simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_ava... | 656 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : Optional[int] = logging.get_logger(__name__)
__A : int = {
"shi-la... | 656 | """simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.... | 656 | 1 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class __lowerCAmelCase ( tf.keras.layers.Layer):
'''simp... | 656 | """simple docstring"""
__A : Union[str, Any] = {str(digit): digit**5 for digit in range(10)}
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(UpperCamelCase ) )
def lowercase ( ):
... | 656 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
__A : Union[str, Any] = {"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Beit... | 656 | """simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention,... | 656 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=_UpperCamelCase):
'''simple docstring'''
__magic_name__ : Any = ["""transformers""", """torch""", """note_seq"""]
def __init__( self : List[... | 656 | """simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_para... | 656 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_para... | 656 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : Any = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONF... | 656 | 1 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __lowerCAmelCase :
'''simple docstring'''
def __init__( ... | 656 | """simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 656 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_... | 656 | """simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils... | 656 | 1 |
"""simple docstring"""
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr,... | 656 | """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 import ModelMixin
... | 656 | 1 |
"""simple docstring"""
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
__A : Tuple = [
"kernels/rwkv/wkv_cuda.cu",
"kernels/rwkv/wkv_op.cpp",
"kernels/deformable_detr/ms_deform_attn.h",
"kernels/deformable_detr/cuda/ms_deform_i... | 656 | """simple docstring"""
import os
def lowercase ( ):
"""simple docstring"""
A__ : List[Any] =os.path.dirname(os.path.realpath(UpperCamelCase ) )
A__ : str =os.path.join(UpperCamelCase , "triangle.txt" )
with open(UpperCamelCase ) as f:
... | 656 | 1 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Any = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
... | 656 | """simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A : int ... | 656 | 1 |
"""simple docstring"""
import math
def lowercase ( UpperCamelCase : int = 100 ):
"""simple docstring"""
A__ : str =sum(i * i for i in range(1 , n + 1 ) )
A__ : Any =int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return squa... | 656 | """simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__A : Any =... | 656 | 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
@require_... | 656 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Any = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __lowerCAmelCa... | 656 | 1 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__A : Tuple = "."
# Internal Tenso... | 656 | """simple docstring"""
from __future__ import annotations
def lowercase ( UpperCamelCase : list[float] ):
"""simple docstring"""
if len(UpperCamelCase ) < 2:
raise ValueError("Monogons and Digons are not polygons in the Euclidean space" )
if any(i <= 0 for i i... | 656 | 1 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A : List[Any] = logg... | 656 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : Optional[Any] = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not ... | 656 | 1 |
"""simple docstring"""
from __future__ import annotations
__A : Optional[int] = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class __lowerCAmelCase :
'''simple docstring... | 656 | """simple docstring"""
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
if num <= 0:
raise ValueError("Input must be a positive integer" )
A__ : Union[str, Any] =[True] * (num + 1)
A__ : Union[str, Any] =2
while p *... | 656 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class __lowerCAmelCase ( _UpperCamelCase):
'''simple docstring'''
def __init__( self : List[str] ):
# test for the above condition
self.test()
def ... | 656 | """simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowerCAmelCase ( unittest.TestCase):
'''simple docstring'''
def ... | 656 | 1 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__A : Dict = logging.get_logger(__name__)
... | 656 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 656 | 1 |
"""simple docstring"""
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __lowerCAmelCase ( _UpperCamelCase):
'''simple docstring'''
__magic_name__ : Optional[int] = CustomTokenizer
pass
| 656 | """simple docstring"""
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 : Optional[int] = l... | 656 | 1 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def lowercase ( UpperCamelCase : str="ro" , UpperCamelCase : Tuple="en" , UpperCamelCase : Tuple="wmt16" , UpperCamelCase : Any=None ):
"""simple docstring"""... | 656 | """simple docstring"""
def lowercase ( UpperCamelCase : int , UpperCamelCase : list[int] , UpperCamelCase : int ):
"""simple docstring"""
def count_of_possible_combinations(UpperCamelCase : int ) -> int:
if target < 0:
... | 656 | 1 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , UpperCamelCase__ : Tuple ):
A__ : Optional[int] =str(id_ )
... | 656 | """simple docstring"""
import math
import tensorflow as tf
from packaging import version
def lowercase ( UpperCamelCase : Optional[Any] ):
"""simple docstring"""
A__ : List[Any] =tf.convert_to_tensor(UpperCamelCase )
A__ : List[Any] =0.5 * (1.0 + tf.ma... | 656 | 1 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from t... | 656 | """simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from... | 656 | 1 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__A : Optional[Any] = d... | 656 | """simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_ava... | 656 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[str] = logging.get_logger(__name__)
__A : List[Any] = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-as... | 656 | """simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.... | 656 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
__A : Optional[Any] = {
"google/tapas-base-finetuned-sqa": (
"https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"
),
"google/tapas-base-finetuned-wtq": (
"https://hug... | 656 | """simple docstring"""
__A : Union[str, Any] = {str(digit): digit**5 for digit in range(10)}
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(UpperCamelCase ) )
def lowercase ( ):
... | 656 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : int = {
"kssteven/ibert-... | 656 | """simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention,... | 656 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : str = {
"configuration_blenderbot": [
"BLENDERBO... | 656 | """simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_para... | 656 | 1 |
"""simple docstring"""
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 # ... | 656 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : Any = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONF... | 656 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_com... | 656 | """simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 656 | 1 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowerCAmelCase ( unittest.TestCase):
'''simple docstring'''
def ... | 656 | """simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils... | 656 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : Tuple , UpperCamelCase : List[str] ):
"""simple docstring"""
A__ : Dict =[0 for i in range(r + 1 )]
# nc0 = 1
A__ : Any =1
for i in range(1 , n + 1 ):
# ... | 656 | """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 import ModelMixin
... | 656 | 1 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
__A : List[Any] = datasets.logging.get_logger(__name__)
__A : Any = "\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, ... | 656 | """simple docstring"""
import os
def lowercase ( ):
"""simple docstring"""
A__ : List[Any] =os.path.dirname(os.path.realpath(UpperCamelCase ) )
A__ : str =os.path.join(UpperCamelCase , "triangle.txt" )
with open(UpperCamelCase ) as f:
... | 656 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : int , UpperCamelCase : int ):
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCamelCase , int(b / 2 ) ) * actual_power(UpperCamelCase ... | 656 | """simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A : int ... | 656 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__A : Union[str, Any] = {"tokenization_byt5": ["ByT5Tokenizer"]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__A : Any = _LazyModule(__name__, glo... | 656 | """simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__A : Any =... | 656 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : Dict , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : float = 0 ):
... | 656 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Any = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __lowerCAmelCa... | 656 | 1 |
"""simple docstring"""
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__A : Optional[int] = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simpl... | 656 | """simple docstring"""
from __future__ import annotations
def lowercase ( UpperCamelCase : list[float] ):
"""simple docstring"""
if len(UpperCamelCase ) < 2:
raise ValueError("Monogons and Digons are not polygons in the Euclidean space" )
if any(i <= 0 for i i... | 656 | 1 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from tr... | 656 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A : Optional[Any] = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not ... | 656 | 1 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
__A : str = "scheduler_config.json"
class __lowerCA... | 656 | """simple docstring"""
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
if num <= 0:
raise ValueError("Input must be a positive integer" )
A__ : Union[str, Any] =[True] * (num + 1)
A__ : Union[str, Any] =2
while p *... | 656 | 1 |
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__A : str = "http://www.mocks... | 656 | """simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowerCAmelCase ( unittest.TestCase):
'''simple docstring'''
def ... | 656 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A : Optional[Any] = {
"configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFI... | 656 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 656 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params ... | 656 | """simple docstring"""
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 : Optional[int] = l... | 656 | 1 |
"""simple docstring"""
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__A : Any = (
"This metric will be removed from the library ... | 656 | """simple docstring"""
def lowercase ( UpperCamelCase : int , UpperCamelCase : list[int] , UpperCamelCase : int ):
"""simple docstring"""
def count_of_possible_combinations(UpperCamelCase : int ) -> int:
if target < 0:
... | 656 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : list[int] , UpperCamelCase : list[int] ):
"""simple docstring"""
A__ : Optional[int] =len(UpperCamelCase )
print("The following activities are selected:" )
# The first activity is always ... | 656 | """simple docstring"""
import math
import tensorflow as tf
from packaging import version
def lowercase ( UpperCamelCase : Optional[Any] ):
"""simple docstring"""
A__ : List[Any] =tf.convert_to_tensor(UpperCamelCase )
A__ : List[Any] =0.5 * (1.0 + tf.ma... | 656 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_e... | 656 | """simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from... | 656 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A : int ... | 656 | """simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_ava... | 656 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A : Dict = ... | 656 | """simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.... | 656 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from... | 656 | """simple docstring"""
__A : Union[str, Any] = {str(digit): digit**5 for digit in range(10)}
def lowercase ( UpperCamelCase : int ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(UpperCamelCase ) )
def lowercase ( ):
... | 656 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : int , UpperCamelCase : list[int] , UpperCamelCase : int ):
"""simple docstring"""
def count_of_possible_combinations(UpperCamelCase : int ) -> int:
if target < 0:
... | 656 | """simple docstring"""
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention,... | 656 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.... | 656 | """simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_para... | 656 | 1 |
"""simple docstring"""
import unittest
from transformers import BertGenerationConfig, 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 ...test_modeling_co... | 656 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : Any = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONF... | 656 | 1 |
"""simple docstring"""
from __future__ import annotations
from scipy.special import comb # type: ignore
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self : List[str] , UpperCamelCase__ : list[tuple[float, float]] ):
A__ : List[str]... | 656 | """simple docstring"""
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 656 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def lowercase ( UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : bool , UpperCamelCase : list[int] , UpperCamelCase : float ):
"""simple docs... | 656 | """simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils... | 656 | 1 |
"""simple docstring"""
__A : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.60_9344,
"knot": 1.852,
}
__A : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.2_7777_7778,
"mph": 0.6_2137_1192,
"knot": 0.5_3995_6803,
}
def lowercase ( Uppe... | 656 | """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 import ModelMixin
... | 656 | 1 |
"""simple docstring"""
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowerCAmelCase ( _UpperCamelCase):
'''simple... | 656 | """simple docstring"""
import os
def lowercase ( ):
"""simple docstring"""
A__ : List[Any] =os.path.dirname(os.path.realpath(UpperCamelCase ) )
A__ : str =os.path.join(UpperCamelCase , "triangle.txt" )
with open(UpperCamelCase ) as f:
... | 656 | 1 |
"""simple docstring"""
def lowercase ( UpperCamelCase : int , UpperCamelCase : int ):
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError("both inputs must be positive integers" )
A__ : Tuple =str(bin(UpperCamelCase... | 656 | """simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__A : int ... | 656 | 1 |
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