code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case_( a__ ):
@staticmethod
@abstractmethod
def lowerCamelCase__ ( UpperCamelCase_ : ArgumentParser ):
raise NotImplementedError()
@... | 714 |
"""simple docstring"""
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 637 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : Optional[Any] ):
stooge(_snake_case , 0 , len(_snake_case ) - 1 )
return arr
def _snake_case ( _snake_case : Optional[int] , _snake_case : List[Any] , _snake_case : Optional[An... | 715 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Paddin... | 637 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : float , _snake_case : list[float] ) -> Tuple:
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list can... | 716 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 4000000 ):
lowerCAmelCase : int = [0, 1]
lowerCAmelCase : List[str] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
... | 637 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=a__ )
class snake_case_( a__ ):
__UpperCamelCase = field(default='''automatic-spe... | 717 |
"""simple docstring"""
def _snake_case ( _snake_case : float , _snake_case : list[float] ):
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty'''... | 637 | 0 |
"""simple docstring"""
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class snake_case_( ... | 718 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : list[int] , _snake_case : int ):
if len(_snake_case ) == 0:
return False
lowerCAmelCase : List[Any] = len(_snake_case ) // 2
if a_list[midpoint] ... | 637 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
snake_case__ : int = logging.get_logger(__name__) # pylint: disable=invalid-name
class snake_case_( a__ ):
... | 719 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
snake_case__ : Optional[Any] = namedtuple(
''... | 637 | 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__ : Optional[Any] = logging.get_logger(__name__)
snake_case__ : Union[str, Any]... | 720 |
"""simple docstring"""
def _snake_case ( _snake_case : int , _snake_case : int ):
return base * power(_snake_case , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
snake_case__ : Un... | 637 | 0 |
"""simple docstring"""
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_d... | 721 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeli... | 637 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
snake_case__ = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
... | 638 | from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
requi... | 638 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Whis... | 638 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
_lowerCamelCase = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __magic_name__... | 638 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
s... | 638 | import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
... | 638 | 1 |
snake_case__ = 'Alexander Joslin'
import operator as op
from .stack import Stack
def __magic_name__( __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
_lowerCamelC... | 638 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 638 | 1 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
requi... | 638 | from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
],
}
try:
... | 638 | 1 |
from typing import Any
class UpperCamelCase :
'''simple docstring'''
def __init__( self , A_ ) -> Dict:
"""simple docstring"""
_lowerCamelCase = data
_lowerCamelCase = None
def __repr__( se... | 638 | from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils ... | 638 | 1 |
def __magic_name__( __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = len(__UpperCAmelCase )
_lowerCamelCase = len(matrix[0] )
_lowerCamelCase = min(__UpperCAmelCase , __UpperCAmelCase )
for row in range(__... | 638 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case__ = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbe... | 638 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
snake_case__ = TypeVar('T')
class UpperCamelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self , A_ , A_ ) -> None:
... | 638 | import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcess... | 638 | 1 |
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> bool:
'''simple docstring'''
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod() | 638 | import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.model... | 638 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case__ = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAva... | 638 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase = len(__UpperCAmelCase )
_lowerCamelCase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of z... | 638 | 1 |
import unittest
from knapsack import greedy_knapsack as kp
class UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
_lowerCamelCase = [10, 20, 30,... | 638 | from typing import List
import numpy as np
def __magic_name__( __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = {key: len(__UpperCAmelCase ) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase , __UpperCAmelCase )}
if le... | 638 | 1 |
def __magic_name__( __UpperCAmelCase ) -> str:
'''simple docstring'''
_lowerCamelCase = [0] * len(__UpperCAmelCase )
_lowerCamelCase = []
_lowerCamelCase = []
_lowerCamelCase = 0
for values in graph.values():
... | 638 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 638 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__lowercase )
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
A_ = field(def... | 638 | import argparse
import json
from tqdm import tqdm
def __magic_name__( ) -> List[str]:
'''simple docstring'''
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=__UpperCAmelCase ... | 638 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
snake_case__ = logging.get_logger(__name__)
snake_case__ =... | 638 | import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerF... | 638 | 1 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__( __UpperCAmelCase ) -> List[Tuple[int, ...]]:
'''si... | 638 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Whis... | 638 | 1 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/con... | 638 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def __magic_name__( __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def... | 638 | 1 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __magic_name__( __UpperCAmelCase , __Up... | 638 | from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipeline... | 638 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_inf... | 638 | import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowercase ) , 'Tatoeba direc... | 638 | 1 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase_ ( self ) -> ... | 638 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_inf... | 638 | 1 |
from __future__ import annotations
import math
import random
from typing import Any
class UpperCamelCase :
'''simple docstring'''
def __init__( self ) -> None:
"""simple docstring"""
_lowerCamelCase = []
_lowerCamelCase... | 638 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
... | 638 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Convers... | 638 | import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
snake_case__ = logging.get_logger(__name__)
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
def __init__( self , *A_ , **A_ ) -> None:
... | 638 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...u... | 638 | import argparse
import json
import subprocess
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = []
_lowerCamelCase = (
F'curl -H "Accept: application/vnd.github+json" -H "Authoriz... | 638 | 1 |
# 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
#
# Unless required by a... | 638 | from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
requi... | 638 | 1 |
from __future__ import annotations
class UpperCamelCase :
'''simple docstring'''
def __init__( self , A_ , A_ ) -> int:
"""simple docstring"""
_lowerCamelCase , _lowerCamelCase = text, pattern
_lowerCamelC... | 638 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
_lowerCamelCase = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __magic_name__... | 638 | 1 |
import numpy as np
def __magic_name__( __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __magic_name__( __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
return vector * sigmoid(__UpperCAm... | 638 | import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
... | 638 | 1 |
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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 638 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 638 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ = {
'configuration_xlm_roberta_xl': [
'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XLMRobertaXLConfig',
'XLMRobertaXLOnnxConfig',
],
}
... | 638 | from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
],
}
try:
... | 638 | 1 |
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_common import ModelTe... | 638 | from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils ... | 638 | 1 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipeline... | 638 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case__ = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbe... | 638 | 1 |
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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
... | 638 | import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcess... | 638 | 1 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'nvidia/s... | 638 | import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.model... | 638 | 1 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
... | 638 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase = len(__UpperCAmelCase )
_lowerCamelCase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of z... | 638 | 1 |
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 import RobertaTokenizer... | 638 | from typing import List
import numpy as np
def __magic_name__( __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = {key: len(__UpperCAmelCase ) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase , __UpperCAmelCase )}
if le... | 638 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and ... | 638 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 638 | 1 |
from __future__ import annotations
from PIL import Image
# Define glider example
snake_case__ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0,... | 638 | import argparse
import json
from tqdm import tqdm
def __magic_name__( ) -> List[str]:
'''simple docstring'''
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=__UpperCAmelCase ... | 638 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.model... | 638 | import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerF... | 638 | 1 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEn... | 638 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Whis... | 638 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {'vocab_file': 'vocab.json... | 638 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def __magic_name__( __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def... | 638 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ = {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}
tr... | 638 | from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipeline... | 638 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_avai... | 638 | import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowercase ) , 'Tatoeba direc... | 638 | 1 |
from __future__ import annotations
def __magic_name__( __UpperCAmelCase ) -> bool:
'''simple docstring'''
if len(__UpperCAmelCase ) < 2:
raise ValueError('''Monogons and Digons are not polygons in the Euclidean space''' )
if any(i <= 0 for i in nums ):
... | 638 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_inf... | 638 | 1 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
snake_case__ = 0B101100111110110010010000011110111011000110011110
# bin(x)[2:] gives bi... | 638 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
... | 638 | 1 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class UpperCamelCase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 638 | import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
snake_case__ = logging.get_logger(__name__)
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
def __init__( self , *A_ , **A_ ) -> None:
... | 638 | 1 |
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase = len(__UpperCAmelCase )
_lowerCamelCase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of z... | 638 | import argparse
import json
import subprocess
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = []
_lowerCamelCase = (
F'curl -H "Accept: application/vnd.github+json" -H "Authoriz... | 638 | 1 |
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def __magic_name__( ) -> None:
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
asse... | 638 | from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
requi... | 638 | 1 |
def __magic_name__( ) -> Optional[Any]:
'''simple docstring'''
_lowerCamelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_lowerCamelCase = 6
_lowerCamelCase = 1
_lowerCamelCase = 1901
_lowerCamelCase ... | 638 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
_lowerCamelCase = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __magic_name__... | 638 | 1 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# 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
snake_case__ = 'src/dif... | 638 | import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
... | 638 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils imp... | 638 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 638 | 1 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __magic_name__( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
if not is_accelerate_available():
return method
... | 638 | from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
],
}
try:
... | 638 | 1 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def __magic_name__( __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def... | 638 | from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils ... | 638 | 1 |
import os
from collections.abc import Iterator
def __magic_name__( __UpperCAmelCase = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(__UpperCAmelCase ):
_lowerCamelCase = [d for d in dir_names if d != '''scripts... | 638 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case__ = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbe... | 638 | 1 |
snake_case__ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def __magic_name__( ) -> None:
'''simple docstring'''
_lowerCamelCase = input('''Enter message: ''' )
_lowerCamelCase = input('''Enter key [alphanumeric]: ''' )
_lowerCamelCase = input('''Encr... | 638 | import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcess... | 638 | 1 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from ... | 638 | import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.model... | 638 | 1 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_... | 638 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase = len(__UpperCAmelCase )
_lowerCamelCase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of z... | 638 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__lowercase )
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
A_ = field(default='question-... | 638 | from typing import List
import numpy as np
def __magic_name__( __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = {key: len(__UpperCAmelCase ) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase , __UpperCAmelCase )}
if le... | 638 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __magic_name__( __UpperCAmelCase , __U... | 638 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 638 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case__ = {
'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
'tokenization_mvp': ['MvpTokenizer'],
}
try:
... | 638 | import argparse
import json
from tqdm import tqdm
def __magic_name__( ) -> List[str]:
'''simple docstring'''
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=__UpperCAmelCase ... | 638 | 1 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .be... | 638 | import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerF... | 638 | 1 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
snake_ca... | 638 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Whis... | 638 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
l... | 638 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def __magic_name__( __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def... | 638 | 1 |
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 .embeddings_flax import FlaxTimestepEmbeddi... | 638 | from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipeline... | 638 | 1 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, Rea... | 638 | import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowercase ) , 'Tatoeba direc... | 638 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
'ClapTextConfig',
],
'proces... | 638 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_inf... | 638 | 1 |
def __magic_name__( __UpperCAmelCase = 1000 ) -> int:
'''simple docstring'''
return sum(e for e in range(3 , __UpperCAmelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'''{solution() = }''') | 638 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
... | 638 | 1 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing imp... | 638 | import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
snake_case__ = logging.get_logger(__name__)
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
def __init__( self , *A_ , **A_ ) -> None:
... | 638 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available():
raise OptionalD... | 638 | import argparse
import json
import subprocess
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = []
_lowerCamelCase = (
F'curl -H "Accept: application/vnd.github+json" -H "Authoriz... | 638 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def __magic_name__( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
def decorator(__UpperCAmelCase ):
_lowerCamelCase = getattr(__UpperCAmelCase , '''handle_key''' , [] )
... | 638 | from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
requi... | 638 | 1 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerF... | 638 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
_lowerCamelCase = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __magic_name__... | 638 | 1 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
snake_case__ = logging.get_logger(__name__)
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
def __init__( self , *A_ , **A_ ) -> None:
... | 638 | import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
... | 638 | 1 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import Dat... | 638 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 638 | 1 |
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , ) -> float:
'''simple docstring'''
_lowerCamelCase = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 fo... | 638 | from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
],
}
try:
... | 638 | 1 |
import numpy
class UpperCamelCase :
'''simple docstring'''
def __init__( self , A_ , A_ ) -> None:
"""simple docstring"""
_lowerCamelCase = input_array
# Random initial weights are assigned where first argument... | 638 | from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils ... | 638 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterM... | 638 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case__ = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbe... | 638 | 1 |
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_mvp import MvpTokenizer
sn... | 638 | import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcess... | 638 | 1 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
snake_case__ = True
ex... | 638 | import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.model... | 638 | 1 |
print((lambda quine: quine % quine)('print((lambda quine: quine %% quine)(%r))')) | 638 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase = len(__UpperCAmelCase )
_lowerCamelCase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of z... | 638 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
snake_case__ = logging.getLogger(__name__)
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
def __init__( ... | 638 | from typing import List
import numpy as np
def __magic_name__( __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = {key: len(__UpperCAmelCase ) for key, value in gen_kwargs.items() if isinstance(__UpperCAmelCase , __UpperCAmelCase )}
if le... | 638 | 1 |
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
_lowerCamelCase = [0 for i in range(r + 1 )]
# nc0 = 1
_lowerCamelCase = 1
for i in range(1 , n + 1 ):
# to compute c... | 638 | import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 638 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
't5-... | 638 | import argparse
import json
from tqdm import tqdm
def __magic_name__( ) -> List[str]:
'''simple docstring'''
_lowerCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=__UpperCAmelCase ... | 638 | 1 |
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`') | 638 | import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerF... | 638 | 1 |
class UpperCamelCase :
'''simple docstring'''
def __init__( self ) -> None:
"""simple docstring"""
_lowerCamelCase = {} # Mapping from char to TrieNode
_lowerCamelCase = False
def UpperCamelCase_ ( ... | 638 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case__ = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Whis... | 638 | 1 |
snake_case__ = range(2, 20 + 1)
snake_case__ = [10**k for k in range(ks[-1] + 1)]
snake_case__ = {}
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
_lowerCamelCase = ... | 638 | import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='%(message)s')
def __magic_name__( __UpperCAmelCase ) -> np.ndarray:
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def... | 638 | 1 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_... | 638 | from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipeline... | 638 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required... | 638 | import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowercase ) , 'Tatoeba direc... | 638 | 1 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.uti... | 638 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_inf... | 638 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, E... | 638 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
... | 638 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker... | 638 | import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
snake_case__ = logging.get_logger(__name__)
class UpperCamelCase ( __lowercase ):
'''simple docstring'''
def __init__( self , *A_ , **A_ ) -> None:
... | 638 | 1 |
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=__lowercase ):
'''simple docstring'''
A_ = ['flax']
def __init__( self , *A_ , **A_ ) -> Any:
"""simple docstring"""
requires_b... | 638 | import argparse
import json
import subprocess
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> int:
'''simple docstring'''
_lowerCamelCase = []
_lowerCamelCase = (
F'curl -H "Accept: application/vnd.github+json" -H "Authoriz... | 638 | 1 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common im... | 638 | from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
requi... | 638 | 1 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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, random_attention_mas... | 638 | def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
_lowerCamelCase = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __magic_name__... | 638 | 1 |
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase ) -> float:
'''simple docstring'''
def get_matched_characters(__UpperCAmelCase , __UpperCAmelCase ) -> str:
_lowerCamelCase = []
_lowerCamelCase = min(len(_stra ) , ... | 638 | import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
... | 638 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenizatio... | 638 | from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_... | 638 | 1 |
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