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 |
|---|---|---|---|---|
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
s... | 709 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 46 | 0 |
def a(lowercase__ = 50 ):
'''simple docstring'''
snake_case_ = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_le... | 710 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
tr... | 46 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import ... | 711 |
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': o... | 46 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig',... | 712 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/... | 46 | 0 |
'''simple docstring'''
from __future__ import annotations
def a(lowercase__ ):
'''simple docstring'''
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(lowercase__ ) ):
matrix[i][0]... | 713 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREA... | 46 | 0 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""transformers""", """torch""", """note_seq"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple doc... | 714 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin impo... | 46 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .... | 715 |
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replac... | 46 | 0 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__=5 ):
'''simple docstring'''
assert masked_input.count('<mask>' ) == 1
snake_case_ = torch.tensor(tokenizer.encode(lowercase__ , add_special_tokens=low... | 716 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
... | 46 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class SCREAMING_SNAKE_CASE ( __snake_case ):
""... | 717 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils i... | 46 | 0 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
A = [
# tf -> hf
('/', '.'),
('layer_', 'layers.'),
('kernel', 'weight'... | 718 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 46 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common imp... | 719 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docst... | 46 | 0 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
while second != 0:
snake_case_ = first & second
first ^= second
snake_case_ = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
A = int(input('Enter the first number: ').strip(... | 720 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_t... | 46 | 0 |
import re
def a(lowercase__ ):
'''simple docstring'''
if len(re.findall('[ATCG]' , lowercase__ ) ) != len(lowercase__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 721 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_a... | 46 | 0 |
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 .tokenization_barth... | 700 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , low... | 46 | 0 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f"""{price_plus_tax(100, 0.2_5) = }""")
print(f"""{price_plus_tax(125.50, 0.0_5) = }""")
| 701 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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 ConfigTest... | 46 | 0 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
cl... | 702 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
A = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='En... | 46 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelT... | 703 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolv... | 46 | 0 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 704 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = name
snake_case_ = val
def __str__( self ):
"""simple docstring"""
return f"""{self... | 46 | 0 |
def a(lowercase__ ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('\'str\' object cannot be interpreted as an integer' )
if num == 0:
return "0b... | 705 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOn... | 46 | 0 |
'''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_py... | 706 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
'starting number must be\n ... | 46 | 0 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from... | 707 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 1.5
snake_case_ ... | 46 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chan... | 708 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 46 | 0 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_a... | 709 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 46 | 0 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
def get_matched_characters(lowercase__ , lowercase__ ) -> str:
snake_case_ = []
snake_case_ = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
snake_case_ = int(max(0 , i - limit ) )
snake_cas... | 710 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
tr... | 46 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'configuration_table_transformer': [
'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TableTransformerConfig',
'TableTransformerOnnxConfig',
]... | 711 |
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': o... | 46 | 0 |
def a(lowercase__ = 100 ):
'''simple docstring'''
snake_case_ = set()
snake_case_ = 0
snake_case_ = n + 1 # maximum limit
for a in range(2 , lowercase__ ):
for b in range(2 , lowercase__ ):
snake_case_ = a**b # calculates the current power
collect_powers.add(lowe... | 712 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/... | 46 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
A = logging.get_logger(__name__)
A = {'vocab_file': 'vocab.json', 'merges_... | 713 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREA... | 46 | 0 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
__A = field(
default="""codeparrot/codeparrot""" , metadata={"""help""": """Model name or path of model to be trained."""} )
__A ... | 714 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin impo... | 46 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 715 |
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replac... | 46 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __init__( self , *__UpperCamelCase , **__UpperCame... | 716 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
... | 46 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
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_common import BackboneT... | 717 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils i... | 46 | 0 |
'''simple docstring'''
def a(lowercase__ = 1000 ):
'''simple docstring'''
snake_case_ , snake_case_ = 1, 1
snake_case_ = []
for i in range(1 , n + 1 ):
snake_case_ = prev_numerator + 2 * prev_denominator
snake_case_ = prev_numerator + prev_denominator
if len(st... | 718 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 46 | 0 |
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_configurat... | 719 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docst... | 46 | 0 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
fr... | 720 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_t... | 46 | 0 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
A = '<<<<<<< This should probably be modified because it mentions: '
A = '=======\n>>>>>>>\n'
A ... | 721 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_a... | 46 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 700 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , low... | 46 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/... | 701 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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 ConfigTest... | 46 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {'vocab_file... | 702 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
A = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='En... | 46 | 0 |
from __future__ import annotations
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) < k or k < 0:
raise ValueError('Invalid Input' )
snake_case_ = snake_case_ = sum(array[:k] )
for i in range(len(lowercase__ ) - k ):
snake_case_ = current_sum - ar... | 703 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolv... | 46 | 0 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
# Construct mo... | 704 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = name
snake_case_ = val
def __str__( self ):
"""simple docstring"""
return f"""{self... | 46 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
A = _LazyModule(__name__, globals()... | 705 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOn... | 46 | 0 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def a(lowercase__ ):
'''simple docstring'''
# A local function to see if a dot lands in the circle.
def is_in_circle(lowercase__ , lowercase__ ) -> bool:
... | 706 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
'starting number must be\n ... | 46 | 0 |
from __future__ import annotations
from collections.abc import Callable
A = list[list[float | int]]
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = len(lowercase__ )
snake_case_ = [[0 for _ in range(size + 1 )] for _ in range(lowercase__ )]
snake_c... | 707 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 1.5
snake_case_ ... | 46 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxCon... | 708 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 46 | 0 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = LxmertConfig.from_jso... | 709 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 46 | 0 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def a(*lowercase__ , lowercase__ = None , lowercase__=True , lowercase__=2 ):
'''simple docstring'''
from .. import __version__
snake_case_ = take_from
snake_case_ = ()
if no... | 710 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
tr... | 46 | 0 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
A = parse(importlib.metadata.version('torch'))
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if operation not in STR_... | 711 |
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': o... | 46 | 0 |
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
A = 'src/diffusers'
A ... | 712 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/... | 46 | 0 |
'''simple docstring'''
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case... | 713 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREA... | 46 | 0 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_form... | 714 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin impo... | 46 | 0 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import Tokeni... | 715 |
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replac... | 46 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
A = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
'processing_troc... | 716 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
... | 46 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = list(lowercase__ )
snake_case_ = list(lowercase__ )
snake_case_ = 0
for i in ran... | 717 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils i... | 46 | 0 |
'''simple docstring'''
import os
def a():
'''simple docstring'''
with open(os.path.dirname(lowercase__ ) + '/p022_names.txt' ) as file:
snake_case_ = str(file.readlines()[0] )
snake_case_ = names.replace('"' , '' ).split(',' )
names.sort()
snake_case_ = 0
snake_case_ ... | 718 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 46 | 0 |
from PIL import Image
def a(lowercase__ ):
'''simple docstring'''
snake_case_ , snake_case_ = image.size
snake_case_ = 0
snake_case_ = image.load()
for i in range(lowercase__ ):
for j in range(lowercase__ ):
snake_case_ = pixels[j, i]
mean += pixel
mean //= wi... | 719 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docst... | 46 | 0 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
A = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A = [ord(letter) for letter in string.ascii_lowercase]
A = {ord(char) for... | 720 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_t... | 46 | 0 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 721 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_a... | 46 | 0 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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 ... | 700 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , low... | 46 | 0 |
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, CLIPTokenizerFast
from u... | 701 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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 ConfigTest... | 46 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A = {
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}
try... | 702 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
A = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='En... | 46 | 0 |
from math import sqrt
def a(lowercase__ = 1000000 ):
'''simple docstring'''
snake_case_ = 0
snake_case_ = 0
snake_case_ = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_shortest_sides**2 + max... | 703 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolv... | 46 | 0 |
import sys
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = len(lowercase__ )
snake_case_ = [[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
snake_case_ = [[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
for chain_length in range(2 , lowerc... | 704 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = name
snake_case_ = val
def __str__( self ):
"""simple docstring"""
return f"""{self... | 46 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine': ['CanineTokenizer'],
}
try:
if not i... | 705 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOn... | 46 | 0 |
'''simple docstring'''
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 706 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
'starting number must be\n ... | 46 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
A = logging.get_logger(__name__)
A = {'... | 707 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 1.5
snake_case_ ... | 46 | 0 |
'''simple docstring'''
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def a():
'''simple... | 708 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 46 | 0 |
from math import factorial
def a(lowercase__ = 100 ):
'''simple docstring'''
return sum(map(lowercase__ , str(factorial(lowercase__ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 709 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 46 | 0 |
def a(lowercase__ = 50 ):
'''simple docstring'''
snake_case_ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[row_length] += ways_number[
row_length - bl... | 710 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
tr... | 46 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class SCR... | 711 |
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': o... | 46 | 0 |
import math
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = 2
snake_case_ = int(math.sqrt(lowercase__ ) ) # Size of every segment
snake_case_ = [True] * (end + 1)
snake_case_ = []
while start <= end:
if temp[start] is True:
in_prime... | 712 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/... | 46 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 713 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREA... | 46 | 0 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if... | 714 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline_mixin impo... | 46 | 0 |
import math
import sys
def a(lowercase__ ):
'''simple docstring'''
if number != int(lowercase__ ):
raise ValueError('the value of input must be a natural number' )
if number < 0:
raise ValueError('the value of input must not be a negative number' )
if number == 0:
return 1
snake_case_ = [-... | 715 |
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replac... | 46 | 0 |
from __future__ import annotations
def a(lowercase__ ):
'''simple docstring'''
return len(set(lowercase__ ) ) == len(lowercase__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 716 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
... | 46 | 0 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_u... | 717 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils i... | 46 | 0 |
'''simple docstring'''
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.... | 718 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 46 | 0 |
def a(lowercase__ ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
snake_case_ , snake_case_ = head.next, head
while fast and fast.next:
snake_case_ = fast.next.next
snake_case_ = slow.next
snake_case_ = slow.next
snake_case_ =... | 719 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docst... | 46 | 0 |
from __future__ import annotations
from typing import Generic, TypeVar
A = TypeVar('T')
class SCREAMING_SNAKE_CASE ( Generic[T] ):
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = data
snake... | 720 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_t... | 46 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 721 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_a... | 46 | 0 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
UpperCAmelCa... | 47 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : Dict = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d... | 47 | 1 |
'''simple docstring'''
def _a ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
if not len(lowerCAmelCase_ ) == len(lowerCAmelCase_ ) == 3:
raise ValueError('''Please enter a valid equation.''' )
if equationa[0] == equationa[1] == equationa[0] ... | 47 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case : List[Any] = 0
if start < end:
... | 47 | 1 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def _a ( lowerCAmelCase_ ):
"""simple docstring"""
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or ... | 47 |
'''simple docstring'''
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 47 | 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_mvp impor... | 47 |
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_t... | 47 | 1 |
'''simple docstring'''
from math import pow, sqrt
def _a ( *lowerCAmelCase_ ):
"""simple docstring"""
_snake_case : Optional[int] = len(lowerCAmelCase_ ) > 0 and all(value > 0.0 for value in values )
return result
def _a ( lowerCAmelCase_ ... | 47 |
'''simple docstring'''
from collections.abc import Generator
def _a ( ):
"""simple docstring"""
_snake_case , _snake_case : Union[str, Any] = 0, 1
while True:
_snake_case , _snake_case : List[str] = b, a + b
... | 47 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
i... | 47 |
'''simple docstring'''
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
UpperCAmelCase : str = logging.getLogger(__name__)
UpperCAmelCas... | 47 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
UpperCAmelCase : List[str] = logging.get_logger(__name__)
class lowerCamelCase (a__ ):
def __init__( self , *lowercase__ , **lowercase__... | 47 |
'''simple docstring'''
from __future__ import annotations
def _a ( lowerCAmelCase_ , lowerCAmelCase_ = None , lowerCAmelCase_ = None ):
"""simple docstring"""
if start is None:
_snake_case : Optional[Any] = 0
if end is None:
... | 47 | 1 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _a ( lowerCAmelCase_ ):
"""simple docstring"""
if not is_accelerate_available():
return method
_sn... | 47 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers i... | 47 | 1 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transform... | 47 |
'''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 ):
def UpperCAmelCase_ ( self ) ... | 47 | 1 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_confi... | 47 |
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = tuple[float, float, float]
UpperCAmelCase : int = tuple[float, float, float]
def _a ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case : str = end_poin... | 47 | 1 |
'''simple docstring'''
def _a ( ):
"""simple docstring"""
_snake_case : Any = 0
for i in range(1 , 1_001 ):
total += i**i
return str(lowerCAmelCase_ )[-10:]
if __name__ == "__main__":
print(solution())
| 47 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCAmelCase : List[st... | 47 | 1 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
UpperCAmelCase : Optional[Any] = ... | 47 |
'''simple docstring'''
def _a ( lowerCAmelCase_ ):
"""simple docstring"""
if n == 1 or not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return 0
elif n == 2:
return 1
else:
_snake_case : Union[str, Any] = [0, ... | 47 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase (a__ , unittest.Tes... | 47 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
UpperCAmelCase : Any = TypeVar('T')
UpperCAmelCase : str = TypeVar('U')
class lowerCamelCase (Generic[T, U] ):
def __init__( self ... | 47 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase : Union[str, An... | 47 |
'''simple docstring'''
import os
import numpy
import onnx
def _a ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case : List[Any] = a.name
_snake_case : List[Any] = b.name
_snake_case : Tuple = ... | 47 | 1 |
'''simple docstring'''
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
UpperCAmelCase : Union[str, Any] = False
class lowerCamelCase (unittest.TestCa... | 47 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase : int = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available()... | 47 | 1 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCAmelCase : Union[str, Any] = {
# 1536-bit
5: {... | 47 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImage... | 47 | 1 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Acc... | 47 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from... | 47 | 1 |
'''simple docstring'''
UpperCAmelCase : Dict = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCAmelCase ... | 47 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
Up... | 47 | 1 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class lowerCamelCase :
def __init__( self , lowercase__ ) -> Dict:
"""simple docstring"""
_snake_case : Optional[int] = str(id_ )
_snake_... | 47 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def _a ( lowerCAmelCase_ ):
"""simple docstring"""
if num <= 0:
raise ValueError('''math domain error''' )
return quad(lowerCAmelCase_ , 0 , lowerCAmelCa... | 47 | 1 |
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 47 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 47 | 1 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class lowerCamelCase :
def __init__( self , lowercase__ , lowercase__ , lowercase__ , lowercase__=None , lowercase__=None ) -> List[Any]:
"""simple ... | 47 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 47 | 1 |
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