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'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
... | 44 |
'''simple docstring'''
def __UpperCamelCase ( a : int , a : int ) ->int:
while b:
snake_case , snake_case = b, a % b
return a
def __UpperCamelCase ( a : int , a : int ) ->int:
return a if b == 0 else euclidean_gcd_recursive(a , ... | 44 | 1 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
fr... | 44 |
'''simple docstring'''
import argparse
import copy
def __UpperCamelCase ( a : Union[str, Any] ) ->Tuple:
snake_case = {}
with open(a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case = []
_list.append([line.split... | 44 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin,... | 44 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
exc... | 44 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class _lowercase ... | 44 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowercase ( __a ):
_UpperCAmelCase = '''WhisperFeatureExtractor'''
_UpperCAmelCase = '''WhisperTokenizer'''
def __init__( self , A__ ... | 44 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageP... | 44 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class _... | 44 | 1 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_w... | 44 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xo... | 44 | 1 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
_lowercase = datasets.logging.get_logger(__name__)
_lowercase = ... | 44 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,... | 44 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class _lowercase :
def __init__( self , A__=2 , A__=3 , A__=64 , A__=None ) -> Dict:... | 44 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __UpperCamelCase ( a : Optional[int] ) ->Dict:
snake_case = [
'''encoder.version''',
'''decoder.v... | 44 | 1 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def __UpperCamelCase ( a : dict , a : str , a : set , a : set , a : dict , a : dict , a : PriorityQueue , a : dict ,... | 44 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=__a ):
_UpperCAmelCase = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A__ , **A__ ) -> Union... | 44 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.o... | 44 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = value
snake_case = None
snake_case = None
cla... | 44 | 1 |
'''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.o... | 44 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVi... | 44 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase ... | 44 |
'''simple docstring'''
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 PoolFormerConfig, PoolFormerForImageClassificatio... | 44 | 1 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffus... | 44 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowercase = [
os.path.join(os.path.dirname(__file__), dirname)
... | 44 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel... | 44 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairs... | 44 | 1 |
'''simple docstring'''
import numpy
class _lowercase :
def __init__( self , A__ , A__ ) -> None:
snake_case = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer... | 44 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .... | 44 | 1 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
_lowercase = 'naver-clova-ix/donut-base'
class _lowercase ( unittest.TestCase ):
def UpperCamelCase ( self ) -> Dict:
snake_case = D... | 44 |
'''simple docstring'''
_lowercase = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-d... | 44 | 1 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torc... | 44 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, to... | 44 | 1 |
'''simple docstring'''
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,
ConditionalDet... | 44 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase ... | 44 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __UpperCamelCase ( a : Optional[int] ) ->Tuple:
# e... | 44 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowercase ( yaml.SafeLoader ):
def UpperCamelCase ( self , A__ ) -> List[str]:
snake_case =... | 44 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __UpperCamelCase ( a : Tuple , a : str ... | 44 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from... | 44 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_i... | 44 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
... | 44 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowercase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except Opt... | 44 |
'''simple docstring'''
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
fro... | 44 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
... | 44 |
'''simple docstring'''
def __UpperCamelCase ( a : int , a : int ) ->int:
while b:
snake_case , snake_case = b, a % b
return a
def __UpperCamelCase ( a : int , a : int ) ->int:
return a if b == 0 else euclidean_gcd_recursive(a , ... | 44 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenizati... | 44 |
'''simple docstring'''
import argparse
import copy
def __UpperCamelCase ( a : Union[str, Any] ) ->Tuple:
snake_case = {}
with open(a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case = []
_list.append([line.split... | 44 | 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
... | 44 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
exc... | 44 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/... | 44 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowercase ( __a ):
_UpperCAmelCase = '''WhisperFeatureExtractor'''
_UpperCAmelCase = '''WhisperTokenizer'''
def __init__( self , A__ ... | 44 | 1 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_... | 44 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class _... | 44 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowercase ( __a ):
... | 44 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xo... | 44 | 1 |
'''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 impor... | 44 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,... | 44 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def __UpperCamelCase ( a : float , a : float , a : float ) ->dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('''One and only... | 44 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __UpperCamelCase ( a : Optional[int] ) ->Dict:
snake_case = [
'''encoder.version''',
'''decoder.v... | 44 | 1 |
'''simple docstring'''
def __UpperCamelCase ( a : int ) ->list:
snake_case = int(a )
if n_element < 1:
snake_case = ValueError('''a should be a positive number''' )
raise my_error
snake_case = [1]
snake_case , snake_case , snake_case ... | 44 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=__a ):
_UpperCAmelCase = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A__ , **A__ ) -> Union... | 44 | 1 |
'''simple docstring'''
from ....utils import logging
_lowercase = logging.get_logger(__name__)
class _lowercase ( __a ):
def __init__( self , A__ , A__=None , A__=20_48 ) -> Tuple:
snake_case = config.__di... | 44 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = value
snake_case = None
snake_case = None
cla... | 44 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( a : float , a : float , a : float , ) ->tuple[str, float]:
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 v... | 44 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVi... | 44 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( a : list ) ->list:
if len(a ) == 0:
return []
snake_case , snake_case = min(a ), max(a )
snake_case = int(max_value - min_value ) + 1
snake_case = [[] fo... | 44 |
'''simple docstring'''
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 PoolFormerConfig, PoolFormerForImageClassificatio... | 44 | 1 |
'''simple docstring'''
def __UpperCamelCase ( a : list[int] , a : list[int] , a : int ) ->bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(a ) )
def __UpperCamelCase ( a : list[list[i... | 44 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowercase = [
os.path.join(os.path.dirname(__file__), dirname)
... | 44 | 1 |
'''simple docstring'''
def __UpperCamelCase ( a : Optional[int] ) ->str:
snake_case = len(a )
snake_case = sum(a )
snake_case = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
snake_case =... | 44 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairs... | 44 | 1 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class _lowercase :
def __init__( self ) -> Tuple:
snake_case = {}
def UpperCamelCase ( self ,... | 44 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .... | 44 | 1 |
'''simple docstring'''
import doctest
from collections import deque
import numpy as np
class _lowercase :
def __init__( self ) -> None:
snake_case = [2, 1, 2, -1]
snake_case = [1, 2, 3, 4]
def UpperCamelCase ... | 44 |
'''simple docstring'''
_lowercase = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-d... | 44 | 1 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def __UpperCamelCase ( a : int , a : float = 0.0 , a : float = 1.0 ) ->int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import ... | 44 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, to... | 44 | 1 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def __UpperCamelCase ( a : int ) ->i... | 44 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase ... | 44 | 1 |
'''simple docstring'''
_lowercase = [
'Audio',
'Array2D',
'Array3D',
'Array4D',
'Array5D',
'ClassLabel',
'Features',
'Sequence',
'Value',
'Image',
'Translation',
'TranslationVariableLanguages',
]
from .audio import Au... | 44 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowercase ( yaml.SafeLoader ):
def UpperCamelCase ( self , A__ ) -> List[str]:
snake_case =... | 44 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCamelCase ( a : Union[str, Any] , a : Optiona... | 44 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from... | 44 | 1 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 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.o... | 44 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
... | 44 | 1 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,... | 44 |
'''simple docstring'''
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
fro... | 44 | 1 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
exc... | 44 |
'''simple docstring'''
def __UpperCamelCase ( a : int , a : int ) ->int:
while b:
snake_case , snake_case = b, a % b
return a
def __UpperCamelCase ( a : int , a : int ) ->int:
return a if b == 0 else euclidean_gcd_recursive(a , ... | 44 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from tran... | 44 |
'''simple docstring'''
import argparse
import copy
def __UpperCamelCase ( a : Union[str, Any] ) ->Tuple:
snake_case = {}
with open(a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case = []
_list.append([line.split... | 44 | 1 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def __UpperCamelCase ( a : Optional[Any] , a : int , a : List[Any] , a : Union[str, Any]=1024 ) ->Tuple:
... | 44 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
exc... | 44 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def __UpperCamelCase ( a : float , a : float , a : float ) ->tuple:
snake_case = namedtuple('''result''' , '''name value''' )
if (voltage, current,... | 44 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowercase ( __a ):
_UpperCAmelCase = '''WhisperFeatureExtractor'''
_UpperCAmelCase = '''WhisperTokenizer'''
def __init__( self , A__ ... | 44 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedL... | 44 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class _... | 44 | 1 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .... | 44 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xo... | 44 | 1 |
'''simple docstring'''
from __future__ import annotations
_lowercase = 'Muhammad Umer Farooq'
_lowercase = 'MIT'
_lowercase = '1.0.0'
_lowercase = 'Muhammad Umer Farooq'
_lowercase = 'contact@muhammadumerfarooq.me'
... | 44 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,... | 44 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowercase = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_... | 44 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __UpperCamelCase ( a : Optional[int] ) ->Dict:
snake_case = [
'''encoder.version''',
'''decoder.v... | 44 | 1 |
'''simple docstring'''
from math import sqrt
def __UpperCamelCase ( a : int ) ->int:
snake_case = 0
for i in range(1 , int(sqrt(a ) + 1 ) ):
if n % i == 0 and i != sqrt(a ):
total += i + n // i
elif i == sqrt(a ):
total += i
retu... | 44 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=__a ):
_UpperCAmelCase = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A__ , **A__ ) -> Union... | 44 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, to... | 44 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = value
snake_case = None
snake_case = None
cla... | 44 | 1 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_lowercase = [
'good first issue',
'feature request',
'wip',
]
def __UpperCamelCase ( ) ->List[Any]:
snake_case = Github(os.environ['''GITHUB_TOKE... | 44 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVi... | 44 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
_lowercase = logging.getLogger(__name__)
def __UpperCamelCase ( ) ->Dict:
snake_case = argparse.ArgumentParse... | 44 |
'''simple docstring'''
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 PoolFormerConfig, PoolFormerForImageClassificatio... | 44 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _lowercase ( unittest.TestCase ):
def UpperCamelCase ( self )... | 44 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowercase = [
os.path.join(os.path.dirname(__file__), dirname)
... | 44 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transfor... | 44 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairs... | 44 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = ... | 44 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .... | 44 | 1 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
_lowercase = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the grou... | 44 |
'''simple docstring'''
_lowercase = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-d... | 44 | 1 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
_lowercase = False
_lowercase = True
_lowercase = False
... | 44 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, to... | 44 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_propert... | 44 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase ... | 44 | 1 |
'''simple docstring'''
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,
B... | 44 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowercase ( yaml.SafeLoader ):
def UpperCamelCase ( self , A__ ) -> List[str]:
snake_case =... | 44 | 1 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable... | 44 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from... | 44 | 1 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, Scheduler... | 44 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
... | 44 | 1 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_lowercase = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0... | 44 |
'''simple docstring'''
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
fro... | 44 | 1 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairs... | 44 |
'''simple docstring'''
def __UpperCamelCase ( a : int , a : int ) ->int:
while b:
snake_case , snake_case = b, a % b
return a
def __UpperCamelCase ( a : int , a : int ) ->int:
return a if b == 0 else euclidean_gcd_recursive(a , ... | 44 | 1 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, reg... | 44 |
'''simple docstring'''
import argparse
import copy
def __UpperCamelCase ( a : Union[str, Any] ) ->Tuple:
snake_case = {}
with open(a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case = []
_list.append([line.split... | 44 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apa... | 44 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
exc... | 44 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __UpperCamelCase ( a : str , a : str ) ->str | Literal[False]:
snake_case = list(a )
snake_case = list(a )
snake_case... | 44 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowercase ( __a ):
_UpperCAmelCase = '''WhisperFeatureExtractor'''
_UpperCAmelCase = '''WhisperTokenizer'''
def __init__( self , A__ ... | 44 | 1 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowercase = [
os.path.join(os.path.dirname(__file__), dirname)
... | 44 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class _... | 44 | 1 |
'''simple docstring'''
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 PoolFormerConfig, PoolFormerForImageClassificatio... | 44 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xo... | 44 | 1 |
'''simple docstring'''
def __UpperCamelCase ( a : int , a : int ) ->int:
return x if y == 0 else greatest_common_divisor(a , x % y )
def __UpperCamelCase ( a : int , a : int ) ->int:
return (x * y) // greatest_common_divisor(a , a ... | 44 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,... | 44 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from .... | 44 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __UpperCamelCase ( a : Optional[int] ) ->Dict:
snake_case = [
'''encoder.version''',
'''decoder.v... | 44 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def __UpperCamelCase ( a : np.ndarray , a : np.ndarray ) ->float:
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(a , a ... | 44 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=__a ):
_UpperCAmelCase = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A__ , **A__ ) -> Union... | 44 | 1 |
'''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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltFor... | 44 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = value
snake_case = None
snake_case = None
cla... | 44 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.o... | 44 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVi... | 44 | 1 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
fro... | 44 |
'''simple docstring'''
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 PoolFormerConfig, PoolFormerForImageClassificatio... | 44 | 1 |
'''simple docstring'''
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import... | 44 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowercase = [
os.path.join(os.path.dirname(__file__), dirname)
... | 44 | 1 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowercase ... | 700 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairs... | 44 | 0 |
'''simple docstring'''
from math import pi, sqrt
def __UpperCamelCase ( a : Optional[Any] ) ->str:
if num <= 0:
raise ValueError('''math domain error''' )
if num > 171.5:
raise OverflowError('''math range error''' )
elif num - int(__A ) not in (0, 0.5):
raise... | 701 |
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .... | 44 | 0 |
'''simple docstring'''
from maths.prime_factors import prime_factors
def __UpperCamelCase ( a : Optional[int] ) ->int:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
snake_case = f"""Input value of [number={number}] must be an integer"""
... | 702 |
'''simple docstring'''
_lowercase = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-d... | 44 | 0 |
'''simple docstring'''
def __UpperCamelCase ( a : List[Any] ) ->bool:
if not isinstance(a , a ):
snake_case = f"""Input value of [number={number}] must be an integer"""
raise TypeError(a )
if number < 0:
return False
snake_case = number * numbe... | 703 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, to... | 44 | 0 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __UpperCamelCase ( a : Dict , a : List[Any] = True , a : int = math.inf , a : Tuple = -math.inf , a : List[str] = math.inf ... | 704 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_lowercase ... | 44 | 0 |
'''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
... | 705 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowercase ( yaml.SafeLoader ):
def UpperCamelCase ( self , A__ ) -> List[str]:
snake_case =... | 44 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowercase ( __lowerCamelCase ):
_UpperCAmelCase = (IPNDMScheduler,)
_UpperCAmelCase = ... | 706 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from... | 44 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowercase = logging.getLogger(__name__)
class _lowercase ( UpperCAme... | 707 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
... | 44 | 0 |
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # cr... | 708 |
'''simple docstring'''
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
fro... | 44 | 0 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
_lowercase = {
'n_samples': 64,
'horizon': 32,
'num_inference_steps': 20,
'n_guide_steps': 2, # can set to 0 for faster samplin... | 709 |
'''simple docstring'''
def __UpperCamelCase ( a : int , a : int ) ->int:
while b:
snake_case , snake_case = b, a % b
return a
def __UpperCamelCase ( a : int , a : int ) ->int:
return a if b == 0 else euclidean_gcd_recursive(a , ... | 44 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( a : Dict ) ->float:
if not nums:
raise ValueError('''List is empty''' )
return sum(lowerCamelCase__ ) / len(lowerCamelCase__ )
if __name__ == "__main__":
import doctes... | 710 |
'''simple docstring'''
import argparse
import copy
def __UpperCamelCase ( a : Union[str, Any] ) ->Tuple:
snake_case = {}
with open(a ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
snake_case = []
_list.append([line.split... | 44 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_lowercase = {
'configuration_trocr': ['TROCR... | 711 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
exc... | 44 | 0 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __UpperCamelCase ( a : str ) ->List[str]:
snake... | 712 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowercase ( __a ):
_UpperCAmelCase = '''WhisperFeatureExtractor'''
_UpperCAmelCase = '''WhisperTokenizer'''
def __init__( self , A__ ... | 44 | 0 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class _lowercase :
def __init__( self , A__ , A__ , A__ , A__=None , A__=None ) -> List[str]:
snake_case = start
snake_case = ... | 713 |
'''simple docstring'''
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class _... | 44 | 0 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
... | 714 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xo... | 44 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSF... | 715 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,... | 44 | 0 |
'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
f... | 716 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __UpperCamelCase ( a : Optional[int] ) ->Dict:
snake_case = [
'''encoder.version''',
'''decoder.v... | 44 | 0 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', def... | 717 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=__a ):
_UpperCAmelCase = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A__ , **A__ ) -> Union... | 44 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_lowercase = logging.get_logger(__name__)
class _lowercase ( _UpperCAmelCase ):
def __init__( self , *A_... | 718 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _lowercase :
def __init__( self , A__ ) -> None:
snake_case = value
snake_case = None
snake_case = None
cla... | 44 | 0 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __UpperCamelCase ( a : List[Any] , a : Any ) ->np.ndarray:
snake_case = math.sqrt(_A )
snake_case = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.exp... | 719 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVi... | 44 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = ... | 720 |
'''simple docstring'''
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 PoolFormerConfig, PoolFormerForImageClassificatio... | 44 | 0 |
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