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
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = [], [], []
for element in data:
if el... | 715 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
UpperCamelCase__ : Optional[int] = datasets.utils.log... | 620 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase__ : Any = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_a... | 716 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 620 | 0 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _inter... | 717 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder,... | 620 | 0 |
from __future__ import annotations
import math
UpperCamelCase__ : Optional[int] = "2020.9.26"
UpperCamelCase__ : str = "xcodz-dot, cclaus, dhruvmanila"
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : ... | 718 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQ... | 620 | 0 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
qu... | 719 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : O... | 620 | 0 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Optional[Any]=1 ):
"""simple docstring"""
if n_shave_prefix_segments ... | 720 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : str = logging.get_logger(__name__)
UpperCamelCase__ : Optional[int] = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/... | 620 | 0 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils imp... | 721 |
import pytest
import datasets
# Import fixture modules as plugins
UpperCamelCase__ : Union[str, Any] = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Dict ):
... | 620 | 0 |
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)
UpperCamelCase__ : str = _sym... | 700 |
from typing import List
import numpy as np
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = {key: len(_SCREAMING_SNAKE_CASE ) for key, value in gen_kwargs.items() if isinstance(_SCREAMING_SNAKE_CASE , _SC... | 620 | 0 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CA... | 701 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : List[str] = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models... | 620 | 0 |
UpperCamelCase__ : Optional[int] = 9.8_06_65
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float = g ):
"""simple docstring"""
if fluid_density <= 0:
raise ValueError('Impossible fl... | 702 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __snake_case ( lower... | 620 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_... | 703 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __snake_case :
def __init__( self , _A , _A , _A , _A , _A , _A=0.2 , _A=0.2):
SCREAMING_SNAKE_CASE_ = bp_numa
SCREAMING_SNAKE_CASE_ = bp_numa
... | 620 | 0 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( _... | 704 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : int=7 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = Non... | 620 | 0 |
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()
except OptionalDependencyNotAvailable:
... | 705 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Any = {
"configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
"tokenization_mvp": ["MvpTokenizer"]... | 620 | 0 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __snake_case ( unittest.TestCase ):
__lowerCAmelCase : Dict = inspec... | 706 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __snake_case ( unittest.TestCase ):
__lowerCAmelCase : Dict = inspec... | 620 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
UpperCamelCase__ : Tuple = {
"configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
... | 620 | 0 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __snake_case ( lowerCAmelCase__ ):
__lowerCAmelCase : List[Any] = CustomTokenizer
pass
| 708 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
UpperCamelCase__ : int = Lock()
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE ... | 620 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( lowerCAmelCase__ ):
__lowerCAmelCase : Optional[Any] = ['image_processor', 'tokenizer']
__lowerCAmelCase : List[str] = 'AutoImageProcessor'
__lowerCAm... | 709 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
UpperCamelCase__ : int = "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teen... | 620 | 0 |
'''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,
nested_simplify,
... | 710 |
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_t... | 620 | 0 |
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... | 711 |
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : int = 200 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = [1, 2, 5, 10, 20, 50, 100, 200]
SCREAMING_SNAKE_CASE_ = [0] * (pence + 1)
SCREAMING_SNAKE_CASE_ = 1 # base case: 1 way to make... | 620 | 0 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
UpperCamelCase__ : List[str] = logging.get_logger(__name__)
class __snake_case ( lowerCAmelCase__ ):
__low... | 712 |
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if index == number_of_items:
return 0
SCRE... | 620 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
... | 713 |
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_v... | 620 | 0 |
'''simple docstring'''
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class __sna... | 714 |
def _UpperCAmelCase ( ):
"""simple docstring"""
for n in range(1 , 1_000_000 ):
yield n * (n + 1) // 2
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_S... | 620 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ : str = {
"configuration_groupvit": [
"GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"GroupViTConfig",
"GroupViTOnnxConfig",
... | 715 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
UpperCamelCase__ : Optional[int] = datasets.utils.log... | 620 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import To... | 716 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 620 | 0 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
... | 717 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder,... | 620 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : List[Any] = logging.get_logger(__name__)
UpperCamelCase__ : List[str] = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models... | 718 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQ... | 620 | 0 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transform... | 719 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCamelCase__ : O... | 620 | 0 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMS... | 720 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : str = logging.get_logger(__name__)
UpperCamelCase__ : Optional[int] = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/... | 620 | 0 |
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_image_inputs
if is_torch_available():
import to... | 721 |
import pytest
import datasets
# Import fixture modules as plugins
UpperCamelCase__ : Union[str, Any] = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Dict ):
... | 620 | 0 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
# TODO Update this
a_ = {
"facebook/esm-1b": "http... | 621 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
... | 621 | 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
a_ = logging... | 621 |
"""simple docstring"""
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,
... | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def a__ ( ) -> Generator[int, None, None]:
_A = {}
_A = 2
while True:
_A = factor_map.pop(__lowercase , __lowercase )
if fac... | 621 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class snake_case ( ... | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase ) -> None:
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
... | 621 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTo... | 621 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ ... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
# Return True if there is node that has not iterated.
_A = [False] * len(__lowercase )
_A = []
queue.append(__lowercase )
_A = True... | 621 | 1 |
"""simple docstring"""
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase=1024 ) -> Any:
_A , _A = [], []
_A ... | 621 |
"""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,
ConditionalDetrForObjectDetect... | 621 | 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__ ( __lowercase ) -> Optional[Any]:
# This defines a "chinese character" as anything in the CJK Unicode... | 621 |
"""simple docstring"""
import random
def a__ ( __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
_A = a[left_index]
_A = left_index + 1
for j in range(left_index + 1 , __lowercase ):
if a[j] < pivot:
_A ... | 621 | 1 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class snake_case ( ... | 621 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 621 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 621 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase , __lowercase ) -> float:
_A = sorted(numsa + numsa )
_A , _A = divmod(len(__lowercase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
... | 621 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/bli... | 621 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/bli... | 621 | 1 |
"""simple docstring"""
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
Defau... | 621 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class snake_case ( unittest.TestCase , _UpperCamelCase):
def a_ ( self : Optional[Any] ) -> List[str]:
'''... | 621 | 1 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class snake_case ( tf.keras.layers.Layer):
... | 621 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from ... | 621 | 1 |
"""simple docstring"""
def a__ ( __lowercase = 1000 ) -> int:
return sum(e for e in range(3 , __lowercase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'''{solution() = }''') | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
while a != 0:
_A , _A = b % a, a
return b
def a__ ( __lowercase , __lowercase ) -> int:
if gcd(__lowercase , __lowercase ) != 1:
_A = f"... | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BigBirdPegasusConfig",
... | 621 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | 621 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/c... | 621 |
"""simple docstring"""
class snake_case :
def __init__( self : Optional[int] , a__ : List[Any] , a__ : List[str] , a__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
_A ... | 621 | 1 |
"""simple docstring"""
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a_ ... | 621 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"configuration_roformer": ["ROFORME... | 621 | 1 |
"""simple docstring"""
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
a_ = get_logger(__name__)
a_ = r"\n Args:\n input_ids (`jnp.ndarray` of s... | 621 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class snake_case ( _UpperCamelCase):
def __init__( self : str , *a__ : Di... | 621 | 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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transform... | 621 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def a__ ( __lowercase ) -> Optional[int]:
_A = [
"encoder.version",
"decoder.version",
"model.enco... | 621 | 1 |
"""simple docstring"""
def a__ ( __lowercase ) -> list:
if any(not isinstance(__lowercase , __lowercase ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(__lowercase ) ):
for i, (rod_uppe... | 621 |
"""simple docstring"""
import numpy as np
def a__ ( __lowercase , __lowercase ) -> np.ndarray:
return np.where(vector > 0 , __lowercase , (alpha * (np.exp(__lowercase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Condit... | 621 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base impor... | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
... | 621 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def a__ ( __lowercase ) -> List[Any]:
_A = os.path.join(args.tf_model_dir , "parameters.json" )
_A ... | 621 | 1 |
"""simple docstring"""
class snake_case :
def __init__( self : Optional[int] ) -> Tuple:
'''simple docstring'''
_A = 0
_A = 0
_A = {}
def a_ ( self : L... | 621 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for... | 621 | 1 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, to... | 621 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
... | 621 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import... | 621 |
"""simple docstring"""
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,
... | 621 | 1 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def a__ ( __lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
_A = ve... | 621 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class snake_case ( ... | 621 | 1 |
"""simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class snake_case :
def __init__( self : Union[str, Any] , a__ : List[str] , a__ : int , a__ : int ) -> List[s... | 621 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTo... | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
# Return True if there is node that has not iterated.
_A = [False] * len(__lowercase )
_A = []
queue.append(__lowercase )
_A = True... | 621 | 1 |
"""simple docstring"""
def a__ ( __lowercase ) -> tuple[int, int]:
try:
_A = float(__lowercase )
except ValueError:
raise ValueError("Please enter a valid number" )
_A = decimal - int(__lowercase )
if fractional_part == 0:
return ... | 621 |
"""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,
ConditionalDetrForObjectDetect... | 621 | 1 |
"""simple docstring"""
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_availa... | 621 |
"""simple docstring"""
import random
def a__ ( __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
_A = a[left_index]
_A = left_index + 1
for j in range(left_index + 1 , __lowercase ):
if a[j] < pivot:
_A ... | 621 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | 621 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 621 | 1 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
a_ = None
try:
import msvcrt
except ImportError:
a_ = None
try:
import fcntl
except ImportError:
a_ = None
... | 621 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase , __lowercase ) -> float:
_A = sorted(numsa + numsa )
_A , _A = divmod(len(__lowercase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
... | 621 | 1 |
"""simple docstring"""
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Op... | 621 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/bli... | 621 | 1 |
"""simple docstring"""
import numpy as np
def a__ ( __lowercase , __lowercase ) -> np.ndarray:
return np.where(vector > 0 , __lowercase , (alpha * (np.exp(__lowercase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 621 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class snake_case ( unittest.TestCase , _UpperCamelCase):
def a_ ( self : Optional[Any] ) -> List[str]:
'''... | 621 | 1 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class snake_case ( tf.keras.optimizers.schedules.Learnin... | 621 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from ... | 621 | 1 |
"""simple docstring"""
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
while a != 0:
_A , _A = b % a, a
return b
def a__ ( __lowercase , __lowercase ) -> int:
if gcd(__lowercase , __lowercase ) != 1:
_A = f"... | 621 | 1 |
"""simple docstring"""
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_ARCH... | 621 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | 621 | 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 .tokenizati... | 621 |
"""simple docstring"""
class snake_case :
def __init__( self : Optional[int] , a__ : List[Any] , a__ : List[str] , a__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
_A ... | 621 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
class snake_case ( _UpperCamelCase):
__UpperCamelCase = 'u... | 621 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"configuration_roformer": ["ROFORME... | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
try:
... | 621 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class snake_case ( _UpperCamelCase):
def __init__( self : str , *a__ : Di... | 621 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils i... | 621 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def a__ ( __lowercase ) -> Optional[int]:
_A = [
"encoder.version",
"decoder.version",
"model.enco... | 621 | 1 |
"""simple docstring"""
def a__ ( __lowercase ) -> list:
if len(__lowercase ) <= 1:
return lst
_A = 1
while i < len(__lowercase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
_A , _A = lst[i], lst[i - 1]
... | 621 |
"""simple docstring"""
import numpy as np
def a__ ( __lowercase , __lowercase ) -> np.ndarray:
return np.where(vector > 0 , __lowercase , (alpha * (np.exp(__lowercase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 621 | 1 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availabl... | 621 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base impor... | 621 | 1 |
"""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 | 621 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def a__ ( __lowercase ) -> List[Any]:
_A = os.path.join(args.tf_model_dir , "parameters.json" )
_A ... | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
_A = [randint(-1000 , 1000 ) for i in range(10 )]
_A = randint(... | 621 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for... | 621 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class snake_case ( _UpperCamelCase):
def __init__( self : Optional[int] , a__ : Optional[int] , a__ : List[Any] ) ... | 621 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
... | 621 | 1 |
"""simple docstring"""
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
a_ = importlib.util.find_spec("s3fs") is not None
if _has_safs:
... | 621 |
"""simple docstring"""
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,
... | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"configuration_roformer": ["ROFORME... | 621 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class snake_case ( ... | 621 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case ( _UpperCamelCase... | 621 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTo... | 621 | 1 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def a__ ( *__lowercase , __lowercase = None , __lowercase=True , __lowercase=2 ) -> List[str]:
from .. import __version__
... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
# Return True if there is node that has not iterated.
_A = [False] * len(__lowercase )
_A = []
queue.append(__lowercase )
_A = True... | 621 | 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 .tokenizati... | 621 |
"""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,
ConditionalDetrForObjectDetect... | 621 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for... | 621 |
"""simple docstring"""
import random
def a__ ( __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
_A = a[left_index]
_A = left_index + 1
for j in range(left_index + 1 , __lowercase ):
if a[j] < pivot:
_A ... | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase , __lowercase ) -> float:
_A = sorted(numsa + numsa )
_A , _A = divmod(len(__lowercase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
... | 621 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFe... | 621 | 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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transf... | 621 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase , __lowercase ) -> float:
_A = sorted(numsa + numsa )
_A , _A = divmod(len(__lowercase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
... | 621 | 1 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.st... | 621 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/bli... | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
def a__ ( __lowercase ) -> float:
_A = 0.00
_A = 0
for resistor in resistors:
if resistor <= 0:
_A = f"""Resistor at index {index} has a negative or zero value!"""
... | 621 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class snake_case ( unittest.TestCase , _UpperCamelCase):
def a_ ( self : Optional[Any] ) -> List[str]:
'''... | 621 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
c... | 621 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from ... | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
a_ = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
class snake_case :
def __init__( sel... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
while a != 0:
_A , _A = b % a, a
return b
def a__ ( __lowercase , __lowercase ) -> int:
if gcd(__lowercase , __lowercase ) != 1:
_A = f"... | 621 | 1 |
"""simple docstring"""
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax... | 621 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | 621 | 1 |
"""simple docstring"""
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
a_ = TypeVar("T")
class snake_case ( Generic[T]):
... | 621 |
"""simple docstring"""
class snake_case :
def __init__( self : Optional[int] , a__ : List[Any] , a__ : List[str] , a__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
_A ... | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
try:
if not i... | 621 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"configuration_roformer": ["ROFORME... | 621 | 1 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
return abs(__lowercase ) if a == 0 else greatest_common_divisor(b % a , __lowercase )
def a__ ( __lowercase , __lowercase ) -> int:
while y: # --> when y=0 then loop will ter... | 621 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
a_ = logging.get_logger(__name__)
class snake_case ( _UpperCamelCase):
def __init__( self : str , *a__ : Di... | 621 | 1 |
"""simple docstring"""
a_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def a__ ( ) -> None:
_A = input("Enter message: " )
_A = input("Enter key [alphanumeric]: " )
_A = input("Encrypt/Decrypt [e/d]: " )
if mode.lower().startswith("e"... | 621 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def a__ ( __lowercase ) -> Optional[int]:
_A = [
"encoder.version",
"decoder.version",
"model.enco... | 621 | 1 |
"""simple docstring"""
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
a_ = {
"tiny.en": "https://openaipublic.azureedge... | 621 |
"""simple docstring"""
import numpy as np
def a__ ( __lowercase , __lowercase ) -> np.ndarray:
return np.where(vector > 0 , __lowercase , (alpha * (np.exp(__lowercase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 621 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"configuration_roberta": ["ROBERTA_... | 621 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base impor... | 621 | 1 |
"""simple docstring"""
import random
def a__ ( __lowercase , __lowercase , __lowercase ) -> Optional[Any]:
_A = a[left_index]
_A = left_index + 1
for j in range(left_index + 1 , __lowercase ):
if a[j] < pivot:
_A ... | 621 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def a__ ( __lowercase ) -> List[Any]:
_A = os.path.join(args.tf_model_dir , "parameters.json" )
_A ... | 621 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | 621 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for... | 621 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import Co... | 621 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
... | 621 | 1 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import To... | 621 |
"""simple docstring"""
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,
... | 621 | 1 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
a_ = "src/transformers"
# Matches is_xxx_available()
a_ = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
a_ = re.compile... | 621 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ = False
class snake_case ( ... | 621 | 1 |
"""simple docstring"""
import math
import sys
def a__ ( __lowercase ) -> int:
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 num... | 621 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTo... | 621 | 1 |
"""simple docstring"""
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils impor... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
# Return True if there is node that has not iterated.
_A = [False] * len(__lowercase )
_A = []
queue.append(__lowercase )
_A = True... | 621 | 1 |
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