code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import colorsys
from PIL import Image # type: ignore
def lowerCamelCase_ ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : int ) -> float:
"""simple docstring"""
__lowerCamelCase = x
__lowe... | 348 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils imp... | 348 | 1 |
from typing import List
from .keymap import KEYMAP, get_character
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> Optional[Any]:
"""simple docstring"""
def decorator(UpperCamelCase__ : Optional[int] ):
__lowerCamelCase = get... | 348 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerat... | 348 | 1 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_... | 348 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClass... | 348 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import as... | 348 |
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_gpu, require_vision, slow, torch_device
... | 348 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils impor... | 348 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-smal... | 348 | 1 |
from numpy import exp, pi, sqrt
def lowerCamelCase_ ( UpperCamelCase__ : Any , UpperCamelCase__ : float = 0.0 , UpperCamelCase__ : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x -... | 348 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention... | 348 | 1 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCamelCase_ ( UpperCamelCase__ : Namespace ) -> Optional[int]:
"""simple docstring"""
return ConvertCommand(
args.... | 348 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self ... | 348 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabl... | 348 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
... | 348 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = ... | 348 |
import requests
__A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__lowerCamelCase = requests.get(_NEWS_API + bbc_news_api_key ).jso... | 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> int:
"""simple docstring"""
__lowerCamelCase = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__lowerCamelCase = n - k
... | 348 |
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_dat... | 348 | 1 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__A = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruk... | 348 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 348 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[float] , UpperCamelCase__ : list[float] ) -> float:
"""simple docstring"""
__lowerCamelCase = sorted(numsa + numsa )
__lowerCamelCase , __low... | 348 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models... | 348 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"facebook/levit-128S": "https:... | 348 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils i... | 348 | 1 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_... | 348 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also ... | 348 | 1 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_... | 348 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@... | 348 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered... | 348 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEF... | 348 | 1 |
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
__A = logging.get_logger(__name__)
class... | 348 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[float] , UpperCamelCase__ : list[float] ) -> float:
"""simple docstring"""
__lowerCamelCase = sorted(numsa + numsa )
__lowerCamelCase , __low... | 348 | 1 |
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.net/main/whisper/models/d3dd57d32... | 348 |
__A = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
"electro... | 348 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 348 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 348 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"kssteven/ibert-roberta-base": "https://huggingface.co/kssteven... | 348 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available... | 348 | 1 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import ... | 348 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ... | 348 | 1 |
from collections.abc import Sequence
def lowerCamelCase_ ( UpperCamelCase__ : Sequence[int] | None = None ) -> int:
"""simple docstring"""
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
__lowerC... | 348 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_te... | 348 | 1 |
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
__A = datasets.logging.get_logger(__name__)
__A = "\\n@InProceedings{moosavi2019minimum,\n author = { Nafise Sadat Mo... | 348 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils imp... | 348 | 1 |
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_c... | 348 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerat... | 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A = {
"configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "Mobile... | 348 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClass... | 348 | 1 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
... | 348 |
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_gpu, require_vision, slow, torch_device
... | 348 | 1 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassificatio... | 348 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-smal... | 348 | 1 |
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_warmup, set_seed
from accelerate impor... | 348 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention... | 348 | 1 |
__A = 0 # The first color of the flag.
__A = 1 # The second color of the flag.
__A = 2 # The third color of the flag.
__A = (red, white, blue)
def lowerCamelCase_ ( UpperCamelCase__ : list ) -> list:
"""simple docstring"""
if... | 348 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self ... | 348 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProces... | 348 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
... | 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
global f # a global dp table for knapsack... | 348 |
import requests
__A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__lowerCamelCase = requests.get(_NEWS_API + bbc_news_api_key ).jso... | 348 | 1 |
import operator
def lowerCamelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : bool = False , UpperCamelCase__ : list | None = None ) -> list:
"""simple docstring"""
__lowerCamelCase = operator.lt if reverse else operat... | 348 |
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_dat... | 348 | 1 |
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 __lowerCAmelCase ( __magic_name__ ):
... | 348 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> bool:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 348 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models... | 348 | 1 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
__A = logging.getLogger(__name... | 348 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils i... | 348 | 1 |
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ ) -> None:
'''simple docstring'''
__lowerCamelCase = size
__lowerCamelCase = [0] * size
__lowerCamelCase = [0] * size
... | 348 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also ... | 348 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ) -> list:
"""simple docstring"""
__lowerCamelCase = []... | 348 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@... | 348 | 1 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.jso... | 348 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEF... | 348 | 1 |
import heapq
def lowerCamelCase_ ( UpperCamelCase__ : dict ) -> set[int]:
"""simple docstring"""
__lowerCamelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue wil... | 348 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[float] , UpperCamelCase__ : list[float] ) -> float:
"""simple docstring"""
__lowerCamelCase = sorted(numsa + numsa )
__lowerCamelCase , __low... | 348 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def lowercase_ ( lowerCamelCase__ ) -> int:
'''simple... | 348 |
__A = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
"electro... | 348 | 1 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
__A = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
__A = requests.get(url, headers={"UserAgent":... | 348 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {"configuration_opt": ["OPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OPTConfig"]}
... | 348 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available... | 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int = 200 ) -> int:
"""simple docstring"""
__lowerCamelCase = [1, 2, 5, 10, 20, 50, 100, 200]
__lowerCamelCase = [0] * (pence + 1)
__lowerCamelCase = 1 # base case: 1 way to make 0 pence
for coin... | 348 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ... | 348 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if... | 348 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_te... | 348 | 1 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common imp... | 348 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils imp... | 348 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils imp... | 348 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerat... | 348 | 1 |
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self , lowerCamelCase__ , lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
__lowerCamelCase = name
__lowerCamelCase = val
def __... | 348 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClass... | 348 | 1 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__A = logging.get_logger(__name__)
def lowerCamelCase_ ( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Optional[i... | 348 |
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_gpu, require_vision, slow, torch_device
... | 348 | 1 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_at... | 348 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-smal... | 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int = 1000 ) -> int:
"""simple docstring"""
__lowerCamelCase = 2**power
__lowerCamelCase = 0
while n:
__lowerCamelCase , __lowerCamelCase = r + n % 10, n // 10
return r
if __name_... | 348 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention... | 348 | 1 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
... | 348 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self ... | 348 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 348 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
... | 348 | 1 |
import math
__A = 10
__A = 7
__A = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCamelCase_ ( UpperCamelCase__ : int = 20 ) -> str:
"""simple docstring"""
__lowerCamelCase = math.comb(UpperCamelCase__ , UpperCamelCase__ )
... | 348 |
import requests
__A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__lowerCamelCase = requests.get(_NEWS_API + bbc_news_api_key ).jso... | 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : list[int] ) -> int:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(UpperCamelCase__ , (list, tuple) ) or not all(
isinstance(UpperCamelCase__ , UpperCamelCase__ ) ... | 348 |
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_dat... | 348 | 1 |
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_dat... | 348 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 348 | 1 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__A = 4
__A = 3
class __lowerCAmelCase ( __magic_name__ ):
"""... | 348 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models... | 348 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> str:
"""simple docstring"""
def is_in_circle(UpperCamelCase__ : float , ... | 348 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils i... | 348 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
... | 348 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also ... | 348 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import... | 348 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@... | 348 | 1 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
__A = logging.get_logger(__name__)
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
def __init__( self , *lower... | 348 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEF... | 348 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
... | 348 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[float] , UpperCamelCase__ : list[float] ) -> float:
"""simple docstring"""
__lowerCamelCase = sorted(numsa + numsa )
__lowerCamelCase , __low... | 348 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first c... | 348 |
__A = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
"electro... | 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> str:
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError('both inputs must be positive integers' )
__lowerCamelCase = str(bin(UpperCa... | 348 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnn... | 348 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available... | 348 | 1 |
__A = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
"electro... | 348 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ... | 348 | 1 |
import argparse
import os
import re
__A = "src/transformers"
# Pattern that looks at the indentation in a line.
__A = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__A = re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure... | 348 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_te... | 348 | 1 |
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 348 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils imp... | 348 | 1 |
from random import shuffle
import tensorflow as tf
from numpy import array
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int ) -> Optional[Any]:
"""simple docstring"""
__lowerCamelCase = int(UpperCamel... | 348 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerat... | 348 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[float] ) -> float:
"""simple docstring"""
__lowerCamelCase = 0.00
__lowerCamelCase = 0
for resistor in resistors:
if resistor <= 0:
... | 348 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClass... | 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> bool:
"""simple docstring"""
__lowerCamelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def lowerCamelCase_ ( UpperCamelCase__ : int = 5000 ) -> int:
... | 348 |
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_gpu, require_vision, slow, torch_device
... | 348 | 1 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self ) -> List[st... | 348 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-smal... | 348 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : str , **UpperCamelCase__ : Tuple ) -> List[str]:
"""simple docstring"""
... | 348 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention... | 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
__lowerCamelCase = str(bin(UpperCamelCase__ ... | 348 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self ... | 348 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__A = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true"... | 348 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
... | 348 | 1 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : Any=False ) -> List[Any]:
"""simple docstring"""
__lo... | 348 |
import requests
__A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__lowerCamelCase = requests.get(_NEWS_API + bbc_news_api_key ).jso... | 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
"configuration_table_transformer": [
"TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TableTransformerConfig",
"TableTransformerOnnxConf... | 348 |
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_dat... | 348 | 1 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = (DDPMParallelScheduler,)
def lowercase_ ( self ... | 348 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 348 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.tex... | 348 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models... | 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int = 1000 ) -> int:
"""simple docstring"""
__lowerCamelCase = -1
__lowerCamelCase = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
... | 348 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils i... | 348 | 1 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"google/efficientnet-b7"... | 348 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also ... | 348 | 1 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import T... | 348 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@... | 348 | 1 |
import numpy as np
def lowerCamelCase_ ( UpperCamelCase__ : np.ndarray , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : float = 1E-12 , UpperCamelCase__ : int = 100 , ) -> tuple[float, np.ndarray]:
"""simple docstring... | 348 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEF... | 348 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] ) -> Any:
"""simple docstring"""
__lowerCamelCase = os... | 348 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[float] , UpperCamelCase__ : list[float] ) -> float:
"""simple docstring"""
__lowerCamelCase = sorted(numsa + numsa )
__lowerCamelCase , __low... | 348 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
__A = ["small", "medium", "large"]
__A = "lm_head.decoder.weight"
__A = "lm_head.weight"
def lowerCamelCase_ ( UpperCamelCase__ : str , UpperCamelCase__ : s... | 348 |
__A = {
"joule": 1.0,
"kilojoule": 10_00,
"megajoule": 1_00_00_00,
"gigajoule": 10_00_00_00_00,
"wattsecond": 1.0,
"watthour": 36_00,
"kilowatthour": 3_60_00_00,
"newtonmeter": 1.0,
"calorie_nutr": 41_86.8,
"kilocalorie_nutr": 4_18_68_00.00,
"electro... | 348 | 1 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''MCTCTFeatureExtractor'''
snake_case_ = '''AutoTokenizer... | 348 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : list[list[int]] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : set ) -> int:
"""simple docstring"""
__lowerCamelCase , __lowerCamelCase = len(UpperCamelCase... | 348 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available... | 348 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class __lowerCAmelCase ( __magic_name__ ):
"""simple do... | 348 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=__magic_name__ ):
"""simple docstring"""
snake_case_ = ['''sentencepiece''']
def __init__( self , *lowerCamelCase__ , **lowerCamelCase__ ... | 348 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def ... | 348 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_te... | 348 | 1 |
import qiskit
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> qiskit.result.counts.Counts:
"""simple docstring"""
__lowerCamelCase = qiskit.Aer.get_backend('aer_simulator' )
__lowerCamelCase = q... | 348 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils imp... | 348 | 1 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self ... | 348 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerat... | 348 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
# See all AltCLIP models ... | 348 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClass... | 348 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEF... | 348 |
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_gpu, require_vision, slow, torch_device
... | 348 | 1 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, ... | 348 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-smal... | 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__A = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
... | 348 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention... | 348 | 1 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
__A = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
... | 348 |
import sys
from collections import defaultdict
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
__lowerCamelCase = []
def lowercase_ ( self ... | 348 | 1 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaToken... | 348 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
... | 348 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : Any ) -> Optional[Any]:
"""simple docstring"""
__lowerCamelCase = []
__lowerCamelCase = []
__lowerCamelCase = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
... | 348 |
import requests
__A = "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> None:
"""simple docstring"""
__lowerCamelCase = requests.get(_NEWS_API + bbc_news_api_key ).jso... | 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 348 |
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_dat... | 348 | 1 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is... | 348 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 348 | 1 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils i... | 348 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models... | 348 | 1 |
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