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 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a : Dict = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
'PoolForm... | 147 | import collections
import importlib.util
import os
import re
from pathlib import Path
_SCREAMING_SNAKE_CASE = 'src/transformers'
# Matches is_xxx_available()
_SCREAMING_SNAKE_CASE = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
_SCREAMI... | 180 | 0 |
"""simple docstring"""
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipel... | 150 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Union[str, Any] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
... | 150 | 1 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copie... | 221 | """simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms... | 221 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
_a : Optional[int] = log... | 46 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _lowerCAmelCase ( lowercase ) -> Callable:
@wraps(lowercase )
def _inner_fn(*lowercase , **lowercase ):
warnings.warn(
(f'\'{fn.__name__}\' is expe... | 46 | 1 |
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = 2
_UpperCAmelCase = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(lowercase ... | 289 | """simple docstring"""
from sklearn.metrics import recall_score
import datasets
UpperCAmelCase__ = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN... | 289 | 1 |
import functools
def a_ ( _lowercase , _lowercase ):
# Validation
if not isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) or not all(isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) for day in days ):
... | 370 |
"""simple docstring"""
from __future__ import annotations
import math
def a_ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
... | 128 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
UpperCamelCase : str = logging.get_logger(__name__)
class __lowerCAmelCase ( snake_case_ ):
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
... | 316 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | 312 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/... | 361 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
"conf... | 149 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_available():
rais... | 309 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : int = 1_00 ) -> int:
_lowerCAmelCase : Optional[Any] = (n * (n + 1) // 2) ** 2
_lowerCAmelCase : str = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
... | 309 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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_tenso... | 361 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cas... | 17 | 0 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --hos... | 226 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
P... | 226 | 1 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer ... | 198 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import loggin... | 198 | 1 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
lowerCAmelCase : Any = Mapping[str, np.ndarray]
lowerCAmelCase : int = Mapping[str, Any] # Is a nested dict.... | 13 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def snake_case( ) -> int:
'''simple docstring'''
lowercase : List[str] = ArgumentP... | 308 | 0 |
"""simple docstring"""
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
lowerCa... | 358 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thing... | 34 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def _UpperCamelCase ( __A , __A ) -> list:
'''simple docstring'''
if len(__A ) != 2 or len(a[0] ) != 2 or len(__A ) != 2 or len(b[0] ) != 2:
raise Exception("Matric... | 80 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase: List[str] = {}
try:
if not is_s... | 255 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
UpperCamelCase_ : Dict = logging.get_logger(__name__)
class _a ( __lowerCAmelCase ):
def __init__( self ,*_SCREAMING_SNAKE_CASE ,... | 142 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeniz... | 142 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError(''... | 45 |
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 import Accelerator, Dist... | 122 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import 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_... | 190 |
'''simple docstring'''
import argparse
lowercase__ : Any = '''docs/source/_static/js/custom.js'''
def _lowerCAmelCase ( __snake_case : Union[str, Any] ) -> str:
with open(__snake_case , encoding='utf-8' , newline='\n' )... | 190 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Dict = logging.get_logger(__name__)
lowerCamelCase_ : List[Any] = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-... | 81 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _SCREAMING_SNAKE_CASE ( lowercase : Union[dict, list, tuple, torch... | 204 | 0 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, Trai... | 11 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
lowercase_ = "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed... | 11 | 1 |
"""simple docstring"""
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 ... | 81 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[Any] = {}
try:
... | 81 | 1 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :str , lowerCAmelCase__ :list[str] | None = None , lowerCAmelCase__ :dict[str, float] | None = None , lowerCAmelCase__ :bool = False , ) -> tuple[int, float, str]:
... | 356 |
'''simple docstring'''
def _A (lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
assert (
isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and number_of_steps > 0
), f'number_of_steps needs to be positive integer, your input {... | 104 | 0 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_comm... | 209 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import... | 209 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = '▁'
lowercase__ = ... | 203 | """simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowercase__ = re.compile(r'\b(a|an|the)\b', re.UNICODE)
lowercase__ = None
def __a ( ) ->List[Any]:
a__: Dict = argparse.Ar... | 203 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart impor... | 27 | """simple docstring"""
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase=2_8_1_2_3 ) -> Any:
lowercase__: Optional[Any] = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k... | 177 | 0 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> list[str]:
if partitions <= 0:
raise ValueError("""partitions must be a positive number!""" )
... | 364 |
'''simple docstring'''
def __lowerCamelCase ( __lowerCAmelCase : Dict ) -> Optional[Any]:
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
... | 3 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prep... | 145 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, pr... | 86 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_m... | 371 |
"""simple docstring"""
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
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__... | 233 | 0 |
import doctest
from collections import deque
import numpy as np
class __snake_case :
def __init__( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE__ = [1, 2, 3... | 219 | import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class __snake_case ( lowerCamelCase_ ... | 219 | 1 |
"""simple docstring"""
_a : List[str] = {
'Pillow': 'Pillow',
'accelerate': 'accelerate>=0.11.0',
'compel': 'compel==0.1.8',
'black': 'black~=23.1',
'datasets': 'datasets',
'filelock': 'filelock',
'flax': 'flax>=0.4.1',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'hug... | 371 | """simple docstring"""
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state im... | 126 | 0 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE_ : list , SCREAMING_SNAKE_CASE_ : int ):
# Checks if the entire collection has been sorted
if len(SCREAMING_SNAKE_CASE_ ) <= 1 or n <= 1:
return
insert_next(SCREAMING_SNA... | 92 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"""The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionImg2ImgPipeline` instead."""
)
| 92 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 363 |
from PIL import Image
def __magic_name__ ( __a : Image ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ = image.size
UpperCamelCase__ = 0
UpperCamelCase__ = image.load()
for i in range(__a ):
for j in range(__a ... | 178 | 0 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__a :Tuple = WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])
def __snake_case ( __UpperCamelCase ... | 312 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
fro... | 312 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase: str = {
"""configuration_gpt_neo""": ["""GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoConfig""", """GPTNeoOnnx... | 356 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax imp... | 96 | 0 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.dat... | 201 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResamplin... | 201 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class snake_case_ (lowerCamelCase_ ):
def lowerCamelCase__( self :Union[str, Any] ) -> Union[str, Any]:
return [
{"col_1": 3, "col_2": "a"}... | 109 |
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
from flax.j... | 109 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A ={
'configuration_longformer': [
'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 34 |
'''simple docstring'''
class __lowerCAmelCase : # Public class to implement a graph
'''simple docstring'''
def __init__(self : int , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : list[list[bool]] ):
'''simple ... | 2 | 0 |
from heapq import heappop, heappush
import numpy as np
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , ):
UpperCAmelCase : Optional[Any] = grid.shape
UpperCAmelCase : List[str] = [-1, 1, 0, 0]
UpperCAmelCase : Opti... | 357 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowercase__ = {
"facebook/maskformer-s... | 280 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCamelCase_ ( _UpperCAmelCase : Optional[int] = "https://www.worldometers.info/coronavirus" ) -> int:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = Beautifu... | 31 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_avail... | 149 | 0 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_lowerCAmelCase : ... | 361 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFA... | 298 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pip... | 129 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreT... | 129 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> int:
while second != 0:
lowercase__: List[Any] = first & second
first ^= second
lowercase__: List[Any] = c << 1
return first
if __name__ == "__main__":
i... | 2 | """simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class UpperCAmelCase :
"""simple docstring"""
_UpperCAmelCase :s... | 2 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_UpperCAmelCase : str = {
"configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
"tokenization_mvp": ["MvpTokenizer"],
}
... | 236 |
import numpy
# List of input, output pairs
_UpperCAmelCase : List[str] = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
_UpperCAmelCase : Optional[Any] = (((515, 22, 13), 555), ((61, 35, 49), 150))
_UpperCAmelCase : Tuple = [2, 4, 1, 5... | 236 | 1 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from d... | 367 |
def lowerCAmelCase_ ( __UpperCAmelCase: int ) -> list[int]:
if length <= 0 or not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(__UpperCAmelCase )]
... | 247 | 0 |
_SCREAMING_SNAKE_CASE = {str(digit): digit**5 for digit in range(1_0)}
def lowercase( UpperCamelCase_ ) -> int:
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(UpperCamelCase_ ) )
def lowercase( ) -> int:
... | 343 | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface i... | 343 | 1 |
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : int = 4_0_0_0_0_0_0 ):
'''simple docstring'''
__snake_case : int = [0, 1]
__snake_case : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
bre... | 20 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 20 | 1 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
_lowerCamelCase : Union[str, Any] = "2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse(... | 336 |
import argparse
from collections import defaultdict
import yaml
__a = 'docs/source/en/_toctree.yml'
def a ( snake_case__: Dict ):
'''simple docstring'''
lowercase_ = defaultdict(snake_case__ )
for doc in model_doc:
counts[doc["... | 30 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase_ = {'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig',... | 368 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBe... | 174 | 0 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default... | 118 | from ...processing_utils import ProcessorMixin
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = ['''image_processor''', '''feature_extractor''']
lowerCamelCase__ = '''TvltImageProcessor'''
lowerCamelCase__ = '''TvltFeatureExtractor'''
... | 118 | 1 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ):
lowercase__ : list[list[int]] = []
lowercase__ : list[int] = []
lowercase__ : Union[str, Any] = 0
lowercase__ : Union[str, Any] = sum(Uppe... | 214 | '''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pip... | 214 | 1 |
'''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 diffusers.utils... | 27 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=_lowercase):
snake_case__ = ['''flax''']
def __init__( self : int , *__UpperCamelCase : Optional[int] , **__UpperCamelCase : ... | 256 | 0 |
'''simple docstring'''
def lowercase (_A ):
"""simple docstring"""
return 1_0 - x * x
def lowercase (_A , _A ):
"""simple docstring"""
if equation(_A ) * equation(_A ) >= 0... | 25 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase : List[Any] = logging.get_logger(__name__)
lowerCAmelC... | 25 | 1 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __lowerCamelCase ( lowerCamelCase__ : str , lowerCamelCase__ : str , lowerCamelCase__ : Union[str, Any] ):
'''simple docstring'''
lowerCamelCase ... | 252 |
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> list:
'''simple docstring'''
__UpperCAmelCase = len(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = [[0] * n for i in range(SCREAMING_SNAKE_CASE )]
for i... | 333 | 0 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def A__ ( __lowerCamelCase, __lowerCamelCase = "cpu", __lowerCamelCase = None ):
SCREAMING_SNAKE_CASE_ = torch.load(__lowerCamelCase, map_location=__lowerCamelCase )
for k, v in tqdm(state_dict.items()... | 257 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator... | 257 | 1 |
from math import ceil
def _snake_case( SCREAMING_SNAKE_CASE__ = 1_001 ) -> int:
lowercase : List[Any] = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
lowercase : List[Any] = 2 * i + 1
lowercase ... | 20 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
lowerCAmelCase__ = logging.getLogger(__name__)
class a__ ( snake_case ):
"""simple docstring"""
def __init__( self ... | 68 | 0 |
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_toke... | 368 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokeni... | 301 | 0 |
import requests
from bsa import BeautifulSoup
def A ( _lowerCamelCase = "AAPL" ):
'''simple docstring'''
_lowerCAmelCase : str = F"https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"
_lowerCAmelCase : Optional[int] = BeautifulSoup(r... | 36 |
from PIL import Image
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : int = image.size
_lowerCAmelCase : Any = 0
_lowerCAmelCase : Tuple = image.load()
for i in ra... | 36 | 1 |
from __future__ import annotations
def _A ( lowerCAmelCase_ : int = 4 ):
"""simple docstring"""
lowerCAmelCase__ = abs(lowerCAmelCase_ ) or 4
return [[1 + x + y * row_size for x in range(lowerCAmelCase_ )] for y in range(lowerCAmelCase... | 356 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _A ( lowerCAmelCase_ : str , lowerCAmelCase_ : str , **lowerCAmelCase_ : str ):
"""simple docstring"""
lowerCAmelCase__ = AutoConfig... | 221 | 0 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowerCAmelCase_ (lowerCAmelCase__: str ):
"""simple docstring"""
if "model" in orig_key:
UpperCAmelCase_: List[Any] = orig_key.replace("""model.""" ... | 147 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer... | 147 | 1 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
# TODO Update this
lowercase__ = {
... | 364 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowercase__ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
... | 12 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils... | 275 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.im... | 296 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase__ = {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitCo... | 30 | """simple docstring"""
import mpmath # for roots of unity
import numpy as np
class a :
def __init__( self : Tuple , __lowerCAmelCase : Dict=None , __lowerCAmelCase : Union[str, Any]=None ):
# Input as list
_UpperCAmelCase = list(poly_a or [0] )[:]
_Upp... | 30 | 1 |
"""simple docstring"""
from __future__ import annotations
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> None:
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
... | 291 | '''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_REC... | 1 | 0 |
"""simple docstring"""
import math
def snake_case_ ( A_ : Any, A_ : Dict = 0, A_ : Union[str, Any] = 0 ):
'''simple docstring'''
_lowerCamelCase : Tuple = end or len(A_ )
for i in range(A_, A_ ):
_lowerCamelCase ... | 358 |
"""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,
ConditionalDetrForObjectDetection,
Cond... | 175 | 0 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_... | 345 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTe... | 345 | 1 |
from __future__ import annotations
from math import pi
def __lowerCamelCase ( __snake_case : float, __snake_case : float, __snake_case : float ) -> dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
rais... | 370 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__snake_case : Optional[int] = WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])
def __l... | 136 | 0 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODE... | 66 |
"""simple docstring"""
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPMod... | 243 | 0 |
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 Optional
import huggingface_hub.utils as hf_hub_ut... | 367 | import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = [
'word_embeddi... | 81 | 0 |
from __future__ import annotations
def lowercase_ ( _lowerCamelCase : str , _lowerCamelCase : list[str] | None = None , _lowerCamelCase : dict[str, float] | None = None , _lowerCamelCase : bool = False , ):
lowercase__ : List[Any] = cipher... | 87 |
"""simple docstring"""
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelF... | 316 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case = {
"""configuration_blenderbot_small""": [
"""BLENDERBOT_SMALL_PRETRAI... | 169 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
__snake_case = False
try:
__snake_case = _is_packa... | 169 | 1 |
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils impor... | 286 |
"""simple docstring"""
import qiskit
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : Tuple = qiskit.Aer.get_backend('aer_simulator' )
A_ : str = qiskit.QuantumCircuit(4 , 2 )
# enc... | 286 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_availab... | 115 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 115 | 1 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
SCREAMING_SNAKE_CASE : Dict = re.compile(r"\b(a|an|the)\b", re.UNICODE)
SCREAMING_SNAKE_CASE : List[str] = None
def UpperCamelCase_( ) -> ... | 21 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import re... | 257 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin... | 362 |
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE_:Optional[Any] = logging.get_lo... | 115 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : str = {
"configuration_x... | 51 |
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... | 51 | 1 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.... | 355 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_uti... | 168 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCAmelCase ( )->List[Any]:
'''simple docstring'''
snake_case_ = ArgumentParser(
description... | 159 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_mod... | 19 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnn... | 35 | from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
... | 35 | 1 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class A__(a_ ):
"""sim... | 248 |
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENA... | 248 | 1 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.... | 59 |
import os
def lowerCamelCase_ ( _a : str = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(_a ) , _a ) ) as input_file:
UpperCAmelCase_ : Dict = [
[int(_a ) for element in line.split(""",""" )]
... | 59 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ : int = {
'configuration_convbert': ['CONVBERT_... | 25 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
... | 79 | 0 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def UpperCAmelCase_ (__a : bytes ):
"""simple docstring"""
if len(__a ) != 3_2:
raise ValueError('Input must be of length 32' )
_a : Any = b''
for i in [3, 2, 1, 0]:
... | 5 |
'''simple docstring'''
__lowerCAmelCase = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""... | 5 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 55 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
UpperCAmelCase_ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('', '|', '|'),
datarow=DataRow... | 12 | 0 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> bool:
'''simple docstring'''
if num < 0:
return False
__UpperCamelCase : int = num
__UpperCamelCase : int = 0
while num > 0:
... | 351 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
lowercase : List[str] = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : ... | 151 | 0 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 342 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__magic_name__: str = logging.get_logger(__name__)
__magic_name__: ... | 342 | 1 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, 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_... | 10 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def UpperCamelCase ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int , __lowerCa... | 10 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _snake_case( SCREAMING_SNAKE_CAS... | 7 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
fro... | 291 | 0 |
'''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 B... | 147 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowerCamelCase : list ):
if len(__lowerCamelCase ) <= 1:
return lst
lowercase_ :Optional[Any] = 1
while i < len(__lowerCamelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
... | 147 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
... | 224 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Tuple = logging.get_logger(__name__)
lowercase__ : Any = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface... | 224 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a = logging.get_logger(__name__)
_a = {
"""facebook/convnex... | 100 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Optional[int]:
"""simple docstring"""
if len(__snake_case ) <= 1 or n <= 1:
return
insert_next(__snake_case, ... | 100 | 1 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def a ( __a="ro" , __a="en" , __a="wmt16" , __a=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise Import... | 97 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"""The `image_to_image.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionImg2ImgPipeline` instead."""
)
| 92 | 0 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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_... | 99 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def a__ ( UpperCAmelCase : int ) -> Dict:
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCAmelCase : float , Upper... | 99 | 1 |
"""simple docstring"""
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 ... | 316 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCamelCase : Union[str, Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def ... | 316 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Dict = logging.get_logger(__name__)
lowercase : Any = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',... | 357 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAme... | 36 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.... | 53 | __lowerCamelCase : List[Any] = 6_5521
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 0
for plain_chr in plain_text:
S... | 219 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase: Dict = logging.get_logger(__name__)
UpperCAmelCase: Dict = {
"""MIT/ast-finetuned-audioset-10-10-0.4593""": (
"""https://huggingface.co/MI... | 336 |
"""simple docstring"""
import argparse
from collections import defaultdict
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : str = F"""{file}_{class_name}_{test_n... | 336 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_... | 116 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : List[str] = (DDIMParallelScheduler,)
__lowerCamelCase : int ... | 116 | 1 |
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
# TODO Update this
lowerCAmelCase_ = {
"""facebook/esm-1b""":... | 371 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase_ ( lowerCAmelCase: List[str] , lowerCAmelCase: Dict , lowerCAm... | 260 | 0 |
import numpy as np
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : np.array ) -> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 38 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class _SCREAMING_SNAKE_CASE ( _a ):
def __init__( self : List[Any] , __lowerCamelCase : Callable , __low... | 38 | 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
__UpperCamelCase : List[str] = 4
__UpperCamelCase : List[Any] = 3
class __lowerCAme... | 347 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
d... | 347 | 1 |
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