code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import Any
def __UpperCAmelCase ( lowerCamelCase_ : list ) -> list[Any]:
"""simple docstring"""
if not input_list:
return []
SCREAMING_SNAKE_CASE_ : Any = [input_list.count(lowerCamelCase_ ) for value in input_list]
SCREAMIN... | 105 | '''simple docstring'''
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
... | 370 | 0 |
from __future__ import annotations
import time
import numpy as np
a = [8, 5, 9, 7]
a = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
a = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
... | 650 |
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_array, valid_... | 650 | 1 |
from __future__ import annotations
def _lowercase ( UpperCamelCase_ ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = len(_A ) // 2
# choose the middle 3 elements
SCREAMING_SNAKE_CASE__ = lst[m - 1 : m + 2]
# if middle element is p... | 472 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __SCREAMING_SNAKE_CASE( a_ , unittest.TestCase ):
_UpperCAmelCase = ... | 328 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
sm... | 701 | '''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __... | 389 | 0 |
"""simple docstring"""
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, rand... | 575 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ja... | 123 | 0 |
'''simple docstring'''
lowerCamelCase_ : Union[str, Any] = 8.3_1_4_4_5_9_8
def __magic_name__( _A , _A ):
'''simple docstring'''
if temperature < 0:
raise Exception("""Temperature cannot be less than 0 K""" )
if molar_mass <= 0:
... | 265 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILIma... | 265 | 1 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pyt... | 55 |
"""simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizatio... | 512 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import C... | 115 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def _a ( Upper... | 115 | 1 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common im... | 528 | """simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
SCREAMING_SNAKE_CASE__:Optional[A... | 528 | 1 |
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 706 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase):
... | 75 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax... | 653 |
'''simple docstring'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ )-> str:
"""simple docstring"""
if not isinstance(__magic_name__ ,__magic_name__ ):
raise ValueError("iterations must be defined as integers" )
if not isinstanc... | 653 | 1 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class lowerCamelCase_ ( _SCREAMING_SNAKE_CASE ):
_lowerCamelCase : Op... | 403 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : Optional[int] ) -> int:
"""simple docstring"""
return getitem, k
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : Dict , U... | 403 | 1 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
__UpperCAmelCase = version.parse(version.parse(... | 651 |
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
__UpperCAmelCase = logging.getLo... | 651 | 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
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__... | 705 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils imp... | 474 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a ={
"""configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
"""tokenizati... | 652 |
"""simple docstring"""
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 ... | 388 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_AR... | 704 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : int=7 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = Non... | 620 | 0 |
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> --host <host> --key_... | 10 |
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf i... | 460 | 0 |
def lowerCamelCase_ ( UpperCamelCase__ : Tuple ):
'''simple docstring'''
return str(__UpperCAmelCase ) == str(__UpperCAmelCase )[::-1]
def lowerCamelCase_ ( UpperCamelCase__ : Tuple ):
'''simple docstring'''
... | 713 | import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def lowerCamelCase_ ( UpperCamelCase__ : Any ):
'''s... | 591 | 0 |
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__A : str = "http://www.mocks... | 656 | """simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __lowerCAmelCase ( unittest.TestCase):
'''simple docstring'''
def ... | 656 | 1 |
import re
from filelock import FileLock
try:
import nltk
lowercase : Optional[Any] = True
except (ImportError, ModuleNotFoundError):
lowercase : Any = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
... | 94 |
# Copyright 2021 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 requir... | 94 | 1 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Ac... | 18 |
"""simple docstring"""
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... | 553 | 0 |
"""simple docstring"""
import string
from math import logaa
def _SCREAMING_SNAKE_CASE ( _lowercase : str , _lowercase : str ) ->int:
'''simple docstring'''
a : Tuple = document.translate(
str.maketrans("" , "" ... | 31 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _SCREAMING_SNAKE_CASE ( _lowercase : List[str] ) ->int:
... | 31 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
... | 285 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase ={
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
raise ... | 285 | 1 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowercase ( tf.keras.layers.Layer ):
def __init__( self , _a , ... | 54 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@req... | 54 | 1 |
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 impor... | 280 |
from __future__ import annotations
def lowercase_ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
snake_case__ : list[list[int]] =[]
create_all_state(1 , SCREAMING_SNAKE_CASE , SCREAM... | 381 | 0 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionM... | 712 |
'''simple docstring'''
from __future__ import annotations
def __a(SCREAMING_SNAKE_CASE_ : list[float] , SCREAMING_SNAKE_CASE_ : list[float] ):
'''simple docstring'''
_lowerCAmelCase = sorted(numsa + numsa )
_lowerCAmelCase , _lowerCAmelCase = div... | 489 | 0 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCAmelCase : List[str] = 'MCTCTFeatureExtractor'... | 695 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
lowercase_ = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">... | 695 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def _snake_case ( lowerCamelCase__ : Callable[[int | float], int | float] , lowerCamelCase__ : int | float , lowerCamelCase__ : int | float , lowerCame... | 700 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
A__ : int = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Sys... | 244 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=_lowerCAmelCase )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :str = field(default='au... | 80 |
"""simple docstring"""
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 589 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
lowerCAmelCase__ : Tuple = {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}
... | 502 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class a ... | 502 | 1 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accel... | 94 |
def lowercase_ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
snake_case__ : List[Any] =word.split()
def justify(SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : int , SCREAMING_S... | 381 | 0 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 711 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
def _lowerCamelCase (... | 297 | 0 |
'''simple docstring'''
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def _A ( _lowerCAmelCase ):
... | 474 |
'''simple docstring'''
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class _UpperCamelCase ( A , unittest.TestCase ):
'''simple docstring'''
lowerCAm... | 474 | 1 |
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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbos... | 604 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is... | 604 | 1 |
from __future__ import annotations
from collections.abc import Callable
def a__ ( snake_case , snake_case , snake_case , snake_case = 100 , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = x_start
__SCREAMING_SNAKE_CASE : List[Any] ... | 74 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _lowerCamelCase ( ):
'''simple docstring'''
print('''Making key files...''' )
make_key_files(''... | 256 | 0 |
from __future__ import annotations
import math
def __UpperCamelCase ( _A : int , _A : int , _A : bool , _A : list[int] , _A : float ) ->int:
"""simple docstring"""
if depth < 0:
raise ValueError(... | 75 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__A : List[Any] = 'src/transformers'
__A : Tuple ... | 75 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowercase ( unittest.TestCase ):
'''simpl... | 696 |
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_... | 21 | 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 = {
"""configuration_xlm_roberta""": [
... | 611 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_... | 611 | 1 |
"""simple docstring"""
from manim import *
class lowerCAmelCase ( lowerCamelCase_ ):
'''simple docstring'''
def __A ( self ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCRE... | 247 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'''facebook/con... | 247 | 1 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowerCAmelCase_ ( nn.Module ):
SCREAMING_SNAKE_CASE_ : int
SCREAMING_SNAKE_CASE_ : int
SCREAMING_SNAKE... | 416 |
def _A ( _UpperCamelCase , _UpperCamelCase ):
_UpperCAmelCase : Tuple = len(_UpperCamelCase )
_UpperCAmelCase : Tuple = len(_UpperCamelCase )
_UpperCAmelCase : Dict = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
_UpperCAmelCase : List[A... | 416 | 1 |
'''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,
... | 3 |
def UpperCAmelCase_ ( UpperCAmelCase__ = "The quick brown fox jumps over the lazy dog" , ):
lowercase_ = set()
# Replace all the whitespace in our sentence
lowercase_ = input_str.replace(""" """ , """""" )
for alpha in input_str:
if "a" <= alph... | 412 | 0 |
'''simple docstring'''
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
... | 568 |
'''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():
from .toke... | 568 | 1 |
"""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
a : Tuple = logging.get_logger(... | 218 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
__lowerCamelCase = "WhisperFeatureExtractor"
__lowerCamelCase = "WhisperT... | 218 | 1 |
"""simple docstring"""
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
assert (
isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and number_of_steps > 0
), F'number_of_steps needs to be positive integer, your input {number_of_st... | 717 |
"""simple docstring"""
import warnings
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__ : str= logging.get_logger(__... | 20 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vis... | 589 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def A__ ( __lowerCamelCase ):
"""simple docstring"""
# enc... | 589 | 1 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_fo... | 707 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Optional[int] = {
"configuration_blenderbot": [
"BLENDERBOT_PRETRAINED_CON... | 642 | 0 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from tr... | 44 |
import math
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
return math.pow(_UpperCAmelCase , 2 ) - a
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> float:
return 2 * x
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ... | 562 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ ={
"""configuration_xlm_roberta_xl""": [
"""XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XLMRobertaXLConfig""",
"""XLMRobertaXLOnnxConfig"... | 701 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ =logging.get_logger(__name__)
UpperCAmelCase_ ={
"""vocab_file""": """vocab.txt""",
"""... | 33 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available... | 311 | import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
a_ = '\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evaluation of... | 417 | 0 |
from statistics import mean
import numpy as np
def _snake_case ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : int = 0
# Number of processes finished
_lowerCamelCase : str = 0
# ... | 718 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretra... | 492 | 0 |
'''simple docstring'''
class snake_case :
"""simple docstring"""
def __init__( self ) -> None:
"""simple docstring"""
snake_case__ : dict[str, TrieNode] = {} # Mapping from char to TrieNode
snake_case__ : int = False
d... | 261 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _A ( snake_case__ : List[str]="ro" , snake_case__ : int="en" , snake_case__ : Any="wmt16" , snake_case__ : Optional[Any]=None ):
try:
import datasets
except ... | 261 | 1 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,... | 717 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__snake_case : Union[str, Any] ... | 687 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
"funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/config.json"... | 468 |
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_utils import DUMMY_UNKNOWN_I... | 181 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowercase__ : str = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
... | 139 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCAmelCase_ = 42
lowerCAmelCase_ = None
lowerCAmelCase_ = None
... | 139 | 1 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : int ... | 85 |
"""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
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
... | 624 | 0 |
"""simple docstring"""
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import... | 715 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorForma... | 19 | 0 |
"""simple docstring"""
UpperCAmelCase__ = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
U... | 224 |
"""simple docstring"""
class lowerCAmelCase__ :
def __init__( self : List[Any] , _lowerCamelCase : str = "" , _lowerCamelCase : bool = False ):
# Mapping from the first character of the prefix of the node
_snake_case = {... | 224 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
... | 44 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,... | 44 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
fr... | 642 |
"""simple docstring"""
def lowercase__ ( lowerCAmelCase__ : str ) -> list[int]:
'''simple docstring'''
a__ : List[str] = [0 for i in range(len(lowerCAmelCase__ ) )]
# initialize interval's left pointer and right pointer
a__ , a__ : int = 0, 0
for i in ra... | 642 | 1 |
from datetime import datetime as dt
import os
from github import Github
a__ : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def UpperCAmelCase_( ):
"""simple docstring"... | 703 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def UpperCAmelCase_( a__ , a__ , a__ , a__ , a__ = None , a__ = None , a__ = None , ):
"""simple docstring"""
if config... | 333 | 0 |
"""simple docstring"""
import numpy as np
def A_ ( snake_case_ : Tuple ,snake_case_ : Any ,snake_case_ : str ,snake_case_ : Optional[int] ,snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : int = int(np... | 499 |
"""simple docstring"""
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, _concate... | 499 | 1 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, requi... | 539 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class snake_case (unittest.TestCase , UpperCamelCase ):
def _a ( self ) -> List[str]:
lowercase__ ... | 539 | 1 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils impo... | 439 |
"""simple docstring"""
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
A: int = logging.get_logger(__name__)
A: List[str] = [
["attention", "attn"],
... | 160 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
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 ... | 295 |
"""simple docstring"""
import operator as op
def _lowerCAmelCase ( lowerCamelCase__ : Tuple ) -> List[str]:
_SCREAMING_SNAKE_CASE : Optional[int] = []
_SCREAMING_SNAKE_CASE : str = lambda lowerCamelCase__, lowerCamelCase__ : int(x / ... | 295 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def a__ ( SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
lowerCAmelCase , lowerCAmelCase : Any = np.shape(__snake_case )
if rows != columns:
low... | 645 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class _snake_case ( lowerCamelCase ):
"""simple docstring"""
def __init__( self , a , a ) -> List[str]... | 317 | 0 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorc... | 701 |
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCAmelCase__ = logging.getLogger(__name__)
class lowercase ( _lowercase ):
"""simple docstring"""
a__ = "masked_bert"
def __init__( self , __snake_case=3_05_22 , __s... | 648 | 0 |
from __future__ import annotations
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> set[str]:
UpperCamelCase_ ,UpperCamelCase_: Dict = set(UpperCAmelCase__ ), [start]
while stack:
UpperCamelCase_: Any = stack.pop()
e... | 57 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRo... | 428 | 0 |
'''simple docstring'''
def _a ( __lowerCAmelCase : int = 10_00 ):
"""simple docstring"""
snake_case__ : Optional[Any] = -1
snake_case__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and ... | 502 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fa... | 502 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""facebook/data2vec-text... | 5 |
from collections.abc import Sequence
def UpperCamelCase ( snake_case__ = None):
if nums is None or not nums:
raise ValueError("Input sequence should not be empty")
lowerCAmelCase_ : Dict = nums[0]
for i in range(1 , len(snake_case__)):
lowerCAme... | 659 | 0 |
"""simple docstring"""
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... | 668 | """simple docstring"""
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_t... | 668 | 1 |
"""simple docstring"""
class lowerCamelCase_:
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=None , lowerCamelCase__=None ):
_lowerCamelCase = data
_lowerCamelCase = previous
_lowerCamel... | 661 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
UpperCAmelCase__ : float
UpperCAmelCase__ : TreeNode | None = None
UpperCAmelCase__ : TreeNode | ... | 14 | 0 |
from __future__ import annotations
import math
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Callable[[int | float], int | float] , SCREAMING_SNAKE_CASE : int | float , SCREAMING_SNAKE_CASE : int | float , SCREAMING_SNAKE_CA... | 688 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 688 | 1 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
snake_case_ = True
exce... | 164 |
'''simple docstring'''
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should... | 236 | 0 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_a = logging.get_logger(__name__)
class __A ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCa... | 700 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """ClapFeatureExtractor"""
lowerCAmelCase_ = ("""RobertaTokenizer""", """RobertaToken... | 29 | 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)
lower... | 41 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqCo... | 41 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from... | 253 |
from collections.abc import Generator
from math import sin
def __a ( __UpperCAmelCase : bytes ) -> bytes:
"""simple docstring"""
if len(__UpperCAmelCase ) != 32:
raise ValueError("Input must be of length 32" )
lowerCamelCase_ : Optional[Any... | 253 | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class UpperCamelCase__ ( _lowerCAmelCase ):
"""simple docstring"""
A__ : Tuple = "SpeechT5FeatureExtractor"
A__ : List[Any] = "SpeechT5Tokenizer"
... | 104 |
def A_ ( _lowerCAmelCase ) -> bool:
return str(_lowerCAmelCase ) == str(_lowerCAmelCase )[::-1]
def A_ ( _lowerCAmelCase ) -> int:
return int(_lowerCAmelCase ) + int(str(_lowerCAmelCase )[::-1] )
def A_ ( _lowerCAmelCase = 1_0000 ) -> int:
UpperCamelCase... | 629 | 0 |
'''simple docstring'''
import math
def __UpperCamelCase( _A : int ):
'''simple docstring'''
UpperCAmelCase__ : str = 0
UpperCAmelCase__ : List[Any] = 0
while num > 0:
UpperCAmelCase__ : Union[str, Any] = num % 8
UpperCAmelCase__... | 496 | '''simple docstring'''
def __UpperCamelCase( _A : int ):
'''simple docstring'''
if length <= 0 or not isinstance(_A , _A ):
raise ValueError('''Length must be a positive integer.''' )
return [n * (2 * n - 1) for n in range(_A )]
if __name__ == "__main__":
print(hexagonal_num... | 496 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config... | 18 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/mai... | 18 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionP... | 229 |
"""simple docstring"""
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_tran... | 229 | 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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD... | 556 |
def lowerCAmelCase_ (lowerCAmelCase__: Union[str, Any] , lowerCAmelCase__: Optional[int] ):
"""simple docstring"""
UpperCAmelCase_: List[str] = 0
UpperCAmelCase_: Tuple = len(lowerCAmelCase__ ) - 1
while left <= right:
# avoid d... | 556 | 1 |
from __future__ import annotations
from math import ceil, floor, sqrt
def A ( _lowerCamelCase = 2_000_000 ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = [0]
_lowerCAmelCase : Union[str, Any] = 42
f... | 716 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_snake_case = logging.get_logger(__name__)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'upernet'
def __init__( se... | 658 | 0 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def ... | 39 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase_ = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
"""configuration... | 458 | 0 |
'''simple docstring'''
from typing import Any
def UpperCamelCase__ ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : dict , __magic_name__ : dict , __magic_name__ : dict , ) -> list:
'''simple docstring'''... | 715 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor... | 419 | 0 |
class _A :
"""simple docstring"""
def __init__( self : Optional[Any] ) -> Optional[Any]:
__UpperCAmelCase ={}
def _a ( self : str ) -> None:
print(self.vertex )
for i in self.vertex:
... | 68 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 68 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_... | 255 |
"""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 numpy as np
import tensorflow as tf
from tran... | 255 | 1 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
... | 660 | '''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 660 | 1 |
"""simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization... | 498 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class __S... | 498 | 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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.uti... | 453 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
SCREAMING_SNAKE_CASE : Optional[int] = TableFormat(
lineabove=None,
linebelowheader=None,
linebetwee... | 260 | 0 |
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__)
lowerCamelCase : Optional[int] = {
... | 721 |
from __future__ import annotations
from fractions import Fraction
def _SCREAMING_SNAKE_CASE ( lowercase : int , lowercase : int ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
... | 651 | 0 |
from pathlib import Path
import fire
def lowercase__ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase) -> Optional[int]:
"""simple docstring"""
UpperCamelCase = Path(_UpperCamelCase)
UpperCamelCase = Path(_UpperCamelCa... | 280 |
import torch
from torch import nn
class A__ ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CA... | 280 | 1 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__snake_case =logging.get_logger(__nam... | 701 |
'''simple docstring'''
class UpperCAmelCase_ :
def __init__( self : List[str] , UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Union[str, Any]=None ) -> str:
lowerCAmelCase = data
lowerCA... | 513 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gp... | 96 |
'''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():
from .tok... | 538 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
... | 553 |
"""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, TrainingJobAnalytics
f... | 553 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : str , _snake_case : str ) -> bool:
'''simple docstring'''
_A = get_failure_array(_snake_case )
# 2) Step through text searching for pattern
_A ... | 7 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}
c... | 7 | 1 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_... | 644 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ : str = logging.get_logger(__name__)
snake_case_ ... | 644 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class ... | 291 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowercase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# This is the re... | 291 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
lowerCAmelCase_ : List[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
... | 204 | '''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import l... | 204 | 1 |
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.c... | 623 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ : Tuple = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
... | 623 | 1 |
import heapq
def _SCREAMING_SNAKE_CASE ( a ) -> set[int]:
__A : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works w... | 77 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _A( nn.Module ):
... | 77 | 1 |
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