


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__     					=			{
    "configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		SCREAMING_SNAKE_CASE__     					=			[
		    "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "NezhaForNextSentencePrediction",
		    "NezhaForMaskedLM",
		    "NezhaForPreTraining",
		    "NezhaForMultipleChoice",
		    "NezhaForQuestionAnswering",
		    "NezhaForSequenceClassification",
		    "NezhaForTokenClassification",
		    "NezhaModel",
		    "NezhaPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_nezha import (
				    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    NezhaForMaskedLM,
				    NezhaForMultipleChoice,
				    NezhaForNextSentencePrediction,
				    NezhaForPreTraining,
				    NezhaForQuestionAnswering,
				    NezhaForSequenceClassification,
				    NezhaForTokenClassification,
				    NezhaModel,
				    NezhaPreTrainedModel,
				)


else:
		import sys

		SCREAMING_SNAKE_CASE__     					=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __init__(   self       ,  *_lowercase       ,  **_lowercase ):

												"""simple docstring"""



												warnings.warn(
												    """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
												    """ use DonutImageProcessor instead."""       ,  _lowercase       ,  )
												super().__init__(*_lowercase       ,  **_lowercase )
