


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase     :     Dict        =	{
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase     :     List[Any]        =	['BertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase     :     Optional[Any]        =	[
	    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BertForMaskedLM',
	    'BertForMultipleChoice',
	    'BertForNextSentencePrediction',
	    'BertForPreTraining',
	    'BertForQuestionAnswering',
	    'BertForSequenceClassification',
	    'BertForTokenClassification',
	    'BertLayer',
	    'BertLMHeadModel',
	    'BertModel',
	    'BertPreTrainedModel',
	    'load_tf_weights_in_bert',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase     :     Dict        =	[
	    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFBertEmbeddings',
	    'TFBertForMaskedLM',
	    'TFBertForMultipleChoice',
	    'TFBertForNextSentencePrediction',
	    'TFBertForPreTraining',
	    'TFBertForQuestionAnswering',
	    'TFBertForSequenceClassification',
	    'TFBertForTokenClassification',
	    'TFBertLMHeadModel',
	    'TFBertMainLayer',
	    'TFBertModel',
	    'TFBertPreTrainedModel',
	]
try:
	if not is_tensorflow_text_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase     :     int        =	['TFBertTokenizer']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase     :     str        =	[
	    'FlaxBertForCausalLM',
	    'FlaxBertForMaskedLM',
	    'FlaxBertForMultipleChoice',
	    'FlaxBertForNextSentencePrediction',
	    'FlaxBertForPreTraining',
	    'FlaxBertForQuestionAnswering',
	    'FlaxBertForSequenceClassification',
	    'FlaxBertForTokenClassification',
	    'FlaxBertModel',
	    'FlaxBertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
	from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_bert_fast import BertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bert import (
		    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BertForMaskedLM,
		    BertForMultipleChoice,
		    BertForNextSentencePrediction,
		    BertForPreTraining,
		    BertForQuestionAnswering,
		    BertForSequenceClassification,
		    BertForTokenClassification,
		    BertLayer,
		    BertLMHeadModel,
		    BertModel,
		    BertPreTrainedModel,
		    load_tf_weights_in_bert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_bert import (
		    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFBertEmbeddings,
		    TFBertForMaskedLM,
		    TFBertForMultipleChoice,
		    TFBertForNextSentencePrediction,
		    TFBertForPreTraining,
		    TFBertForQuestionAnswering,
		    TFBertForSequenceClassification,
		    TFBertForTokenClassification,
		    TFBertLMHeadModel,
		    TFBertMainLayer,
		    TFBertModel,
		    TFBertPreTrainedModel,
		)

	try:
		if not is_tensorflow_text_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_bert_tf import TFBertTokenizer

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_bert import (
		    FlaxBertForCausalLM,
		    FlaxBertForMaskedLM,
		    FlaxBertForMultipleChoice,
		    FlaxBertForNextSentencePrediction,
		    FlaxBertForPreTraining,
		    FlaxBertForQuestionAnswering,
		    FlaxBertForSequenceClassification,
		    FlaxBertForTokenClassification,
		    FlaxBertModel,
		    FlaxBertPreTrainedModel,
		)

else:
	import sys

	_lowercase     :     Optional[int]        =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








'''simple docstring'''




import warnings

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


_a					:			List[Any]							=    logging.get_logger(__name__)



class 							lowercase_						(		a			):

  '''simple docstring'''





  def __init__(  self ,			*a_ ,			**a_  )				->					None:


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


