





'''simple docstring'''




from typing import TYPE_CHECKING

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


a__					:			List[str]          =     {
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			pass

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			int          =     [
			    '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RoCBertForCausalLM''',
			    '''RoCBertForMaskedLM''',
			    '''RoCBertForMultipleChoice''',
			    '''RoCBertForPreTraining''',
			    '''RoCBertForQuestionAnswering''',
			    '''RoCBertForSequenceClassification''',
			    '''RoCBertForTokenClassification''',
			    '''RoCBertLayer''',
			    '''RoCBertModel''',
			    '''RoCBertPreTrainedModel''',
			    '''load_tf_weights_in_roc_bert''',
			]

if TYPE_CHECKING:
			from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
			from .tokenization_roc_bert import RoCBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						raise OptionalDependencyNotAvailable()

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roc_bert import (
						    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RoCBertForCausalLM,
						    RoCBertForMaskedLM,
						    RoCBertForMultipleChoice,
						    RoCBertForPreTraining,
						    RoCBertForQuestionAnswering,
						    RoCBertForSequenceClassification,
						    RoCBertForTokenClassification,
						    RoCBertLayer,
						    RoCBertModel,
						    RoCBertPreTrainedModel,
						    load_tf_weights_in_roc_bert,
						)


else:
			import sys

			a__					:			str          =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










'''simple docstring'''




import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


a__					:			int          =     logging.get_logger(__name__)






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       *UpperCamelCase_      ,       **UpperCamelCase_		)      ->		None:
		warnings.warn(
		    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
		    ' Please use PerceiverImageProcessor instead.'      ,       UpperCamelCase_      ,       )
		super().__init__(*UpperCamelCase_      ,       **UpperCamelCase_		)



