


'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 							__UpperCamelCase    (		SCREAMING_SNAKE_CASE_ ):
	A_           =   ["image_processor", "tokenizer"]
	A_           =   "CLIPImageProcessor"
	A_           =   ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")




	def __init__(       self						,    __a=None						,    __a=None						,    **__a							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  None
						if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'						,    snake_case__						,    )
											__a      :	Tuple    =  kwargs.pop('feature_extractor'							)

						__a      :	Union[str, Any]    =  image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
											raise ValueError('You need to specify an `image_processor`.'							)
						if tokenizer is None:
											raise ValueError('You need to specify a `tokenizer`.'							)

						super().__init__(snake_case__						,    snake_case__							)




	def __call__(       self						,    __a=None						,    __a=None						,    __a=None						,    **__a							):

						'''simple docstring'''





						if text is None and images is None:
											raise ValueError('You have to specify either text or images. Both cannot be none.'							)

						if text is not None:
											__a      :	Dict    =  self.tokenizer(snake_case__						,    return_tensors=snake_case__						,    **snake_case__							)

						if images is not None:
											__a      :	Union[str, Any]    =  self.image_processor(snake_case__						,    return_tensors=snake_case__						,    **snake_case__							)

						if text is not None and images is not None:
											__a      :	List[str]    =  image_features.pixel_values
											return encoding
						elif text is not None:
											return encoding
						else:
											return BatchEncoding(data=dict(**snake_case__							)						,    tensor_type=snake_case__							)




	def 				__UpperCAmelCase							(       self						,    *__a						,    **__a							):

						'''simple docstring'''





						return self.tokenizer.batch_decode(*snake_case__						,    **snake_case__							)




	def 				__UpperCAmelCase							(       self						,    *__a						,    **__a							):

						'''simple docstring'''





						return self.tokenizer.decode(*snake_case__						,    **snake_case__							)





	@property
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  self.tokenizer.model_input_names
						__a      :	Dict    =  self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)







'''simple docstring'''



from typing import TYPE_CHECKING

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


__lowercase     :						Union[str, Any]	=     {
    '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:
	__lowercase     :						List[str]	=     [
	    '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

	__lowercase     :						Any	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



