


import warnings

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







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					List[str]    =		['image_processor', 'tokenizer']
			_UpperCamelCase		:					int    =		'CLIPImageProcessor'
			_UpperCamelCase		:					Optional[int]    =		('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')




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

						'''simple docstring'''


						UpperCamelCase__					=       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      ,						)
									UpperCamelCase__					=       kwargs.pop("feature_extractor"     )

						UpperCamelCase__					=       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      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						**snake_case     ):

						'''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:
									UpperCamelCase__					=       self.tokenizer(snake_case      ,						return_tensors=snake_case      ,						**snake_case     )

						if images is not None:
									UpperCamelCase__					=       self.image_processor(snake_case      ,						return_tensors=snake_case      ,						**snake_case     )

						if text is not None and images is not None:
									UpperCamelCase__					=       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 				snake_case__				(							self      ,						*snake_case      ,						**snake_case     ):

						'''simple docstring'''


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




			def 				snake_case__				(							self      ,						*snake_case      ,						**snake_case     ):

						'''simple docstring'''


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







			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.tokenizer.model_input_names
						UpperCamelCase__					=       self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )






from abc import ABC, abstractmethod
from argparse import ArgumentParser







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@staticmethod
			@abstractmethod
			def 				snake_case__				(							snake_case     ):

						'''simple docstring'''


						raise NotImplementedError()







			@abstractmethod
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						raise NotImplementedError()


