'''simple docstring'''

import warnings
from functools import wraps
from typing import Callable



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						@wraps(a__      )
						def _inner_fn(*a__			,      **a__      ):
												warnings.warn(
												    (F'''\'{fn.__name__}\' is experimental and might be subject to breaking changes in the future.''')			,      a__			,      )
												return fn(*a__			,      **a__      )

						return _inner_fn






'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Any       =							['image_processor', 'tokenizer']
		lowercase   :      Any       =							'CLIPImageProcessor'
		lowercase   :      int       =							('CLIPTokenizer', 'CLIPTokenizerFast')






		def __init__(   self		,						__A=None		,						__A=None		,						**__A				) ->     str:
								_lowerCAmelCase										=None
								if "feature_extractor" in kwargs:
														warnings.warn(
														    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
														    ' instead.'		,						__A		,						)
														_lowerCAmelCase										=kwargs.pop('feature_extractor'				)

								_lowerCAmelCase										=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__(__A		,						__A				)






		def __call__(   self		,						__A=None		,						__A=None		,						__A=None		,						**__A				) ->     Optional[int]:

								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:
														_lowerCAmelCase										=self.tokenizer(__A		,						return_tensors=__A		,						**__A				)

								if images is not None:
														_lowerCAmelCase										=self.image_processor(__A		,						return_tensors=__A		,						**__A				)

								if text is not None and images is not None:
														_lowerCAmelCase										=image_features.pixel_values
														return encoding
								elif text is not None:
														return encoding
								else:
														return BatchEncoding(data=dict(**__A				)		,						tensor_type=__A				)






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Any:
								return self.tokenizer.batch_decode(*__A		,						**__A				)






		def 		UpperCamelCase__       (   self		,						*__A		,						**__A				) ->     Optional[int]:
								return self.tokenizer.decode(*__A		,						**__A				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=self.tokenizer.model_input_names
								_lowerCAmelCase										=self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,						__A		,						)
								return self.image_processor_class






		@property
		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'		,						__A		,						)
								return self.image_processor





