'''simple docstring'''




import warnings

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




class 	__UpperCAmelCase				(				_lowerCAmelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				["image_processor", "tokenizer"]
       __lowercase					:					Any    =				"AutoImageProcessor"
       __lowercase					:					Any    =				"AutoTokenizer"

       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=None    ,     **_SCREAMING_SNAKE_CASE )		->     List[str]:
           A_     =   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__    ,     )
               A_     =   kwargs.pop('''feature_extractor''' )

           A_     =   image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError('''You have to specify an image_processor.''' )
           if tokenizer is None:
               raise ValueError('''You have to specify a tokenizer.''' )

           super().__init__(A__    ,     A__ )
           A_     =   self.image_processor

       def __call__(      self    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=None    ,     **_SCREAMING_SNAKE_CASE )		->     Dict:

           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_     =   self.tokenizer(A__    ,     return_tensors=A__    ,     **A__ )

           if images is not None:
               A_     =   self.image_processor(A__    ,     return_tensors=A__    ,     **A__ )

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

       def 					__A       (      self    ,     *_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE )		->     Optional[Any]:
           return self.tokenizer.batch_decode(*A__    ,     **A__ )

       def 					__A       (      self    ,     *_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE )		->     Any:
           return self.tokenizer.decode(*A__    ,     **A__ )

       @property
       def 					__A       (      self )		->     List[Any]:
           A_     =   self.tokenizer.model_input_names
           A_     =   self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )

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












import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 	A	(						unittest.TestCase						):
     """simple docstring"""

     @cached_property
     def        __snake_case      (			self     :      Union[str, Any]	) ->				str:
           return BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'	)




     @cached_property
     def        __snake_case      (			self     :      Tuple	) ->				Dict:
           return BlenderbotTokenizerFast.from_pretrained('facebook/blenderbot-3B'	)




     def        __snake_case      (			self     :      List[str]	) ->				int:
           lowerCamelCase__					     = self.tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      List[str]	) ->				str:
           lowerCamelCase__					     = self.rust_tokenizer_ab
           lowerCamelCase__					     = ' I am a small frog.'
           lowerCamelCase__					     = tok([src_text]		,     padding=A__		,     truncation=A__	)['input_ids']
           lowerCamelCase__					     = tok.batch_decode(A__		,     skip_special_tokens=A__		,     clean_up_tokenization_spaces=A__	)[0]
           assert src_text == decoded




     def        __snake_case      (			self     :      Any	) ->				str:
           assert self.tokenizer_ab.add_prefix_space
           assert self.tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]




     def        __snake_case      (			self     :      str	) ->				Union[str, Any]:
           assert self.rust_tokenizer_ab.add_prefix_space
           assert self.rust_tokenizer_ab([' Sam', 'Sam']	).input_ids == [[5_5_0_2, 2], [5_5_0_2, 2]]