
'''simple docstring'''
A__ :		List[str]					=[0.4_8_5, 0.4_5_6, 0.4_0_6]
A__ :		Any					=[0.2_2_9, 0.2_2_4, 0.2_2_5]
A__ :		List[str]					=[0.5, 0.5, 0.5]
A__ :		Optional[Any]					=[0.5, 0.5, 0.5]
A__ :		List[str]					=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
A__ :		List[str]					=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]





'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												=['''image_processor''', '''tokenizer''']
 lowerCamelCase												='''EfficientNetImageProcessor'''
 lowerCamelCase												=('''BertTokenizer''', '''BertTokenizerFast''')
 def __init__( self      :     str					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     Tuple							):



   """simple docstring"""





   super().__init__(lowerCamelCase					, lowerCamelCase							)
 def __call__( self      :     Tuple					, lowerCamelCase      :     str=None					, lowerCamelCase      :     Tuple=None					, lowerCamelCase      :     int="max_length"					, lowerCamelCase      :     int=64					, lowerCamelCase      :     Union[str, Any]=None					, **lowerCamelCase      :     Dict							):



   """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		:  str       				=		self.tokenizer(
         lowerCamelCase					, padding=lowerCamelCase					, max_length=lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if images is not None:
     __A		:  Optional[Any]       				=		self.image_processor(lowerCamelCase					, return_tensors=lowerCamelCase					, **lowerCamelCase							)

   if text is not None and images is not None:
     __A		:  Optional[int]       				=		image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**lowerCamelCase							)					, tensor_type=lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     Optional[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





   return self.tokenizer.batch_decode(*lowerCamelCase					, **lowerCamelCase							)
 def 		lowercase_( self      :     Any					, *lowerCamelCase      :     List[Any]					, **lowerCamelCase      :     Union[str, Any]							):



   """simple docstring"""





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






 @property
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  Dict       				=		self.tokenizer.model_input_names
   __A		:  int       				=		self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



