





'''simple docstring'''
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=["image_processor", "tokenizer"]
 SCREAMING_SNAKE_CASE_	:					Dict					="BlipImageProcessor"
 SCREAMING_SNAKE_CASE_	:					Optional[int]					="AutoTokenizer"






 def __init__(       self       :							int							,	__A       :							List[str]							,	__A       :							Tuple	):
        __UpperCamelCase       =  False
        super().__init__(__A							,	__A	)
        __UpperCamelCase       =  self.image_processor






 def __call__(       self       :							Union[str, Any]							,	__A       :							ImageInput = None							,	__A       :							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,	__A       :							bool = True							,	__A       :							Union[bool, str, PaddingStrategy] = False							,	__A       :							Union[bool, str, TruncationStrategy] = None							,	__A       :							Optional[int] = None							,	__A       :							int = 0							,	__A       :							Optional[int] = None							,	__A       :							Optional[bool] = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							bool = True							,	__A       :							Optional[Union[str, TensorType]] = None							,	**__A       :							Optional[int]							,	):
        if images is None and text is None:
               raise ValueError('You have to specify either images or text.'	)

        # Get only text
        if images is None:
               __UpperCamelCase       =  self.tokenizer
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
               return text_encoding

        # add pixel_values
        __UpperCamelCase       =  self.image_processor(__A							,	return_tensors=__A	)

        if text is not None:
               __UpperCamelCase       =  self.tokenizer(
                   text=__A							,	add_special_tokens=__A							,	padding=__A							,	truncation=__A							,	max_length=__A							,	stride=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	return_overflowing_tokens=__A							,	return_special_tokens_mask=__A							,	return_offsets_mapping=__A							,	return_token_type_ids=__A							,	return_length=__A							,	verbose=__A							,	return_tensors=__A							,	**__A							,	)
        else:
               __UpperCamelCase       =  None

        if text_encoding is not None:
               encoding_image_processor.update(__A	)

        return encoding_image_processor






 def        _lowerCamelCase					(       self       :							Optional[Any]							,	*__A       :							List[str]							,	**__A       :							List[str]	):
        return self.tokenizer.batch_decode(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	*__A       :							Dict							,	**__A       :							Optional[Any]	):
        return self.tokenizer.decode(*__A							,	**__A	)






 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  self.tokenizer.model_input_names
        __UpperCamelCase       =  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



def      a      (			A__	:   str      ,   A__	:   bool = False     )    ->      str:





      """simple docstring"""



      if not isinstance(A__      ,   A__     ):
            _lowercase	       =F'''Expected string as input, found {type(A__     )}'''
            raise ValueError(A__     )
      if not isinstance(A__      ,   A__     ):
            _lowercase	       =F'''Expected boolean as use_pascal parameter, found {type(A__     )}'''
            raise ValueError(A__     )

      _lowercase	       =input_str.split('_'     )

      _lowercase	       =0 if use_pascal else 1

      _lowercase	       =words[start_index:]

      _lowercase	       =[word[0].upper() + word[1:] for word in words_to_capitalize]

      _lowercase	       ='' if use_pascal else words[0]

      return "".join([initial_word, *capitalized_words]     )


if __name__ == "__main__":
  from doctest import testmod

  testmod()


