






'''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 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: List[Any]									=     ['image_processor', 'tokenizer']
    __lowerCamelCase: int									=     'BlipImageProcessor'
    __lowerCamelCase: Optional[int]									=     ('BertTokenizer', 'BertTokenizerFast')




    def __init__(      self	:							int	,  a	:							Optional[int]	,  a	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  False
       super().__init__(a	,  a      )
       lowercase_						:     Any							     =  self.image_processor




    def __call__(      self	:							Optional[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	:							List[str]	,  ):






       '''simple docstring'''






       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:
          lowercase_						:     Union[str, Any]							     =  self.tokenizer
          lowercase_						:     int							     =  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
       lowercase_						:     Tuple							     =  self.image_processor(a	,  return_tensors=a      )

       if text is not None:
          lowercase_						:     List[Any]							     =  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:
          lowercase_						:     Any							     =  None

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

       return encoding_image_processor




    def 					lowerCAmelCase__					(      self	:							Any	,  *a	:							str	,  **a	:							Dict      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Optional[Any]	,  *a	:							Dict	,  **a	:							Optional[int]      ):






       '''simple docstring'''






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





    @property
    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  self.tokenizer.model_input_names
       lowercase_						:     Union[str, Any]							     =  self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )












'''simple docstring'''
from __future__ import annotations




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  2
   lowercase_						:     Tuple							     =  []
   while i * i <= n:
      if n % i:
         i += 1
      else:
         n //= i
         factors.append(_UpperCamelCase				)
   if n > 1:
      factors.append(_UpperCamelCase				)
   return factors


if __name__ == "__main__":
 import doctest

 doctest.testmod()




