

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

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer

class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       ['image_processor', 'tokenizer']
 UpperCamelCase__     =       'BlipImageProcessor'
 UpperCamelCase__     =       'AutoTokenizer'







 def __init__(       self  , snake_case_  , snake_case_  , snake_case_   ):
   super().__init__(snake_case_  , snake_case_   )

   # add QFormer tokenizer
   lowercase									=qformer_tokenizer







 def __call__(       self  , snake_case_ = None  , snake_case_ = None  , snake_case_ = True  , snake_case_ = False  , snake_case_ = None  , snake_case_ = None  , snake_case_ = 0  , snake_case_ = None  , snake_case_ = None  , snake_case_ = False  , snake_case_ = False  , snake_case_ = False  , snake_case_ = False  , snake_case_ = False  , snake_case_ = True  , snake_case_ = None  , **snake_case_  , ):
   if images is None and text is None:
     raise ValueError('''You have to specify at least images or text.'''   )

   lowercase									=BatchFeature()

   if text is not None:
     lowercase									=self.tokenizer(
         text=snake_case_  , add_special_tokens=snake_case_  , padding=snake_case_  , truncation=snake_case_  , max_length=snake_case_  , stride=snake_case_  , pad_to_multiple_of=snake_case_  , return_attention_mask=snake_case_  , return_overflowing_tokens=snake_case_  , return_special_tokens_mask=snake_case_  , return_offsets_mapping=snake_case_  , return_token_type_ids=snake_case_  , return_length=snake_case_  , verbose=snake_case_  , return_tensors=snake_case_  , **snake_case_  , )
     encoding.update(snake_case_   )
     lowercase									=self.qformer_tokenizer(
         text=snake_case_  , add_special_tokens=snake_case_  , padding=snake_case_  , truncation=snake_case_  , max_length=snake_case_  , stride=snake_case_  , pad_to_multiple_of=snake_case_  , return_attention_mask=snake_case_  , return_overflowing_tokens=snake_case_  , return_special_tokens_mask=snake_case_  , return_offsets_mapping=snake_case_  , return_token_type_ids=snake_case_  , return_length=snake_case_  , verbose=snake_case_  , return_tensors=snake_case_  , **snake_case_  , )
     lowercase									=qformer_text_encoding.pop('''input_ids'''   )
     lowercase									=qformer_text_encoding.pop('''attention_mask'''   )

   if images is not None:
     lowercase									=self.image_processor(snake_case_  , return_tensors=snake_case_   )
     encoding.update(snake_case_   )

   return encoding







 def 						_A(       self  , *snake_case_  , **snake_case_   ):
   return self.tokenizer.batch_decode(*snake_case_  , **snake_case_   )







 def 						_A(       self  , *snake_case_  , **snake_case_   ):
   return self.tokenizer.decode(*snake_case_  , **snake_case_   )







 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def 						_A(       self   ):
   lowercase									=self.tokenizer.model_input_names
   lowercase									=self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







 def 						_A(       self  , snake_case_  , **snake_case_   ):
   if os.path.isfile(snake_case_   ):
     raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'   )
   os.makedirs(snake_case_  , exist_ok=snake_case_   )
   lowercase									=os.path.join(snake_case_  , '''qformer_tokenizer'''   )
   self.qformer_tokenizer.save_pretrained(snake_case_   )
   return super().save_pretrained(snake_case_  , **snake_case_   )







 @classmethod
 def 						_A(       cls  , snake_case_  , **snake_case_   ):
   lowercase									=AutoTokenizer.from_pretrained(snake_case_  , subfolder='''qformer_tokenizer'''   )
   lowercase									=cls._get_arguments_from_pretrained(snake_case_  , **snake_case_   )
   args.append(snake_case_   )
   return cls(*snake_case_   )







def      A__       (    SCREAMING_SNAKE_CASE_   :      int						)  ->  bool:






   """simple docstring"""







   if not isinstance(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						):
      _UpperCAmelCase           =   F'''Input value of [number={number}] must be an integer'''
      raise TypeError(SCREAMING_SNAKE_CASE_						)
   if number < 0:
      return False
   _UpperCAmelCase           =   number * number
   while number > 0:
      if number % 10 != number_square % 10:
         return False
      number //= 10
      number_square //= 10
   return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()