


from typing import List, Optional, Union

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







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       ['image_processor', 'tokenizer']
 SCREAMING_SNAKE_CASE__      =       'Pix2StructImageProcessor'
 SCREAMING_SNAKE_CASE__      =       ('T5Tokenizer', 'T5TokenizerFast')




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Optional[int] 							=       False
       super().__init__(_lowerCamelCase					,  _lowerCamelCase    )




 def __call__(		self					,  _lowerCamelCase=None					,  _lowerCamelCase = None					,  _lowerCamelCase = True					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = 2048					,  _lowerCamelCase = 0					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = False					,  _lowerCamelCase = False					,  _lowerCamelCase = False					,  _lowerCamelCase = False					,  _lowerCamelCase = False					,  _lowerCamelCase = True					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       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 and not self.image_processor.is_vqa:
             a						:int 							=       self.tokenizer
             a						:int 							=       self.tokenizer(
                 text=_lowerCamelCase					,  add_special_tokens=_lowerCamelCase					,  padding=_lowerCamelCase					,  truncation=_lowerCamelCase					,  max_length=_lowerCamelCase					,  stride=_lowerCamelCase					,  pad_to_multiple_of=_lowerCamelCase					,  return_attention_mask=_lowerCamelCase					,  return_overflowing_tokens=_lowerCamelCase					,  return_special_tokens_mask=_lowerCamelCase					,  return_offsets_mapping=_lowerCamelCase					,  return_token_type_ids=_lowerCamelCase					,  return_length=_lowerCamelCase					,  verbose=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase					,  )
             return text_encoding

       if not self.image_processor.is_vqa:
             # add pixel_values
             a						:Optional[Any] 							=       self.image_processor(
                 _lowerCamelCase					,  return_tensors=_lowerCamelCase					,  max_patches=_lowerCamelCase					,  **_lowerCamelCase    )
       else:
             # add pixel_values and bbox
             a						:Optional[int] 							=       self.image_processor(
                 _lowerCamelCase					,  return_tensors=_lowerCamelCase					,  max_patches=_lowerCamelCase					,  header_text=_lowerCamelCase					,  **_lowerCamelCase    )

       if text is not None and not self.image_processor.is_vqa:
             a						:Any 							=       self.tokenizer(
                 text=_lowerCamelCase					,  add_special_tokens=_lowerCamelCase					,  padding=_lowerCamelCase					,  truncation=_lowerCamelCase					,  max_length=_lowerCamelCase					,  stride=_lowerCamelCase					,  pad_to_multiple_of=_lowerCamelCase					,  return_attention_mask=_lowerCamelCase					,  return_overflowing_tokens=_lowerCamelCase					,  return_special_tokens_mask=_lowerCamelCase					,  return_offsets_mapping=_lowerCamelCase					,  return_token_type_ids=_lowerCamelCase					,  return_length=_lowerCamelCase					,  verbose=_lowerCamelCase					,  return_tensors=_lowerCamelCase					,  **_lowerCamelCase					,  )

             if "attention_mask" in text_encoding:
                   a						:List[str] 							=       text_encoding.pop('''attention_mask'''    )
             if "input_ids" in text_encoding:
                   a						:List[Any] 							=       text_encoding.pop('''input_ids'''    )
       else:
             a						:str 							=       None

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

       return encoding_image_processor




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.batch_decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  *_lowerCamelCase					,  **_lowerCamelCase    ):
       return self.tokenizer.decode(*_lowerCamelCase					,  **_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Union[str, Any] 							=       self.tokenizer.model_input_names
       a						:Dict 							=       self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )









from maths.prime_factors import prime_factors
def 				__lowerCamelCase						( UpperCAmelCase_						:		int						):




      """simple docstring"""





      if not isinstance(UpperCAmelCase_ ,  UpperCAmelCase_						):
            a						:Dict 							=       F'''Input value of [number={number}] must be an integer'''
            raise TypeError(UpperCAmelCase_						)
      if number < 1:
            raise ValueError('''Input must be a positive integer'''						)
      return -1 if len(prime_factors(UpperCAmelCase_						)						) % 2 else 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()





