

'''simple docstring'''
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 							A__   (	_a  ):
  A__					=    ["""image_processor""", """tokenizer"""]
  A__					=    """Pix2StructImageProcessor"""
  A__					=    ("""T5Tokenizer""", """T5TokenizerFast""")





  def __init__(		self				:     Optional[Any]							, _a				:     Union[str, Any]							, _a				:     Any			)     ->   Dict:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =False
       super().__init__(_a							, _a			)





  def __call__(		self				:     Any							, _a				:     Optional[int]=None							, _a				:     Optional[int] = None							, _a				:     List[str] = True							, _a				:     Dict = False							, _a				:     List[str] = None							, _a				:     Optional[Any] = None							, _a				:     Optional[int] = 2048							, _a				:     Tuple = 0							, _a				:     Optional[int] = None							, _a				:     Optional[Any] = None							, _a				:     Any = False							, _a				:     Optional[Any] = False							, _a				:     Dict = False							, _a				:     int = False							, _a				:     int = False							, _a				:     Optional[int] = True							, _a				:     Tuple = None							, **_a				:     Union[str, Any]							, )     ->   BatchEncoding:





       '''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 and not self.image_processor.is_vqa:
            _SCREAMING_SNAKE_CASE							   =self.tokenizer
            _SCREAMING_SNAKE_CASE							   =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

       if not self.image_processor.is_vqa:
            # add pixel_values
            _SCREAMING_SNAKE_CASE							   =self.image_processor(
                _a							, return_tensors=_a							, max_patches=_a							, **_a			)
       else:
            # add pixel_values and bbox
            _SCREAMING_SNAKE_CASE							   =self.image_processor(
                _a							, return_tensors=_a							, max_patches=_a							, header_text=_a							, **_a			)

       if text is not None and not self.image_processor.is_vqa:
            _SCREAMING_SNAKE_CASE							   =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							, )

            if "attention_mask" in text_encoding:
                 _SCREAMING_SNAKE_CASE							   =text_encoding.pop('attention_mask'			)
            if "input_ids" in text_encoding:
                 _SCREAMING_SNAKE_CASE							   =text_encoding.pop('input_ids'			)
       else:
            _SCREAMING_SNAKE_CASE							   =None

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

       return encoding_image_processor





  def 						A  (		self				:     List[Any]							, *_a				:     Union[str, Any]							, **_a				:     str			)     ->   Dict:





       '''simple docstring'''






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





  def 						A  (		self				:     Tuple							, *_a				:     int							, **_a				:     Dict			)     ->   Tuple:





       '''simple docstring'''






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





  @property
  def 						A  (		self				:     str			)     ->   Optional[int]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =self.tokenizer.model_input_names
       _SCREAMING_SNAKE_CASE							   =self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)



'''simple docstring'''
def 	_lowerCAmelCase (						_UpperCamelCase						:					str    )				->      bool:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =0
     for ch in input_str:
          _SCREAMING_SNAKE_CASE							   =ord(_UpperCamelCase    )
          _SCREAMING_SNAKE_CASE							   =pow(2							,			_UpperCamelCase    )

          # If we already turned on bit for current character's unicode
          if bitmap >> ch_unicode & 1 == 1:
               return False
          bitmap |= ch_bit_index_on
     return True


if __name__ == "__main__":
     import doctest

     doctest.testmod()
