



'''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  snake_case__						(    SCREAMING_SNAKE_CASE_				):
 A__				= ['''image_processor''', '''tokenizer''']
 A__				= '''Pix2StructImageProcessor'''
 A__				= ('''T5Tokenizer''', '''T5TokenizerFast''')
 def __init__(   self						:							Dict      ,     __a						:							Tuple      ,     __a						:							List[str]  )      ->   Dict:







       '''simple docstring'''
       __snake_case :   str													=    False
       super().__init__(__a      ,     __a  )
 def __call__(   self						:							List[Any]      ,     __a						:							List[Any]=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						:							Optional[int] = 2048      ,     __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						:							int      ,     )      ->   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:
             __snake_case :   List[str]													=    self.tokenizer
             __snake_case :   Dict													=    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
             __snake_case :   Union[str, Any]													=    self.image_processor(
                 __a      ,     return_tensors=__a      ,     max_patches=__a      ,     **__a  )
       else:
             # add pixel_values and bbox
             __snake_case :   Dict													=    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:
             __snake_case :   Dict													=    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:
                   __snake_case :   Dict													=    text_encoding.pop('attention_mask'  )
             if "input_ids" in text_encoding:
                   __snake_case :   List[Any]													=    text_encoding.pop('input_ids'  )
       else:
             __snake_case :   int													=    None

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

       return encoding_image_processor
 def A_   (   self						:							Dict      ,     *__a						:							Dict      ,     **__a						:							List[str]  )      ->   Tuple:







       '''simple docstring'''
       return self.tokenizer.batch_decode(*__a      ,     **__a  )
 def A_   (   self						:							Tuple      ,     *__a						:							Tuple      ,     **__a						:							str  )      ->   Optional[Any]:







       '''simple docstring'''
       return self.tokenizer.decode(*__a      ,     **__a  )





 @property
 def A_   (   self						:							int  )      ->   str:







       '''simple docstring'''
       __snake_case :   Any													=    self.tokenizer.model_input_names
       __snake_case :   Tuple													=    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )








'''simple docstring'''





from __future__ import annotations





def   a_   (		_UpperCAmelCase    :      list[int]				)    ->  bool:
      return len(set(_UpperCAmelCase				)				) == len(_UpperCAmelCase				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



