

'''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__  (							UpperCamelCase__  ):
   _SCREAMING_SNAKE_CASE   :					Union[str, Any]     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					Tuple     	=							'Pix2StructImageProcessor'
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							('T5Tokenizer', 'T5TokenizerFast')


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       False
      super().__init__(__lowerCamelCase      ,    __lowerCamelCase  )


   def __call__(			self      ,    _UpperCamelCase=None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    _UpperCamelCase = False      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 2048      ,    _UpperCamelCase = 0      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = True      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """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:
         _lowercase      :				Any      					=       self.tokenizer
         _lowercase      :				List[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      ,    )
         return text_encoding

      if not self.image_processor.is_vqa:
         # add pixel_values
         _lowercase      :				str      					=       self.image_processor(
             __lowerCamelCase      ,    return_tensors=__lowerCamelCase      ,    max_patches=__lowerCamelCase      ,    **__lowerCamelCase  )
      else:
         # add pixel_values and bbox
         _lowercase      :				Optional[Any]      					=       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:
         _lowercase      :				Union[str, 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:
            _lowercase      :				Any      					=       text_encoding.pop("attention_mask"  )
         if "input_ids" in text_encoding:
            _lowercase      :				int      					=       text_encoding.pop("input_ids"  )
      else:
         _lowercase      :				Tuple      					=       None

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

      return encoding_image_processor


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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






   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       self.tokenizer.model_input_names
      _lowercase      :				List[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  _A		(     snake_case		,							snake_case = None		,							snake_case = None					)      ->     None:
   if start is None:
      _lowercase      :				Dict      					=       0

   if end is None:
      _lowercase      :				List[Any]      					=       len(snake_case					) - 1

   if start >= end:
      return

   _lowercase      :				int      					=       (start + end) // 2

   slowsort(snake_case		,							snake_case		,							snake_case					)
   slowsort(snake_case		,							mid + 1		,							snake_case					)

   if sequence[end] < sequence[mid]:
      _lowercase					,				_lowercase      :				Any      					=       sequence[mid], sequence[end]

   slowsort(snake_case		,							snake_case		,							end - 1					)


if __name__ == "__main__":
    from doctest import testmod

    testmod()






