





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     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			["""image_processor""", """tokenizer"""]
  a_      =			"""Pix2StructImageProcessor"""
  a_      =			("""T5Tokenizer""", """T5TokenizerFast""")






  def __init__(    self			:		str ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		str					)							->						List[Any]:
     __lowerCAmelCase							     =     False
     super().__init__(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def __call__(    self			:		Tuple ,  lowerCAmelCase_			:		Optional[Any]=None ,  lowerCAmelCase_			:		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		Union[bool, str, PaddingStrategy] = False ,  lowerCAmelCase_			:		Union[bool, str, TruncationStrategy] = None ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		Optional[int] = 2_0_4_8 ,  lowerCAmelCase_			:		int = 0 ,  lowerCAmelCase_			:		Optional[int] = None ,  lowerCAmelCase_			:		Optional[bool] = None ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		bool = True ,  lowerCAmelCase_			:		Optional[Union[str, TensorType]] = None ,  **lowerCAmelCase_			:		Dict ,  )							->						BatchEncoding:
     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:
        __lowerCAmelCase							     =     self.tokenizer
        __lowerCAmelCase							     =     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
        __lowerCAmelCase							     =     self.image_processor(
            lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_ ,  max_patches=lowerCAmelCase_ ,  **lowerCAmelCase_					)
     else:
        # add pixel_values and bbox
        __lowerCAmelCase							     =     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:
        __lowerCAmelCase							     =     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:
           __lowerCAmelCase							     =     text_encoding.pop('attention_mask'					)
        if "input_ids" in text_encoding:
           __lowerCAmelCase							     =     text_encoding.pop('input_ids'					)
     else:
        __lowerCAmelCase							     =     None

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

     return encoding_image_processor






  def     lowercase		(    self			:		List[str] ,  *lowerCAmelCase_			:		List[Any] ,  **lowerCAmelCase_			:		str					)							->						List[Any]:
     return self.tokenizer.batch_decode(*lowerCAmelCase_ ,  **lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[int] ,  *lowerCAmelCase_			:		Dict ,  **lowerCAmelCase_			:		str					)							->						Optional[Any]:
     return self.tokenizer.decode(*lowerCAmelCase_ ,  **lowerCAmelCase_					)






  @property
  def     lowercase		(    self			:		List[Any]					)							->						str:
     __lowerCAmelCase							     =     self.tokenizer.model_input_names
     __lowerCAmelCase							     =     self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)








class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Dict					)							->						List[str]:
     __lowerCAmelCase							     =     name
     __lowerCAmelCase							     =     value
     __lowerCAmelCase							     =     weight






  def __repr__(    self			:		Union[str, Any]					)							->						List[str]:
     return f"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""






  def     lowercase		(    self			:		int					)							->						Optional[int]:
     return self.value






  def     lowercase		(    self			:		Optional[Any]					)							->						Union[str, Any]:
     return self.name






  def     lowercase		(    self			:		List[Any]					)							->						Tuple:
     return self.weight






  def     lowercase		(    self			:		int					)							->						Dict:
     return self.value / self.weight





def   a_   (			lowerCAmelCase_				:      Dict,     lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Optional[int]     ):
   __lowerCAmelCase							     =     []
   for i in range(len(lowerCAmelCase_     )     ):
      menu.append(Things(name[i],     value[i],     weight[i]     )     )
   return menu

def   a_   (			lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   __lowerCAmelCase							     =     sorted(lowerCAmelCase_,     key=lowerCAmelCase_,     reverse=lowerCAmelCase_     )
   __lowerCAmelCase							     =     []
   __lowerCAmelCase     ,     __lowerCAmelCase							     =     0.0, 0.0
   for i in range(len(lowerCAmelCase_     )     ):
      if (total_cost + items_copy[i].get_weight()) <= max_cost:
         result.append(items_copy[i]     )
         total_cost += items_copy[i].get_weight()
         total_value += items_copy[i].get_value()
   return (result, total_value)

def   a_   (			):
   pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()

