
'''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    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				List[str]	=			["""image_processor""", """tokenizer"""]
 A_				:				str	=			"""Pix2StructImageProcessor"""
 A_				:				List[Any]	=			("""T5Tokenizer""", """T5TokenizerFast""")


 def __init__(    self  : Union[str, Any]			,							a__  : Dict			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	False
  super().__init__(a__			,							a__    )


 def __call__(    self  : Tuple			,							a__  : Dict=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__  : str			,							):
  '''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:
   lowerCAmelCase__     :   Optional[Any]     		=	self.tokenizer
   lowerCAmelCase__     :   Any     		=	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
   lowerCAmelCase__     :   Optional[int]     		=	self.image_processor(
       a__			,							return_tensors=a__			,							max_patches=a__			,							**a__    )
  else:
   # add pixel_values and bbox
   lowerCAmelCase__     :   str     		=	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:
   lowerCAmelCase__     :   List[Any]     		=	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:
    lowerCAmelCase__     :   List[str]     		=	text_encoding.pop("attention_mask"    )
   if "input_ids" in text_encoding:
    lowerCAmelCase__     :   Dict     		=	text_encoding.pop("input_ids"    )
  else:
   lowerCAmelCase__     :   Dict     		=	None

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

  return encoding_image_processor


 def    _A	(    self  : Optional[int]			,							*a__  : Optional[Any]			,							**a__  : Tuple    ):
  '''simple docstring'''

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


 def    _A	(    self  : List[str]			,							*a__  : List[Any]			,							**a__  : Any    ):
  '''simple docstring'''

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


 @property
 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	self.tokenizer.model_input_names
  lowerCAmelCase__     :   Optional[int]     		=	self.image_processor.model_input_names
  return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )







'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__		,     lowerCAmelCase__     :   Tuple     		=	img.shape[0], img.shape[1]

 # converting each pixel's color to its negative
 for i in range(lowerCamelCase_			):
  for j in range(lowerCamelCase_			):
   lowerCAmelCase__     :   Optional[int]     		=	[2_5_5, 2_5_5, 2_5_5] - img[i][j]

 return img


if __name__ == "__main__":
     # read original image
     snake_case									=    imread("""image_data/lena.jpg""", 1)

     # convert to its negative
     snake_case									=    convert_to_negative(img)

     # show result image
     imshow("""negative of original image""", img)
     waitKey(0)
     destroyAllWindows()





