




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__      ):
						UpperCamelCase      =       ["""image_processor""", """tokenizer"""]
						UpperCamelCase      =       """Pix2StructImageProcessor"""
						UpperCamelCase      =       ("""T5Tokenizer""", """T5TokenizerFast""")

						def __init__(		self :Optional[Any]     ,			__snake_case :int     ,			__snake_case :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=False
										super().__init__(__snake_case     ,			__snake_case			)

						def __call__(		self :Any     ,			__snake_case :List[Any]=None     ,			__snake_case :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,			__snake_case :bool = True     ,			__snake_case :Union[bool, str, PaddingStrategy] = False     ,			__snake_case :Union[bool, str, TruncationStrategy] = None     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[int] = 20_48     ,			__snake_case :int = 0     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[bool] = None     ,			__snake_case :bool = False     ,			__snake_case :bool = False     ,			__snake_case :bool = False     ,			__snake_case :bool = False     ,			__snake_case :bool = False     ,			__snake_case :bool = True     ,			__snake_case :Optional[Union[str, TensorType]] = None     ,			**__snake_case :Any     ,			):
										'''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:
														__magic_name__						:						Any										=self.tokenizer
														__magic_name__						:						Dict										=self.tokenizer(
														    text=__snake_case     ,			add_special_tokens=__snake_case     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=__snake_case     ,			stride=__snake_case     ,			pad_to_multiple_of=__snake_case     ,			return_attention_mask=__snake_case     ,			return_overflowing_tokens=__snake_case     ,			return_special_tokens_mask=__snake_case     ,			return_offsets_mapping=__snake_case     ,			return_token_type_ids=__snake_case     ,			return_length=__snake_case     ,			verbose=__snake_case     ,			return_tensors=__snake_case     ,			**__snake_case     ,			)
														return text_encoding

										if not self.image_processor.is_vqa:
														# add pixel_values
														__magic_name__						:						Dict										=self.image_processor(
														    __snake_case     ,			return_tensors=__snake_case     ,			max_patches=__snake_case     ,			**__snake_case			)
										else:
														# add pixel_values and bbox
														__magic_name__						:						Optional[int]										=self.image_processor(
														    __snake_case     ,			return_tensors=__snake_case     ,			max_patches=__snake_case     ,			header_text=__snake_case     ,			**__snake_case			)

										if text is not None and not self.image_processor.is_vqa:
														__magic_name__						:						int										=self.tokenizer(
														    text=__snake_case     ,			add_special_tokens=__snake_case     ,			padding=__snake_case     ,			truncation=__snake_case     ,			max_length=__snake_case     ,			stride=__snake_case     ,			pad_to_multiple_of=__snake_case     ,			return_attention_mask=__snake_case     ,			return_overflowing_tokens=__snake_case     ,			return_special_tokens_mask=__snake_case     ,			return_offsets_mapping=__snake_case     ,			return_token_type_ids=__snake_case     ,			return_length=__snake_case     ,			verbose=__snake_case     ,			return_tensors=__snake_case     ,			**__snake_case     ,			)

														if "attention_mask" in text_encoding:
																		__magic_name__						:						Tuple										=text_encoding.pop("""attention_mask"""			)
														if "input_ids" in text_encoding:
																		__magic_name__						:						List[str]										=text_encoding.pop("""input_ids"""			)
										else:
														__magic_name__						:						Any										=None

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

										return encoding_image_processor

						def      A__    (		self :List[Any]     ,			*__snake_case :Union[str, Any]     ,			**__snake_case :List[Any]			):
										'''simple docstring'''

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

						def      A__    (		self :Optional[Any]     ,			*__snake_case :Optional[int]     ,			**__snake_case :str			):
										'''simple docstring'''

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


						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										__magic_name__						:						int										=self.tokenizer.model_input_names
										__magic_name__						:						Any										=self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)








'''simple docstring'''


import functools


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)
     __A	:       int      =							len(SCREAMING_SNAKE_CASE							)

     @functools.cache
     def min_distance(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							) -> int:
          # if first word index is overflow - delete all from the second word
          if indexa >= len_worda:
               return len_worda - indexa
          # if second word index is overflow - delete all from the first word
          if indexa >= len_worda:
               return len_worda - indexa
          __A	:       Optional[int]      =							int(worda[indexa] != worda[indexa]							)  # current letters not identical
          return min(
              1 + min_distance(indexa + 1       ,					SCREAMING_SNAKE_CASE							)       ,					1 + min_distance(SCREAMING_SNAKE_CASE       ,					indexa + 1							)       ,					diff + min_distance(indexa + 1       ,					indexa + 1							)       ,					)

     return min_distance(0       ,					0							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

