
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_ ):
				_lowercase    	=['''image_processor''', '''tokenizer''']
				_lowercase    	='''BridgeTowerImageProcessor'''
				_lowercase    	=('''RobertaTokenizer''', '''RobertaTokenizerFast''')




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   List[Any]:
					super().__init__(_UpperCamelCase       ,       _UpperCamelCase  )




				def __call__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None       ,       _UpperCamelCase = True       ,       _UpperCamelCase = False       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = 0       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = False       ,       _UpperCamelCase = False       ,       _UpperCamelCase = False       ,       _UpperCamelCase = False       ,       _UpperCamelCase = True       ,       _UpperCamelCase = None       ,       **_UpperCamelCase       ,       )       ->   BatchEncoding:
					lowerCAmelCase_      			=			self.tokenizer(
					    text=_UpperCamelCase       ,       add_special_tokens=_UpperCamelCase       ,       padding=_UpperCamelCase       ,       truncation=_UpperCamelCase       ,       max_length=_UpperCamelCase       ,       stride=_UpperCamelCase       ,       pad_to_multiple_of=_UpperCamelCase       ,       return_token_type_ids=_UpperCamelCase       ,       return_attention_mask=_UpperCamelCase       ,       return_overflowing_tokens=_UpperCamelCase       ,       return_special_tokens_mask=_UpperCamelCase       ,       return_offsets_mapping=_UpperCamelCase       ,       return_length=_UpperCamelCase       ,       verbose=_UpperCamelCase       ,       return_tensors=_UpperCamelCase       ,       **_UpperCamelCase       ,       )
					# add pixel_values + pixel_mask
					lowerCAmelCase_      			=			self.image_processor(
					    _UpperCamelCase       ,       return_tensors=_UpperCamelCase       ,       do_normalize=_UpperCamelCase       ,       do_center_crop=_UpperCamelCase       ,       **_UpperCamelCase  )
					encoding.update(_UpperCamelCase  )

					return encoding




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   str:
					return self.tokenizer.batch_decode(*_UpperCamelCase       ,       **_UpperCamelCase  )




				def   __a		(      self       ,       *_UpperCamelCase       ,       **_UpperCamelCase  )       ->   int:
					return self.tokenizer.decode(*_UpperCamelCase       ,       **_UpperCamelCase  )




				@property
				def   __a		(      self  )       ->   Any:
					lowerCAmelCase_      			=			self.tokenizer.model_input_names
					lowerCAmelCase_      			=			self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )





import functools






def      lowerCamelCase__       (       __lowerCAmelCase						:						str					,							__lowerCAmelCase						:						str       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			len(__lowerCAmelCase       )
			lowerCAmelCase_      			=			len(__lowerCAmelCase       )

			@functools.cache
			def min_distance(__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						int       ) -> 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
						lowerCAmelCase_      			=			int(worda[indexa] != worda[indexa]       )  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1					,							__lowerCAmelCase       )					,							1 + min_distance(__lowerCAmelCase					,							indexa + 1       )					,							diff + min_distance(indexa + 1					,							indexa + 1       )					,							)

			return min_distance(0					,							0       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



