




"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_    ,				)		->	tuple[list[list[int]], list[list[str]]]:
 A__ =       list(lowercase_		)
 A__ =       list(lowercase_		)
 A__ =       len(lowercase_		)
 A__ =       len(lowercase_		)

 A__ =       [
     [0 for _ in range(len_destination_seq + 1		)] for _ in range(len_source_seq + 1		)
 ]
 A__ =       [
     ["0" for _ in range(len_destination_seq + 1		)] for _ in range(len_source_seq + 1		)
 ]

 for i in range(1    ,				len_source_seq + 1		):
  A__ =       i * delete_cost
  A__ =       f"""D{source_seq[i - 1]:c}"""

 for i in range(1    ,				len_destination_seq + 1		):
  A__ =       i * insert_cost
  A__ =       f"""I{destination_seq[i - 1]:c}"""

 for i in range(1    ,				len_source_seq + 1		):
  for j in range(1    ,				len_destination_seq + 1		):
   if source_seq[i - 1] == destination_seq[j - 1]:
    A__ =       costs[i - 1][j - 1] + copy_cost
    A__ =       f"""C{source_seq[i - 1]:c}"""
   else:
    A__ =       costs[i - 1][j - 1] + replace_cost
    A__ =       f"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]		)

   if costs[i - 1][j] + delete_cost < costs[i][j]:
    A__ =       costs[i - 1][j] + delete_cost
    A__ =       f"""D{source_seq[i - 1]:c}"""

   if costs[i][j - 1] + insert_cost < costs[i][j]:
    A__ =       costs[i][j - 1] + insert_cost
    A__ =       f"""I{destination_seq[j - 1]:c}"""

 return costs, ops







def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	list[str]:
 if i == 0 and j == 0:
  return []
 else:
  if ops[i][j][0] == "C" or ops[i][j][0] == "R":
   A__ =       assemble_transformation(lowercase_    ,				i - 1    ,				j - 1		)
   seq.append(ops[i][j]		)
   return seq
  elif ops[i][j][0] == "D":
   A__ =       assemble_transformation(lowercase_    ,				i - 1    ,				lowercase_		)
   seq.append(ops[i][j]		)
   return seq
  else:
   A__ =       assemble_transformation(lowercase_    ,				lowercase_    ,				j - 1		)
   seq.append(ops[i][j]		)
   return seq


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE       = compute_transform_tables("Python", "Algorithms", -1, 1, 2, 2)

      SCREAMING_SNAKE_CASE       = len(operations)
      SCREAMING_SNAKE_CASE       = len(operations[0])
      SCREAMING_SNAKE_CASE       = assemble_transformation(operations, m - 1, n - 1)

      SCREAMING_SNAKE_CASE       = list("Python")
      SCREAMING_SNAKE_CASE       = 0
      SCREAMING_SNAKE_CASE       = 0

      with open("min_cost.txt", "w") as file:
            for op in sequence:
                  print("".join(string))

                  if op[0] == "C":
                        file.write("%-16s" % "Copy %c" % op[1])
                        file.write("\t\t\t" + "".join(string))
                        file.write("\r\n")

                        cost -= 1
                  elif op[0] == "R":
                        SCREAMING_SNAKE_CASE       = op[2]

                        file.write("%-16s" % ("Replace %c" % op[1] + " with " + str(op[2])))
                        file.write("\t\t" + "".join(string))
                        file.write("\r\n")

                        cost += 1
                  elif op[0] == "D":
                        string.pop(i)

                        file.write("%-16s" % "Delete %c" % op[1])
                        file.write("\t\t\t" + "".join(string))
                        file.write("\r\n")

                        cost += 2
                  else:
                        string.insert(i, op[1])

                        file.write("%-16s" % "Insert %c" % op[1])
                        file.write("\t\t\t" + "".join(string))
                        file.write("\r\n")

                        cost += 2

                  i += 1

            print("".join(string))
            print("Cost: ", cost)

            file.write("\r\nMinimum cost: " + str(cost))











"""simple docstring"""



from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy




class 			UpperCAmelCase_						(		A_							):
    lowercase__		      =   '''MarkupLMFeatureExtractor'''
    lowercase__		      =   ('''MarkupLMTokenizer''', '''MarkupLMTokenizerFast''')
    lowercase__		      =   True




    def __call__(					self		: Dict			,				snake_case_		: Optional[int]=None			,				snake_case_		: Union[str, Any]=None			,				snake_case_		: Tuple=None			,				snake_case_		: str=None			,				snake_case_		: Optional[Any]=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_		: int = 0			,				snake_case_		: Optional[int] = None			,				snake_case_		: Optional[bool] = 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 = True			,				snake_case_		: Optional[Union[str, TensorType]] = None			,				**snake_case_		: Dict			,				)   ->							BatchEncoding:




     '''simple docstring'''

     if self.parse_html:
      if html_strings is None:
       raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"   )

      if nodes is not None or xpaths is not None or node_labels is not None:
       raise ValueError(
           "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"   )

      A__ =       self.feature_extractor(snake_case_   )
      A__ =       features["nodes"]
      A__ =       features["xpaths"]
     else:
      if html_strings is not None:
       raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."   )
      if nodes is None or xpaths is None:
       raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"   )

        # # second, apply the tokenizer
     if questions is not None and self.parse_html:
      if isinstance(snake_case_			,				snake_case_   ):
       A__ =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

     A__ =       self.tokenizer(
         text=questions if questions is not None else nodes			,				text_pair=nodes if questions is not None else None			,				xpaths=snake_case_			,				node_labels=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_token_type_ids=snake_case_			,				return_attention_mask=snake_case_			,				return_overflowing_tokens=snake_case_			,				return_special_tokens_mask=snake_case_			,				return_offsets_mapping=snake_case_			,				return_length=snake_case_			,				verbose=snake_case_			,				return_tensors=snake_case_			,				**snake_case_			,				)

     return encoded_inputs




    def 							__magic_name__							(					self		: Optional[Any]			,				*snake_case_		: Any			,				**snake_case_		: Any   )   ->							int:




     '''simple docstring'''

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




    def 							__magic_name__							(					self		: Tuple			,				*snake_case_		: Optional[Any]			,				**snake_case_		: List[Any]   )   ->							Any:




     '''simple docstring'''

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




    @property
    def 							__magic_name__							(					self		: List[Any]   )   ->							Optional[int]:




     '''simple docstring'''

     A__ =       self.tokenizer.model_input_names
     return tokenizer_input_names





