code: def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				)				->tuple[list[list[int]], list[list[str]]]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     list(lowerCamelCase__					)
    a__     		=     list(lowerCamelCase__					)
    a__     		=     len(lowerCamelCase__					)
    a__     		=     len(lowerCamelCase__					)
  
    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 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->list[str]:
  
    '''simple docstring'''
  
  
  
  
    if i == 0 and j == 0:
      return []
    else:
      if ops[i][j][0] == "C" or ops[i][j][0] == "R":
        a__     		=     assemble_transformation(lowerCamelCase__ ,				i - 1 ,				j - 1					)
        seq.append(ops[i][j]					)
        return seq
      elif ops[i][j][0] == "D":
        a__     		=     assemble_transformation(lowerCamelCase__ ,				i - 1 ,				lowerCamelCase__					)
        seq.append(ops[i][j]					)
        return seq
      else:
        a__     		=     assemble_transformation(lowerCamelCase__ ,				lowerCamelCase__ ,				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))
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE   						=    {
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
       if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizer"""]
  
  try:
       if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizerFast"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    [
           """OpenLlamaForCausalLM""",
           """OpenLlamaModel""",
           """OpenLlamaPreTrainedModel""",
           """OpenLlamaForSequenceClassification""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
       try:
            if not is_sentencepiece_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizer
  
       try:
            if not is_tokenizers_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizerFast
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )
  
  
  else:
       import sys
  
       _SCREAMING_SNAKE_CASE   						=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  