
"""simple docstring"""
def 					a__ ( a :					str							,  a :					str							,  a :					int							,  a :					int							,  a :					int							,  a :					int							,  ):

  """simple docstring"""







  _snake_case  :    Union[str, Any]            = list(a		)
  _snake_case  :    List[str]            = list(a		)
  _snake_case  :    Dict            = len(a		)
  _snake_case  :    List[str]            = len(a		)

  _snake_case  :    Union[str, Any]            = [
      [0 for _ in range(len_destination_seq + 1		)] for _ in range(len_source_seq + 1		)
  ]
  _snake_case  :    Union[str, Any]            = [
      ["0" for _ in range(len_destination_seq + 1		)] for _ in range(len_source_seq + 1		)
  ]

  for i in range(1							,  len_source_seq + 1		):
    _snake_case  :    Dict            = i * delete_cost
    _snake_case  :    Any            = f'D{source_seq[i - 1]:c}'

  for i in range(1							,  len_destination_seq + 1		):
    _snake_case  :    Optional[Any]            = i * insert_cost
    _snake_case  :    str            = 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]:
        _snake_case  :    List[str]            = costs[i - 1][j - 1] + copy_cost
        _snake_case  :    List[Any]            = f'C{source_seq[i - 1]:c}'
      else:
        _snake_case  :    str            = costs[i - 1][j - 1] + replace_cost
        _snake_case  :    Any            = f'R{source_seq[i - 1]:c}' + str(destination_seq[j - 1]		)

      if costs[i - 1][j] + delete_cost < costs[i][j]:
        _snake_case  :    List[Any]            = costs[i - 1][j] + delete_cost
        _snake_case  :    str            = f'D{source_seq[i - 1]:c}'

      if costs[i][j - 1] + insert_cost < costs[i][j]:
        _snake_case  :    List[str]            = costs[i][j - 1] + insert_cost
        _snake_case  :    int            = f'I{destination_seq[j - 1]:c}'

  return costs, ops
def 					a__ ( a :					list[list[str]]							,  a :					int							,  a :					int		):

  """simple docstring"""







  if i == 0 and j == 0:
    return []
  else:
    if ops[i][j][0] == "C" or ops[i][j][0] == "R":
      _snake_case  :    int            = assemble_transformation(a							,  i - 1							,  j - 1		)
      seq.append(ops[i][j]		)
      return seq
    elif ops[i][j][0] == "D":
      _snake_case  :    Dict            = assemble_transformation(a							,  i - 1							,  a		)
      seq.append(ops[i][j]		)
      return seq
    else:
      _snake_case  :    Any            = assemble_transformation(a							,  a							,  j - 1		)
      seq.append(ops[i][j]		)
      return seq


if __name__ == "__main__":
    _a,      _a   :      List[str]       =				compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)

    _a   :      str       =				len(operations)
    _a   :      str       =				len(operations[0])
    _a   :      Dict       =				assemble_transformation(operations, m - 1, n - 1)

    _a   :      List[str]       =				list("""Python""")
    _a   :      Optional[int]       =				0
    _a   :      Union[str, Any]       =				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":
                _a   :      Dict       =				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"""







# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
	import tensorflow as tf







class 							_SCREAMING_SNAKE_CASE    (	TensorFormatter[Mapping, 'tf.Tensor', Mapping]				):





							"""simple docstring"""





							def __init__( self:  Union[str, Any]					,		__A:  Union[str, Any]=None					,		**__A:  Union[str, Any]     ):

												'''simple docstring'''

												super().__init__(features=__A     )
												a__ 							=	tf_tensor_kwargs
												import tensorflow as tf  # noqa: import tf at initialization




							def 			lowercase		( self:  Optional[int]					,		__A:  List[Any]     ):

												'''simple docstring'''

												import tensorflow as tf

												if isinstance(__A					,		__A     ) and column:
																	if all(
																	    isinstance(__A					,		tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
																						return tf.stack(__A     )
																	elif all(
																	    isinstance(__A					,		(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
																	    for x in column     ):
																						# only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
																						return tf.ragged.stack(__A     )

												return column




							def 			lowercase		( self:  int					,		__A:  Optional[Any]     ):

												'''simple docstring'''

												import tensorflow as tf

												if value is None:
																	return value

												a__ 							=	{}

												if isinstance(__A					,		(np.number, np.ndarray)     ) and np.issubdtype(value.dtype					,		np.integer     ):
																	a__ 							=	{'''dtype''': tf.intaa}
												elif isinstance(__A					,		(np.number, np.ndarray)     ) and np.issubdtype(value.dtype					,		np.floating     ):
																	a__ 							=	{'''dtype''': tf.floataa}
												elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																	import PIL.Image

																	if isinstance(__A					,		PIL.Image.Image     ):
																						a__ 							=	np.asarray(__A     )

												return tf.convert_to_tensor(__A					,		**{**default_dtype, **self.tf_tensor_kwargs}     )




							def 			lowercase		( self:  List[Any]					,		__A:  str     ):

												'''simple docstring'''

												import tensorflow as tf

												# support for torch, tf, jax etc.
												if config.TORCH_AVAILABLE and "torch" in sys.modules:
																	import torch

																	if isinstance(__A					,		torch.Tensor     ):
																						return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
												if hasattr(__A					,		'''__array__'''     ) and not isinstance(__A					,		tf.Tensor     ):
																	a__ 							=	data_struct.__array__()
												# support for nested types like struct of list of struct
												if isinstance(__A					,		np.ndarray     ):
																	if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
																						return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
												elif isinstance(__A					,		(list, tuple)     ):
																	return self._consolidate([self.recursive_tensorize(__A     ) for substruct in data_struct]     )
												return self._tensorize(__A     )




							def 			lowercase		( self:  int					,		__A:  dict     ):

												'''simple docstring'''

												return map_nested(self._recursive_tensorize					,		__A					,		map_list=__A     )




							def 			lowercase		( self:  Tuple					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_row(__A     )
												a__ 							=	self.python_features_decoder.decode_row(__A     )
												return self.recursive_tensorize(__A     )




							def 			lowercase		( self:  Dict					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_column(__A     )
												a__ 							=	self.python_features_decoder.decode_column(__A					,		pa_table.column_names[0]     )
												a__ 							=	self.recursive_tensorize(__A     )
												a__ 							=	self._consolidate(__A     )
												return column





							def 			lowercase		( self:  Tuple					,		__A:  pa.Table     ):

												'''simple docstring'''

												a__ 							=	self.numpy_arrow_extractor().extract_batch(__A     )
												a__ 							=	self.python_features_decoder.decode_batch(__A     )
												a__ 							=	self.recursive_tensorize(__A     )
												for column_name in batch:
																	a__ 							=	self._consolidate(batch[column_name]     )
												return batch


