
def 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__		,					a__		,					a__		,					) ->						tuple[list[list[int]], list[list[str]]]:
				__a    							=					list(a_						)
				__a    							=					list(a_						)
				__a    							=					len(a_						)
				__a    							=					len(a_						)

				__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 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						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(a_		,					i - 1		,					j - 1						)
												seq.append(ops[i][j]						)
												return seq
								elif ops[i][j][0] == "D":
												__a    							=					assemble_transformation(a_		,					i - 1		,					a_						)
												seq.append(ops[i][j]						)
												return seq
								else:
												__a    							=					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  :			Tuple     				=				len(operations)
					A  :			int     				=				len(operations[0])
					A  :			Dict     				=				assemble_transformation(operations, m - 1, n - 1)

					A  :			int     				=				list('Python')
					A  :			Tuple     				=				0
					A  :			Tuple     				=				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  :			Any     				=				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))








# 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     __A(				TensorFormatter[Mapping, '''tf.Tensor''', Mapping]    ):




				def __init__(  self	,  _snake_case=None	,  **_snake_case       )					->   Dict:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if isinstance(_snake_case	,  _snake_case       ) and column:
												if all(
												    isinstance(_snake_case	,  tf.Tensor       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
																return tf.stack(_snake_case       )
												elif all(
												    isinstance(_snake_case	,  (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(_snake_case       )

								return column




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Optional[Any]:


								'''simple docstring'''




								import tensorflow as tf

								if value is None:
												return value

								__a    							=					{}

								if isinstance(_snake_case	,  (np.number, np.ndarray)       ) and np.issubdtype(value.dtype	,  np.integer       ):
												__a    							=					{'''dtype''': tf.intaa}
								elif isinstance(_snake_case	,  (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(_snake_case	,  PIL.Image.Image       ):
																__a    							=					np.asarray(_snake_case       )

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




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




								__a    							=					self.numpy_arrow_extractor().extract_row(_snake_case       )
								__a    							=					self.python_features_decoder.decode_row(_snake_case       )
								return self.recursive_tensorize(_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   "tf.Tensor":


								'''simple docstring'''




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




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Mapping:


								'''simple docstring'''




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