



def   UpperCAmelCase							(		A__				,  A__				,  A__				,  A__				,  A__				,  A__				,  )							->					tuple[list[list[int]], list[list[str]]]:
	_snake_case					:							str					     =   list(A__     )
	_snake_case					:							Dict					     =   list(A__     )
	_snake_case					:							Tuple					     =   len(A__     )
	_snake_case					:							int					     =   len(A__     )

	_snake_case					:							Dict					     =   [
	    [0 for _ in range(len_destination_seq + 1     )] for _ in range(len_source_seq + 1     )
	]
	_snake_case					:							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					:							Optional[int]					     =   i * delete_cost
		_snake_case					:							Dict					     =   f'''D{source_seq[i - 1]:c}'''

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

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

			if costs[i][j - 1] + insert_cost < costs[i][j]:
				_snake_case					:							Union[str, Any]					     =   costs[i][j - 1] + insert_cost
				_snake_case					:							Dict					     =   f'''I{destination_seq[j - 1]:c}'''

	return costs, ops
def   UpperCAmelCase							(		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":
			_snake_case					:							Union[str, Any]					     =   assemble_transformation(A__				,  i - 1				,  j - 1     )
			seq.append(ops[i][j]     )
			return seq
		elif ops[i][j][0] == "D":
			_snake_case					:							Optional[int]					     =   assemble_transformation(A__				,  i - 1				,  A__     )
			seq.append(ops[i][j]     )
			return seq
		else:
			_snake_case					:							List[str]					     =   assemble_transformation(A__				,  A__				,  j - 1     )
			seq.append(ops[i][j]     )
			return seq


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

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

						UpperCAmelCase_			      =  list('''Python''')
						UpperCAmelCase_			      =  0
						UpperCAmelCase_			      =  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":
																								UpperCAmelCase_			      =  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))










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_			      =  {'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase_			      =  [
						    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''QDQBertForMaskedLM''',
						    '''QDQBertForMultipleChoice''',
						    '''QDQBertForNextSentencePrediction''',
						    '''QDQBertForQuestionAnswering''',
						    '''QDQBertForSequenceClassification''',
						    '''QDQBertForTokenClassification''',
						    '''QDQBertLayer''',
						    '''QDQBertLMHeadModel''',
						    '''QDQBertModel''',
						    '''QDQBertPreTrainedModel''',
						    '''load_tf_weights_in_qdqbert''',
						]


if TYPE_CHECKING:
						from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_qdqbert import (
												    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    QDQBertForMaskedLM,
												    QDQBertForMultipleChoice,
												    QDQBertForNextSentencePrediction,
												    QDQBertForQuestionAnswering,
												    QDQBertForSequenceClassification,
												    QDQBertForTokenClassification,
												    QDQBertLayer,
												    QDQBertLMHeadModel,
												    QDQBertModel,
												    QDQBertPreTrainedModel,
												    load_tf_weights_in_qdqbert,
												)


else:
						import sys

						UpperCAmelCase_			      =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





