def 		UpperCAmelCase    (			a_	,     a_	,     a_	,     a_	,     a_	,     a_	,     )	->			int:







				"""simple docstring"""



				__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 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			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(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__":
					SCREAMING_SNAKE_CASE							:Dict											=       compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

					SCREAMING_SNAKE_CASE							:List[Any]											=       len(operations)
					SCREAMING_SNAKE_CASE							:Optional[int]											=       len(operations[0])
					SCREAMING_SNAKE_CASE							:Dict											=       assemble_transformation(operations, m - 1, n - 1)

					SCREAMING_SNAKE_CASE							:int											=       list('Python')
					SCREAMING_SNAKE_CASE							:Optional[int]											=       0
					SCREAMING_SNAKE_CASE							:List[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":
																				SCREAMING_SNAKE_CASE							:Tuple											=       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))


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 		UpperCAmelCase    (			a_		)	->			Any:







				"""simple docstring"""



				if "model" in orig_key:
								__A         =				orig_key.replace("model."	,     ""		)
				if "norm1" in orig_key:
								__A         =				orig_key.replace("norm1"	,     "attention.output.LayerNorm"		)
				if "norm2" in orig_key:
								__A         =				orig_key.replace("norm2"	,     "output.LayerNorm"		)
				if "norm" in orig_key:
								__A         =				orig_key.replace("norm"	,     "LayerNorm"		)
				if "transformer" in orig_key:
								__A         =				orig_key.split("."		)[0].split("_"		)[-1]
								__A         =				orig_key.replace(F'''transformer_{layer_num}'''	,     F'''encoder.layer.{layer_num}'''		)
				if "mha.attn" in orig_key:
								__A         =				orig_key.replace("mha.attn"	,     "attention.self"		)
				if "mha" in orig_key:
								__A         =				orig_key.replace("mha"	,     "attention"		)
				if "W_q" in orig_key:
								__A         =				orig_key.replace("W_q"	,     "self.query"		)
				if "W_k" in orig_key:
								__A         =				orig_key.replace("W_k"	,     "self.key"		)
				if "W_v" in orig_key:
								__A         =				orig_key.replace("W_v"	,     "self.value"		)
				if "ff.0" in orig_key:
								__A         =				orig_key.replace("ff.0"	,     "intermediate.dense"		)
				if "ff.2" in orig_key:
								__A         =				orig_key.replace("ff.2"	,     "output.dense"		)
				if "ff" in orig_key:
								__A         =				orig_key.replace("ff"	,     "output.dense"		)
				if "mlm_class" in orig_key:
								__A         =				orig_key.replace("mlm.mlm_class"	,     "cls.predictions.decoder"		)
				if "mlm" in orig_key:
								__A         =				orig_key.replace("mlm"	,     "cls.predictions.transform"		)
				if "backbone.backbone.encoders" in orig_key:
								__A         =				orig_key.replace("backbone.backbone.encoders"	,     "encoder.layer"		)
				if "cls" not in orig_key:
								__A         =				"mra." + orig_key

				return orig_key


def 		UpperCAmelCase    (			a_	,     a_		)	->			List[str]:







				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__A         =				orig_state_dict.pop(a_		)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__A         =				val

				__A         =				orig_state_dict["cls.predictions.decoder.bias"]
				__A         =				torch.arange(a_		).expand((1, -1)		) + 2

				return orig_state_dict


def 		UpperCAmelCase    (			a_	,     a_	,     a_		)	->			Dict:







				"""simple docstring"""



				__A         =				torch.load(a_	,     map_location="cpu"		)["model_state_dict"]
				__A         =				MraConfig.from_json_file(a_		)
				__A         =				MraForMaskedLM(a_		)

				__A         =				convert_checkpoint_helper(config.max_position_embeddings	,     a_		)

				print(model.load_state_dict(a_		)		)
				model.eval()
				model.save_pretrained(a_		)

				print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''		)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Optional[Any]											=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for Mra model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					SCREAMING_SNAKE_CASE							:Any											=       parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
