





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM







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

					return orig_key







def  A					(				UpperCAmelCase	,	UpperCAmelCase					):
					for key in orig_state_dict.copy().keys():
										_snake_case							:       Any												= orig_state_dict.pop(UpperCAmelCase					)

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

					_snake_case							:       str												= orig_state_dict["cls.predictions.decoder.bias"]
					_snake_case							:       Optional[Any]												= torch.arange(UpperCAmelCase					).expand((1, -1)					) + 2

					return orig_state_dict







def  A					(				UpperCAmelCase	,	UpperCAmelCase	,	UpperCAmelCase					):
					_snake_case							:       List[Any]												= torch.load(UpperCAmelCase	,	map_location="cpu"					)["model_state_dict"]
					_snake_case							:       str												= MraConfig.from_json_file(UpperCAmelCase					)
					_snake_case							:       str												= MraForMaskedLM(UpperCAmelCase					)

					_snake_case							:       List[Any]												= convert_checkpoint_helper(config.max_position_embeddings	,	UpperCAmelCase					)

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

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


if __name__ == "__main__":
							__lowerCAmelCase			:str	 =     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.'
							)
							__lowerCAmelCase			:Union[str, Any]	 =     parser.parse_args()
							convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






from __future__ import annotations

from collections.abc import Generator







def  A					(				UpperCAmelCase					):
					if not isinstance(UpperCAmelCase	,	UpperCAmelCase					) or n < 1:
										raise Exception("Sequence only defined for positive integers"					)

					yield n
					while n != 1:
										if n % 2 == 0:
															n //= 2
										else:
															_snake_case							:       Any												= 3 * n + 1
										yield n







def  A					(				):
					_snake_case							:       Dict												= 43
					_snake_case							:       List[str]												= tuple(collatz_sequence(UpperCAmelCase					)					)
					print(UpperCAmelCase					)
					print(F"""Collatz sequence from {n} took {len(UpperCAmelCase					)} steps."""					)


if __name__ == "__main__":
							main()