





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

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

		return orig_key

def      a    (   _lowercase :  Optional[Any]			,    _lowercase :  Tuple							)  ->Dict:
		for key in orig_state_dict.copy().keys():
				UpperCamelCase__								=						orig_state_dict.pop(_snake_case							)

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

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

		return orig_state_dict

def      a    (   _lowercase :  Tuple			,    _lowercase :  int			,    _lowercase :  int							)  ->Union[str, Any]:
		UpperCamelCase__								=						torch.load(_snake_case			,    map_location="""cpu"""							)["""model_state_dict"""]
		UpperCamelCase__								=						MraConfig.from_json_file(_snake_case							)
		UpperCamelCase__								=						MraForMaskedLM(_snake_case							)

		UpperCamelCase__								=						convert_checkpoint_helper(config.max_position_embeddings			,    _snake_case							)

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

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


if __name__ == "__main__":
				__lowerCAmelCase 	=      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 	=      parser.parse_args()
				convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)











from itertools import permutations



def    UpperCAmelCase							(  _snake_case = 5				):
							if gon_side < 3 or gon_side > 5:
														raise ValueError('''gon_side must be in the range [3, 5]'''				)

							# Since it's 16, we know 10 is on the outer ring
							# Put the big numbers at the end so that they are never the first number
							lowerCAmelCase =		list(range(gon_side + 1    ,			0    ,			-1				)				)
							lowerCAmelCase =		list(range(gon_side + 2    ,			gon_side * 2 + 1				)				)

							for perm in permutations(small_numbers + big_numbers				):
														lowerCAmelCase =		generate_gon_ring(_snake_case    ,			list(_snake_case				)				)
														if is_magic_gon(_snake_case				):
																					return int(''''''.join(str(_snake_case				) for n in numbers				)				)

							lowerCAmelCase =		F"""Magic {gon_side}-gon ring is impossible"""
							raise ValueError(_snake_case				)



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		[0] * (gon_side * 3)
							lowerCAmelCase =		perm[0:3]
							perm.append(perm[1]				)

							lowerCAmelCase =		1 if gon_side < 5 else 2

							for i in range(1    ,			len(_snake_case				) // 3 + magic_number				):
														lowerCAmelCase =		perm[2 * i + 1]
														lowerCAmelCase =		result[3 * i - 1]
														lowerCAmelCase =		perm[2 * i + 2]

							return result



def    UpperCAmelCase							(  _snake_case				):
							if len(_snake_case				) % 3 != 0:
														raise ValueError('''a gon ring should have a length that is a multiple of 3'''				)

							if min(numbers[::3]				) != numbers[0]:
														return False

							lowerCAmelCase =		sum(numbers[:3]				)

							return all(sum(numbers[i : i + 3]				) == total for i in range(3    ,			len(_snake_case				)    ,			3				)				)


if __name__ == "__main__":
		print(solution())



