
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM

def    UpperCamelCase_ (	a_		)     ->Any:
		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_		)     ->Any:
		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_		)     ->str:
		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__":
	__a							  =						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."""
	)
	__a							  =						parser.parse_args()
	convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






from __future__ import annotations

import math
import random

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->list[int]:
		A				=d * e - 1
		A				=0
		A				=0
		while p == 0:
				A				=random.randint(2  ,				n - 1		)
				A				=k
				while True:
						if t % 2 == 0:
								A				=t // 2
								A				=(g**t) % n
								A				=math.gcd(x - 1  ,				a_		)
								if x > 1 and y > 1:
										A				=y
										A				=n // y
										break  # find the correct factors
						else:
								break  # t is not divisible by 2, break and choose another g
		return sorted([p, q]		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()




