


'''simple docstring'''







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM



def 				_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



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

				return orig_key



def 				_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				for key in orig_state_dict.copy().keys():
								__magic_name__				:					List[str]						       =       orig_state_dict.pop(a_		)

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

				__magic_name__				:					List[str]						       =       orig_state_dict["cls.predictions.decoder.bias"]
				__magic_name__				:					Dict						       =       torch.arange(a_		).expand((1, -1)		) + 2

				return orig_state_dict



def 				_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					List[Any]						       =       torch.load(a_     ,   map_location="cpu"		)["model_state_dict"]
				__magic_name__				:					int						       =       MraConfig.from_json_file(a_		)
				__magic_name__				:					Any						       =       MraForMaskedLM(a_		)

				__magic_name__				:					Optional[Any]						       =       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       :   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."
		)
		_SCREAMING_SNAKE_CASE       :   Dict        = parser.parse_args()
		convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



'''simple docstring'''







import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput






@dataclass
class 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						__snake_case   =      42
						__snake_case   =      None
						__snake_case   =      None






class 		_snake_case    (				unittest.TestCase		):






						'''simple docstring'''







						def lowerCAmelCase__ (  self:	List[str]     )							->	str:
										__magic_name__				:					List[Any]						       =       ModelOutputTest(a=30     )
										self.assertEqual(x.a      ,							30     )
										self.assertIsNone(x.b     )
										self.assertIsNone(x.c     )
										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					Optional[int]						       =       x.d







						def lowerCAmelCase__ (  self:	Dict     )							->	Dict:
										__magic_name__				:					List[Any]						       =       ModelOutputTest(a=30      ,							b=10     )
										self.assertEqual(x[0]      ,							30     )
										self.assertEqual(x[1]      ,							10     )
										self.assertEqual(x[:2]      ,							(30, 10)     )
										self.assertEqual(x[:]      ,							(30, 10)     )

										__magic_name__				:					str						       =       ModelOutputTest(a=30      ,							c=10     )
										self.assertEqual(x[0]      ,							30     )
										self.assertEqual(x[1]      ,							10     )
										self.assertEqual(x[:2]      ,							(30, 10)     )
										self.assertEqual(x[:]      ,							(30, 10)     )







						def lowerCAmelCase__ (  self:	List[Any]     )							->	Optional[int]:
										__magic_name__				:					Optional[Any]						       =       ModelOutputTest(a=30      ,							b=10     )
										self.assertEqual(x["a"]      ,							30     )
										self.assertEqual(x["b"]      ,							10     )
										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					str						       =       x["c"]

										__magic_name__				:					Optional[Any]						       =       ModelOutputTest(a=30      ,							c=10     )
										self.assertEqual(x["a"]      ,							30     )
										self.assertEqual(x["c"]      ,							10     )
										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					Union[str, Any]						       =       x["b"]







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Any:
										__magic_name__				:					Any						       =       ModelOutputTest(a=30     )
										self.assertEqual(list(x.keys()     )      ,							["a"]     )
										self.assertEqual(list(x.values()     )      ,							[30]     )
										self.assertEqual(list(x.items()     )      ,							[("a", 30)]     )
										self.assertEqual(list(__UpperCamelCase     )      ,							["a"]     )

										__magic_name__				:					Dict						       =       ModelOutputTest(a=30      ,							b=10     )
										self.assertEqual(list(x.keys()     )      ,							["a", "b"]     )
										self.assertEqual(list(x.values()     )      ,							[30, 10]     )
										self.assertEqual(list(x.items()     )      ,							[("a", 30), ("b", 10)]     )
										self.assertEqual(list(__UpperCamelCase     )      ,							["a", "b"]     )

										__magic_name__				:					int						       =       ModelOutputTest(a=30      ,							c=10     )
										self.assertEqual(list(x.keys()     )      ,							["a", "c"]     )
										self.assertEqual(list(x.values()     )      ,							[30, 10]     )
										self.assertEqual(list(x.items()     )      ,							[("a", 30), ("c", 10)]     )
										self.assertEqual(list(__UpperCamelCase     )      ,							["a", "c"]     )

										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					str						       =       x.update({"d": 20}     )
										with self.assertRaises(__UpperCamelCase     ):
														del x["a"]
										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					List[str]						       =       x.pop("a"     )
										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					Optional[Any]						       =       x.setdefault("d"      ,							32     )







						def lowerCAmelCase__ (  self:	Dict     )							->	int:
										__magic_name__				:					Any						       =       ModelOutputTest(a=30     )
										__magic_name__				:					Tuple						       =       10
										self.assertEqual(x.a      ,							10     )
										self.assertEqual(x["a"]      ,							10     )







						def lowerCAmelCase__ (  self:	Dict     )							->	Union[str, Any]:
										__magic_name__				:					Tuple						       =       ModelOutputTest(a=30     )
										__magic_name__				:					Union[str, Any]						       =       10
										self.assertEqual(x.a      ,							10     )
										self.assertEqual(x["a"]      ,							10     )







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Optional[int]:
										__magic_name__				:					Tuple						       =       ModelOutputTest({"a": 30, "b": 10}     )
										self.assertEqual(list(x.keys()     )      ,							["a", "b"]     )
										self.assertEqual(x.a      ,							30     )
										self.assertEqual(x.b      ,							10     )







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Optional[Any]:
										__magic_name__				:					List[str]						       =       ModelOutputTest([("a", 30), ("b", 10)]     )
										self.assertEqual(list(x.keys()     )      ,							["a", "b"]     )
										self.assertEqual(x.a      ,							30     )
										self.assertEqual(x.b      ,							10     )

										with self.assertRaises(__UpperCamelCase     ):
														__magic_name__				:					Any						       =       ModelOutputTest([("a", 30), (10, 10)]     )

										__magic_name__				:					Any						       =       ModelOutputTest(a=(30, 30)     )
										self.assertEqual(list(x.keys()     )      ,							["a"]     )
										self.assertEqual(x.a      ,							(30, 30)     )