from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap





@total_ordering
class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				str							,A      :				str							,A      :				int				):
					__A         =				word
					__A         =				count






	def __eq__( self      :				str							,A      :				object				):
					if not isinstance(A							,A				):
									return NotImplemented
					return self.count == other.count







	def __lt__( self      :				Union[str, Any]							,A      :				object				):
					if not isinstance(A							,A				):
									return NotImplemented
					return self.count < other.count





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







				"""simple docstring"""



				__A         =				Heap()
				__A         =				Counter(a_		)
				heap.build_max_heap(
				    [WordCount(a_	,     a_		) for word, count in count_by_word.items()]		)
				return [heap.extract_max().word for _ in range(min(a_	,     len(a_		)		)		)]


if __name__ == "__main__":
					import doctest

					doctest.testmod()


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)
