



import argparse

import torch

from transformers import MraConfig, MraForMaskedLM
def 						snake_case__							(       __lowercase							)		->		List[Any]:







						"""simple docstring"""

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

						return orig_key
def 						snake_case__							(       __lowercase				,  __lowercase							)		->		List[str]:







						"""simple docstring"""

						for key in orig_state_dict.copy().keys():
												A__						:				Dict         =			orig_state_dict.pop(__lowercase							)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		A__						:				List[Any]         =			val

						A__						:				List[str]         =			orig_state_dict["cls.predictions.decoder.bias"]
						A__						:				int         =			torch.arange(__lowercase							).expand((1, -1)							) + 2

						return orig_state_dict
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase							)		->		List[Any]:







						"""simple docstring"""

						A__						:				int         =			torch.load(__lowercase				,  map_location="cpu"							)["model_state_dict"]
						A__						:				Union[str, Any]         =			MraConfig.from_json_file(__lowercase							)
						A__						:				int         =			MraForMaskedLM(__lowercase							)

						A__						:				Tuple         =			convert_checkpoint_helper(config.max_position_embeddings				,  __lowercase							)

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

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


if __name__ == "__main__":
		snake_case     :    List[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.'
		)
		snake_case     :    str						=    parser.parse_args()
		convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




class 				lowerCAmelCase__	:







					def __init__(							self   :  Union[str, Any]):
											A__						:				Optional[int]         =			[0]
											A__						:				Any         =			0







					def 			_lowercase      (							self   :  Optional[int]      ,		_A   :  int):
											A__						:				Optional[Any]         =			self.__heap[i]
											while i // 2 > 0:
																	if self.__heap[i] > self.__heap[i // 2]:
																							A__						:				Union[str, Any]         =			self.__heap[i // 2]
																							A__						:				List[str]         =			temporary
																	i //= 2







					def 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  int):
											self.__heap.append(_A)
											self.__size += 1
											self.__swap_up(self.__size)







					def 			_lowercase      (							self   :  Any      ,		_A   :  int):
											while self.__size >= 2 * i:
																	if 2 * i + 1 > self.__size:
																							A__						:				str         =			2 * i
																	else:
																							if self.__heap[2 * i] > self.__heap[2 * i + 1]:
																													A__						:				Tuple         =			2 * i
																							else:
																													A__						:				Tuple         =			2 * i + 1
																	A__						:				Any         =			self.__heap[i]
																	if self.__heap[i] < self.__heap[bigger_child]:
																							A__						:				List[str]         =			self.__heap[bigger_child]
																							A__						:				Tuple         =			temporary
																	A__						:				Tuple         =			bigger_child







					def 			_lowercase      (							self   :  int):
											A__						:				str         =			self.__heap[1]
											A__						:				Tuple         =			self.__heap[self.__size]
											self.__size -= 1
											self.__heap.pop()
											self.__swap_down(1)
											return max_value







					@property
					def 			_lowercase      (							self   :  Any):
											return self.__heap[1:]







					def __len__(							self   :  Union[str, Any]):
											return self.__size


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# create an instance of BinaryHeap
		snake_case     :    List[Any]						=    BinaryHeap()
		binary_heap.insert(6)
		binary_heap.insert(1_0)
		binary_heap.insert(1_5)
		binary_heap.insert(1_2)
		# pop root(max-values because it is max heap)
		print(binary_heap.pop())  # 15
		print(binary_heap.pop())  # 12
		# get the list and size after operations
		print(binary_heap.get_list)
		print(len(binary_heap))