




import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


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

		return orig_key


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Any:
		for key in orig_state_dict.copy().keys():
				SCREAMING_SNAKE_CASE_							=       orig_state_dict.pop(lowerCAmelCase   )

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

		SCREAMING_SNAKE_CASE_							=       orig_state_dict['cls.predictions.decoder.bias']
		SCREAMING_SNAKE_CASE_							=       torch.arange(lowerCAmelCase   ).expand((1, -1)   ) + 2

		return orig_state_dict


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Optional[int]:
		SCREAMING_SNAKE_CASE_							=       torch.load(lowerCAmelCase						,	map_location='cpu'   )['model_state_dict']
		SCREAMING_SNAKE_CASE_							=       MraConfig.from_json_file(lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       MraForMaskedLM(lowerCAmelCase   )

		SCREAMING_SNAKE_CASE_							=       convert_checkpoint_helper(config.max_position_embeddings						,	lowerCAmelCase   )

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

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


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    List[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:    Optional[Any]											=   parser.parse_args()
						convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





from __future__ import annotations


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Union[str, Any]:
		SCREAMING_SNAKE_CASE_							=       0
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   ) - 1
		while left <= right:
				SCREAMING_SNAKE_CASE_							=       (left + right) // 2
				if left == right:
						if sorted_list[middle] < item:
								SCREAMING_SNAKE_CASE_							=       middle + 1
						break
				elif sorted_list[middle] < item:
						SCREAMING_SNAKE_CASE_							=       middle + 1
				else:
						SCREAMING_SNAKE_CASE_							=       middle - 1
		sorted_list.insert(lowerCAmelCase						,	lowerCAmelCase   )
		return sorted_list


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Any:
		SCREAMING_SNAKE_CASE_							=       []
		while left and right:
				if left[0][0] < right[0][0]:
						result.append(left.pop(0   )   )
				else:
						result.append(right.pop(0   )   )
		return result + left + right


def 				_a       (				lowerCAmelCase   )->  List[str]:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if length <= 1:
				return list_ad
		SCREAMING_SNAKE_CASE_							=       length // 2
		return merge(sortlist_ad(list_ad[:middle]   )						,	sortlist_ad(list_ad[middle:]   )   )


def 				_a       (				lowerCAmelCase   )->  list[int]:

		if len(lowerCAmelCase   ) <= 1:
				return collection
		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(0						,	len(lowerCAmelCase   )						,	2   ):
				if i == len(lowerCAmelCase   ) - 1:
						SCREAMING_SNAKE_CASE_							=       True
				else:
						if collection[i] < collection[i + 1]:
								two_paired_list.append([collection[i], collection[i + 1]]   )
						else:
								two_paired_list.append([collection[i + 1], collection[i]]   )
		SCREAMING_SNAKE_CASE_							=       sortlist_ad(lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       [i[0] for i in sorted_list_ad]
		result.append(sorted_list_ad[-1][1]   )
		if has_last_odd_item:
				SCREAMING_SNAKE_CASE_							=       collection[-1]
				SCREAMING_SNAKE_CASE_							=       binary_search_insertion(lowerCAmelCase						,	lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(len(lowerCAmelCase   ) - 1   ):
				if result[i] == collection[-1] and has_last_odd_item:
						SCREAMING_SNAKE_CASE_							=       True
				SCREAMING_SNAKE_CASE_							=       sorted_list_ad[i][1]
				# If last_odd_item is inserted before the item's index,
				# you should forward index one more.
				if is_last_odd_item_inserted_before_this_index:
						SCREAMING_SNAKE_CASE_							=       result[: i + 2] + binary_search_insertion(result[i + 2 :]						,	lowerCAmelCase   )
				else:
						SCREAMING_SNAKE_CASE_							=       result[: i + 1] + binary_search_insertion(result[i + 1 :]						,	lowerCAmelCase   )

		return result


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    List[str]											=   input('''Enter numbers separated by a comma:\n''').strip()
						SCREAMING_SNAKE_CASE:    Any											=   [int(item) for item in user_input.split(''',''')]
						print(merge_insertion_sort(unsorted))