





"""simple docstring"""




from collections.abc import Callable


class 			__UpperCamelCase							:






							def __init__(  self ,   lowerCAmelCase__ = None )      ->					None:
								# Stores actual heap items.
								a :						list      =							[]
								# Stores indexes of each item for supporting updates and deletion.
								a :						dict      =							{}
								# Stores current size of heap.
								a :						List[Any]      =							0
								# Stores function used to evaluate the score of an item on which basis ordering
								# will be done.
								a :						str      =							key or (lambda lowerCAmelCase__ : x)






							def        __a  (  self ,   lowerCAmelCase__ )      ->					int | None:
								return int((i - 1) / 2 ) if i > 0 else None






							def        __a  (  self ,   lowerCAmelCase__ )      ->					int | None:
								a :						List[Any]      =							int(2 * i + 1 )
								return left if 0 < left < self.size else None






							def        __a  (  self ,   lowerCAmelCase__ )      ->					int | None:
								a :						Dict      =							int(2 * i + 2 )
								return right if 0 < right < self.size else None






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					None:
								a,			a :						Optional[int]      =							(
								    self.pos_map[self.arr[j][0]],
								    self.pos_map[self.arr[i][0]],
								)
								# Then swap the items in the list.
								a,			a :						Optional[int]      =							self.arr[j], self.arr[i]






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					bool:
								return self.arr[i][1] < self.arr[j][1]






							def        __a  (  self ,   lowerCAmelCase__ )      ->					int:
								a :						Any      =							self._left(lowerCAmelCase__ )
								a :						Tuple      =							self._right(lowerCAmelCase__ )
								a :						List[Any]      =							i

								if left is not None and not self._cmp(lowerCAmelCase__ ,   lowerCAmelCase__ ):
									a :						str      =							left
								if right is not None and not self._cmp(lowerCAmelCase__ ,   lowerCAmelCase__ ):
									a :						Any      =							right

								return valid_parent






							def        __a  (  self ,   lowerCAmelCase__ )      ->					None:
								a :						Union[str, Any]      =							self._parent(lowerCAmelCase__ )
								while parent is not None and not self._cmp(lowerCAmelCase__ ,   lowerCAmelCase__ ):
									self._swap(lowerCAmelCase__ ,   lowerCAmelCase__ )
									a,			a :						Optional[int]      =							parent, self._parent(lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ )      ->					None:
								a :						Any      =							self._get_valid_parent(lowerCAmelCase__ )
								while valid_parent != index:
									self._swap(lowerCAmelCase__ ,   lowerCAmelCase__ )
									a,			a :						int      =							valid_parent, self._get_valid_parent(lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					None:
								if item not in self.pos_map:
									return
								a :						str      =							self.pos_map[item]
								a :						Any      =							[item, self.key(lowerCAmelCase__ )]
								# Make sure heap is right in both up and down direction.
								# Ideally only one of them will make any change.
								self._heapify_up(lowerCAmelCase__ )
								self._heapify_down(lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ )      ->					None:
								if item not in self.pos_map:
									return
								a :						Any      =							self.pos_map[item]
								del self.pos_map[item]
								a :						Any      =							self.arr[self.size - 1]
								a :						Any      =							index
								self.size -= 1
								# Make sure heap is right in both up and down direction. Ideally only one
								# of them will make any change- so no performance loss in calling both.
								if self.size > index:
									self._heapify_up(lowerCAmelCase__ )
									self._heapify_down(lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					None:
								a :						List[str]      =							len(self.arr )
								if arr_len == self.size:
									self.arr.append([item, self.key(lowerCAmelCase__ )] )
								else:
									a :						Union[str, Any]      =							[item, self.key(lowerCAmelCase__ )]
								a :						Tuple      =							self.size
								self.size += 1
								self._heapify_up(self.size - 1 )






							def        __a  (  self )      ->					tuple | None:
								return self.arr[0] if self.size else None






							def        __a  (  self )      ->					tuple | None:
								a :						Union[str, Any]      =							self.get_top()
								if top_item_tuple:
									self.delete_item(top_item_tuple[0] )
								return top_item_tuple






def  _SCREAMING_SNAKE_CASE						(      )					->None:






	'''simple docstring'''








if __name__ == "__main__":
		import doctest

		doctest.testmod()









"""simple docstring"""




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a		:					Optional[int]	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       str   )					->int:






	'''simple docstring'''






	a :						Optional[Any]      =							RobertaPreLayerNormConfig.from_pretrained(
	    _lowercase       ,			architectures=["RobertaPreLayerNormForMaskedLM"]   )

	# convert state_dict
	a :						Any      =							torch.load(hf_hub_download(repo_id=_lowercase       ,			filename="pytorch_model.bin"   )   )
	a :						int      =							{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("roberta."   ):
			a :						List[Any]      =							"roberta_prelayernorm." + tensor_key[len("roberta."   ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(".self.LayerNorm.weight"   ) or tensor_key.endswith(".self.LayerNorm.bias"   ):
			continue

		a :						Dict      =							tensor_value

	a :						Optional[Any]      =							RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_lowercase       ,			config=_lowercase       ,			state_dict=_lowercase   )
	model.save_pretrained(_lowercase   )

	# convert tokenizer
	a :						List[Any]      =							AutoTokenizer.from_pretrained(_lowercase   )
	tokenizer.save_pretrained(_lowercase   )


if __name__ == "__main__":
		a		:					Any	      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint-repo''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		a		:					Tuple	      =     parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


