
'''simple docstring'''





from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

a__		: Optional[Any]  							=							TypeVar("T", bound=bool)


class 			UpperCamelCase__	(     Generic[T]):






 def __init__(      self	:List[Any]						,					_A	:T      )					->      None:







       '''simple docstring'''



       __A													=      value
       __A													=      None
       __A													=      None






 @property
 def 						lowercase_    (      self	:Tuple      )					->      T:







       '''simple docstring'''



       return self._value






 @staticmethod
 def 						lowercase_    (      _A	:SkewNode[T] | None						,					_A	:SkewNode[T] | None      )					->      SkewNode[T] | None:







       '''simple docstring'''



       if not roota:
             return roota

       if not roota:
             return roota

       if roota.value > roota.value:
             __A						,   __A													=      roota, roota

       __A													=      roota
       __A													=      roota.right
       __A													=      roota.left
       __A													=      SkewNode.merge(_A						,					_A      )

       return result


class 			UpperCamelCase__	(     Generic[T]):






 def __init__(      self	:Optional[int]						,					_A	:Iterable[T] | None = ()      )					->      None:







       '''simple docstring'''



       __A													=      None
       if data:
             for item in data:
                   self.insert(_A      )






 def __bool__(      self	:List[str]      )					->      bool:







       '''simple docstring'''



       return self._root is not None






 def __iter__(      self	:Dict      )					->      Iterator[T]:







       '''simple docstring'''



       __A													=      []
       while self:
             result.append(self.pop()      )

       # Pushing items back to the heap not to clear it.
       for item in result:
             self.insert(_A      )

       return iter(_A      )






 def 						lowercase_    (      self	:Tuple						,					_A	:T      )					->      None:







       '''simple docstring'''



       __A													=      SkewNode.merge(self._root						,					SkewNode(_A      )      )






 def 						lowercase_    (      self	:Dict      )					->      T | None:







       '''simple docstring'''



       __A													=      self.top()
       __A													=      (
           SkewNode.merge(self._root.left						,					self._root.right      ) if self._root else None
       )

       return result






 def 						lowercase_    (      self	:int      )					->      T:







       '''simple docstring'''



       if not self._root:
             raise IndexError('Can\'t get top element for the empty heap.'      )
       return self._root.value






 def 						lowercase_    (      self	:int      )					->      None:







       '''simple docstring'''



       __A													=      None


if __name__ == "__main__":
 import doctest

 doctest.testmod()








'''simple docstring'''





import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # Initialise PyTorch model
      __A													=      RoFormerConfig.from_json_file(UpperCAmelCase						)
      print(f'Building PyTorch model from configuration: {config}'						)
      __A													=      RoFormerForMaskedLM(UpperCAmelCase						)

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)

      # Save pytorch-model
      print(f'Save PyTorch model to {pytorch_dump_path}'						)
      torch.save(model.state_dict()   ,	UpperCAmelCase   ,	_use_new_zipfile_serialization=UpperCAmelCase						)


if __name__ == "__main__":
 a__		: Tuple  							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--bert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained BERT model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a__		: Union[str, Any]  							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






