code: 
  
  
  
  
  '''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 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = RoFormerConfig.from_json_file(lowercase_						)
  		print(f'''Building PyTorch model from configuration: {config}'''						)
  		_snake_case			 = RoFormerForMaskedLM(lowercase_						)
  
  		# Load weights from tf checkpoint
  		load_tf_weights_in_roformer(lowercase_		,							lowercase_		,							lowercase_						)
  
  		# Save pytorch-model
  		print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
  		torch.save(model.state_dict()		,							lowercase_		,							_use_new_zipfile_serialization=lowercase_						)
  
  
  if __name__ == "__main__":
  						_lowerCamelCase							:		Any										=						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.'
  						)
  						_lowerCamelCase							:		Optional[Any]										=						parser.parse_args()
  						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterable, Iterator
  from typing import Any, Generic, TypeVar
  
  _lowerCamelCase							:		List[str]										=						TypeVar('T', bound=bool)
  
  
  
  
  
  class   lowerCamelCase_							( Generic[T]							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase				):
  							_snake_case			 = value
  							_snake_case			 = None
  							_snake_case			 = None
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return self._value
  
  
  
  
  
  
  					@staticmethod
  					def        __SCREAMING_SNAKE_CASE			(					__UpperCamelCase,						__UpperCamelCase				):
  							if not roota:
  									return roota
  
  							if not roota:
  									return roota
  
  							if roota.value > roota.value:
  									_snake_case ,					_snake_case			 = roota, roota
  
  							_snake_case			 = roota
  							_snake_case			 = roota.right
  							_snake_case			 = roota.left
  							_snake_case			 = SkewNode.merge(__UpperCamelCase,						__UpperCamelCase				)
  
  							return result
  
  
  
  
  
  class   lowerCamelCase_							( Generic[T]							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase = ()				):
  							_snake_case			 = None
  							if data:
  									for item in data:
  											self.insert(__UpperCamelCase				)
  
  
  
  
  
  
  					def __bool__(					self				):
  							return self._root is not None
  
  
  
  
  
  
  					def __iter__(					self				):
  							_snake_case			 = []
  							while self:
  									result.append(self.pop()				)
  
  							# Pushing items back to the heap not to clear it.
  							for item in result:
  									self.insert(__UpperCamelCase				)
  
  							return iter(__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = SkewNode.merge(self._root,						SkewNode(__UpperCamelCase				)				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.top()
  							_snake_case			 = (
  							    SkewNode.merge(self._root.left,						self._root.right				) if self._root else None
  							)
  
  							return result
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							if not self._root:
  									raise IndexError("Can't get top element for the empty heap."				)
  							return self._root.value
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = None
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  
  
  
  
  