



"""simple docstring"""


from __future__ import annotations

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

snake_case_								=			TypeVar("""T""", bound=bool)







class  A_     (	Generic[T]							):



						"""simple docstring"""






						def __init__(   self					:Optional[int]  ,    lowercase_					:T   )							->     None:
												UpperCAmelCase			 =					value
												UpperCAmelCase			 =					None
												UpperCAmelCase			 =					None






						@property
						def       UpperCAmelCase__	(   self					:Dict   )							->     T:
												return self._value






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:RandomizedHeapNode[T] | None  ,    lowercase_					:RandomizedHeapNode[T] | None   )							->     RandomizedHeapNode[T] | None:
												if not roota:
																		return roota

												if not roota:
																		return roota

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

												if random.choice([True, False]   ):
																		UpperCAmelCase ,       UpperCAmelCase			 =					roota.right, roota.left

												UpperCAmelCase			 =					RandomizedHeapNode.merge(roota.left  ,    lowercase_   )

												return roota







class  A_     (	Generic[T]							):



						"""simple docstring"""






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

												if data:
																		for item in data:
																								self.insert(lowercase_   )






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:T   )							->     None:
												UpperCAmelCase			 =					RandomizedHeapNode.merge(self._root  ,    RandomizedHeapNode(lowercase_   )   )






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     T | None:
												UpperCAmelCase			 =					self.top()

												if self._root is None:
																		return None

												UpperCAmelCase			 =					RandomizedHeapNode.merge(self._root.left  ,    self._root.right   )

												return result






						def       UpperCAmelCase__	(   self					:str   )							->     T:
												if not self._root:
																		raise IndexError('Can\'t get top element for the empty heap.'   )
												return self._root.value






						def       UpperCAmelCase__	(   self					:List[str]   )							->     None:
												UpperCAmelCase			 =					None






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     list[Any]:
												UpperCAmelCase			 =					[]
												while self:
																		result.append(self.pop()   )

												return result






						def __bool__(   self					:Dict   )							->     bool:
												return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()







import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()
def        __lowercase(							__snake_case				:		Dict    ,__snake_case				:		List[str]    ,__snake_case				:		List[Any]					)       ->   str:
       __snake_case						       =   ConvBertConfig.from_json_file(__snake_case					)
       __snake_case						       =   ConvBertModel(__snake_case					)

       __snake_case						       =   load_tf_weights_in_convbert(__snake_case    ,__snake_case    ,__snake_case					)
       model.save_pretrained(__snake_case					)

       __snake_case						       =   TFConvBertModel.from_pretrained(__snake_case    ,from_pt=__snake_case					)
       tf_model.save_pretrained(__snake_case					)


if __name__ == "__main__":
 lowerCamelCase_				:							List[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(
     "--convbert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained ConvBERT 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_				:							Union[str, Any]       =    parser.parse_args()
 convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
