



import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase      ,					_UpperCamelCase			):
				# Initialise PyTorch model
				_UpperCAmelCase						:  Optional[int]     					=	BertConfig.from_pretrained(
				    '''bert-large-cased'''      ,					vocab_size=_UpperCamelCase      ,					max_position_embeddings=512      ,					is_decoder=_UpperCamelCase      ,					add_cross_attention=_UpperCamelCase      ,					)
				_UpperCAmelCase						:  str     					=	bert_config.to_dict()
				del bert_config_dict["type_vocab_size"]
				_UpperCAmelCase						:  int     					=	BertGenerationConfig(**_UpperCamelCase			)
				if is_encoder:
								_UpperCAmelCase						:  Optional[int]     					=	BertGenerationEncoder(_UpperCamelCase			)
				else:
								_UpperCAmelCase						:  Tuple     					=	BertGenerationDecoder(_UpperCamelCase			)
				print(F'''Building PyTorch model from configuration: {config}'''			)

				# Load weights from tf checkpoint
				load_tf_weights_in_bert_generation(
				    _UpperCamelCase      ,					_UpperCamelCase      ,					model_class='''bert'''      ,					is_encoder_named_decoder=_UpperCamelCase      ,					is_encoder=_UpperCamelCase      ,					)

				# Save pytorch-model
				print(F'''Save PyTorch model and config to {pytorch_dump_path}'''			)
				model.save_pretrained(_UpperCamelCase			)


if __name__ == "__main__":
					UpperCAmelCase__			:   Dict      			=    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					parser.add_argument(
					    '--is_encoder_named_decoder',
					    action='store_true',
					    help='If decoder has to be renamed to encoder in PyTorch model.',
					)
					parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
					parser.add_argument('--vocab_size', default=50358, type=int, help='Vocab size of model')
					UpperCAmelCase__			:   Optional[Any]      			=    parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_hub_path,
					    args.pytorch_dump_path,
					    args.is_encoder_named_decoder,
					    args.vocab_size,
					    is_encoder=args.is_encoder,
					)





class 							lowerCAmelCase_		:







		def __init__(    self  :   int       ,		UpperCAmelCase_  :   int					)       ->		None:


						'''simple docstring'''







						_UpperCAmelCase						:  Tuple     					=	data
						_UpperCAmelCase						:  BinaryTreeNode | None     					=	None
						_UpperCAmelCase						:  BinaryTreeNode | None     					=	None


def 	_A	(   _UpperCamelCase      ,					_UpperCamelCase			):
				if node is None:
								_UpperCAmelCase						:  Union[str, Any]     					=	BinaryTreeNode(_UpperCamelCase			)
								return node

				# binary search tree is not empty,
				# so we will insert it into the tree
				# if new_value is less than value of data in node,
				#  add it to left subtree and proceed recursively
				if new_value < node.data:
								_UpperCAmelCase						:  Any     					=	insert(node.left_child      ,					_UpperCamelCase			)
				else:
								# if new_value is greater than value of data in node,
								#  add it to right subtree and proceed recursively
								_UpperCAmelCase						:  List[Any]     					=	insert(node.right_child      ,					_UpperCamelCase			)
				return node


def 	_A	(   _UpperCamelCase			):  # if node is None,return
				if node:
								_UpperCAmelCase						:  str     					=	inorder(node.left_child			)
								_UpperCAmelCase						:  Union[str, Any]     					=	[*inorder_array, node.data]
								_UpperCAmelCase						:  Optional[int]     					=	inorder_array + inorder(node.right_child			)
				else:
								_UpperCAmelCase						:  Tuple     					=	[]
				return inorder_array


def 	_A	(   ):
				_UpperCAmelCase						:  Dict     					=	insert(_UpperCamelCase      ,					15			)
				insert(_UpperCamelCase      ,					10			)
				insert(_UpperCamelCase      ,					25			)
				insert(_UpperCamelCase      ,					6			)
				insert(_UpperCamelCase      ,					14			)
				insert(_UpperCamelCase      ,					20			)
				insert(_UpperCamelCase      ,					60			)
				return root


def 	_A	(   ):
				# main function
				_UpperCAmelCase						:  Tuple     					=	make_tree()
				print('''Printing values of binary search tree in Inorder Traversal.'''			)
				inorder(_UpperCamelCase			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
