


'''simple docstring'''


from __future__ import annotations

import queue






class 	lowerCAmelCase__			:




			def __init__(		self  :     int	,  lowerCamelCase__  :     Tuple )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      data
					_UpperCAmelCase		:       Any								=      None
					_UpperCAmelCase		:       Tuple								=      None






def  __lowerCAmelCase ():
		print("\n********Press N to stop entering at any point of time********\n"   )
		_UpperCAmelCase		:       Optional[Any]								=      input("Enter the value of the root node: "   ).strip().lower()
		_UpperCAmelCase		:       queue.Queue								=      queue.Queue()
		_UpperCAmelCase		:       List[Any]								=      TreeNode(int(__lowerCAmelCase   )   )
		q.put(__lowerCAmelCase   )
		while not q.empty():
				_UpperCAmelCase		:       Tuple								=      q.get()
				_UpperCAmelCase		:       Union[str, Any]								=      F"""Enter the left node of {node_found.data}: """
				_UpperCAmelCase		:       Any								=      input(__lowerCAmelCase   ).strip().lower() or "n"
				if check == "n":
						return tree_node
				_UpperCAmelCase		:       List[Any]								=      TreeNode(int(__lowerCAmelCase   )   )
				_UpperCAmelCase		:       List[Any]								=      left_node
				q.put(__lowerCAmelCase   )
				_UpperCAmelCase		:       Any								=      F"""Enter the right node of {node_found.data}: """
				_UpperCAmelCase		:       str								=      input(__lowerCAmelCase   ).strip().lower() or "n"
				if check == "n":
						return tree_node
				_UpperCAmelCase		:       Dict								=      TreeNode(int(__lowerCAmelCase   )   )
				_UpperCAmelCase		:       List[str]								=      right_node
				q.put(__lowerCAmelCase   )
		raise






def  __lowerCAmelCase (__lowerCAmelCase   ):
		if not isinstance(__lowerCAmelCase      , __lowerCAmelCase   ) or not node:
				return
		print(node.data      , end=","   )
		pre_order(node.left   )
		pre_order(node.right   )






def  __lowerCAmelCase (__lowerCAmelCase   ):
		if not isinstance(__lowerCAmelCase      , __lowerCAmelCase   ) or not node:
				return
		in_order(node.left   )
		print(node.data      , end=","   )
		in_order(node.right   )






def  __lowerCAmelCase (__lowerCAmelCase   ):
		if not isinstance(__lowerCAmelCase      , __lowerCAmelCase   ) or not node:
				return
		post_order(node.left   )
		post_order(node.right   )
		print(node.data      , end=","   )






def  __lowerCAmelCase (__lowerCAmelCase   ):
		if not isinstance(__lowerCAmelCase      , __lowerCAmelCase   ) or not node:
				return
		_UpperCAmelCase		:       queue.Queue								=      queue.Queue()
		q.put(__lowerCAmelCase   )
		while not q.empty():
				_UpperCAmelCase		:       List[str]								=      q.get()
				print(node_dequeued.data      , end=","   )
				if node_dequeued.left:
						q.put(node_dequeued.left   )
				if node_dequeued.right:
						q.put(node_dequeued.right   )






def  __lowerCAmelCase (__lowerCAmelCase   ):
		if not isinstance(__lowerCAmelCase      , __lowerCAmelCase   ) or not node:
				return
		_UpperCAmelCase		:       queue.Queue								=      queue.Queue()
		q.put(__lowerCAmelCase   )
		while not q.empty():
				_UpperCAmelCase		:       Optional[int]								=      []
				while not q.empty():
						_UpperCAmelCase		:       List[Any]								=      q.get()
						print(node_dequeued.data      , end=","   )
						if node_dequeued.left:
								list_.append(node_dequeued.left   )
						if node_dequeued.right:
								list_.append(node_dequeued.right   )
				print()
				for node in list_:
						q.put(__lowerCAmelCase   )






def  __lowerCAmelCase (__lowerCAmelCase   ):
		if not isinstance(__lowerCAmelCase      , __lowerCAmelCase   ) or not node:
				return
		_UpperCAmelCase		:       list[TreeNode]								=      []
		_UpperCAmelCase		:       List[Any]								=      node
		while n or stack:
				while n:  # start from root node, find its left child
						print(n.data      , end=","   )
						stack.append(__lowerCAmelCase   )
						_UpperCAmelCase		:       Any								=      n.left
				# end of while means current node doesn't have left child
				_UpperCAmelCase		:       str								=      stack.pop()
				# start to traverse its right child
				_UpperCAmelCase		:       List[Any]								=      n.right






def  __lowerCAmelCase (__lowerCAmelCase   ):
		if not isinstance(__lowerCAmelCase      , __lowerCAmelCase   ) or not node:
				return
		_UpperCAmelCase		:       list[TreeNode]								=      []
		_UpperCAmelCase		:       Optional[Any]								=      node
		while n or stack:
				while n:
						stack.append(__lowerCAmelCase   )
						_UpperCAmelCase		:       int								=      n.left
				_UpperCAmelCase		:       int								=      stack.pop()
				print(n.data      , end=","   )
				_UpperCAmelCase		:       int								=      n.right






def  __lowerCAmelCase (__lowerCAmelCase   ):
		if not isinstance(__lowerCAmelCase      , __lowerCAmelCase   ) or not node:
				return
		_UpperCAmelCase					,    _UpperCAmelCase		:       Dict								=      [], []
		_UpperCAmelCase		:       Any								=      node
		stacka.append(__lowerCAmelCase   )
		while stacka:  # to find the reversed order of post order, store it in stack2
				_UpperCAmelCase		:       List[Any]								=      stacka.pop()
				if n.left:
						stacka.append(n.left   )
				if n.right:
						stacka.append(n.right   )
				stacka.append(__lowerCAmelCase   )
		while stacka:  # pop up from stack2 will be the post order
				print(stacka.pop().data      , end=","   )






def  __lowerCAmelCase (__lowerCAmelCase = ""      , __lowerCAmelCase=50      , __lowerCAmelCase="*"   ):
		if not s:
				return "\n" + width * char
		_UpperCAmelCase					,    _UpperCAmelCase		:       Optional[Any]								=      divmod(width - len(__lowerCAmelCase   ) - 2      , 2   )
		return F"""{left * char} {s} {(left + extra) * char}"""


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(prompt('Binary Tree Traversals'))

						lowerCamelCase__    	=     build_tree()
						print(prompt('Pre Order Traversal'))
						pre_order(node)
						print(prompt() + '\n')

						print(prompt('In Order Traversal'))
						in_order(node)
						print(prompt() + '\n')

						print(prompt('Post Order Traversal'))
						post_order(node)
						print(prompt() + '\n')

						print(prompt('Level Order Traversal'))
						level_order(node)
						print(prompt() + '\n')

						print(prompt('Actual Level Order Traversal'))
						level_order_actual(node)
						print('*' * 50 + '\n')

						print(prompt('Pre Order Traversal - Iteration Version'))
						pre_order_iter(node)
						print(prompt() + '\n')

						print(prompt('In Order Traversal - Iteration Version'))
						in_order_iter(node)
						print(prompt() + '\n')

						print(prompt('Post Order Traversal - Iteration Version'))
						post_order_iter(node)
						print(prompt())







'''simple docstring'''


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
						lowerCamelCase__    	=     argparse.ArgumentParser(
						    description=(
						        'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
						        ' Distillation'
						    )
						)
						parser.add_argument('--model_type', default='bert', choices=['bert'])
						parser.add_argument('--model_name', default='bert-base-uncased', type=str)
						parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
						parser.add_argument('--vocab_transform', action='store_true')
						lowerCamelCase__    	=     parser.parse_args()

						if args.model_type == "bert":
												lowerCamelCase__    	=     BertForMaskedLM.from_pretrained(args.model_name)
												lowerCamelCase__    	=     'bert'
						else:
												raise ValueError('args.model_type should be "bert".')

						lowerCamelCase__    	=     model.state_dict()
						lowerCamelCase__    	=     {}

						for w in ["word_embeddings", "position_embeddings"]:
												lowerCamelCase__    	=     state_dict[F'''{prefix}.embeddings.{w}.weight''']
						for w in ["weight", "bias"]:
												lowerCamelCase__    	=     state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

						lowerCamelCase__    	=     0
						for teacher_idx in [0, 2, 4, 7, 9, 11]:
												for w in ["weight", "bias"]:
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
																		]

																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
																		]

																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
																		]
												std_idx += 1

						lowerCamelCase__    	=     state_dict['cls.predictions.decoder.weight']
						lowerCamelCase__    	=     state_dict['cls.predictions.bias']
						if args.vocab_transform:
												for w in ["weight", "bias"]:
																		lowerCamelCase__    	=     state_dict[F'''cls.predictions.transform.dense.{w}''']
																		lowerCamelCase__    	=     state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

						print(F'''N layers selected for distillation: {std_idx}''')
						print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

						print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
						torch.save(compressed_sd, args.dump_checkpoint)



