






from __future__ import annotations

import queue



class   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_    :      Optional[int]            =						data
							UpperCAmelCase_    :      str            =						None
							UpperCAmelCase_    :      Optional[Any]            =						None





def      lowercase__      (     ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							TreeNode  ):


					'''simple docstring'''


					if not isinstance(_snake_case     ,       _snake_case  ) or not node:
										return
					print(node.data     ,       end=','  )
					pre_order(node.left  )
					pre_order(node.right  )




def      lowercase__      (     __snake_case    :							TreeNode  ):


					'''simple docstring'''


					if not isinstance(_snake_case     ,       _snake_case  ) or not node:
										return
					in_order(node.left  )
					print(node.data     ,       end=','  )
					in_order(node.right  )




def      lowercase__      (     __snake_case    :							TreeNode  ):


					'''simple docstring'''


					if not isinstance(_snake_case     ,       _snake_case  ) or not node:
										return
					post_order(node.left  )
					post_order(node.right  )
					print(node.data     ,       end=','  )




def      lowercase__      (     __snake_case    :							TreeNode  ):


					'''simple docstring'''


					if not isinstance(_snake_case     ,       _snake_case  ) or not node:
										return
					UpperCAmelCase_    :      Dict            =						queue.Queue()
					q.put(_snake_case  )
					while not q.empty():
										UpperCAmelCase_    :      Union[str, Any]            =						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      lowercase__      (     __snake_case    :							TreeNode  ):


					'''simple docstring'''


					if not isinstance(_snake_case     ,       _snake_case  ) or not node:
										return
					UpperCAmelCase_    :      Optional[int]            =						queue.Queue()
					q.put(_snake_case  )
					while not q.empty():
										UpperCAmelCase_    :      int            =						[]
										while not q.empty():
															UpperCAmelCase_    :      List[str]            =						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(_snake_case  )




def      lowercase__      (     __snake_case    :							TreeNode  ):


					'''simple docstring'''


					if not isinstance(_snake_case     ,       _snake_case  ) or not node:
										return
					UpperCAmelCase_    :      Union[str, Any]            =						[]
					UpperCAmelCase_    :      List[str]            =						node
					while n or stack:
										while n:  # start from root node, find its left child
															print(n.data     ,       end=','  )
															stack.append(_snake_case  )
															UpperCAmelCase_    :      Tuple            =						n.left
										# end of while means current node doesn't have left child
										UpperCAmelCase_    :      Dict            =						stack.pop()
										# start to traverse its right child
										UpperCAmelCase_    :      Optional[Any]            =						n.right




def      lowercase__      (     __snake_case    :							TreeNode  ):


					'''simple docstring'''


					if not isinstance(_snake_case     ,       _snake_case  ) or not node:
										return
					UpperCAmelCase_    :      Any            =						[]
					UpperCAmelCase_    :      Optional[int]            =						node
					while n or stack:
										while n:
															stack.append(_snake_case  )
															UpperCAmelCase_    :      List[str]            =						n.left
										UpperCAmelCase_    :      List[str]            =						stack.pop()
										print(n.data     ,       end=','  )
										UpperCAmelCase_    :      List[str]            =						n.right




def      lowercase__      (     __snake_case    :							TreeNode  ):


					'''simple docstring'''


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




def      lowercase__      (     __snake_case    :							str = ""     ,       __snake_case    :							Optional[int]=50     ,       __snake_case    :							Optional[Any]="*"  ):


					'''simple docstring'''


					if not s:
										return "\n" + width * char
					UpperCAmelCase_       ,  UpperCAmelCase_    :      List[str]            =						divmod(width - len(_snake_case  ) - 2     ,       2  )
					return F"{left * char} {s} {(left + extra) * char}"


if __name__ == "__main__":
		import doctest

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

		__UpperCAmelCase								=						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())









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			__a        =[
			    'decoder.version',
			    'decoder.output_projection.weight',
			    '_float_tensor',
			    'decoder.embed_positions._float_tensor',
			]
			for k in ignore_keys:
						state_dict.pop(_snake_case       ,     _snake_case	)






def        UpperCamelCase_(							_snake_case   :  Any	):




			"""simple docstring"""






			__a      ,    __a        =emb.weight.shape
			__a        =nn.Linear(_snake_case       ,     _snake_case       ,     bias=_snake_case	)
			__a        =emb.weight.data
			return lin_layer






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        =Namespace(**checkpoint['cfg']['model']	)
			__a        =checkpoint['model']
			remove_ignore_keys_(_snake_case	)
			__a        =state_dict['decoder.embed_tokens.weight'].shape[0]

			__a        ={key.replace('decoder'       ,     'model'	): val for key, val in state_dict.items()}

			__a        =XGLMConfig(
			    vocab_size=_snake_case       ,     max_position_embeddings=args.max_target_positions       ,     num_layers=args.decoder_layers       ,     attention_heads=args.decoder_attention_heads       ,     ffn_dim=args.decoder_ffn_embed_dim       ,     d_model=args.decoder_embed_dim       ,     layerdrop=args.decoder_layerdrop       ,     dropout=args.dropout       ,     attention_dropout=args.attention_dropout       ,     activation_dropout=args.activation_dropout       ,     activation_function='gelu'       ,     scale_embedding=not args.no_scale_embedding       ,     tie_word_embeddings=args.share_decoder_input_output_embed       ,     )

			__a        =XGLMForCausalLM(_snake_case	)
			__a        =model.load_state_dict(_snake_case       ,     strict=_snake_case	)
			print(_snake_case	)
			__a        =make_linear_from_emb(model.model.embed_tokens	)

			return model


if __name__ == "__main__":
						_lowerCAmelCase					:			str  				=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
						parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
						_lowerCAmelCase					:			str  				=			parser.parse_args()
						_lowerCAmelCase					:			Union[str, Any]  				=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)






