


'''simple docstring'''

from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any





@dataclass
class      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  42
  _lowerCamelCase		    =  None
  _lowerCamelCase		    =  None





def __snake_case					(      ):
 lowerCamelCase_      =  Node(1		)
 lowerCamelCase_      =  Node(2		)
 lowerCamelCase_      =  Node(3		)
 lowerCamelCase_      =  Node(4		)
 lowerCamelCase_      =  Node(5		)
 return tree





def __snake_case					(      UpperCAmelCase_						:						Dict		):
 return [root.data, *preorder(root.left		), *preorder(root.right		)] if root else []





def __snake_case					(      UpperCAmelCase_						:						List[Any]		):
 return postorder(root.left		) + postorder(root.right		) + [root.data] if root else []





def __snake_case					(      UpperCAmelCase_						:						Dict		):
 return [*inorder(root.left		), root.data, *inorder(root.right		)] if root else []





def __snake_case					(      UpperCAmelCase_						:						Any		):
 return (max(height(root.left		)					,				height(root.right		)		) + 1) if root else 0





def __snake_case					(      UpperCAmelCase_						:						Any		):
 lowerCamelCase_      =  []

 if root is None:
  return output

 lowerCamelCase_      =  deque([root]		)

 while process_queue:
  lowerCamelCase_      =  process_queue.popleft()
  output.append(node.data		)

  if node.left:
   process_queue.append(node.left		)
  if node.right:
   process_queue.append(node.right		)
 return output





def __snake_case					(      UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						List[str]		):
 lowerCamelCase_      =  []

 def populate_output(UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						Union[str, Any]		) -> None:
  if not root:
   return
  if level == 1:
   output.append(root.data		)
  elif level > 1:
   populate_output(root.left					,				level - 1		)
   populate_output(root.right					,				level - 1		)

 populate_output(_UpperCamelCase					,				_UpperCamelCase		)
 return output





def __snake_case					(      UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						List[str]		):
 lowerCamelCase_      =  []

 def populate_output(UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						Any		) -> None:
  if root is None:
   return
  if level == 1:
   output.append(root.data		)
  elif level > 1:
   populate_output(root.right					,				level - 1		)
   populate_output(root.left					,				level - 1		)

 populate_output(_UpperCamelCase					,				_UpperCamelCase		)
 return output





def __snake_case					(      UpperCAmelCase_						:						List[Any]		):




 if root is None:
  return []

 lowerCamelCase_      =  []

 lowerCamelCase_      =  0
 lowerCamelCase_      =  height(_UpperCamelCase		)

 for h in range(1					,				height_tree + 1		):
  if not flag:
   output.append(get_nodes_from_left_to_right(_UpperCamelCase					,				_UpperCamelCase		)		)
   lowerCamelCase_      =  1
  else:
   output.append(get_nodes_from_right_to_left(_UpperCamelCase					,				_UpperCamelCase		)		)
   lowerCamelCase_      =  0

 return output





def __snake_case					(      ):  # Main function for testing.
 lowerCamelCase_      =  make_tree()
 print(F'''In-order Traversal: {inorder(_UpperCamelCase		)}'''		)
 print(F'''Pre-order Traversal: {preorder(_UpperCamelCase		)}'''		)
 print(F'''Post-order Traversal: {postorder(_UpperCamelCase		)}'''					,				"\n"		)

 print(F'''Height of Tree: {height(_UpperCamelCase		)}'''					,				"\n"		)

 print("Complete Level Order Traversal: "		)
 print(level_order(_UpperCamelCase		)					,				"\n"		)

 print("Level-wise order Traversal: "		)

 for level in range(1					,				height(_UpperCamelCase		) + 1		):
  print(F'''Level {level}:'''					,				get_nodes_from_left_to_right(_UpperCamelCase					,				level=_UpperCamelCase		)		)

 print("\nZigZag order Traversal: "		)
 print(zigzag(_UpperCamelCase		)		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()







"""simple docstring"""


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel
class   _UpperCamelCase  (   pl.LightningModule		):
							'''simple docstring'''







							def __init__(     self    ,   __a	):
										super().__init__()
										__lowerCAmelCase				    =			model
										__lowerCAmelCase				    =			2
										__lowerCAmelCase				    =			nn.Linear(self.model.config.hidden_size    ,   self.num_labels	)



							def 			snake_case				(     self	):
										pass


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			LongformerModel.from_pretrained(_UpperCamelCase      )
			__lowerCAmelCase				    =			LightningModel(_UpperCamelCase      )

			__lowerCAmelCase				    =			torch.load(_UpperCamelCase    , map_location=torch.device("cpu"      )      )
			lightning_model.load_state_dict(ckpt["state_dict"]      )

			# init longformer question answering model
			__lowerCAmelCase				    =			LongformerForQuestionAnswering.from_pretrained(_UpperCamelCase      )

			# transfer weights
			longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()      )
			longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()      )
			longformer_for_qa.eval()

			# save model
			longformer_for_qa.save_pretrained(_UpperCamelCase      )

			print(f"Conversion successful. Model saved under {pytorch_dump_folder_path}"      )


if __name__ == "__main__":
	A     :   Tuple			    =			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--longformer_model",
	    default=None,
	    type=str,
	    required=True,
	    help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
	)
	parser.add_argument(
	    "--longformer_question_answering_ckpt_path",
	    default=None,
	    type=str,
	    required=True,
	    help="Path the official PyTorch Lightning Checkpoint.",
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
	)
	A     :   Optional[int]			    =			parser.parse_args()
	convert_longformer_qa_checkpoint_to_pytorch(
	    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
	)




