


# using dfs for finding eulerian path traversal
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None	):


		"""simple docstring"""




		lowercase__				= (path or []) + [u]
		for v in graph[u]:
				if visited_edge[u][v] is False:
						lowercase__	,						lowercase__				= True, True
						lowercase__				= dfs(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
		return path
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= 0
		lowercase__				= -1
		for i in range(SCREAMING_SNAKE_CASE	):
				if i not in graph.keys():
						continue
				if len(graph[i]	) % 2 == 1:
						odd_degree_nodes += 1
						lowercase__				= i
		if odd_degree_nodes == 0:
				return 1, odd_node
		if odd_degree_nodes == 2:
				return 2, odd_node
		return 3, odd_node
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= [[False for _ in range(max_node + 1	)] for _ in range(max_node + 1	)]
		lowercase__	,						lowercase__				= check_circuit_or_path(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
		if check == 3:
				print('''graph is not Eulerian'''	)
				print('''no path'''	)
				return
		lowercase__				= 1
		if check == 2:
				lowercase__				= odd_node
				print('''graph has a Euler path'''	)
		if check == 1:
				print('''graph has a Euler cycle'''	)
		lowercase__				= dfs(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
		print(SCREAMING_SNAKE_CASE	)
def      _a					( ):


		"""simple docstring"""




		lowercase__				= {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
		lowercase__				= {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
		lowercase__				= {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
		lowercase__				= {1: [2, 3], 2: [1, 3], 3: [1, 2]}
		lowercase__				= {
		    1: [],
		    2: []
		    # all degree is zero
		}
		lowercase__				= 10
		check_euler(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
		check_euler(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
		check_euler(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
		check_euler(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
		check_euler(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
			main()









import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class 	__lowerCAmelCase      (       pl.LightningModule	):





   def __init__(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





      super().__init__()
      a__						:    int   		=	model
      a__						:    Any   		=	2
      a__						:    List[str]   		=	nn.Linear(self.model.config.hidden_size       ,      self.num_labels   )





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      pass







def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # load longformer model from model identifier
   a__						:    List[str]   		=	LongformerModel.from_pretrained(lowerCamelCase )
   a__						:    Dict   		=	LightningModel(lowerCamelCase )

   a__						:    str   		=	torch.load(lowerCamelCase				,			map_location=torch.device("cpu" ) )
   lightning_model.load_state_dict(ckpt["state_dict"] )

   # init longformer question answering model
   a__						:    Dict   		=	LongformerForQuestionAnswering.from_pretrained(lowerCamelCase )

   # 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(lowerCamelCase )

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Any         =    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."""
  )
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

