
import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class 				lowercase		(					pl.LightningModule		):




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





							def  A__      (				self):
												pass



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					# load longformer model from model identifier
					lowercase							=						LongformerModel.from_pretrained(snake_case__		)
					lowercase							=						LightningModel(snake_case__		)

					lowercase							=						torch.load(snake_case__ ,			map_location=torch.device('''cpu'''		)		)
					lightning_model.load_state_dict(ckpt['''state_dict''']		)

					# init longformer question answering model
					lowercase							=						LongformerForQuestionAnswering.from_pretrained(snake_case__		)

					# 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(snake_case__		)

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


if __name__ == "__main__":
							lowercase__		:str   =    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."
							)
							lowercase__		: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
							)





from __future__ import annotations

_SCREAMING_SNAKE_CASE										=						{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					None:
        _A        =  graph
        # mapping node to its parent in resulting breadth first tree
        _A        =  {}
        _A        =  source_vertex



 def      UpperCAmelCase							(					self							)  ->					None:
        _A        =  {self.source_vertex}
        _A        =  None
        _A        =  [self.source_vertex]  # first in first out queue

        while queue:
               _A        =  queue.pop(0							)
               for adjacent_vertex in self.graph[vertex]:
                      if adjacent_vertex not in visited:
                             visited.add(lowerCAmelCase_							)
                             _A        =  vertex
                             queue.append(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					str:
        if target_vertex == self.source_vertex:
               return self.source_vertex

        _A        =  self.parent.get(lowerCAmelCase_							)
        if target_vertex_parent is None:
               _A        =  (
                   F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
               )
               raise ValueError(lowerCAmelCase_							)

        return self.shortest_path(lowerCAmelCase_							) + F'''->{target_vertex}'''


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))




