




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class     snake_case__							(pl.LightningModule   ):
				"""simple docstring"""



				def __init__(      self				:     Any					,      __lowerCamelCase				:     Optional[int]							)					->     List[str]:
							super().__init__()
							a    =			model
							a    =			2
							a    =			nn.Linear(self.model.config.hidden_size					,      self.num_labels							)





				def 							__UpperCAmelCase							(      self				:     str							)					->     int:
							pass




def 			__magic_name__				( A		:					str,  A		:					str,  A		:					str   ):






			'''simple docstring'''







			a    =			LongformerModel.from_pretrained(A   )
			a    =			LightningModel(A   )

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

			# init longformer question answering model
			a    =			LongformerForQuestionAnswering.from_pretrained(A   )

			# 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(A   )

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


if __name__ == "__main__":
		__lowerCAmelCase		:				Optional[int]       			=		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.'
		)
		__lowerCAmelCase		:				List[str]       			=		parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)












'''simple docstring'''







from queue import PriorityQueue
from typing import Any

import numpy as np





def 						snake_case_							(_a	:							dict    ,   _a	:							str    ,   _a	:							set    ,   _a	:							set    ,   _a	:							dict    ,   _a	:							dict    ,   _a	:							PriorityQueue    ,   _a	:							dict    ,   _a	:							float | int    ,   ):
    for nxt, d in graph[v]:
        if nxt in visited_forward:
            continue
        UpperCAmelCase			       =		cst_fwd.get(_a    ,   np.inf     )
        UpperCAmelCase			       =		cst_fwd[v] + d
        if new_cost_f < old_cost_f:
            queue.put((new_cost_f, nxt)     )
            UpperCAmelCase			       =		new_cost_f
            UpperCAmelCase			       =		v
        if nxt in visited_backward:
            if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
                UpperCAmelCase			       =		cst_fwd[v] + d + cst_bwd[nxt]
    return shortest_distance





def 						snake_case_							(_a	:							str    ,   _a	:							str    ,   _a	:							dict    ,   _a	:							dict     ):
    UpperCAmelCase			       =		-1

    UpperCAmelCase			       =		set()
    UpperCAmelCase			       =		set()
    UpperCAmelCase			       =		{source: 0}
    UpperCAmelCase			       =		{destination: 0}
    UpperCAmelCase			       =		{source: None}
    UpperCAmelCase			       =		{destination: None}
    UpperCAmelCase			       =		PriorityQueue()
    UpperCAmelCase			       =		PriorityQueue()

    UpperCAmelCase			       =		np.inf

    queue_forward.put((0, source)     )
    queue_backward.put((0, destination)     )

    if source == destination:
        return 0

    while not queue_forward.empty() and not queue_backward.empty():
        UpperCAmelCase       ,     UpperCAmelCase			       =		queue_forward.get()
        visited_forward.add(_a     )

        UpperCAmelCase       ,     UpperCAmelCase			       =		queue_backward.get()
        visited_backward.add(_a     )

        UpperCAmelCase			       =		pass_and_relaxation(
            _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   )

        UpperCAmelCase			       =		pass_and_relaxation(
            _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   )

        if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
            break

    if shortest_distance != np.inf:
        UpperCAmelCase			       =		shortest_distance
    return shortest_path_distance


A     ={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A     ={
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
   import doctest

   doctest.testmod()




