

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class 						__UpperCamelCase    (					pl.LightningModule						):




	def __init__(		self	:						List[Any]		,	lowerCAmelCase	:						List[str]							):


		'''simple docstring'''







		super().__init__()
		UpperCAmelCase_       				=			model
		UpperCAmelCase_       				=			2
		UpperCAmelCase_       				=			nn.Linear(self.model.config.hidden_size		,	self.num_labels							)




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		pass



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	# load longformer model from model identifier
	UpperCAmelCase_       				=			LongformerModel.from_pretrained(A    )
	UpperCAmelCase_       				=			LightningModel(A    )

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

	# init longformer question answering model
	UpperCAmelCase_       				=			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__":
				_a:    List[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."""
				)
				_a:    Any 							=			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

from collections import Counter
from random import random

class 						__UpperCamelCase    :




	def __init__(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						Union[str, Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						float							):


		'''simple docstring'''







		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		UpperCAmelCase_       				=			probability




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		return list(self.connections							)




	def       __A					(		self	:						str		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			0
		UpperCAmelCase_       				=			random()

		for dest in self.connections[node]:
			current_probability += self.connections[node][dest]
			if current_probability > random_value:
				return dest
		return ""



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(A       ,     A       ,     A    )

	UpperCAmelCase_       				=			Counter(graph.get_nodes()    )
	UpperCAmelCase_       				=			start

	for _ in range(A    ):
		UpperCAmelCase_       				=			graph.transition(A    )
		visited[node] += 1

	return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()