



'''simple docstring'''
import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel


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


	def __init__(		self       , A      )			->				Tuple:
				super().__init__()
				A:   Optional[int]								= model
				A:   int								= 2
				A:   int								= nn.Linear(self.model.config.hidden_size       , self.num_labels      )





	def   a__			(		self      )			->				Dict:
				pass







def 		_SCREAMING_SNAKE_CASE				(					lowerCamelCase__ :   Any				,				lowerCamelCase__ :   Optional[int]				,				lowerCamelCase__ :   int					):


			'''simple docstring'''







			A:   int								= LongformerModel.from_pretrained(UpperCamelCase__					)
			A:   List[Any]								= LightningModel(UpperCamelCase__					)

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

			# init longformer question answering model
			A:   Dict								= 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__":
					__SCREAMING_SNAKE_CASE			:   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.'
					)
					__SCREAMING_SNAKE_CASE			:   Dict							   =parser.parse_args()
					convert_longformer_qa_checkpoint_to_pytorch(
					    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
					)








import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											snake_case_     							=       10




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4]
											snake_case_     							=       [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
											snake_case_							,       snake_case_     							=       process_story(snake_case			)
											self.assertEqual(snake_case ,  []			)




				def        a				(   self			):
											snake_case_     							=       ''
											snake_case_							,       snake_case_     							=       process_story(snake_case			)
											self.assertEqual(snake_case ,  []			)
											self.assertEqual(snake_case ,  []			)




				def        a				(   self			):
											snake_case_     							=       (
											    'It was the year of Our Lord one thousand seven hundred and '
											    'seventy-five\n\nSpiritual revelations were conceded to England '
											    'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
											)
											snake_case_							,       snake_case_     							=       process_story(snake_case			)

											snake_case_     							=       [
											    'It was the year of Our Lord one thousand seven hundred and seventy-five.',
											    'Spiritual revelations were conceded to England at that favoured period, as at this.',
											]
											self.assertEqual(snake_case ,  snake_case			)

											snake_case_     							=       ['It was the best of times.']
											self.assertEqual(snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([1, 2, 3, 4]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  0			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([1, 2, 3, 4, 23, 23, 23]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1, 0, 0, 0]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  23			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([8, 2, 3, 4, 1, 1, 1]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1, 0, 0, 0]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  1			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       101
											snake_case_     							=       torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]			)
											snake_case_     							=       torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]			)

											snake_case_     							=       compute_token_type_ids(snake_case ,  snake_case			)
											np.testing.assert_array_equal(snake_case ,  snake_case			)




