




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
		)












from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray




def 			__magic_name__				( A		:					NDArray[floataa],  A		:					NDArray[floataa],  A		:					list[int],  A		:					int,  ):






			'''simple docstring'''







			a    ,		a    =			coefficient_matrix.shape
			a    ,		a    =			constant_matrix.shape

			if rowsa != colsa:
						a    =			F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if colsa != 1:
						a    =			F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if rowsa != rowsa:
						a    =			(
						    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
						    F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
						)
						raise ValueError(A   )

			if len(A   ) != rowsa:
						a    =			(
						    "Number of initial values must be equal to number of rows in coefficient "
						    F"""matrix but received {len(A   )} and {rowsa}"""
						)
						raise ValueError(A   )

			if iterations <= 0:
						raise ValueError("Iterations must be at least 1"   )

			a    =			np.concatenate(
			    (coefficient_matrix, constant_matrix),  axis=1   )

			a    ,		a    =			table.shape

			strictly_diagonally_dominant(A   )

			# Iterates the whole matrix for given number of times
			for _ in range(A   ):
						a    =			[]
						for row in range(A   ):
									a    =			0
									for col in range(A   ):
												if col == row:
															a    =			table[row][col]
												elif col == cols - 1:
															a    =			table[row][col]
												else:
															temp += (-1) * table[row][col] * init_val[col]
									a    =			(temp + val) / denom
									new_val.append(A   )
						a    =			new_val

			return [float(A   ) for i in new_val]




def 			__magic_name__				( A		:					NDArray[floataa]   ):






			'''simple docstring'''







			a    ,		a    =			table.shape

			a    =			True

			for i in range(0,  A   ):
						a    =			0
						for j in range(0,  cols - 1   ):
									if i == j:
												continue
									else:
												total += table[i][j]

						if table[i][i] <= total:
									raise ValueError("Coefficient matrix is not strictly diagonally dominant"   )

			return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()






