






"""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		):


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						Optional[Any]							):
													'''simple docstring'''





													super().__init__()
													A__						:  Union[str, Any]		  =  model
													A__						:  Any		  =  2
													A__						:  Tuple		  =  nn.Linear(self.model.config.hidden_size			,    self.num_labels							)


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													pass




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					str,						UpperCAmelCase__       :					str      )		->Union[str, Any]:
						# load longformer model from model identifier
						A__						:  int		  =  LongformerModel.from_pretrained(UpperCAmelCase__      )
						A__						:  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__						:  Optional[Any]		  =  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__":
						A_    =							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_    =							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 __future__ import annotations

A_    =							10




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[int]      )		->list[int]:
						A__						:  Any		  =  1
						A__						:  Optional[int]		  =  max(UpperCAmelCase__      )
						while placement <= max_digit:
												# declare and initialize empty buckets
												A__						:  list[list]		  =  [[] for _ in range(UpperCAmelCase__      )]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		A__						:  List[Any]		  =  int((i / placement) % RADIX      )
																		buckets[tmp].append(UpperCAmelCase__      )
												# put each buckets' contents into list_of_ints
												A__						:  Union[str, Any]		  =  0
												for b in range(UpperCAmelCase__      ):
																		for i in buckets[b]:
																								A__						:  str		  =  i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
						import doctest

						doctest.testmod()






