




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel


class  A		(  pl.LightningModule     ):



		def __init__(     self,	UpperCamelCase__   ):







								"""simple docstring"""



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







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass







def __UpperCamelCase			(       _A ,  _A ,  _A  ):
						# load longformer model from model identifier
						lowerCAmelCase_      			=  LongformerModel.from_pretrained(_A  )
						lowerCAmelCase_      			=  LightningModel(_A  )

						lowerCAmelCase_      			=  torch.load(_A ,  map_location=torch.device('''cpu'''  )  )
						lightning_model.load_state_dict(ckpt['''state_dict''']  )

						# init longformer question answering model
						lowerCAmelCase_      			=  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     =			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
						)










import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


_A     =			logging.get_logger(__name__)


class  A		(  __UpperCAmelCase     ):



		def __init__(     self,	*UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								warnings.warn(
								    '''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
								    ''' use BeitImageProcessor instead.''',	UpperCamelCase__,	)
								super().__init__(*UpperCamelCase__,	**UpperCamelCase__   )




