


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	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	int:




											'''simple docstring'''

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	List[Any]:




											'''simple docstring'''

											pass





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	Optional[Any]:


						'''simple docstring'''






						__snake_case				=		LongformerModel.from_pretrained(_lowerCAmelCase )
						__snake_case				=		LightningModel(_lowerCAmelCase )

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

						# init longformer question answering model
						__snake_case				=		LongformerForQuestionAnswering.from_pretrained(_lowerCAmelCase )

						# 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(_lowerCAmelCase )

						print(F'''Conversion successful. Model saved under {pytorch_dump_folder_path}''' )


if __name__ == "__main__":
	A							:					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.'
	)
	A							:					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 warnings

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


A							:					Any     						=     logging.get_logger(__name__)






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[int]     ,    *SCREAMING_SNAKE_CASE	:		Tuple     ,    **SCREAMING_SNAKE_CASE	:		List[Any]						)			->	None:




											'''simple docstring'''

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





