




"""simple docstring"""






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      :	Dict     ,a__      :	Union[str, Any])						->       List[Any]:




											"""simple docstring"""







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







						def       __UpperCamelCase     (							self      :	List[str])						->       Union[str, Any]:




											"""simple docstring"""







											pass





def 							UpperCAmelCase		(		snake_case			:					str      ,       snake_case			:					str      ,       snake_case			:					str    ):
					# load longformer model from model identifier
					_lowerCAmelCase:List[str]									=  LongformerModel.from_pretrained(lowerCamelCase_    )
					_lowerCAmelCase:Optional[int]									=  LightningModel(lowerCamelCase_    )

					_lowerCAmelCase:Dict									=  torch.load(lowerCamelCase_      ,       map_location=torch.device('''cpu'''    )    )
					lightning_model.load_state_dict(ckpt['''state_dict''']    )

					# init longformer question answering model
					_lowerCAmelCase:Tuple									=  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__":
							UpperCamelCase__						=			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.'''
							)
							UpperCamelCase__						=			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 ...configuration_utils import PretrainedConfig




class   a__			(       UpperCamelCase_  ):
						snake_case__             = '''bert-generation'''





						def __init__(							self      :	Dict     ,a__      :	str=5_0358     ,a__      :	List[str]=1024     ,a__      :	int=24     ,a__      :	Optional[Any]=16     ,a__      :	List[str]=4096     ,a__      :	Optional[int]="gelu"     ,a__      :	str=0.1     ,a__      :	Union[str, Any]=0.1     ,a__      :	int=512     ,a__      :	Dict=0.02     ,a__      :	List[Any]=1E-12     ,a__      :	List[Any]=0     ,a__      :	int=2     ,a__      :	str=1     ,a__      :	Dict="absolute"     ,a__      :	int=True     ,**a__      :	List[str]     ,)						->       List[Any]:




											"""simple docstring"""







											super().__init__(pad_token_id=a__     ,bos_token_id=a__     ,eos_token_id=a__     ,**a__)

											_lowerCAmelCase:Optional[Any]									=  vocab_size
											_lowerCAmelCase:Union[str, Any]									=  hidden_size
											_lowerCAmelCase:Any									=  num_hidden_layers
											_lowerCAmelCase:int									=  num_attention_heads
											_lowerCAmelCase:int									=  hidden_act
											_lowerCAmelCase:List[Any]									=  intermediate_size
											_lowerCAmelCase:Optional[Any]									=  hidden_dropout_prob
											_lowerCAmelCase:int									=  attention_probs_dropout_prob
											_lowerCAmelCase:Optional[int]									=  max_position_embeddings
											_lowerCAmelCase:Dict									=  initializer_range
											_lowerCAmelCase:Union[str, Any]									=  layer_norm_eps
											_lowerCAmelCase:int									=  position_embedding_type
											_lowerCAmelCase:Tuple									=  use_cache






