




import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=10_24		, __UpperCamelCase=10_24		, __UpperCamelCase=False		, **__UpperCamelCase)      ->					Dict:
     a     =				AutoTokenizer.from_pretrained(snake_case_)
     a     =				SeqaSeqDataset(snake_case_		, snake_case_		, snake_case_		, snake_case_		, type_path="train"		, **snake_case_)
     a     =				tok.pad_token_id

     def get_lens(__UpperCamelCase):
          a     =				tqdm(
              DataLoader(snake_case_		, batch_size=5_12		, num_workers=8		, shuffle=snake_case_		, collate_fn=ds.collate_fn)		, desc=str(ds.len_file)		, )
          a     =				[]
          for batch in dl:
               a     =				batch["""input_ids"""].ne(snake_case_).sum(1).tolist()
               a     =				batch["""labels"""].ne(snake_case_).sum(1).tolist()
               if consider_target:
                    for src, tgt in zip(snake_case_		, snake_case_):
                         max_lens.append(max(snake_case_		, snake_case_))
               else:
                    max_lens.extend(snake_case_)
          return max_lens

     a     =				get_lens(snake_case_)
     a     =				SeqaSeqDataset(snake_case_		, snake_case_		, snake_case_		, snake_case_		, type_path="val"		, **snake_case_)
     a     =				get_lens(snake_case_)
     pickle_save(snake_case_		, train_ds.len_file)
     pickle_save(snake_case_		, val_ds.len_file)


if __name__ == "__main__":
   fire.Fire(save_len_file)









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  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



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







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



          '''simple docstring'''



          pass







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     # load longformer model from model identifier
     a     =				LongformerModel.from_pretrained(__UpperCamelCase)
     a     =				LightningModel(__UpperCamelCase)

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

     # init longformer question answering model
     a     =				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__":
   lowercase__				:      Optional[Any]	   = 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."
   )
   lowercase__				:      Union[str, Any]	   = parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )

