

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class        a__       (   pl.LightningModule       ):







  """simple docstring"""




  def __init__(			self    : int				,				UpperCAmelCase__    : int		)					->Optional[int]:
        """simple docstring"""







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




  def  _lowercase      (			self    : Optional[Any]		)					->Dict:
        """simple docstring"""







        pass






def 					__lowercase      (     _A						,    _A						,    _A			)     ->						Optional[int]:
      # load longformer model from model identifier
      SCREAMING_SNAKE_CASE :      Tuple       =   LongformerModel.from_pretrained(_A			)
      SCREAMING_SNAKE_CASE :      Dict       =   LightningModel(_A			)

      SCREAMING_SNAKE_CASE :      List[Any]       =   torch.load(_A						,    map_location=torch.device("""cpu"""			)			)
      lightning_model.load_state_dict(ckpt["""state_dict"""]			)

      # init longformer question answering model
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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__":
   UpperCAmelCase__ :     Tuple				    =    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__ :     Dict				    =    parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )







def 					__lowercase      (     _A			)     ->						list[int]:
      SCREAMING_SNAKE_CASE :      int       =   [0 for i in range(len(_A			)			)]

      # initialize interval's left pointer and right pointer
      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Dict       =   0, 0

      for i in range(1						,    len(_A			)			):
            # case when current index is inside the interval
            if i <= right_pointer:
                  SCREAMING_SNAKE_CASE :      Any       =   min(right_pointer - i + 1						,    z_result[i - left_pointer]			)
                  SCREAMING_SNAKE_CASE :      Optional[Any]       =   min_edge

            while go_next(_A						,    _A						,    _A			):
                  z_result[i] += 1

            # if new index's result gives us more right interval,
            # we've to update left_pointer and right_pointer
            if i + z_result[i] - 1 > right_pointer:
                  SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[Any]       =   i, i + z_result[i] - 1

      return z_result






def 					__lowercase      (     _A						,    _A						,    _A			)     ->						bool:
      return i + z_result[i] < len(_A			) and s[z_result[i]] == s[i + z_result[i]]






def 					__lowercase      (     _A						,    _A			)     ->						int:
      SCREAMING_SNAKE_CASE :      Any       =   0
      # concatenate 'pattern' and 'input_str' and call z_function
      # with concatenated string
      SCREAMING_SNAKE_CASE :      List[Any]       =   z_function(pattern + input_str			)

      for val in z_result:
            # if value is greater then length of the pattern string
            # that means this index is starting position of substring
            # which is equal to pattern string
            if val >= len(_A			):
                  answer += 1

      return answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()




