


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase				:				List[str]  				= logging.get_logger(__name__)

class 					__magic_name__		(   a__):
     A:						int								=					"encoder-decoder"
     A:						int								=					True


     def __init__(					self							:   Union[str, Any]							,		**lowerCamelCase__							:   str	)     ->					int:





      '''simple docstring'''






      super().__init__(**__lowerCAmelCase	)
      assert (
          "encoder" in kwargs and "decoder" in kwargs
      ), "Config has to be initialized with encoder and decoder config"
      UpperCamelCase__    :			Optional[int]      				=      kwargs.pop('''encoder'''	)
      UpperCamelCase__    :			Union[str, Any]      				=      encoder_config.pop('''model_type'''	)
      UpperCamelCase__    :			Any      				=      kwargs.pop('''decoder'''	)
      UpperCamelCase__    :			Any      				=      decoder_config.pop('''model_type'''	)

      from ..auto.configuration_auto import AutoConfig

      UpperCamelCase__    :			Union[str, Any]      				=      AutoConfig.for_model(__lowerCAmelCase							,		**__lowerCAmelCase	)
      UpperCamelCase__    :			Optional[Any]      				=      AutoConfig.for_model(__lowerCAmelCase							,		**__lowerCAmelCase	)
      UpperCamelCase__    :			Tuple      				=      True


     @classmethod
     def    UpperCAmelCase__		(					cls							:   List[Any]							,		lowerCamelCase__							:   Optional[int]							,		lowerCamelCase__							:   List[str]							,		**lowerCamelCase__							:   Union[str, Any]	)     ->					PretrainedConfig:





      '''simple docstring'''






      logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''	)
      UpperCamelCase__    :			Union[str, Any]      				=      True
      UpperCamelCase__    :			Any      				=      True

      return cls(encoder=encoder_config.to_dict()							,		decoder=decoder_config.to_dict()							,		**__lowerCAmelCase	)


     def    UpperCAmelCase__		(					self							:   str	)     ->					Any:





      '''simple docstring'''






      UpperCamelCase__    :			Any      				=      copy.deepcopy(self.__dict__	)
      UpperCamelCase__    :			Optional[Any]      				=      self.encoder.to_dict()
      UpperCamelCase__    :			Tuple      				=      self.decoder.to_dict()
      UpperCamelCase__    :			Dict      				=      self.__class__.model_type
      return output



'''simple docstring'''

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class 			UpperCAmelCase  (						pl.LightningModule					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase )  ->  List[str]:
        super().__init__()
        lowercase__				:    List[str]  =					model
        lowercase__				:    Dict  =					2
        lowercase__				:    Any  =					nn.Linear(self.model.config.hidden_size	,   self.num_labels )


       def _lowerCAmelCase(						self )  ->  str:
        pass


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 # load longformer model from model identifier
 lowercase__				:    Dict  =					LongformerModel.from_pretrained(UpperCAmelCase     )
 lowercase__				:    List[str]  =					LightningModel(UpperCAmelCase     )

 lowercase__				:    List[Any]  =					torch.load(UpperCAmelCase		,     map_location=torch.device('''cpu'''     )     )
 lightning_model.load_state_dict(ckpt['''state_dict''']     )

 # init longformer question answering model
 lowercase__				:    Optional[int]  =					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__":
  __a: List[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."""
  )
  __a: Tuple          =   parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

