





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						:    List[str]						,lowerCamelCase__						:    List[Any]       )     ->		List[str]:




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Any:




           '''simple docstring'''


           pass



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   str:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						LongformerModel.from_pretrained(_A	)
      SCREAMING_SNAKE_CASE		=						LightningModel(_A	)

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

      # init longformer question answering model
      SCREAMING_SNAKE_CASE		=						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__":
      SCREAMING_SNAKE_CASE_						   =       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."""
      )
      SCREAMING_SNAKE_CASE_						   =       parser.parse_args()
      convert_longformer_qa_checkpoint_to_pytorch(
          args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
      )








from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel




