

'''simple docstring'''



import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class 				_snake_case					(   pl.LightningModule			):

  def __init__(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

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


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      pass



def       snake_case   (	snake_case      : List[str]					,		snake_case      : Any					,		snake_case      : Optional[int]			) ->  Dict:


    """simple docstring"""


    lowerCAmelCase 						=							LongformerModel.from_pretrained(A__			)
    lowerCAmelCase 						=							LightningModel(A__			)

    lowerCAmelCase 						=							torch.load(A__					,		map_location=torch.device('cpu'			)			)
    lightning_model.load_state_dict(ckpt['state_dict']			)

    # init longformer question answering model
    lowerCAmelCase 						=							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						:  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."
     )
     _UpperCamelCase						:  Optional[int]							       =				parser.parse_args()
     convert_longformer_qa_checkpoint_to_pytorch(
         args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
     )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__			:      str								={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__			:      Optional[Any]								=[
				    'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'CLIPSegModel',
				    'CLIPSegPreTrainedModel',
				    'CLIPSegTextModel',
				    'CLIPSegVisionModel',
				    'CLIPSegForImageSegmentation',
				]

if TYPE_CHECKING:
				from .configuration_clipseg import (
				    CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    CLIPSegConfig,
				    CLIPSegTextConfig,
				    CLIPSegVisionConfig,
				)
				from .processing_clipseg import CLIPSegProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_clipseg import (
								    CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CLIPSegForImageSegmentation,
								    CLIPSegModel,
								    CLIPSegPreTrainedModel,
								    CLIPSegTextModel,
								    CLIPSegVisionModel,
								)

else:
				import sys

				lowerCAmelCase__			:      str								=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





