





'''simple docstring'''

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel






class        UpperCAmelCase     (	pl.LightningModule    ):
 def __init__(    self				:Tuple						,	lowercase_				:List[str]							)->							Dict:
     super().__init__()
     A__    =      model
     A__    =      2
     A__    =      nn.Linear(self.model.config.hidden_size						,	self.num_labels							)



 def 					UpperCAmelCase_							(    self				:int							)->							int:
     pass





def 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : str						,	_lowerCamelCase : str	):
    # load longformer model from model identifier
    A__    =      LongformerModel.from_pretrained(_lowerCamelCase	)
    A__    =      LightningModel(_lowerCamelCase	)

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

    # init longformer question answering model
    A__    =      LongformerForQuestionAnswering.from_pretrained(_lowerCamelCase	)

    # 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(_lowerCamelCase	)

    print(F"Conversion successful. Model saved under {pytorch_dump_folder_path}"	)


if __name__ == "__main__":
  __lowerCAmelCase							:     List[str]		     =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."
  )
  __lowerCAmelCase							:     str		     =parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )










'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase							:     Any		     ={
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase							:     Tuple		     =[
      "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Swinv2ForImageClassification",
      "Swinv2ForMaskedImageModeling",
      "Swinv2Model",
      "Swinv2PreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_swinva import (
        SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwinvaForImageClassification,
        SwinvaForMaskedImageModeling,
        SwinvaModel,
        SwinvaPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase							:     List[Any]		     =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



