

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
  )







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__ :     str				    =    {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Any				    =    ["""LayoutXLMTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     List[str]				    =    ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
  from .processing_layoutxlm import LayoutXLMProcessor

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm import LayoutXLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
  import sys

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




