





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    {
    """configuration_pix2struct""": [
        """PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Pix2StructConfig""",
        """Pix2StructTextConfig""",
        """Pix2StructVisionConfig""",
    ],
    """processing_pix2struct""": ["""Pix2StructProcessor"""],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__							:      List[str]         =    ["""Pix2StructImageProcessor"""]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__							:      str         =    [
      """PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Pix2StructPreTrainedModel""",
      """Pix2StructForConditionalGeneration""",
      """Pix2StructVisionModel""",
      """Pix2StructTextModel""",
  ]

if TYPE_CHECKING:
  from .configuration_pixastruct import (
      PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      PixaStructConfig,
      PixaStructTextConfig,
      PixaStructVisionConfig,
  )
  from .processing_pixastruct import PixaStructProcessor

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_pixastruct import PixaStructImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_pixastruct import (
        PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
        PixaStructForConditionalGeneration,
        PixaStructPreTrainedModel,
        PixaStructTextModel,
        PixaStructVisionModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel







class 	__lowerCAmelCase      (       pl.LightningModule	):





   def __init__(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





      super().__init__()
      a__						:    int   		=	model
      a__						:    Any   		=	2
      a__						:    List[str]   		=	nn.Linear(self.model.config.hidden_size       ,      self.num_labels   )





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





      pass







def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   # load longformer model from model identifier
   a__						:    List[str]   		=	LongformerModel.from_pretrained(lowerCamelCase )
   a__						:    Dict   		=	LightningModel(lowerCamelCase )

   a__						:    str   		=	torch.load(lowerCamelCase				,			map_location=torch.device("cpu" ) )
   lightning_model.load_state_dict(ckpt["state_dict"] )

   # init longformer question answering model
   a__						:    Dict   		=	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__":
  SCREAMING_SNAKE_CASE__							:      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."""
  )
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

