


'''simple docstring'''







import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel


class 			__lowerCamelCase			(	pl.LightningModule	):
    """simple docstring"""


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							int):
           super().__init__()
           _A					:     Dict     = model
           _A					:     Optional[int]     = 2
           _A					:     List[str]     = nn.Linear(self.model.config.hidden_size				,							self.num_labels)


    def        A   (  self      :							Dict):
           pass






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

       _A					:     int     = torch.load(lowerCamelCase  ,map_location=torch.device('cpu'       )       )
       lightning_model.load_state_dict(ckpt['state_dict']       )

       # init longformer question answering model
       _A					:     Tuple     = 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__":
   A				:					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.'''
   )
   A				:					int    						=			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, is_vision_available


A				:					Optional[int]    						=			{
    '''configuration_mask2former''': [
        '''MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Mask2FormerConfig''',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Tuple    						=			['''Mask2FormerImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Dict    						=			[
       '''MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Mask2FormerForUniversalSegmentation''',
       '''Mask2FormerModel''',
       '''Mask2FormerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_maskaformer import MaskaFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskaformer import (
          MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskaFormerForUniversalSegmentation,
          MaskaFormerModel,
          MaskaFormerPreTrainedModel,
      )


else:
   import sys

   A				:					Optional[Any]    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)






