

'''simple docstring'''





import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class        _a    (						pl.LightningModule							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE   )							->      Any:
          super().__init__()
          _snake_case							=				model
          _snake_case							=				2
          _snake_case							=				nn.Linear(self.model.config.hidden_size ,self.num_labels   )






     def     _lowercase     (			self   )							->      Tuple:
          pass







def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Optional[Any]   )						->			Tuple:

     """simple docstring"""




     _snake_case							=				LongformerModel.from_pretrained(_UpperCAmelCase   )
     _snake_case							=				LightningModel(_UpperCAmelCase   )

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

     # init longformer question answering model
     _snake_case							=				LongformerForQuestionAnswering.from_pretrained(_UpperCAmelCase   )

     # 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(_UpperCAmelCase   )

     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_						:      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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:      int       =						logging.get_logger(__name__)

UpperCamelCase_						:      Tuple       =						{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						"""vit_msn"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1e-06 ,_SCREAMING_SNAKE_CASE=224 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=True ,**_SCREAMING_SNAKE_CASE ,)							->      int:
          super().__init__(**_SCREAMING_SNAKE_CASE   )

          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_attention_heads
          _snake_case							=				intermediate_size
          _snake_case							=				hidden_act
          _snake_case							=				hidden_dropout_prob
          _snake_case							=				attention_probs_dropout_prob
          _snake_case							=				initializer_range
          _snake_case							=				layer_norm_eps
          _snake_case							=				image_size
          _snake_case							=				patch_size
          _snake_case							=				num_channels
          _snake_case							=				qkv_bias


