






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE 							=						{
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class 							A_    (						__lowercase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Dict =					"xlm-roberta-xl"


 def __init__(	self							,  _A=250880							,  _A=2560							,  _A=36							,  _A=32							,  _A=10240							,  _A="gelu"							,  _A=0.1							,  _A=0.1							,  _A=514							,  _A=1							,  _A=0.02							,  _A=1e-05							,  _A=1							,  _A=0							,  _A=2							,  _A="absolute"							,  _A=True							,  _A=None							,  **_A							,  ) ->		Any:

     """simple docstring"""



     super().__init__(pad_token_id=_A							,  bos_token_id=_A							,  eos_token_id=_A							,  **_A)
     _UpperCAmelCase				:      Any			  =				vocab_size
     _UpperCAmelCase				:      Union[str, Any]			  =				hidden_size
     _UpperCAmelCase				:      Tuple			  =				num_hidden_layers
     _UpperCAmelCase				:      Union[str, Any]			  =				num_attention_heads
     _UpperCAmelCase				:      str			  =				hidden_act
     _UpperCAmelCase				:      Union[str, Any]			  =				intermediate_size
     _UpperCAmelCase				:      Any			  =				hidden_dropout_prob
     _UpperCAmelCase				:      int			  =				attention_probs_dropout_prob
     _UpperCAmelCase				:      int			  =				max_position_embeddings
     _UpperCAmelCase				:      Tuple			  =				type_vocab_size
     _UpperCAmelCase				:      Any			  =				initializer_range
     _UpperCAmelCase				:      str			  =				layer_norm_eps
     _UpperCAmelCase				:      Any			  =				position_embedding_type
     _UpperCAmelCase				:      int			  =				use_cache
     _UpperCAmelCase				:      int			  =				classifier_dropout



class 							A_    (						__lowercase			):






 '''simple docstring'''




 @property
 def  snake_case__				(	self) ->		Mapping[str, Mapping[int, str]]:

     """simple docstring"""



     if self.task == "multiple-choice":
         _UpperCAmelCase				:      List[Any]			  =				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         _UpperCAmelCase				:      List[Any]			  =				{0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ])












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							,  _A) ->		List[str]:

     """simple docstring"""



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


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     pass



def      _lowerCamelCase	(						__A    :							str		,		__A    :							str		,		__A    :							str )      ->					List[str]:
    # load longformer model from model identifier
    _UpperCAmelCase				:      Optional[Any]			  =				LongformerModel.from_pretrained(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				LightningModel(__A )

    _UpperCAmelCase				:      Optional[int]			  =				torch.load(__A		,		map_location=torch.device('''cpu''' ) )
    lightning_model.load_state_dict(ckpt['''state_dict'''] )

    # init longformer question answering model
    _UpperCAmelCase				:      int			  =				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__":
  SCREAMING_SNAKE_CASE 							=						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 							=						parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )




