






"""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     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							str					)     ->		Tuple:
      super().__init__()
      lowerCamelCase_         =						model
      lowerCamelCase_         =						2
      lowerCamelCase_         =						nn.Linear(self.model.config.hidden_size       ,						self.num_labels					)






    def    UpperCamelCase       (       self     :							int					)     ->		Tuple:
      pass







def      lowerCamelCase__       (							_lowerCamelCase       :		str		,				_lowerCamelCase       :		str		,				_lowerCamelCase       :		str )      ->				Any:
  # load longformer model from model identifier
  lowerCamelCase_         =						LongformerModel.from_pretrained(_lowerCamelCase )
  lowerCamelCase_         =						LightningModel(_lowerCamelCase )

  lowerCamelCase_         =						torch.load(_lowerCamelCase		,				map_location=torch.device('cpu' ) )
  lightning_model.load_state_dict(ckpt['state_dict'] )

  # init longformer question answering model
  lowerCamelCase_         =						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					:				Dict					    =						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					:				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 math import pow, sqrt







def      lowerCamelCase__       (							*_lowerCamelCase       :		float )      ->				bool:
  lowerCamelCase_         =						len(_lowerCamelCase ) > 0 and all(value > 0.0 for value in values )
  return result







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase )
      else ValueError('Input Error: Molar mass values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(effusion_rate * sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(effusion_rate / sqrt(molar_mass_a / molar_mass_a )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(molar_mass / pow(effusion_rate_a / effusion_rate_a		,				2 )		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )







def      lowerCamelCase__       (							_lowerCamelCase       :		float		,				_lowerCamelCase       :		float		,				_lowerCamelCase       :		float )      ->				float | ValueError:
  return (
      round(pow(effusion_rate_a / effusion_rate_a		,				2 ) / molar_mass		,				6 )
      if validate(_lowerCamelCase		,				_lowerCamelCase		,				_lowerCamelCase )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.' )
  )




