

"""simple docstring"""





from math import pow, sqrt




def   _A  (  *UpperCamelCase_     :       float)	->						bool:
   '''simple docstring'''


   __lowercase								=						len(UpperCamelCase_) > 0 and all(value > 0.0 for value in values)
   return result




def   _A  (  UpperCamelCase_     :       float,			UpperCamelCase_     :       float)	->						float | ValueError:
   '''simple docstring'''


   return (
       round(sqrt(molar_mass_a / molar_mass_a),			6)
       if validate(UpperCamelCase_,			UpperCamelCase_)
       else ValueError("Input Error: Molar mass values must greater than 0.")
   )




def   _A  (  UpperCamelCase_     :       float,			UpperCamelCase_     :       float,			UpperCamelCase_     :       float)	->						float | ValueError:
   '''simple docstring'''


   return (
       round(effusion_rate * sqrt(molar_mass_a / molar_mass_a),			6)
       if validate(UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)
       else ValueError(
           "Input Error: Molar mass and effusion rate values must greater than 0.")
   )




def   _A  (  UpperCamelCase_     :       float,			UpperCamelCase_     :       float,			UpperCamelCase_     :       float)	->						float | ValueError:
   '''simple docstring'''


   return (
       round(effusion_rate / sqrt(molar_mass_a / molar_mass_a),			6)
       if validate(UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)
       else ValueError(
           "Input Error: Molar mass and effusion rate values must greater than 0.")
   )




def   _A  (  UpperCamelCase_     :       float,			UpperCamelCase_     :       float,			UpperCamelCase_     :       float)	->						float | ValueError:
   '''simple docstring'''


   return (
       round(molar_mass / pow(effusion_rate_a / effusion_rate_a,			2),			6)
       if validate(UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)
       else ValueError(
           "Input Error: Molar mass and effusion rate values must greater than 0.")
   )




def   _A  (  UpperCamelCase_     :       float,			UpperCamelCase_     :       float,			UpperCamelCase_     :       float)	->						float | ValueError:
   '''simple docstring'''


   return (
       round(pow(effusion_rate_a / effusion_rate_a,			2) / molar_mass,			6)
       if validate(UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)
       else ValueError(
           "Input Error: Molar mass and effusion rate values must greater than 0.")
   )







"""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							:			Optional[Any],					UpperCAmelCase__							:			str	):
    super().__init__()
    __lowercase								=						model
    __lowercase								=						2
    __lowercase								=						nn.Linear(self.model.config.hidden_size,					self.num_labels	)



 def 					_lowercase (			self							:			Optional[int]	):
    pass







def   _A  (  UpperCamelCase_     :       str,			UpperCamelCase_     :       str,			UpperCamelCase_     :       str)	->						str:
   '''simple docstring'''


   __lowercase								=						LongformerModel.from_pretrained(UpperCamelCase_)
   __lowercase								=						LightningModel(UpperCamelCase_)

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

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




