


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			:    List[str]       ,      lowerCamelCase			:    List[str]							)      ->       Any:
														super().__init__()
														__snake_case						:     Any									=  model
														__snake_case						:     List[str]									=  2
														__snake_case						:     List[Any]									=  nn.Linear(self.model.config.hidden_size       ,      self.num_labels							)






							def        __snake_case   (				self			:    List[Any]							)      ->       List[Any]:
														pass







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      ):
							# load longformer model from model identifier
							__snake_case						:     List[str]									=  LongformerModel.from_pretrained(__lowerCamelCase      )
							__snake_case						:     Union[str, Any]									=  LightningModel(__lowerCamelCase      )

							__snake_case						:     Dict									=  torch.load(__lowerCamelCase				,      map_location=torch.device("cpu"      )      )
							lightning_model.load_state_dict(ckpt["state_dict"]      )

							# init longformer question answering model
							__snake_case						:     List[str]									=  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__":
			_snake_case	:						int         =							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."
			)
			_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 pi, sqrt
def 						snake_case_       (UpperCamelCase			:    float ):





							'''simple docstring'''


							if num <= 0:
														raise ValueError('''math domain error''' )
							if num > 171.5:
														raise OverflowError('''math range error''' )
							elif num - int(UpperCamelCase ) not in (0, 0.5):
														raise NotImplementedError('''num must be an integer or a half-integer''' )
							elif num == 0.5:
														return sqrt(UpperCamelCase )
							else:
														return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def 						snake_case_       ():





							'''simple docstring'''


							assert gamma(0.5 ) == sqrt(UpperCamelCase )
							assert gamma(1 ) == 1.0
							assert gamma(2 ) == 1.0


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				_snake_case : Optional[Any]  				=						1.0
				while num:
								_snake_case : Dict  				=						float(input('Gamma of: '))
								print(F'''gamma({num}) = {gamma(num)}''')
								print('\nEnter 0 to exit...')

