


from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


_SCREAMING_SNAKE_CASE			:       Dict      =						logging.get_logger(__name__)

class 			UpperCamelCase__			:
      a__ :  Optional[int]		    =    42
      a__ :  int		    =    None






      @staticmethod
      def 						__lowercase(  )	->			Optional[Any]:
           raise NotImplementedError






      def 						__lowercase(  self       : Optional[Any],		__lowerCamelCase       : List[Any],		__lowerCamelCase       : int,		__lowerCamelCase       : str,		**__lowerCamelCase       : Optional[Any] )	->			List[str]:
           raise NotImplementedError






      def 						__lowercase(  self       : str,		__lowerCamelCase       : Optional[int] )	->			Optional[int]:
           raise NotImplementedError






      def 						__lowercase(  self       : Optional[Any] )	->			List[str]:
           if not self.is_available():
                raise RuntimeError(
                    f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.' )






      @classmethod
      def 						__lowercase(  cls       : List[Any] )	->			Optional[Any]:
           return f'`pip install {cls.pip_package or cls.name}`'

class 			UpperCamelCase__			(			__lowerCamelCase      ):
      a__ :  List[Any]		    =    'optuna'






      @staticmethod
      def 						__lowercase(  )	->			Optional[Any]:
           return is_optuna_available()






      def 						__lowercase(  self       : Union[str, Any],		__lowerCamelCase       : int,		__lowerCamelCase       : int,		__lowerCamelCase       : str,		**__lowerCamelCase       : Optional[int] )	->			Optional[int]:
           return run_hp_search_optuna(__a,		__a,		__a,		**__a )






      def 						__lowercase(  self       : int,		__lowerCamelCase       : str )	->			str:
           return default_hp_space_optuna(__a )

class 			UpperCamelCase__			(			__lowerCamelCase      ):
      a__ :  List[Any]		    =    'ray'
      a__ :  Optional[Any]		    =    '\'ray[tune]\''






      @staticmethod
      def 						__lowercase(  )	->			Tuple:
           return is_ray_available()






      def 						__lowercase(  self       : Tuple,		__lowerCamelCase       : List[Any],		__lowerCamelCase       : int,		__lowerCamelCase       : str,		**__lowerCamelCase       : str )	->			Dict:
           return run_hp_search_ray(__a,		__a,		__a,		**__a )






      def 						__lowercase(  self       : Union[str, Any],		__lowerCamelCase       : Optional[int] )	->			Tuple:
           return default_hp_space_ray(__a )

class 			UpperCamelCase__			(			__lowerCamelCase      ):
      a__ :  Optional[int]		    =    'sigopt'






      @staticmethod
      def 						__lowercase(  )	->			Dict:
           return is_sigopt_available()






      def 						__lowercase(  self       : Optional[int],		__lowerCamelCase       : Tuple,		__lowerCamelCase       : int,		__lowerCamelCase       : str,		**__lowerCamelCase       : List[Any] )	->			List[Any]:
           return run_hp_search_sigopt(__a,		__a,		__a,		**__a )






      def 						__lowercase(  self       : Tuple,		__lowerCamelCase       : Any )	->			List[Any]:
           return default_hp_space_sigopt(__a )

class 			UpperCamelCase__			(			__lowerCamelCase      ):
      a__ :  Optional[Any]		    =    'wandb'






      @staticmethod
      def 						__lowercase(  )	->			str:
           return is_wandb_available()






      def 						__lowercase(  self       : List[str],		__lowerCamelCase       : Union[str, Any],		__lowerCamelCase       : int,		__lowerCamelCase       : str,		**__lowerCamelCase       : Union[str, Any] )	->			Union[str, Any]:
           return run_hp_search_wandb(__a,		__a,		__a,		**__a )






      def 						__lowercase(  self       : Dict,		__lowerCamelCase       : Any )	->			Tuple:
           return default_hp_space_wandb(__a )

_SCREAMING_SNAKE_CASE			:       List[str]      =						{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}







def     _lowercase							(  )	-> List[Any]:





     '''simple docstring'''





     UpperCamelCase__				:	Any										=  [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
     if len(UpperCamelCase__      ) > 0:
          UpperCamelCase__				:	Optional[Any]										=  available_backends[0].name
          if len(UpperCamelCase__      ) > 1:
               logger.info(
                   F'{len(UpperCamelCase__      )} hyperparameter search backends available. Using {name} as the default.'      )
          return name
     raise RuntimeError(
         '''No hyperparameter search backend available.\n'''
         + '''\n'''.join(
             F' - To install {backend.name} run {backend.pip_install()}'
             for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()      )      )





'''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					:				Dict  ,		__a					:				List[str]						)				->						Tuple:
								super().__init__()
								__UpperCAmelCase      					=      model
								__UpperCAmelCase      					=      2
								__UpperCAmelCase      					=      nn.Linear(self.model.config.hidden_size  ,		self.num_labels						)






							def    snake_case__    (   self					:				int						)				->						int:
								pass





def    lowerCAmelCase       (		UpperCamelCase__					: str     ,       UpperCamelCase__					: str     ,       UpperCamelCase__					: str							):


	"""simple docstring"""
	# load longformer model from model identifier
	__UpperCAmelCase      					=      LongformerModel.from_pretrained(UpperCamelCase__							)
	__UpperCAmelCase      					=      LightningModel(UpperCamelCase__							)

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

	# init longformer question answering model
	__UpperCAmelCase      					=      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__":
					__lowerCAmelCase						:							Optional[Any]    							=						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."
					)
					__lowerCAmelCase						:							List[str]    							=						parser.parse_args()
					convert_longformer_qa_checkpoint_to_pytorch(
					    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
					)
