






"""simple docstring"""



import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str      )		->List[Any]:
						A__						:  Any		  =  args.pruning_method
						A__						:  Tuple		  =  args.threshold

						A__						:  List[Any]		  =  args.model_name_or_path.rstrip("""/"""      )
						A__						:  Optional[int]		  =  args.target_model_path

						print(f'Load fine-pruned model from {model_name_or_path}'      )
						A__						:  Dict		  =  torch.load(os.path.join(UpperCAmelCase__,						"""pytorch_model.bin"""      )      )
						A__						:  Union[str, Any]		  =  {}

						for name, tensor in model.items():
												if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
																		A__						:  Optional[int]		  =  tensor
																		print(f'Copied layer {name}'      )
												elif "classifier" in name or "qa_output" in name:
																		A__						:  Optional[Any]		  =  tensor
																		print(f'Copied layer {name}'      )
												elif "bias" in name:
																		A__						:  Any		  =  tensor
																		print(f'Copied layer {name}'      )
												else:
																		if pruning_method == "magnitude":
																								A__						:  str		  =  MagnitudeBinarizer.apply(inputs=UpperCAmelCase__,						threshold=UpperCAmelCase__      )
																								A__						:  int		  =  tensor * mask
																								print(f'Pruned layer {name}'      )
																		elif pruning_method == "topK":
																								if "mask_scores" in name:
																														continue
																								A__						:  Any		  =  name[:-6]
																								A__						:  Dict		  =  model[f'{prefix_}mask_scores']
																								A__						:  Optional[int]		  =  TopKBinarizer.apply(UpperCAmelCase__,						UpperCAmelCase__      )
																								A__						:  int		  =  tensor * mask
																								print(f'Pruned layer {name}'      )
																		elif pruning_method == "sigmoied_threshold":
																								if "mask_scores" in name:
																														continue
																								A__						:  str		  =  name[:-6]
																								A__						:  Optional[Any]		  =  model[f'{prefix_}mask_scores']
																								A__						:  Tuple		  =  ThresholdBinarizer.apply(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )
																								A__						:  Any		  =  tensor * mask
																								print(f'Pruned layer {name}'      )
																		elif pruning_method == "l0":
																								if "mask_scores" in name:
																														continue
																								A__						:  Optional[int]		  =  name[:-6]
																								A__						:  int		  =  model[f'{prefix_}mask_scores']
																								A__  ,  A__						:  Tuple		  =  -0.1, 1.1
																								A__						:  str		  =  torch.sigmoid(UpperCAmelCase__      )
																								A__						:  int		  =  s * (r - l) + l
																								A__						:  Optional[Any]		  =  s_bar.clamp(min=0.0,						max=1.0      )
																								A__						:  Dict		  =  tensor * mask
																								print(f'Pruned layer {name}'      )
																		else:
																								raise ValueError("""Unknown pruning method"""      )

						if target_model_path is None:
												A__						:  List[str]		  =  os.path.join(
												    os.path.dirname(UpperCAmelCase__      ),						f'bertarized_{os.path.basename(UpperCAmelCase__      )}'      )

						if not os.path.isdir(UpperCAmelCase__      ):
												shutil.copytree(UpperCAmelCase__,						UpperCAmelCase__      )
												print(f'\nCreated folder {target_model_path}'      )

						torch.save(UpperCAmelCase__,						os.path.join(UpperCAmelCase__,						"""pytorch_model.bin"""      )      )
						print("""\nPruned model saved! See you later!"""      )


if __name__ == "__main__":
						A_    =							argparse.ArgumentParser()

						parser.add_argument(
						    '''--pruning_method''',
						    choices=['''l0''', '''magnitude''', '''topK''', '''sigmoied_threshold'''],
						    type=str,
						    required=True,
						    help=(
						        '''Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'''
						        ''' sigmoied_threshold = Soft movement pruning)'''
						    ),
						)
						parser.add_argument(
						    '''--threshold''',
						    type=float,
						    required=False,
						    help=(
						        '''For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
						        '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
						        '''Not needed for `l0`'''
						    ),
						)
						parser.add_argument(
						    '''--model_name_or_path''',
						    type=str,
						    required=True,
						    help='''Folder containing the model that was previously fine-pruned''',
						)
						parser.add_argument(
						    '''--target_model_path''',
						    default=None,
						    type=str,
						    required=False,
						    help='''Folder containing the model that was previously fine-pruned''',
						)

						A_    =							parser.parse_args()

						main(args)














"""simple docstring"""



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


A_    =							logging.get_logger(__name__)






class 							__SCREAMING_SNAKE_CASE						:
							snake_case_     =							42
							snake_case_     =							None


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													raise NotImplementedError


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						int			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Any							):
													'''simple docstring'''





													raise NotImplementedError


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Optional[int]							):
													'''simple docstring'''





													raise NotImplementedError


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													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 						_UpperCamelCase       (			cls      :						Optional[int]							):
													'''simple docstring'''





													return F'`pip install {cls.pip_package or cls.name}`'






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'optuna'


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_optuna_available()


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Any							):
													'''simple docstring'''





													return run_hp_search_optuna(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						int							):
													'''simple docstring'''





													return default_hp_space_optuna(snake_case							)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'ray'
							snake_case_     =							'\'ray[tune]\''


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_ray_available()


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						Tuple			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						List[Any]							):
													'''simple docstring'''





													return run_hp_search_ray(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						Dict							):
													'''simple docstring'''





													return default_hp_space_ray(snake_case							)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'sigopt'


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_sigopt_available()


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return run_hp_search_sigopt(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						Any							):
													'''simple docstring'''





													return default_hp_space_sigopt(snake_case							)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'wandb'


							@staticmethod
							def 						_UpperCamelCase       (			):
													'''simple docstring'''





													return is_wandb_available()


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						int			,    snake_case      :						int			,    snake_case      :						str			,    **snake_case      :						Any							):
													'''simple docstring'''





													return run_hp_search_wandb(snake_case			,    snake_case			,    snake_case			,    **snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return default_hp_space_wandb(snake_case							)




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




def      _lowerCAmelCase		(							)		->str:
						A__						:  List[Any]		  =  [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
						if len(UpperCAmelCase__      ) > 0:
												A__						:  Dict		  =  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()      )      )






