



"""simple docstring"""






import argparse
import os
import shutil

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


def     _lowercase       (						__snake_case			)		->     int:
			__lowerCAmelCase   :						List[str]				= args.pruning_method
			__lowerCAmelCase   :						Tuple				= args.threshold

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

			print(F"""Load fine-pruned model from {model_name_or_path}"""			)
			__lowerCAmelCase   :						Optional[int]				= torch.load(os.path.join(a_      ,"pytorch_model.bin"			)			)
			__lowerCAmelCase   :						List[str]				= {}

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

			if target_model_path is None:
						__lowerCAmelCase   :						int				= os.path.join(
						    os.path.dirname(a_			)      ,F"""bertarized_{os.path.basename(a_			)}"""			)

			if not os.path.isdir(a_			):
						shutil.copytree(a_      ,a_			)
						print(F"""\nCreated folder {target_model_path}"""			)

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


if __name__ == "__main__":
	__snake_case							:     str   =     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',
	)

	__snake_case							:     Any   =     parser.parse_args()

	main(args)




"""simple docstring"""






import logging

from transformers.configuration_utils import PretrainedConfig


__snake_case							:     Tuple   =					logging.getLogger(__name__)



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'masked_bert'

	def __init__(  self:	str ,    _SCREAMING_SNAKE_CASE:	Tuple=3_0522 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=768 ,    _SCREAMING_SNAKE_CASE:	List[str]=12 ,    _SCREAMING_SNAKE_CASE:	Any=12 ,    _SCREAMING_SNAKE_CASE:	Optional[int]=3072 ,    _SCREAMING_SNAKE_CASE:	List[str]="gelu" ,    _SCREAMING_SNAKE_CASE:	List[str]=0.1 ,    _SCREAMING_SNAKE_CASE:	str=0.1 ,    _SCREAMING_SNAKE_CASE:	Dict=512 ,    _SCREAMING_SNAKE_CASE:	List[Any]=2 ,    _SCREAMING_SNAKE_CASE:	Tuple=0.02 ,    _SCREAMING_SNAKE_CASE:	int=1e-12 ,    _SCREAMING_SNAKE_CASE:	int=0 ,    _SCREAMING_SNAKE_CASE:	Tuple="topK" ,    _SCREAMING_SNAKE_CASE:	Any="constant" ,    _SCREAMING_SNAKE_CASE:	int=0.0 ,    **_SCREAMING_SNAKE_CASE:	Any ,    )       -> List[str]:

				"""simple docstring"""

				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Union[str, Any]				= vocab_size
				__lowerCAmelCase   :						List[str]				= hidden_size
				__lowerCAmelCase   :						Any				= num_hidden_layers
				__lowerCAmelCase   :						Dict				= num_attention_heads
				__lowerCAmelCase   :						Union[str, Any]				= hidden_act
				__lowerCAmelCase   :						Optional[Any]				= intermediate_size
				__lowerCAmelCase   :						int				= hidden_dropout_prob
				__lowerCAmelCase   :						List[Any]				= attention_probs_dropout_prob
				__lowerCAmelCase   :						int				= max_position_embeddings
				__lowerCAmelCase   :						str				= type_vocab_size
				__lowerCAmelCase   :						Any				= initializer_range
				__lowerCAmelCase   :						Optional[int]				= layer_norm_eps
				__lowerCAmelCase   :						Tuple				= pruning_method
				__lowerCAmelCase   :						Union[str, Any]				= mask_init
				__lowerCAmelCase   :						str				= mask_scale