



'''simple docstring'''

import argparse
import os
import shutil

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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]       ):
				SCREAMING_SNAKE_CASE  :						List[Any]      					= args.pruning_method
				SCREAMING_SNAKE_CASE  :						List[Any]      					= args.threshold

				SCREAMING_SNAKE_CASE  :						Optional[int]      					= args.model_name_or_path.rstrip('''/'''       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= args.target_model_path

				print(F'''Load fine-pruned model from {model_name_or_path}'''       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.load(os.path.join(_SCREAMING_SNAKE_CASE    ,	'''pytorch_model.bin'''       )       )
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= {}

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

				if target_model_path is None:
								SCREAMING_SNAKE_CASE  :						List[Any]      					= os.path.join(
								    os.path.dirname(_SCREAMING_SNAKE_CASE       )    ,	F'''bertarized_{os.path.basename(_SCREAMING_SNAKE_CASE       )}'''       )

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

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


if __name__ == "__main__":
	snake_case_			=     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_			=     parser.parse_args()

	main(args)









'''simple docstring'''

from ..utils import DummyObject, requires_backends


class 	a__     (				metaclass=_lowercase	):
	__magic_name__		:    Dict	    =     ["torch", "transformers", "onnx"]


	def __init__(self : List[str],					*__UpperCAmelCase : Dict,					**__UpperCAmelCase : List[Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					requires_backends(self,					['''torch''', '''transformers''', '''onnx''']      )


	@classmethod
	def 				lowercase__  (cls : Optional[Any],					*__UpperCAmelCase : Tuple,					**__UpperCAmelCase : int      )     ->			int:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )






	@classmethod
	def 				lowercase__  (cls : Any,					*__UpperCAmelCase : Tuple,					**__UpperCAmelCase : Tuple      )     ->			Tuple:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )



class 	a__     (				metaclass=_lowercase	):
	__magic_name__		:    Any	    =     ["torch", "transformers", "onnx"]


	def __init__(self : Dict,					*__UpperCAmelCase : Union[str, Any],					**__UpperCAmelCase : Optional[Any]      )     ->			int:

					"""simple docstring"""

					requires_backends(self,					['''torch''', '''transformers''', '''onnx''']      )


	@classmethod
	def 				lowercase__  (cls : Any,					*__UpperCAmelCase : Tuple,					**__UpperCAmelCase : List[str]      )     ->			Optional[int]:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )






	@classmethod
	def 				lowercase__  (cls : int,					*__UpperCAmelCase : List[Any],					**__UpperCAmelCase : Any      )     ->			Optional[Any]:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )



class 	a__     (				metaclass=_lowercase	):
	__magic_name__		:    Optional[Any]	    =     ["torch", "transformers", "onnx"]


	def __init__(self : Dict,					*__UpperCAmelCase : Dict,					**__UpperCAmelCase : Any      )     ->			List[str]:

					"""simple docstring"""

					requires_backends(self,					['''torch''', '''transformers''', '''onnx''']      )


	@classmethod
	def 				lowercase__  (cls : Tuple,					*__UpperCAmelCase : Union[str, Any],					**__UpperCAmelCase : int      )     ->			Optional[Any]:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )






	@classmethod
	def 				lowercase__  (cls : Union[str, Any],					*__UpperCAmelCase : List[str],					**__UpperCAmelCase : str      )     ->			Optional[int]:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )



class 	a__     (				metaclass=_lowercase	):
	__magic_name__		:    Tuple	    =     ["torch", "transformers", "onnx"]


	def __init__(self : Union[str, Any],					*__UpperCAmelCase : Optional[int],					**__UpperCAmelCase : List[Any]      )     ->			Tuple:

					"""simple docstring"""

					requires_backends(self,					['''torch''', '''transformers''', '''onnx''']      )


	@classmethod
	def 				lowercase__  (cls : str,					*__UpperCAmelCase : Optional[int],					**__UpperCAmelCase : Tuple      )     ->			str:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )






	@classmethod
	def 				lowercase__  (cls : int,					*__UpperCAmelCase : List[Any],					**__UpperCAmelCase : List[str]      )     ->			Tuple:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )



class 	a__     (				metaclass=_lowercase	):
	__magic_name__		:    List[Any]	    =     ["torch", "transformers", "onnx"]


	def __init__(self : Dict,					*__UpperCAmelCase : Optional[Any],					**__UpperCAmelCase : int      )     ->			List[str]:

					"""simple docstring"""

					requires_backends(self,					['''torch''', '''transformers''', '''onnx''']      )


	@classmethod
	def 				lowercase__  (cls : List[str],					*__UpperCAmelCase : Union[str, Any],					**__UpperCAmelCase : Dict      )     ->			Union[str, Any]:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )






	@classmethod
	def 				lowercase__  (cls : Any,					*__UpperCAmelCase : List[str],					**__UpperCAmelCase : List[Any]      )     ->			Dict:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )



class 	a__     (				metaclass=_lowercase	):
	__magic_name__		:    Dict	    =     ["torch", "transformers", "onnx"]


	def __init__(self : Tuple,					*__UpperCAmelCase : Optional[Any],					**__UpperCAmelCase : List[str]      )     ->			Any:

					"""simple docstring"""

					requires_backends(self,					['''torch''', '''transformers''', '''onnx''']      )


	@classmethod
	def 				lowercase__  (cls : Tuple,					*__UpperCAmelCase : Any,					**__UpperCAmelCase : Union[str, Any]      )     ->			Optional[int]:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )






	@classmethod
	def 				lowercase__  (cls : Optional[Any],					*__UpperCAmelCase : Union[str, Any],					**__UpperCAmelCase : Optional[Any]      )     ->			int:

					"""simple docstring"""

					requires_backends(cls,					['''torch''', '''transformers''', '''onnx''']      )




