






import argparse
import os
import shutil

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

def _UpperCAmelCase   (	a						:	Tuple ):
							snake_case__	  =       args.pruning_method
							snake_case__	  =       args.threshold

							snake_case__	  =       args.model_name_or_path.rstrip("""/""" )
							snake_case__	  =       args.target_model_path

							print(F'''Load fine-pruned model from {model_name_or_path}''' )
							snake_case__	  =       torch.load(os.path.join(a  ,					"""pytorch_model.bin""" ) )
							snake_case__	  =       {}

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

							if target_model_path is None:
														snake_case__	  =       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__":
	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)








from collections.abc import Callable

def _UpperCAmelCase   (	a						:	Callable[[float], float]  ,					a						:	float  ,					a						:	float ):
							snake_case__	  =       a
							snake_case__	  =       b
							if function(a ) == 0:  # one of the a or b is a root for the function
														return a
							elif function(a ) == 0:
														return b
							elif (
							    function(a ) * function(a ) > 0
							):  # if none of these are root and they are both positive or negative,
														# then this algorithm can't find the root
														raise ValueError("""could not find root in given interval.""" )
							else:
														snake_case__	  =       start + (end - start) / 2.0
														while abs(start - mid ) > 10**-7:  # until precisely equals to 10^-7
																					if function(a ) == 0:
																												return mid
																					elif function(a ) * function(a ) < 0:
																												snake_case__	  =       mid
																					else:
																												snake_case__	  =       mid
																					snake_case__	  =       start + (end - start) / 2.0
														return mid

def _UpperCAmelCase   (	a						:	float ):
							return x**3 - 2 * x - 5


if __name__ == "__main__":
	print(bisection(f, 1, 1_0_0_0))

	import doctest

	doctest.testmod()
