






import argparse
import os
import shutil

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






def    __magic_name__    (	SCREAMING_SNAKE_CASE				)     ->    int:
							_lowercase							:   int 							=   args.pruning_method
							_lowercase							:   Optional[int] 							=   args.threshold

							_lowercase							:   str 							=   args.model_name_or_path.rstrip('/'				)
							_lowercase							:   Optional[Any] 							=   args.target_model_path

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

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

							if target_model_path is None:
														_lowercase							:   Tuple 							=   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__":
							UpperCamelCase								=		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",
							)

							UpperCamelCase								=		parser.parse_args()

							main(args)










from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean




def 		lowerCAmelCase_ (						lowerCamelCase ):

				# A local function to see if a dot lands in the circle.
				def is_in_circle(lowerCamelCase						,  lowerCamelCase ) -> bool:
								__magic_name__						:						Dict										=sqrt((x**2) + (y**2) )
								# Our circle has a radius of 1, so a distance
								# greater than 1 would land outside the circle.
								return distance_from_centre <= 1

				# The proportion of guesses that landed in the circle
				__magic_name__						:						Union[str, Any]										=mean(
				    int(is_in_circle(uniform(-1.0						,  1.0 )						,  uniform(-1.0						,  1.0 ) ) )
				    for _ in range(lowerCamelCase ) )
				# The ratio of the area for circle to square is pi/4.
				__magic_name__						:						List[Any]										=proportion * 4
				print(F"The estimated value of pi is {pi_estimate}" )
				print(F"The numpy value of pi is {pi}" )
				print(F"The total error is {abs(pi - pi_estimate )}" )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase = 0.0						,  lowerCamelCase = 1.0						,  ):
				return mean(
				    function_to_integrate(uniform(lowerCamelCase						,  lowerCamelCase ) ) for _ in range(lowerCamelCase ) ) * (max_value - min_value)




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase = 0.0						,  lowerCamelCase = 1.0 ):

				def identity_function(lowerCamelCase ) -> float:
								return x

				__magic_name__						:						Optional[int]										=area_under_curve_estimator(
				    lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )
				__magic_name__						:						str										=(max_value * max_value - min_value * min_value) / 2

				print("""******************""" )
				print(F"Estimating area under y=x where x varies from {min_value} to {max_value}" )
				print(F"Estimated value is {estimated_value}" )
				print(F"Expected value is {expected_value}" )
				print(F"Total error is {abs(estimated_value - expected_value )}" )
				print("""******************""" )




def 		lowerCAmelCase_ (						lowerCamelCase ):

				def function_to_integrate(lowerCamelCase ) -> float:
								return sqrt(4.0 - x * x )

				__magic_name__						:						Dict										=area_under_curve_estimator(
				    lowerCamelCase						,  lowerCamelCase						,  0.0						,  2.0 )

				print("""******************""" )
				print("""Estimating pi using area_under_curve_estimator""" )
				print(F"Estimated value is {estimated_value}" )
				print(F"Expected value is {pi}" )
				print(F"Total error is {abs(estimated_value - pi )}" )
				print("""******************""" )


if __name__ == "__main__":
							import doctest

							doctest.testmod()




