


import argparse
import os
import shutil

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






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Dict				     =					args.pruning_method
							_lowerCAmelCase  :							Dict				     =					args.threshold

							_lowerCAmelCase  :							Any				     =					args.model_name_or_path.rstrip("/"						)
							_lowerCAmelCase  :							str				     =					args.target_model_path

							print(F"Load fine-pruned model from {model_name_or_path}"						)
							_lowerCAmelCase  :							Any				     =					torch.load(os.path.join(_lowerCamelCase       ,				"pytorch_model.bin"						)						)
							_lowerCAmelCase  :							Tuple				     =					{}

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

							if target_model_path is None:
														_lowerCAmelCase  :							Optional[Any]				     =					os.path.join(
														    os.path.dirname(_lowerCamelCase						)       ,				F"bertarized_{os.path.basename(_lowerCamelCase						)}"						)

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

							torch.save(_lowerCamelCase       ,				os.path.join(_lowerCamelCase       ,				"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)





from __future__ import annotations

from collections.abc import MutableSequence



class 						UpperCAmelCase_					:







							def __init__(     self,    __a,    __a):

														'''simple docstring'''

														if len(__a) != degree + 1:
																					raise ValueError(
																					    "The number of coefficients should be equal to the degree + 1.")

														_lowerCAmelCase  :							list[float]				     =					list(__a)
														_lowerCAmelCase  :							Any				     =					degree







							def __add__(     self,    __a):

														'''simple docstring'''

														if self.degree > polynomial_a.degree:
																					_lowerCAmelCase  :							Dict				     =					self.coefficients[:]
																					for i in range(polynomial_a.degree + 1):
																												coefficients[i] += polynomial_a.coefficients[i]
																					return Polynomial(self.degree,    __a)
														else:
																					_lowerCAmelCase  :							Union[str, Any]				     =					polynomial_a.coefficients[:]
																					for i in range(self.degree + 1):
																												coefficients[i] += self.coefficients[i]
																					return Polynomial(polynomial_a.degree,    __a)







							def __sub__(     self,    __a):

														'''simple docstring'''

														return self + polynomial_a * Polynomial(0,    [-1])







							def __neg__(     self):

														'''simple docstring'''

														return Polynomial(self.degree,    [-c for c in self.coefficients])







							def __mul__(     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							list[float]				     =					[0] * (self.degree + polynomial_a.degree + 1)
														for i in range(self.degree + 1):
																					for j in range(polynomial_a.degree + 1):
																												coefficients[i + j] += (
																												    self.coefficients[i] * polynomial_a.coefficients[j]
																												)

														return Polynomial(self.degree + polynomial_a.degree,    __a)







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														_lowerCAmelCase  :							int | float				     =					0
														for i in range(self.degree + 1):
																					result += self.coefficients[i] * (substitution**i)
														return result







							def __str__(     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					""
														for i in range(self.degree,    -1,    -1):
																					if self.coefficients[i] == 0:
																												continue
																					elif self.coefficients[i] > 0:
																												if polynomial:
																																			polynomial += " + "
																					else:
																												polynomial += " - "

																					if i == 0:
																												polynomial += str(abs(self.coefficients[i]))
																					elif i == 1:
																												polynomial += str(abs(self.coefficients[i])) + "x"
																					else:
																												polynomial += str(abs(self.coefficients[i])) + "x^" + str(__a)

														return polynomial







							def __repr__(     self):

														'''simple docstring'''

														return self.__str__()







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							list[float]				     =					[0] * self.degree
														for i in range(self.degree):
																					_lowerCAmelCase  :							List[Any]				     =					self.coefficients[i + 1] * (i + 1)
														return Polynomial(self.degree - 1,    __a)







							def      snake_case__   (     self,    __a = 0):

														'''simple docstring'''

														_lowerCAmelCase  :							list[float]				     =					[0] * (self.degree + 2)
														_lowerCAmelCase  :							Optional[Any]				     =					constant
														for i in range(self.degree + 1):
																					_lowerCAmelCase  :							Dict				     =					self.coefficients[i] / (i + 1)
														return Polynomial(self.degree + 1,    __a)







							def __eq__(     self,    __a):

														'''simple docstring'''

														if not isinstance(__a,    __a):
																					return False

														if self.degree != polynomial_a.degree:
																					return False

														for i in range(self.degree + 1):
																					if self.coefficients[i] != polynomial_a.coefficients[i]:
																												return False

														return True







							def __ne__(     self,    __a):

														'''simple docstring'''

														return not self.__eq__(__a)
