

'''simple docstring'''




import argparse
import os
import shutil

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




def 							lowerCamelCase			(   UpperCAmelCase__ :      List[str]				)    ->   int:
							lowercase_      :    Tuple       		= args.pruning_method
							lowercase_      :    str       		= args.threshold

							lowercase_      :    Tuple       		= args.model_name_or_path.rstrip("""/"""				)
							lowercase_      :    List[str]       		= args.target_model_path

							print(F'''Load fine-pruned model from {model_name_or_path}'''				)
							lowercase_      :    Tuple       		= torch.load(os.path.join(_snake_case   ,   """pytorch_model.bin"""				)				)
							lowercase_      :    List[str]       		= {}

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

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

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

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


if __name__ == "__main__":
						_lowercase		:				Optional[Any]	       =		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",
						)

						_lowercase		:				int	       =		parser.parse_args()

						main(args)






'''simple docstring'''




from collections.abc import Callable

import numpy as np




def 							lowerCamelCase			(   UpperCAmelCase__ :      Callable   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float   ,   UpperCAmelCase__ :      float				)    ->   np.array:
							lowercase_      :    Any       		= int(np.ceil((x_end - xa) / step_size				)				)
							lowercase_      :    List[Any]       		= np.zeros((n + 1,)				)
							lowercase_      :    List[Any]       		= ya
							lowercase_      :    List[str]       		= xa

							for k in range(UpperCAmelCase__				):
														lowercase_      :    Optional[Any]       		= y[k] + step_size * ode_func(UpperCAmelCase__   ,   y[k]				)
														lowercase_      :    List[Any]       		= y[k] + (
														    (step_size / 2) * (ode_func(UpperCAmelCase__   ,   y[k]				) + ode_func(x + step_size   ,   UpperCAmelCase__				))
														)
														x += step_size

							return y


if __name__ == "__main__":
						import doctest

						doctest.testmod()





