




'''simple docstring'''
import argparse
import os
import shutil

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





def 			lowerCamelCase(							SCREAMING_SNAKE_CASE_     )		->		Tuple:
			A_									= args.pruning_method
			A_									= args.threshold

			A_									= args.model_name_or_path.rstrip('/'     )
			A_									= args.target_model_path

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

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

			if target_model_path is None:
						A_									= 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__":
							_SCREAMING_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",
							)

							_SCREAMING_SNAKE_CASE			=						parser.parse_args()

							main(args)










'''simple docstring'''
from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands





def 			lowerCamelCase(							)		->		int:
			A_									= ArgumentParser('Transformers CLI tool'       ,usage='transformers-cli <command> [<args>]'     )
			A_									= parser.add_subparsers(help='transformers-cli command helpers'     )

			# Register commands
			ConvertCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			DownloadCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			EnvironmentCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			RunCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			ServeCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			UserCommands.register_subcommand(SCREAMING_SNAKE_CASE_     )
			AddNewModelCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			AddNewModelLikeCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )
			LfsCommands.register_subcommand(SCREAMING_SNAKE_CASE_     )
			PTtoTFCommand.register_subcommand(SCREAMING_SNAKE_CASE_     )

			# Let's go
			A_									= parser.parse_args()

			if not hasattr(SCREAMING_SNAKE_CASE_       ,'func'     ):
						parser.print_help()
						exit(1     )

			# Run
			A_									= args.func(SCREAMING_SNAKE_CASE_     )
			service.run()


if __name__ == "__main__":
							main()




