





"""simple docstring"""





import argparse
import os
import shutil

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


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	args.pruning_method
		UpperCamelCase   =	args.threshold

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

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

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

		if target_model_path is None:
				UpperCamelCase   =	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__":
						lowerCAmelCase__     			=			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''',
						)

						lowerCAmelCase__     			=			parser.parse_args()

						main(args)













"""simple docstring"""





from diffusers.utils.testing_utils import require_onnxruntime





@require_onnxruntime
class        _lowerCamelCase      :
						pass






