



'''simple docstring'''



import argparse
import os
import shutil

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



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=args.pruning_method
							__lowercase       	=args.threshold

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

							print(f"""Load fine-pruned model from {model_name_or_path}"""					)
							__lowercase       	=torch.load(os.path.join(_lowerCAmelCase  ,   'pytorch_model.bin'					)					)
							__lowercase       	={}

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

							if target_model_path is None:
														__lowercase       	=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__":
		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 typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase	 =       {"""tokenization_bertweet""": ["""BertweetTokenizer"""]}


if TYPE_CHECKING:
		from .tokenization_bertweet import BertweetTokenizer

else:
		import sys

		lowerCamelCase	 =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



