"""simple docstring"""





import argparse
import os
import shutil

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


def    lowercase_   (   _lowerCamelCase:	str      ) ->      Optional[int]:



							'''simple docstring'''

							__lowerCamelCase					:		List[Any]		   =     args.pruning_method
							__lowerCamelCase					:		List[str]		   =     args.threshold

							__lowerCamelCase					:		Any		   =     args.model_name_or_path.rstrip("/"      )
							__lowerCamelCase					:		List[Any]		   =     args.target_model_path

							print(F"""Load fine-pruned model from {model_name_or_path}"""      )
							__lowerCamelCase					:		List[str]		   =     torch.load(os.path.join(_lowerCamelCase						,   "pytorch_model.bin"      )      )
							__lowerCamelCase					:		Union[str, Any]		   =     {}

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

							if target_model_path is None:
														__lowerCamelCase					:		List[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__":
				__A  =				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''',
				)

				__A  =				parser.parse_args()

				main(args)
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import _LazyModule


__A  =				{'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}


if TYPE_CHECKING:
				from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
				import sys

				__A  =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)