



import argparse
import os
import shutil

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






def      _A				(UpperCamelCase		:      List[Any] )  ->Union[str, Any]:



   '''simple docstring'''







   lowerCamelCase__ :      Union[str, Any]								=		args.pruning_method
   lowerCamelCase__ :      Optional[int]								=		args.threshold

   lowerCamelCase__ :      Optional[Any]								=		args.model_name_or_path.rstrip("""/""" )
   lowerCamelCase__ :      List[str]								=		args.target_model_path

   print(f"Load fine-pruned model from {model_name_or_path}" )
   lowerCamelCase__ :      Optional[Any]								=		torch.load(os.path.join(UpperCamelCase      ,			"""pytorch_model.bin""" ) )
   lowerCamelCase__ :      Optional[Any]								=		{}

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

   if target_model_path is None:
      lowerCamelCase__ :      Dict								=		os.path.join(
          os.path.dirname(UpperCamelCase )      ,			f"bertarized_{os.path.basename(UpperCamelCase )}" )

   if not os.path.isdir(UpperCamelCase ):
      shutil.copytree(UpperCamelCase      ,			UpperCamelCase )
      print(f"\nCreated folder {target_model_path}" )

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


if __name__ == "__main__":
    _lowercase     							=						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     							=						parser.parse_args()

    main(args)








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=A_      )
class 				__A					( A_      ):
			UpperCamelCase					:str     =			field(default='''automatic-speech-recognition'''					, metadata={'''include_in_asdict_even_if_is_default''': True}      )
			UpperCamelCase					:ClassVar[Features]     =			Features({'''audio''': Audio()}      )
			UpperCamelCase					:ClassVar[Features]     =			Features({'''transcription''': Value('''string'''      )}      )
			UpperCamelCase					:str     =			"audio"
			UpperCamelCase					:str     =			"transcription"





			def        _snake_case    (self							,    __magic_name__				):
						if self.audio_column not in features:
									raise ValueError(f"Column {self.audio_column} is not present in features."				)
						if not isinstance(features[self.audio_column]							,    __magic_name__				):
									raise ValueError(f"Column {self.audio_column} is not an Audio type."				)
						lowerCamelCase__ :      int								=		copy.deepcopy(self				)
						lowerCamelCase__ :      Tuple								=		self.input_schema.copy()
						lowerCamelCase__ :      Tuple								=		features[self.audio_column]
						lowerCamelCase__ :      List[str]								=		input_schema
						return task_template





			@property
			def        _snake_case    (self				):
						return {self.audio_column: "audio", self.transcription_column: "transcription"}



