



import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def 		UpperCamelCase	(   snake_case__       :    Dict	)	->     List[Any]:
   UpperCamelCase    :  str							=	args.pruning_method
   UpperCamelCase    :  List[Any]							=	args.threshold

   UpperCamelCase    :  Optional[int]							=	args.model_name_or_path.rstrip('/'	)
   UpperCamelCase    :  str							=	args.target_model_path

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

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

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

    __UpperCAmelCase          =       parser.parse_args()

    main(args)








from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = "arrow",			**SCREAMING_SNAKE_CASE_,			)     ->		Optional[int]:
          super().__init__(
              split=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			keep_in_memory=SCREAMING_SNAKE_CASE_,			streaming=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)
          UpperCamelCase    :  List[str]							=	load_from_cache_file
          UpperCamelCase    :  List[str]							=	file_format
          UpperCamelCase    :  Optional[int]							=	Spark(
              df=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			working_dir=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)






       def 					snake_case_       (			self       )     ->		int:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split       )
          UpperCamelCase    :  Union[str, Any]							=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=SCREAMING_SNAKE_CASE_,			file_format=self._file_format,			)
          return self.builder.as_dataset(split=self.split       )



