


import argparse
import os
import shutil

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




def    snake_case_     (  snake_case     )  ->				int:
       lowercase__:   Optional[int]  			=   args.pruning_method
       lowercase__:   Optional[int]  			=   args.threshold

       lowercase__:   Optional[Any]  			=   args.model_name_or_path.rstrip('/'     )
       lowercase__:   Any  			=   args.target_model_path

       print(f'Load fine-pruned model from {model_name_or_path}'     )
       lowercase__:   int  			=   torch.load(os.path.join(__lowerCamelCase			,							'pytorch_model.bin'     )     )
       lowercase__:   List[str]  			=   {}

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

       if target_model_path is None:
              lowercase__:   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__":
 __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'''


import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str] ):
   monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"	,set() )
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any ):
   class       a_   :





        def __init__( self		:					int	,      lowercase		:					int						):





           """simple docstring"""




           lowercase_			:Optional[Any]    			= metric_id







   class       a_   :
        __A      =			[MetricMock(_lowerCAmelCase							) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]





        def 							lowercase__      ( self		:					Union[str, Any]						):





           """simple docstring"""




           return self._metrics







   monkeypatch.setattr("datasets.inspect.huggingface_hub"	,HfhMock() )


@pytest.mark.parametrize(
    "func, args"	,[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					int	,__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					Tuple ):
   if "tmp_path" in args:
      lowercase_			:Union[str, Any]    			= tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
   with pytest.warns(__lowerCamelCase	,match="https://huggingface.co/docs/evaluate" ):
      func(*__lowerCamelCase )


