
from sklearn.metrics import matthews_corrcoef

import datasets


__lowerCamelCase							=       """
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""

__lowerCamelCase							=       """
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results['matthews_correlation'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results['matthews_correlation'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results['matthews_correlation'], 2))
        -0.25
"""

__lowerCamelCase							=       """\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,_KWARGS_DESCRIPTION )
class   UpperCAmelCase		(						datasets.Metric ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	List[str]:


          '''simple docstring'''


          return datasets.MetricInfo(
              description=_DESCRIPTION    ,						citation=_CITATION    ,						inputs_description=_KWARGS_DESCRIPTION    ,						features=datasets.Features(
                  {
                      "predictions": datasets.Value("int32" ),
                      "references": datasets.Value("int32" ),
                  } )    ,						reference_urls=[
                  "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"
              ]    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					List[str]=None )		->	Optional[int]:


          '''simple docstring'''


          return {
              "matthews_correlation": float(matthews_corrcoef(snake_case__    ,						snake_case__    ,						sample_weight=snake_case__ ) ),
          }







import argparse
import os
import shutil

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






def        a_		(       __lowercase   :	Dict ) ->  List[Any]:
			_snake_case		=			args.pruning_method
			_snake_case		=			args.threshold

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

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

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

			if target_model_path is None:
						_snake_case		=			os.path.join(
						    os.path.dirname(__lowercase )       ,					f'''bertarized_{os.path.basename(__lowercase )}''' )

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

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


if __name__ == "__main__":
			_lowerCamelCase       :				Dict       =			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       :				int       =			parser.parse_args()

			main(args)