
import argparse
import os
import shutil

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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]       )  ->     Union[str, Any]:
						_lowercase					       =					args.pruning_method
						_lowercase					       =					args.threshold

						_lowercase					       =					args.model_name_or_path.rstrip("""/"""       )
						_lowercase					       =					args.target_model_path

						print(f"""Load fine-pruned model from {model_name_or_path}"""       )
						_lowercase					       =					torch.load(os.path.join(SCREAMING_SNAKE_CASE_		,   """pytorch_model.bin"""       )       )
						_lowercase					       =					{}

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

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

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

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


if __name__ == "__main__":
		A		: Optional[Any]								=     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		: Any								=     parser.parse_args()

		main(args)

from sklearn.metrics import mean_squared_error

import datasets


A		: List[Any]								=     '''\
@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}
}
'''

A		: str								=     '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


A		: Any								=     '''
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        "raw_values" : Returns a full set of errors in case of multioutput input.

        "uniform_average" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric("mse")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {\'mse\': 0.6123724356957945}

    If you\'re using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric("mse", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mse\': array([0.41666667, 1. ])}
'''



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






			def    UpperCamelCase_				(      self  ):
									return datasets.MetricInfo(
									    description=_DESCRIPTION				,			citation=_CITATION				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(self._get_feature_types()  )				,			reference_urls=[
									        """https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"""
									    ]				,			)






			def    UpperCamelCase_				(      self  ):
									if self.config_name == "multilist":
															return {
															    "predictions": datasets.Sequence(datasets.Value("""float"""  )  ),
															    "references": datasets.Sequence(datasets.Value("""float"""  )  ),
															}
									else:
															return {
															    "predictions": datasets.Value("""float"""  ),
															    "references": datasets.Value("""float"""  ),
															}






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase=None				,			__UpperCamelCase="uniform_average"				,			__UpperCamelCase=True  ):
									_lowercase					       =					mean_squared_error(
									    __UpperCamelCase				,			__UpperCamelCase				,			sample_weight=__UpperCamelCase				,			multioutput=__UpperCamelCase				,			squared=__UpperCamelCase  )

									return {"mse": mse}