





'''simple docstring'''


import gc
import threading
import time

import psutil
import torch

class  lowerCAmelCase_		:




 def __init__(						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   psutil.Process()
     _lowerCAmelCase						      =   False




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   -1

     while True:
         _lowerCAmelCase						      =   max(self.process.memory_info().rss      ,  self.cpu_memory_peak		)

         # can't sleep or will not catch the peak right (this comment is here on purpose)
         if not self.peak_monitoring:
             break




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   True
     _lowerCAmelCase						      =   threading.Thread(target=self.peak_monitor		)
     _lowerCAmelCase						      =   True
     self.thread.start()





 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   False
     self.thread.join()
     return self.cpu_memory_peak







_SCREAMING_SNAKE_CASE									=				PeakCPUMemory()


def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   {"time": time.time()}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    _lowerCAmelCase						      =   psutil.Process().memory_info().rss
    cpu_peak_tracker.start()

    # GPU mem
    for i in range(torch.cuda.device_count()     ):
        _lowerCAmelCase						      =   torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_     )
    torch.cuda.reset_peak_memory_stats()

    return measures


def 						__a(SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   {"time": time.time() - start_measures["time"]}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    _lowerCAmelCase						      =   (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
    _lowerCAmelCase						      =   (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

    # GPU mem
    for i in range(torch.cuda.device_count()     ):
        _lowerCAmelCase						      =   (torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_     ) - start_measures[str(SCREAMING_SNAKE_CASE_     )]) / 2**20
        _lowerCAmelCase						      =   (torch.cuda.max_memory_allocated(SCREAMING_SNAKE_CASE_     ) - start_measures[str(SCREAMING_SNAKE_CASE_     )]) / 2**20

    return measures





def 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       Any     ):



    '''simple docstring'''





    print(F'''{description}:'''     )
    print(F'''- Time: {measures['time']:.2f}s'''     )
    for i in range(torch.cuda.device_count()     ):
        print(F'''- GPU {i} allocated: {measures[str(SCREAMING_SNAKE_CASE_     )]:.2f}MiB'''     )
        _lowerCAmelCase						      =   measures[F'''{i}-peak''']
        print(F'''- GPU {i} peak: {peak:.2f}MiB'''     )
    print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB'''     )
    print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB'''     )









'''simple docstring'''


from sklearn.metrics import mean_squared_error

import datasets


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

_SCREAMING_SNAKE_CASE									=				"\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


_SCREAMING_SNAKE_CASE									=				"\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric(\"mse\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {'mse': 0.6123724356957945}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mse': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mse': array([0.41666667, 1. ])}\n"

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




 def 				_snake_case (						self		)     ->				Dict:
     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 				_snake_case (						self		)     ->				Tuple:
     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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase="uniform_average"      ,  _lowerCAmelCase=True		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   mean_squared_error(
         _lowerCAmelCase      ,  _lowerCAmelCase      ,  sample_weight=_lowerCAmelCase      ,  multioutput=_lowerCAmelCase      ,  squared=_lowerCAmelCase		)

     return {"mse": mse}


