





"""simple docstring"""


def      UpperCamelCase							(SCREAMING_SNAKE_CASE = 10							,     SCREAMING_SNAKE_CASE = 1000							,     SCREAMING_SNAKE_CASE = True     ):
						assert (
						    isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						    and isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						    and isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						), "Invalid type of value(s) specified to function!"

						if min_val > max_val:
												raise ValueError("""Invalid value for min_val or max_val (min_value < max_value)"""     )
						return min_val if option else max_val

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						return int((number_a + number_a) / 2     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						assert (
						    isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						), 'argument values must be type of "int"'

						if lower > higher:
												raise ValueError("""argument value for lower and higher must be(lower > higher)"""     )

						if not lower < to_guess < higher:
												raise ValueError(
												    """guess value must be within the range of lower and higher value"""     )

						def answer(SCREAMING_SNAKE_CASE     ) -> str:
												if number > to_guess:
																		return "high"
												elif number < to_guess:
																		return "low"
												else:
																		return "same"

						print("""started..."""     )

						UpperCamelCase				:		Union[str, Any]				   =			lower
						UpperCamelCase				:		Any				   =			higher

						UpperCamelCase				:		List[str]				   =			[]

						while True:
												UpperCamelCase				:		Optional[int]				   =			get_avg(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
												last_numbers.append(SCREAMING_SNAKE_CASE     )

												if answer(SCREAMING_SNAKE_CASE     ) == "low":
																		UpperCamelCase				:		List[Any]				   =			number
												elif answer(SCREAMING_SNAKE_CASE     ) == "high":
																		UpperCamelCase				:		Optional[Any]				   =			number
												else:
																		break

						print(f"""guess the number : {last_numbers[-1]}"""     )
						print(f"""details : {last_numbers!s}"""     )

def      UpperCamelCase							():
						UpperCamelCase				:		List[str]				   =			int(input("""Enter lower value : """     ).strip()     )
						UpperCamelCase				:		Dict				   =			int(input("""Enter high value : """     ).strip()     )
						UpperCamelCase				:		List[str]				   =			int(input("""Enter value to guess : """     ).strip()     )
						guess_the_number(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
		main()





'''simple docstring'''



from sklearn.metrics import mean_squared_error

import datasets


_lowercase          =						"""\
@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}
}
"""

_lowercase          =						"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


_lowercase          =						"""
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     UpperCAmelCase_	(						datasets.Metric					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



												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    _lowercase      (   self ):

												"""simple docstring"""



												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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase=None       ,  _lowercase="uniform_average"       ,  _lowercase=True ):

												"""simple docstring"""



												_lowerCAmelCase    		=					mean_squared_error(
												    _lowercase       ,  _lowercase       ,  sample_weight=_lowercase       ,  multioutput=_lowercase       ,  squared=_lowercase )

												return {"mse": mse}
