

from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = "isbn/0140328726"       ):
						snake_case_   =	olid.strip().strip('''/'''       )  # Remove leading/trailing whitespace & slashes
						if new_olid.count('''/'''       ) != 1:
												snake_case_   =	F'''{olid} is not a valid Open Library olid'''
												raise ValueError(SCREAMING_SNAKE_CASE__       )
						return requests.get(F'''https://openlibrary.org/{new_olid}.json'''       ).json()


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	{
						    '''title''': '''Title''',
						    '''publish_date''': '''Publish date''',
						    '''authors''': '''Authors''',
						    '''number_of_pages''': '''Number of pages:''',
						    '''first_sentence''': '''First sentence''',
						    '''isbn_10''': '''ISBN (10)''',
						    '''isbn_13''': '''ISBN (13)''',
						}
						snake_case_   =	{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
						snake_case_   =	[
						    get_openlibrary_data(author['''key''']       )['''name'''] for author in data['''Authors''']
						]
						snake_case_   =	data['''First sentence''']['''value''']
						for key, value in data.items():
												if isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
																		snake_case_   =	''', '''.join(SCREAMING_SNAKE_CASE__       )
						return data


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					while True:
										lowerCAmelCase_       	=	input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
										if isbn.lower() in ("", "q", "quit", "exit", "stop"):
															break

										if len(isbn) not in (10, 13) or not isbn.isdigit():
															print(f"""Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.""")
															continue

										print(f"""\nSearching Open Library for ISBN: {isbn}...\n""")

										try:
															lowerCAmelCase_       	=	summarize_book(get_openlibrary_data(f"""isbn/{isbn}"""))
															print('''\n'''.join(f"""{key}: {value}""" for key, value in book_summary.items()))
										except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
															print(f"""Sorry, there are no results for ISBN: {isbn}.""")


from sklearn.metrics import mean_squared_error

import datasets


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}
}
'''

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


lowerCAmelCase_       	=	'''
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 	snake_case_   (		datasets.Metric					):







			'''simple docstring'''

			def 				snake_case__(     self :				Optional[int]							)      ->List[Any]:
									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 :				List[Any]							)      ->Optional[int]:
									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 :				int							,   _UpperCamelCase :				int							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Any=None							,   _UpperCamelCase :				Optional[int]="uniform_average"							,   _UpperCamelCase :				Tuple=True							)      ->Tuple:
									snake_case_   =	mean_squared_error(
									    _UpperCamelCase							,   _UpperCamelCase							,   sample_weight=_UpperCamelCase							,   multioutput=_UpperCamelCase							,   squared=_UpperCamelCase							)

									return {"mse": mse}