





import numpy as np

import datasets


UpperCAmelCase__  :     Optional[Any]   			='''
Compute the Mahalanobis Distance

Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
'''

UpperCAmelCase__  :     List[str]   			='''\
@article{de2000mahalanobis,
  title={The mahalanobis distance},
  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
  journal={Chemometrics and intelligent laboratory systems},
  volume={50},
  number={1},
  pages={1--18},
  year={2000},
  publisher={Elsevier}
}
'''

UpperCAmelCase__  :     Optional[Any]   			='''
Args:
    X: List of datapoints to be compared with the `reference_distribution`.
    reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
    mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:

    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")
    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
    >>> print(results)
    {\'mahalanobis\': array([0.5])}
'''

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


				def _snake_case    (						self							):
								return datasets.MetricInfo(
								    description=_DESCRIPTION				,	citation=_CITATION				,	inputs_description=_KWARGS_DESCRIPTION				,	features=datasets.Features(
								        {
								            """X""": datasets.Sequence(datasets.Value("""float"""				,	id="""sequence"""							)				,	id="""X"""							),
								        }							)				,	)


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_							):
								lowerCamelCase			=np.array(_SCREAMING_SNAKE_CASE							)
								lowerCamelCase			=np.array(_SCREAMING_SNAKE_CASE							)

								# Assert that arrays are 2D
								if len(X.shape							) != 2:
												raise ValueError("""Expected `X` to be a 2D vector"""							)
								if len(reference_distribution.shape							) != 2:
												raise ValueError("""Expected `reference_distribution` to be a 2D vector"""							)
								if reference_distribution.shape[0] < 2:
												raise ValueError(
												    """Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension"""							)

								# Get mahalanobis distance for each prediction
								lowerCamelCase			=X - np.mean(_SCREAMING_SNAKE_CASE							)
								lowerCamelCase			=np.cov(reference_distribution.T							)
								try:
												lowerCamelCase			=np.linalg.inv(_SCREAMING_SNAKE_CASE							)
								except np.linalg.LinAlgError:
												lowerCamelCase			=np.linalg.pinv(_SCREAMING_SNAKE_CASE							)
								lowerCamelCase			=np.dot(_SCREAMING_SNAKE_CASE				,	_SCREAMING_SNAKE_CASE							)
								lowerCamelCase			=np.dot(_SCREAMING_SNAKE_CASE				,	X_minus_mu.T							).diagonal()

								return {"mahalanobis": mahal_dist}












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

import requests





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





def        _lowercase   (  _UpperCAmelCase			)  ->					dict:
				lowerCamelCase			={
				    """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)""",
				}
				lowerCamelCase			={better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
				lowerCamelCase			=[
				    get_openlibrary_data(author["""key"""]			)["""name"""] for author in data["""Authors"""]
				]
				lowerCamelCase			=data["""First sentence"""]["""value"""]
				for key, value in data.items():
								if isinstance(_UpperCAmelCase     , _UpperCAmelCase			):
												lowerCamelCase			=""", """.join(_UpperCAmelCase			)
				return data


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							while True:
														UpperCAmelCase__  :     List[str]   			=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:
																					UpperCAmelCase__  :     Dict   			=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}.")





