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

import requests






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str = "isbn/0140328726"			):
						UpperCamelCase_		:							Dict  =		olid.strip().strip("""/"""			)  # Remove leading/trailing whitespace & slashes
						if new_olid.count("""/"""			) != 1:
												UpperCamelCase_		:							str  =		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     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				dict			):
						UpperCamelCase_		:							Optional[Any]  =		{
						    """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)""",
						}
						UpperCamelCase_		:							List[Any]  =		{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
						UpperCamelCase_		:							int  =		[
						    get_openlibrary_data(author["""key"""]			)["""name"""] for author in data["""Authors"""]
						]
						UpperCamelCase_		:							str  =		data["""First sentence"""]["""value"""]
						for key, value in data.items():
												if isinstance(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			):
																		UpperCamelCase_		:							Dict  =		""", """.join(_SCREAMING_SNAKE_CASE			)
						return data


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	while True:
		SCREAMING_SNAKE_CASE		:     Optional[Any]         =	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:
			SCREAMING_SNAKE_CASE		:     str         =	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}.''')







import numpy as np

import datasets


SCREAMING_SNAKE_CASE		:     Optional[int]         =	"\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n"

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

SCREAMING_SNAKE_CASE		:     List[str]         =	"\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {'mahalanobis': array([0.5])}\n"

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





						def        A_							(self						)				->				str:
												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        A_							(self			,     __UpperCamelCase			,     __UpperCamelCase						)				->				int:
												# convert to numpy arrays
												UpperCamelCase_		:							int  =		np.array(__UpperCamelCase						)
												UpperCamelCase_		:							Union[str, Any]  =		np.array(__UpperCamelCase						)

												# 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
												UpperCamelCase_		:							str  =		X - np.mean(__UpperCamelCase						)
												UpperCamelCase_		:							Dict  =		np.cov(reference_distribution.T						)
												try:
																		UpperCamelCase_		:							str  =		np.linalg.inv(__UpperCamelCase						)
												except np.linalg.LinAlgError:
																		UpperCamelCase_		:							List[Any]  =		np.linalg.pinv(__UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		np.dot(__UpperCamelCase			,     __UpperCamelCase						)
												UpperCamelCase_		:							Tuple  =		np.dot(__UpperCamelCase			,     X_minus_mu.T						).diagonal()

												return {"mahalanobis": mahal_dist}






