"""simple docstring"""


import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


a				=version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
	from nltk import word_tokenize


a				='\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'

a				='\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'

a				='\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results["meteor"], 4))\n    0.6944\n'






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

				def 						_a      ( self ):
										return datasets.MetricInfo(
										    description=_DESCRIPTION      ,					citation=_CITATION      ,					inputs_description=_KWARGS_DESCRIPTION      ,					features=datasets.Features(
										        {
										            "predictions": datasets.Value("string"      ,					id="sequence" ),
										            "references": datasets.Value("string"      ,					id="sequence" ),
										        } )      ,					codebase_urls=["https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"]      ,					reference_urls=[
										        "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
										        "https://en.wikipedia.org/wiki/METEOR",
										    ]      ,					)

				def 						_a      ( self      ,					_lowerCamelCase ):
										import nltk

										nltk.download("wordnet" )
										if NLTK_VERSION >= version.Version("3.6.5" ):
																nltk.download("punkt" )
										if NLTK_VERSION >= version.Version("3.6.6" ):
																nltk.download("omw-1.4" )






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase=0.9      ,					_lowerCamelCase=3      ,					_lowerCamelCase=0.5 ):







										if NLTK_VERSION >= version.Version("3.6.5" ):
																lowerCamelCase__   =[
																    meteor_score.single_meteor_score(
																        word_tokenize(SCREAMING_SNAKE_CASE_ )      ,					word_tokenize(SCREAMING_SNAKE_CASE_ )      ,					alpha=SCREAMING_SNAKE_CASE_      ,					beta=SCREAMING_SNAKE_CASE_      ,					gamma=SCREAMING_SNAKE_CASE_ )
																    for ref, pred in zip(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ )
																]
										else:
																lowerCamelCase__   =[
																    meteor_score.single_meteor_score(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					alpha=SCREAMING_SNAKE_CASE_      ,					beta=SCREAMING_SNAKE_CASE_      ,					gamma=SCREAMING_SNAKE_CASE_ )
																    for ref, pred in zip(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ )
																]

										return {"meteor": np.mean(SCREAMING_SNAKE_CASE_ )}




"""simple docstring"""




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

import requests



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



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_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)''',
		}
		SCREAMING_SNAKE_CASE_    			=			{better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
		SCREAMING_SNAKE_CASE_    			=			[
		    get_openlibrary_data(author['''key''']     )['''name'''] for author in data['''Authors''']
		]
		SCREAMING_SNAKE_CASE_    			=			data['''First sentence''']['''value''']
		for key, value in data.items():
				if isinstance(__a,				__a     ):
						SCREAMING_SNAKE_CASE_    			=			''', '''.join(__a     )
		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}.''')