


import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


A							:					Optional[int]     						=     '\\n@inproceedings{lin-2004-rouge,\n    title = "{ROUGE}: A Package for Automatic Evaluation of Summaries",\n    author = "Lin, Chin-Yew",\n    booktitle = "Text Summarization Branches Out",\n    month = jul,\n    year = "2004",\n    address = "Barcelona, Spain",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W04-1013",\n    pages = "74--81",\n}\n'

A							:					Optional[int]     						=     '\\nROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for\nevaluating automatic summarization and machine translation software in natural language processing.\nThe metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.\n\nNote that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.\n\nThis metrics is a wrapper around Google Research reimplementation of ROUGE:\nhttps://github.com/google-research/google-research/tree/master/rouge\n'

A							:					Optional[Any]     						=     '\nCalculates average rouge scores for a list of hypotheses and 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    rouge_types: A list of rouge types to calculate.\n        Valid names:\n        `"rouge{n}"` (e.g. `"rouge1"`, `"rouge2"`) where: {n} is the n-gram based scoring,\n        `"rougeL"`: Longest common subsequence based scoring.\n        `"rougeLSum"`: rougeLsum splits text using `"\n"`.\n        See details in https://github.com/huggingface/datasets/issues/617\n    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.\n    use_aggregator: Return aggregates if this is set to True\nReturns:\n    rouge1: rouge_1 (precision, recall, f1),\n    rouge2: rouge_2 (precision, recall, f1),\n    rougeL: rouge_l (precision, recall, f1),\n    rougeLsum: rouge_lsum (precision, recall, f1)\nExamples:\n\n    >>> rouge = datasets.load_metric(\'rouge\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> results = rouge.compute(predictions=predictions, references=references)\n    >>> print(list(results.keys()))\n    [\'rouge1\', \'rouge2\', \'rougeL\', \'rougeLsum\']\n    >>> print(results["rouge1"])\n    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))\n    >>> print(results["rouge1"].mid.fmeasure)\n    1.0\n'






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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	List[str]:




											'''simple docstring'''

											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/google-research/google-research/tree/master/rouge"]     ,    reference_urls=[
											        "https://en.wikipedia.org/wiki/ROUGE_(metric)",
											        "https://github.com/google-research/google-research/tree/master/rouge",
											    ]     ,    )






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Dict=None     ,    SCREAMING_SNAKE_CASE	:		str=True     ,    SCREAMING_SNAKE_CASE	:		Tuple=False						)			->	Optional[Any]:




											'''simple docstring'''

											if rouge_types is None:
																	__snake_case				=		["rouge1", "rouge2", "rougeL", "rougeLsum"]

											__snake_case				=		rouge_scorer.RougeScorer(rouge_types=SCREAMING_SNAKE_CASE     ,    use_stemmer=SCREAMING_SNAKE_CASE						)
											if use_aggregator:
																	__snake_case				=		scoring.BootstrapAggregator()
											else:
																	__snake_case				=		[]

											for ref, pred in zip(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						):
																	__snake_case				=		scorer.score(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																	if use_aggregator:
																							aggregator.add_scores(SCREAMING_SNAKE_CASE						)
																	else:
																							scores.append(SCREAMING_SNAKE_CASE						)

											if use_aggregator:
																	__snake_case				=		aggregator.aggregate()
											else:
																	__snake_case				=		{}
																	for key in scores[0]:
																							__snake_case				=		[score[key] for score in scores]

											return result









def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		set()

						# Replace all the whitespace in our sentence
						__snake_case				=		input_str.replace(" "      ,	"" )
						for alpha in input_str:
												if "a" <= alpha.lower() <= "z":
																		frequency.add(alpha.lower() )
						return len(_lowerCAmelCase ) == 26





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						__snake_case				=		[False] * 26
						for char in input_str:
												if char.islower():
																		__snake_case				=		True
												elif char.isupper():
																		__snake_case				=		True
						return all(_lowerCAmelCase )





def 	_lowerCAmelCase       (    _lowerCAmelCase = "The quick brown fox jumps over the lazy dog"      ,	)						->	bool:


						'''simple docstring'''






						return len({char for char in input_str.lower() if char.isalpha()} ) == 26





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from timeit import timeit

						__snake_case				=		"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
						print(timeit("is_pangram()"      ,	setup=_lowerCAmelCase ) )
						print(timeit("is_pangram_faster()"      ,	setup=_lowerCAmelCase ) )
						print(timeit("is_pangram_fastest()"      ,	setup=_lowerCAmelCase ) )
						# 5.348480500048026, 2.6477354579837993, 1.8470395830227062
						# 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





