



'''simple docstring'''




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


lowerCamelCase							:Any       = "\\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"

lowerCamelCase							:Union[str, Any]       = "\\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"

lowerCamelCase							:int       = "\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      _lowerCAmelCase							(      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/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    _a     (self	,				lowercase	,				lowercase	,				lowercase=None	,				lowercase=True	,				lowercase=False							):




				if rouge_types is None:
							A_     :						Union[str, Any]     =					["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

				A_     :						List[str]     =					rouge_scorer.RougeScorer(rouge_types=_SCREAMING_SNAKE_CASE	,				use_stemmer=_SCREAMING_SNAKE_CASE							)
				if use_aggregator:
							A_     :						int     =					scoring.BootstrapAggregator()
				else:
							A_     :						List[str]     =					[]

				for ref, pred in zip(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE							):
							A_     :						List[str]     =					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:
							A_     :						List[str]     =					aggregator.aggregate()
				else:
							A_     :						List[str]     =					{}
							for key in scores[0]:
										A_     :						Dict     =					[score[key] for score in scores]

				return result




'''simple docstring'''




from __future__ import annotations

from functools import lru_cache
from math import ceil

lowerCamelCase							:List[str]       = 1_0_0

lowerCamelCase							:Dict       = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCamelCase							:int

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
						if prime not in primes:
												continue
						primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))



@lru_cache(maxsize=1_00   )
def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			if number_to_partition < 0:
						return set()
			elif number_to_partition == 0:
						return {1}

			A_     :						set[int]     =					set()
			A_     :						int
			A_     :						int

			for prime in primes:
						if prime > number_to_partition:
									continue
						for sub in partition(number_to_partition - prime   ):
									ret.add(sub * prime   )

			return ret



def      a      (			lowerCamelCase__ = 50_00   ):
			'''simple docstring'''





			for number_to_partition in range(1						,				lowerCamelCase__   ):
						if len(partition(lowerCamelCase__   )   ) > number_unique_partitions:
									return number_to_partition
			return None


if __name__ == "__main__":
						print(F"{solution() = }")