


'''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


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

_lowercase      :      int            =					"""\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

_lowercase      :      Dict            =					"""
Calculates average rouge scores for a list of hypotheses and references
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    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))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""






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




				def 					a__(		self      :			List[Any]				)->					int:

										"""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 					a__(		self      :			Dict				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			int=None				,   lowerCAmelCase      :			Optional[Any]=True				,   lowerCAmelCase      :			Optional[Any]=False				)->					int:

										"""simple docstring"""




										if rouge_types is None:
																UpperCAmelCase					    =     ['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

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

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

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

										return result









'''simple docstring'''



_lowercase      :      Any            =					range(2, 20 + 1)
_lowercase      :      str            =					[10**k for k in range(ks[-1] + 1)]
_lowercase      :      dict[int, dict[int, list[list[int]]]]            =					{}


def 				lowerCamelCase__				(	A       :      int				,       A       :      str				,       A       :      List[Any]				,       A       :      Union[str, Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     sum(a_i[j] for j in range(A				,       len(A					)					)					)
						UpperCAmelCase					    =     sum(a_i[j] * base[j] for j in range(min(len(A					)				,       A					)					)					)

						UpperCAmelCase    ,    UpperCAmelCase					    =     0, 0
						UpperCAmelCase					    =     n - i

						UpperCAmelCase					    =     memo.get(A					)

						if sub_memo is not None:
												UpperCAmelCase					    =     sub_memo.get(A					)

												if jumps is not None and len(A					) > 0:
																		# find and make the largest jump without going over
																		UpperCAmelCase					    =     -1
																		for _k in range(len(A					) - 1				,       -1				,       -1					):
																								if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																														UpperCAmelCase					    =     _k
																														break

																		if max_jump >= 0:
																								UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     jumps[max_jump]
																								# since the difference between jumps is cached, add c
																								UpperCAmelCase					    =     diff + c
																								for j in range(min(A				,       len(A					)					)					):
																														UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)
																								if new_c > 0:
																														add(A				,       A				,       A					)

												else:
																		UpperCAmelCase					    =     []
						else:
												UpperCAmelCase					    =     {c: []}
												UpperCAmelCase					    =     sub_memo

						if dn >= max_dn or c + diff >= base[k]:
												return diff, dn

						if k > ks[0]:
												while True:
																		# keep doing smaller jumps
																		UpperCAmelCase    ,    UpperCAmelCase					    =     next_term(A				,       k - 1				,       i + dn				,       A					)
																		diff += _diff
																		dn += terms_jumped

																		if dn >= max_dn or c + diff >= base[k]:
																								break
						else:
												# would be too small a jump, just compute sequential terms instead
												UpperCAmelCase    ,    UpperCAmelCase					    =     compute(A				,       A				,       i + dn				,       A					)
												diff += _diff
												dn += terms_jumped

						UpperCAmelCase					    =     sub_memo[c]

						# keep jumps sorted by # of terms skipped
						UpperCAmelCase					    =     0
						while j < len(A					):
												if jumps[j][1] > dn:
																		break
												j += 1

						# cache the jump for this value digitsum(b) and c
						sub_memo[c].insert(A				,       (diff, dn, k)					)
						return (diff, dn)


def 				lowerCamelCase__				(	A       :      Dict				,       A       :      Optional[int]				,       A       :      List[Any]				,       A       :      int					):




						'''simple docstring'''






						if i >= n:
												return 0, i
						if k > len(A					):
												a_i.extend([0 for _ in range(k - len(A					)					)]					)

						# note: a_i -> b * 10^k + c
						# ds_b -> digitsum(b)
						# ds_c -> digitsum(c)
						UpperCAmelCase					    =     i
						UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     0, 0, 0
						for j in range(len(A					)					):
												if j >= k:
																		ds_b += a_i[j]
												else:
																		ds_c += a_i[j]

						while i < n:
												i += 1
												UpperCAmelCase					    =     ds_c + ds_b
												diff += addend
												UpperCAmelCase					    =     0
												for j in range(A					):
																		UpperCAmelCase					    =     a_i[j] + addend
																		UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)

																		ds_c += a_i[j]

												if addend > 0:
																		break

						if addend > 0:
												add(A				,       A				,       A					)
						return diff, i - start_i


def 				lowerCamelCase__				(	A       :      List[str]				,       A       :      Optional[int]				,       A       :      Optional[Any]					):




						'''simple docstring'''






						for j in range(A				,       len(A					)					):
												UpperCAmelCase					    =     digits[j] + addend
												if s >= 10:
																		UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)
																		UpperCAmelCase					    =     addend // 10 + quotient
												else:
																		UpperCAmelCase					    =     s
																		UpperCAmelCase					    =     addend // 10

												if addend == 0:
																		break

						while addend > 0:
												UpperCAmelCase    ,    UpperCAmelCase					    =     divmod(A				,       10					)
												digits.append(A					)


def 				lowerCamelCase__				(	A       :      int = 10**15					):




						'''simple docstring'''






						UpperCAmelCase					    =     [1]
						UpperCAmelCase					    =     1
						UpperCAmelCase					    =     0
						while True:
												UpperCAmelCase    ,    UpperCAmelCase					    =     next_term(A				,       20				,       i + dn				,       A					)
												dn += terms_jumped
												if dn == n - i:
																		break

						UpperCAmelCase					    =     0
						for j in range(len(A					)					):
												a_n += digits[j] * 10**j
						return a_n


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





