


"""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


__SCREAMING_SNAKE_CASE							:    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'

__SCREAMING_SNAKE_CASE							:    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'

__SCREAMING_SNAKE_CASE							:    List[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      __A     (datasets.Metric):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      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 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      List[Any]=None		,       UpperCAmelCase_							:      List[Any]=True		,       UpperCAmelCase_							:      List[str]=False       )					->Optional[int]:



														"""simple docstring"""



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

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

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

														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







"""simple docstring"""
def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				list:

							# bit count represents no. of bits in the gray code
							if bit_count < 0:
														raise ValueError("""The given input must be positive"""				)

							# get the generated string sequence
							snake_case_           =		gray_code_sequence_string(_SCREAMING_SNAKE_CASE				)
							#
							# convert them to integers
							for i in range(len(_SCREAMING_SNAKE_CASE				)				):
														snake_case_           =		int(sequence[i]				, 2				)

							return sequence







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				list:

							# The approach is a recursive one
							# Base case achieved when either n = 0 or n=1
							if bit_count == 0:
														return ["0"]

							if bit_count == 1:
														return ["0", "1"]

							snake_case_           =		1 << bit_count  # defines the length of the sequence
							# 1<< n is equivalent to 2^n

							# recursive answer will generate answer for n-1 bits
							snake_case_           =		gray_code_sequence_string(bit_count - 1				)

							snake_case_           =		[]

							# append 0 to first half of the smaller sequence generated
							for i in range(seq_len // 2				):
														snake_case_           =		"""0""" + smaller_sequence[i]
														sequence.append(_SCREAMING_SNAKE_CASE				)

							# append 1 to second half ... start from the end of the list
							for i in reversed(range(seq_len // 2				)				):
														snake_case_           =		"""1""" + smaller_sequence[i]
														sequence.append(_SCREAMING_SNAKE_CASE				)

							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()



