
from typing import List

import numpy as np


def 						UpperCAmelCase			(	a_						)		->	int:







				"""simple docstring"""



				__A   			= {key: len(a_						) for key, value in gen_kwargs.items() if isinstance(a_		,   a_						)}
				if len(set(lists_lengths.values()						)						) > 1:
								raise RuntimeError(
								    (
								        "Sharding is ambiguous for this dataset: "
								        + "we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"
								        + "\n".join(F'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()						)
								        + "\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, "
								        + "and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."
								    )						)
				__A   			= max(lists_lengths.values()		,   default=0						)
				return max(1		,   a_						)


def 						UpperCAmelCase			(	a_		,   a_						)		->	List[range]:







				"""simple docstring"""



				__A   			= []
				for group_idx in range(a_						):
								__A   			= num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
								if num_shards_to_add == 0:
												break
								__A   			= shards_indices_per_group[-1].stop if shards_indices_per_group else 0
								__A   			= range(a_		,   start + num_shards_to_add						)
								shards_indices_per_group.append(a_						)
				return shards_indices_per_group


def 						UpperCAmelCase			(	a_		,   a_						)		->	List[dict]:







				"""simple docstring"""



				__A   			= _number_of_shards_in_gen_kwargs(a_						)
				if num_shards == 1:
								return [dict(a_						)]
				else:
								__A   			= _distribute_shards(num_shards=a_		,   max_num_jobs=a_						)
								return [
								    {
								        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
								        if isinstance(a_		,   a_						)
								        else value
								        for key, value in gen_kwargs.items()
								    }
								    for group_idx in range(len(a_						)						)
								]


def 						UpperCAmelCase			(	a_						)		->	dict:







				"""simple docstring"""



				return {
				    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
				    if isinstance(gen_kwargs_list[0][key]		,   a_						)
				    else gen_kwargs_list[0][key]
				    for key in gen_kwargs_list[0]
				}


def 						UpperCAmelCase			(	a_		,   a_						)		->	dict:







				"""simple docstring"""



				__A   			= {len(a_						) for value in gen_kwargs.values() if isinstance(a_		,   a_						)}
				__A   			= {}
				for size in list_sizes:
								__A   			= list(range(a_						)						)
								rng.shuffle(indices_per_size[size]						)
				# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
				__A   			= dict(a_						)
				for key, value in shuffled_kwargs.items():
								if isinstance(a_		,   a_						):
												__A   			= [value[i] for i in indices_per_size[len(a_						)]]
				return shuffled_kwargs




'''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


a_  :				str						= """\
@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\",
}
"""

a_  :				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
"""

a_  :				Tuple						= """
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      snake_case    (    datasets.Metric			):





  """simple docstring"""




  def   snake_case		(    self     ):







   """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   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=False     ):







   """simple docstring"""
   if rouge_types is None:
    lowerCamelCase_      =  ["rouge1", "rouge2", "rougeL", "rougeLsum"]

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

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

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

   return result



