






'''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							=						'\\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							=						'\\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							=						'\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					):




      """simple docstring"""




      def 					A__   (			self      :		Union[str, Any]		)	->	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 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		List[Any]  ,   UpperCAmelCase      :		List[str]  ,   UpperCAmelCase      :		Any=None  ,   UpperCAmelCase      :		Tuple=True  ,   UpperCAmelCase      :		List[str]=False		)	->	Union[str, Any]:


       '''simple docstring'''

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

       lowercase       : Optional[Any]						=rouge_scorer.RougeScorer(rouge_types=UpperCAmelCase  ,   use_stemmer=UpperCAmelCase		)
       if use_aggregator:
        lowercase       : int						=scoring.BootstrapAggregator()
       else:
        lowercase       : Any						=[]

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

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

       return result





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__						=				{
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__						=				[
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

		lowerCamelCase__						=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



