





"""simple docstring"""



import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
       import faiss






@require_faiss
class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Union[str, Any]							=	tempfile.mkdtemp()
     __A       :		Dict							=	8

     # DPR tok
     __A       :		List[str]							=	[
         '''[UNK]''',
         '''[CLS]''',
         '''[SEP]''',
         '''[PAD]''',
         '''[MASK]''',
         '''want''',
         '''##want''',
         '''##ed''',
         '''wa''',
         '''un''',
         '''runn''',
         '''##ing''',
         ''',''',
         '''low''',
         '''lowest''',
     ]
     __A       :		Any							=	os.path.join(self.tmpdirname   ,		'''dpr_tokenizer'''    )
     os.makedirs(__lowerCamelCase   ,		exist_ok=__lowerCamelCase    )
     __A       :		Tuple							=	os.path.join(__lowerCamelCase   ,		DPR_VOCAB_FILES_NAMES['''vocab_file''']    )
     with open(self.vocab_file   ,		'''w'''   ,		encoding='''utf-8'''    ) as vocab_writer:
         vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]    )    )

     # BART tok
     __A       :		Dict							=	[
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''\u0120''',
         '''\u0120l''',
         '''\u0120n''',
         '''\u0120lo''',
         '''\u0120low''',
         '''er''',
         '''\u0120lowest''',
         '''\u0120newer''',
         '''\u0120wider''',
         '''<unk>''',
     ]
     __A       :		str							=	dict(zip(__lowerCamelCase   ,		range(len(__lowerCamelCase    )    )    )    )
     __A       :		Union[str, Any]							=	['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
     __A       :		str							=	{'''unk_token''': '''<unk>'''}

     __A       :		Dict							=	os.path.join(self.tmpdirname   ,		'''bart_tokenizer'''    )
     os.makedirs(__lowerCamelCase   ,		exist_ok=__lowerCamelCase    )
     __A       :		str							=	os.path.join(__lowerCamelCase   ,		BART_VOCAB_FILES_NAMES['''vocab_file''']    )
     __A       :		Any							=	os.path.join(__lowerCamelCase   ,		BART_VOCAB_FILES_NAMES['''merges_file''']    )
     with open(self.vocab_file   ,		'''w'''   ,		encoding='''utf-8'''    ) as fp:
         fp.write(json.dumps(__lowerCamelCase    ) + '''\n'''    )
     with open(self.merges_file   ,		'''w'''   ,		encoding='''utf-8'''    ) as fp:
         fp.write('''\n'''.join(__lowerCamelCase    )    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,		'''dpr_tokenizer'''    )    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,		'''dpr_tokenizer'''    )    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,		'''bart_tokenizer'''    )    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     shutil.rmtree(self.tmpdirname    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[int]							=	Dataset.from_dict(
         {
             '''id''': ['''0''', '''1'''],
             '''text''': ['''foo''', '''bar'''],
             '''title''': ['''Foo''', '''Bar'''],
             '''embeddings''': [np.ones(self.retrieval_vector_size    ), 2 * np.ones(self.retrieval_vector_size    )],
         }    )
     dataset.add_faiss_index('''embeddings'''   ,		string_factory='''Flat'''   ,		metric_type=faiss.METRIC_INNER_PRODUCT    )
     return dataset

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Tuple							=	self.get_dummy_dataset()
     __A       :		Union[str, Any]							=	RagConfig(
         retrieval_vector_size=self.retrieval_vector_size   ,		question_encoder=DPRConfig().to_dict()   ,		generator=BartConfig().to_dict()   ,		)
     with patch('''transformers.models.rag.retrieval_rag.load_dataset'''    ) as mock_load_dataset:
         __A       :		Any							=	dataset
         __A       :		int							=	RagRetriever(
             __lowerCamelCase   ,		question_encoder_tokenizer=self.get_dpr_tokenizer()   ,		generator_tokenizer=self.get_bart_tokenizer()   ,		)
     return retriever

 def    UpperCamelCase__(       self   ,		__lowerCamelCase    ):



     '''simple docstring'''




     __A       :		List[Any]							=	self.get_dummy_dataset()
     __A       :		Optional[int]							=	RagConfig(
         retrieval_vector_size=self.retrieval_vector_size   ,		question_encoder=DPRConfig().to_dict()   ,		generator=BartConfig().to_dict()   ,		index_name='''custom'''   ,		)
     if from_disk:
         __A       :		Dict							=	os.path.join(self.tmpdirname   ,		'''dataset'''    )
         __A       :		Optional[Any]							=	os.path.join(self.tmpdirname   ,		'''index.faiss'''    )
         dataset.get_index('''embeddings'''    ).save(os.path.join(self.tmpdirname   ,		'''index.faiss'''    )    )
         dataset.drop_index('''embeddings'''    )
         dataset.save_to_disk(os.path.join(self.tmpdirname   ,		'''dataset'''    )    )
         del dataset
         __A       :		Union[str, Any]							=	RagRetriever(
             __lowerCamelCase   ,		question_encoder_tokenizer=self.get_dpr_tokenizer()   ,		generator_tokenizer=self.get_bart_tokenizer()   ,		)
     else:
         __A       :		Union[str, Any]							=	RagRetriever(
             __lowerCamelCase   ,		question_encoder_tokenizer=self.get_dpr_tokenizer()   ,		generator_tokenizer=self.get_bart_tokenizer()   ,		index=CustomHFIndex(config.retrieval_vector_size   ,		__lowerCamelCase    )   ,		)
     return retriever

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Union[str, Any]							=	Dataset.from_dict(
         {
             '''id''': ['''0''', '''1'''],
             '''text''': ['''foo''', '''bar'''],
             '''title''': ['''Foo''', '''Bar'''],
             '''embeddings''': [np.ones(self.retrieval_vector_size + 1    ), 2 * np.ones(self.retrieval_vector_size + 1    )],
         }    )
     dataset.add_faiss_index('''embeddings'''   ,		string_factory='''Flat'''   ,		metric_type=faiss.METRIC_INNER_PRODUCT    )

     __A       :		int							=	os.path.join(self.tmpdirname   ,		'''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''    )
     dataset.save_faiss_index('''embeddings'''   ,		index_file_name + '''.index.dpr'''    )
     pickle.dump(dataset['''id''']   ,		open(index_file_name + '''.index_meta.dpr'''   ,		'''wb'''    )    )

     __A       :		Optional[Any]							=	os.path.join(self.tmpdirname   ,		'''psgs_w100.tsv.pkl'''    )
     __A       :		Tuple							=	{sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
     pickle.dump(__lowerCamelCase   ,		open(__lowerCamelCase   ,		'''wb'''    )    )

     __A       :		int							=	RagConfig(
         retrieval_vector_size=self.retrieval_vector_size   ,		question_encoder=DPRConfig().to_dict()   ,		generator=BartConfig().to_dict()   ,		index_name='''legacy'''   ,		index_path=self.tmpdirname   ,		)
     __A       :		Tuple							=	RagRetriever(
         __lowerCamelCase   ,		question_encoder_tokenizer=self.get_dpr_tokenizer()   ,		generator_tokenizer=self.get_bart_tokenizer()    )
     return retriever

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	1
     __A       :		Tuple							=	self.get_dummy_canonical_hf_index_retriever()
     __A       :		int							=	np.array(
         [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]   ,		dtype=np.floataa    )
     __A					,			__A					,			__A       :		str							=	retriever.retrieve(__lowerCamelCase   ,		n_docs=__lowerCamelCase    )
     self.assertEqual(retrieved_doc_embeds.shape   ,		(2, n_docs, self.retrieval_vector_size)    )
     self.assertEqual(len(__lowerCamelCase    )   ,		2    )
     self.assertEqual(sorted(doc_dicts[0]    )   ,		['''embeddings''', '''id''', '''text''', '''title''']    )
     self.assertEqual(len(doc_dicts[0]['''id''']    )   ,		__lowerCamelCase    )
     self.assertEqual(doc_dicts[0]['''id'''][0]   ,		'''1'''    )  # max inner product is reached with second doc
     self.assertEqual(doc_dicts[1]['''id'''][0]   ,		'''0'''    )  # max inner product is reached with first doc
     self.assertListEqual(doc_ids.tolist()   ,		[[1], [0]]    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Dict							=	self.get_dummy_canonical_hf_index_retriever()
     with tempfile.TemporaryDirectory() as tmp_dirname:
         with patch('''transformers.models.rag.retrieval_rag.load_dataset'''    ) as mock_load_dataset:
             __A       :		str							=	self.get_dummy_dataset()
             retriever.save_pretrained(__lowerCamelCase    )
             __A       :		Optional[Any]							=	RagRetriever.from_pretrained(__lowerCamelCase    )
             self.assertIsInstance(__lowerCamelCase   ,		__lowerCamelCase    )
             __A       :		int							=	np.array(
                 [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]   ,		dtype=np.floataa    )
             __A       :		List[str]							=	retriever.retrieve(__lowerCamelCase   ,		n_docs=1    )
             self.assertTrue(out is not None    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Union[str, Any]							=	1
     __A       :		int							=	self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase    )
     __A       :		Tuple							=	np.array(
         [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]   ,		dtype=np.floataa    )
     __A					,			__A					,			__A       :		Tuple							=	retriever.retrieve(__lowerCamelCase   ,		n_docs=__lowerCamelCase    )
     self.assertEqual(retrieved_doc_embeds.shape   ,		(2, n_docs, self.retrieval_vector_size)    )
     self.assertEqual(len(__lowerCamelCase    )   ,		2    )
     self.assertEqual(sorted(doc_dicts[0]    )   ,		['''embeddings''', '''id''', '''text''', '''title''']    )
     self.assertEqual(len(doc_dicts[0]['''id''']    )   ,		__lowerCamelCase    )
     self.assertEqual(doc_dicts[0]['''id'''][0]   ,		'''1'''    )  # max inner product is reached with second doc
     self.assertEqual(doc_dicts[1]['''id'''][0]   ,		'''0'''    )  # max inner product is reached with first doc
     self.assertListEqual(doc_ids.tolist()   ,		[[1], [0]]    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		int							=	self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase    )
     with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(__lowerCamelCase    )
         __A       :		Any							=	RagRetriever.from_pretrained(__lowerCamelCase    )
         self.assertIsInstance(__lowerCamelCase   ,		__lowerCamelCase    )
         __A       :		str							=	np.array(
             [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]   ,		dtype=np.floataa    )
         __A       :		List[Any]							=	retriever.retrieve(__lowerCamelCase   ,		n_docs=1    )
         self.assertTrue(out is not None    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		List[str]							=	1
     __A       :		str							=	self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase    )
     __A       :		str							=	np.array(
         [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]   ,		dtype=np.floataa    )
     __A					,			__A					,			__A       :		Optional[int]							=	retriever.retrieve(__lowerCamelCase   ,		n_docs=__lowerCamelCase    )
     self.assertEqual(retrieved_doc_embeds.shape   ,		(2, n_docs, self.retrieval_vector_size)    )
     self.assertEqual(len(__lowerCamelCase    )   ,		2    )
     self.assertEqual(sorted(doc_dicts[0]    )   ,		['''embeddings''', '''id''', '''text''', '''title''']    )
     self.assertEqual(len(doc_dicts[0]['''id''']    )   ,		__lowerCamelCase    )
     self.assertEqual(doc_dicts[0]['''id'''][0]   ,		'''1'''    )  # max inner product is reached with second doc
     self.assertEqual(doc_dicts[1]['''id'''][0]   ,		'''0'''    )  # max inner product is reached with first doc
     self.assertListEqual(doc_ids.tolist()   ,		[[1], [0]]    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Tuple							=	self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase    )
     with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(__lowerCamelCase    )
         __A       :		Optional[int]							=	RagRetriever.from_pretrained(__lowerCamelCase    )
         self.assertIsInstance(__lowerCamelCase   ,		__lowerCamelCase    )
         __A       :		str							=	np.array(
             [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]   ,		dtype=np.floataa    )
         __A       :		str							=	retriever.retrieve(__lowerCamelCase   ,		n_docs=1    )
         self.assertTrue(out is not None    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Dict							=	1
     __A       :		Any							=	self.get_dummy_legacy_index_retriever()
     __A       :		List[str]							=	np.array(
         [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]   ,		dtype=np.floataa    )
     __A					,			__A					,			__A       :		Optional[int]							=	retriever.retrieve(__lowerCamelCase   ,		n_docs=__lowerCamelCase    )
     self.assertEqual(retrieved_doc_embeds.shape   ,		(2, n_docs, self.retrieval_vector_size)    )
     self.assertEqual(len(__lowerCamelCase    )   ,		2    )
     self.assertEqual(sorted(doc_dicts[0]    )   ,		['''text''', '''title''']    )
     self.assertEqual(len(doc_dicts[0]['''text''']    )   ,		__lowerCamelCase    )
     self.assertEqual(doc_dicts[0]['''text'''][0]   ,		'''bar'''    )  # max inner product is reached with second doc
     self.assertEqual(doc_dicts[1]['''text'''][0]   ,		'''foo'''    )  # max inner product is reached with first doc
     self.assertListEqual(doc_ids.tolist()   ,		[[1], [0]]    )

 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		str							=	self.get_dummy_legacy_index_retriever()
     with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(__lowerCamelCase    )
         __A       :		Optional[Any]							=	RagRetriever.from_pretrained(__lowerCamelCase    )
         self.assertIsInstance(__lowerCamelCase   ,		__lowerCamelCase    )
         __A       :		Dict							=	np.array(
             [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]   ,		dtype=np.floataa    )
         __A       :		List[Any]							=	retriever.retrieve(__lowerCamelCase   ,		n_docs=1    )
         self.assertTrue(out is not None    )

 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     import torch

     __A       :		Any							=	1
     __A       :		Optional[Any]							=	self.get_dummy_canonical_hf_index_retriever()
     __A       :		Union[str, Any]							=	[[5, 7], [10, 11]]
     __A       :		Optional[Any]							=	np.array(
         [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]   ,		dtype=np.floataa    )
     __A       :		str							=	retriever(__lowerCamelCase   ,		__lowerCamelCase   ,		prefix=retriever.config.generator.prefix   ,		n_docs=__lowerCamelCase    )
     __A					,			__A					,			__A       :		int							=	(
         out['''context_input_ids'''],
         out['''context_attention_mask'''],
         out['''retrieved_doc_embeds'''],
     )
     self.assertEqual(retrieved_doc_embeds.shape   ,		(2, n_docs, self.retrieval_vector_size)    )
     self.assertIsInstance(__lowerCamelCase   ,		__lowerCamelCase    )
     self.assertIsInstance(__lowerCamelCase   ,		__lowerCamelCase    )
     self.assertIsInstance(__lowerCamelCase   ,		np.ndarray    )

     __A       :		Any							=	retriever(
         __lowerCamelCase   ,		__lowerCamelCase   ,		prefix=retriever.config.generator.prefix   ,		n_docs=__lowerCamelCase   ,		return_tensors='''pt'''   ,		)
     __A					,			__A					,			__A					,			__A       :		Union[str, Any]							=	(  # noqa: F841
         out['''context_input_ids'''],
         out['''context_attention_mask'''],
         out['''retrieved_doc_embeds'''],
         out['''doc_ids'''],
     )
     self.assertEqual(retrieved_doc_embeds.shape   ,		(2, n_docs, self.retrieval_vector_size)    )
     self.assertIsInstance(__lowerCamelCase   ,		torch.Tensor    )
     self.assertIsInstance(__lowerCamelCase   ,		torch.Tensor    )
     self.assertIsInstance(__lowerCamelCase   ,		torch.Tensor    )







 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     __A       :		Optional[Any]							=	self.get_dpr_ctx_encoder_tokenizer()
     __A       :		List[str]							=	1
     __A       :		List[Any]							=	self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase    )
     retriever.set_ctx_encoder_tokenizer(__lowerCamelCase    )

     __A       :		Optional[Any]							=	[[5, 7], [10, 11]]
     __A       :		Optional[int]							=	np.array(
         [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]   ,		dtype=np.floataa    )
     __A       :		Tuple							=	retriever(__lowerCamelCase   ,		__lowerCamelCase   ,		prefix=retriever.config.generator.prefix   ,		n_docs=__lowerCamelCase    )

     self.assertEqual(
         len(__lowerCamelCase    )   ,		6    )  # check whether the retriever output consist of 6 attributes including tokenized docs
     self.assertEqual(
         all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')    )   ,		__lowerCamelCase    )  # check for doc token related keys in dictionary.











"""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_					=     """\
@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_					=     """\
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_					=     """
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    UpperCamelCase__(       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    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase=None   ,		__lowerCamelCase=True   ,		__lowerCamelCase=False    ):



     '''simple docstring'''




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

     __A       :		Dict							=	rouge_scorer.RougeScorer(rouge_types=__lowerCamelCase   ,		use_stemmer=__lowerCamelCase    )
     if use_aggregator:
         __A       :		Dict							=	scoring.BootstrapAggregator()
     else:
         __A       :		Optional[Any]							=	[]

     for ref, pred in zip(__lowerCamelCase   ,		__lowerCamelCase    ):
         __A       :		List[Any]							=	scorer.score(__lowerCamelCase   ,		__lowerCamelCase    )
         if use_aggregator:
             aggregator.add_scores(__lowerCamelCase    )
         else:
             scores.append(__lowerCamelCase    )

     if use_aggregator:
         __A       :		Optional[Any]							=	aggregator.aggregate()
     else:
         __A       :		List[Any]							=	{}
         for key in scores[0]:
             __A       :		Tuple							=	[score[key] for score in scores]

     return result




