





'''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    lowerCAmelCase_		(		datasets.Metric		):




 def 							_snake_case   (	self		)    ->							Dict:
     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			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase=None			,     _lowerCAmelCase=True			,     _lowerCAmelCase=False		)    ->							Tuple:
     if rouge_types is None:
         _lowerCAmelCase				=      ["rouge1", "rouge2", "rougeL", "rougeLsum"]

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

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

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

     return result









'''simple docstring'''


import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							"T5Config"


def 	__a(SCREAMING_SNAKE_CASE_  :					jnp.array		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      jnp.zeros_like(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      jnp.where(shifted_input_ids == -100		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )
    return shifted_input_ids

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Any         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Dict         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Optional[Any]         =					"mt5"
 __lowerCamelCase						:							str         =					MTaConfig


