


"""simple docstring"""
import sacrebleu as scb
from packaging import version

import datasets


SCREAMING_SNAKE_CASE					:       Tuple							   =							"""\\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"""

SCREAMING_SNAKE_CASE					:       Dict							   =							"""\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich\'s `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"""

SCREAMING_SNAKE_CASE					:       int							   =							"""\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `\'exp\'`. Possible values are:\n        - `\'none\'`: no smoothing\n        - `\'floor\'`: increment zero counts\n        - `\'add-k\'`: increment num/denom by k for n>1\n        - `\'exp\'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method=\'floor\'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method=\'add-k\'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `\'zh\'` for Chinese, `\'ja-mecab\'` for Japanese and `\'13a\'` (mteval) otherwise. Possible values are:\n        - `\'none\'`: No tokenization.\n        - `\'zh\'`: Chinese tokenization.\n        - `\'13a\'`: mimics the `mteval-v13a` script from Moses.\n        - `\'intl\'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `\'char\'`: Language-agnostic character-level tokenization.\n        - `\'ja-mecab\'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    \'score\': BLEU score,\n    \'counts\': Counts,\n    \'totals\': Totals,\n    \'precisions\': Precisions,\n    \'bp\': Brevity penalty,\n    \'sys_len\': predictions length,\n    \'ref_len\': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        [\'score\', \'counts\', \'totals\', \'precisions\', \'bp\', \'sys_len\', \'ref_len\']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,							_KWARGS_DESCRIPTION							)
class      _UpperCAmelCase					(				datasets.Metric							):






		'''simple docstring'''

		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			if version.parse(scb.__version__   ) < version.parse('''1.4.12'''   ):
				raise ImportWarning(
				    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
				    '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''   )
			return datasets.MetricInfo(
			    description=_DESCRIPTION							,			citation=_CITATION							,			homepage='''https://github.com/mjpost/sacreBLEU'''							,			inputs_description=_KWARGS_DESCRIPTION							,			features=datasets.Features(
			        {
			            '''predictions''': datasets.Value('''string'''							,			id='''sequence'''   ),
			            '''references''': datasets.Sequence(datasets.Value('''string'''							,			id='''sequence'''   )							,			id='''references'''   ),
			        }   )							,			codebase_urls=['''https://github.com/mjpost/sacreBLEU''']							,			reference_urls=[
			        '''https://github.com/mjpost/sacreBLEU''',
			        '''https://en.wikipedia.org/wiki/BLEU''',
			        '''https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213''',
			    ]							,			)



		def 							SCREAMING_SNAKE_CASE    (self							,			a_							,			a_							,			a_="exp"							,			a_=None							,			a_=False							,			a_=False							,			a_=None							,			a_=False							,			):





			'''simple docstring'''

			__snake_case      :		Any         =							len(references[0]   )
			if any(len(A_   ) != references_per_prediction for refs in references   ):
				raise ValueError('''Sacrebleu requires the same number of references for each prediction'''   )
			__snake_case      :		str         =							[[refs[i] for refs in references] for i in range(A_   )]
			__snake_case      :		Optional[Any]         =							scb.corpus_bleu(
			    A_							,			A_							,			smooth_method=A_							,			smooth_value=A_							,			force=A_							,			lowercase=A_							,			use_effective_order=A_							,			**({'''tokenize''': tokenize} if tokenize else {})							,			)
			__snake_case      :		List[str]         =							{
			    '''score''': output.score,
			    '''counts''': output.counts,
			    '''totals''': output.totals,
			    '''precisions''': output.precisions,
			    '''bp''': output.bp,
			    '''sys_len''': output.sys_len,
			    '''ref_len''': output.ref_len,
			}
			return output_dict






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
snake_case__						      =			abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def    __magic_name__(	__UpperCAmelCase		)    ->     int:



      '''simple docstring'''

      from transformers.testing_utils import pytest_addoption_shared

      pytest_addoption_shared(__UpperCAmelCase		)




def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      from transformers.testing_utils import pytest_terminal_summary_main

      _lowerCamelCase           =					terminalreporter.config.getoption('''--make-reports'''		)
      if make_reports:
            pytest_terminal_summary_main(__UpperCAmelCase			,    id=__UpperCAmelCase		)