






import sacrebleu as scb
from packaging import version

import datasets


__UpperCAmelCase										=		'\\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'

__UpperCAmelCase										=		'\\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'

__UpperCAmelCase										=		'\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 						lowerCamelCase   (datasets.Metric    ):







		'''simple docstring'''



		def       __UpperCAmelCase    (							self     )			->							Dict:
							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       __UpperCAmelCase    (							self       ,	_UpperCamelCase       ,	_UpperCamelCase       ,	_UpperCamelCase="exp"       ,	_UpperCamelCase=None       ,	_UpperCamelCase=False       ,	_UpperCamelCase=False       ,	_UpperCamelCase=None       ,	_UpperCamelCase=False       ,	)			->							str:
							UpperCAmelCase_					:   Optional[int]   =		len(references[0]     )
							if any(len(_UpperCamelCase     ) != references_per_prediction for refs in references     ):
												raise ValueError('Sacrebleu requires the same number of references for each prediction'     )
							UpperCAmelCase_					:   Dict   =		[[refs[i] for refs in references] for i in range(_UpperCamelCase     )]
							UpperCAmelCase_					:   Optional[Any]   =		scb.corpus_bleu(
							    _UpperCamelCase       ,	_UpperCamelCase       ,	smooth_method=_UpperCamelCase       ,	smooth_value=_UpperCamelCase       ,	force=_UpperCamelCase       ,	lowercase=_UpperCamelCase       ,	use_effective_order=_UpperCamelCase       ,	**({'tokenize': tokenize} if tokenize else {})       ,	)
							UpperCAmelCase_					:   Union[str, Any]   =		{
							    '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














import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 						lowerCamelCase   (_snake_case				,						unittest.TestCase    ):







		'''simple docstring'''



		_snake_case					:					Tuple 		=						BertweetTokenizer
		_snake_case					:					List[str] 		=						False



		def       __UpperCAmelCase    (							self     )			->							Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							UpperCAmelCase_					:   Optional[int]   =		['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
							UpperCAmelCase_					:   Dict   =		dict(zip(_UpperCamelCase       ,	range(len(_UpperCamelCase     )     )     )     )
							UpperCAmelCase_					:   List[Any]   =		['#version: 0.2', 'a m</w>']
							UpperCAmelCase_					:   Union[str, Any]   =		{'unk_token': '<unk>'}

							UpperCAmelCase_					:   Optional[Any]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['vocab_file']     )
							UpperCAmelCase_					:   List[str]   =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES['merges_file']     )
							with open(self.vocab_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												for token in vocab_tokens:
																	fp.write(f"{token} {vocab_tokens[token]}\n"     )
							with open(self.merges_file       ,	'w'       ,	encoding='utf-8'     ) as fp:
												fp.write('\n'.join(_UpperCamelCase     )     )



		def       __UpperCAmelCase    (							self       ,	**_UpperCamelCase     )			->							List[Any]:
							kwargs.update(self.special_tokens_map     )
							return BertweetTokenizer.from_pretrained(self.tmpdirname       ,	**_UpperCamelCase     )



		def       __UpperCAmelCase    (							self       ,	_UpperCamelCase     )			->							List[Any]:
							UpperCAmelCase_					:   Any   =		'I am VinAI Research'
							UpperCAmelCase_					:   Any   =		'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
							return input_text, output_text



		def       __UpperCAmelCase    (							self     )			->							Tuple:
							UpperCAmelCase_					:   Union[str, Any]   =		BertweetTokenizer(self.vocab_file       ,	self.merges_file       ,	**self.special_tokens_map     )
							UpperCAmelCase_					:   Optional[Any]   =		'I am VinAI Research'
							UpperCAmelCase_					:   str   =		'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
							UpperCAmelCase_					:   Any   =		tokenizer.tokenize(_UpperCamelCase     )
							self.assertListEqual(_UpperCamelCase       ,	_UpperCamelCase     )

							UpperCAmelCase_					:   Optional[int]   =		tokens + [tokenizer.unk_token]

							UpperCAmelCase_					:   List[str]   =		[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase     )       ,	_UpperCamelCase     )






