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

import datasets


__lowerCAmelCase  							= '\\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'

__lowerCAmelCase  							= '\\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'

__lowerCAmelCase  							= '\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  lowercase							(self						)  ->     List[Any]:
														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  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						UpperCAmelCase="exp"						,						UpperCAmelCase=None						,						UpperCAmelCase=False						,						UpperCAmelCase=False						,						UpperCAmelCase=None						,						UpperCAmelCase=False						,						)  ->     List[str]:
														_snake_case     =						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"""						)
														_snake_case     =						[[refs[i] for refs in references] for i in range(UpperCAmelCase						)]
														_snake_case     =						scb.corpus_bleu(
														    UpperCAmelCase						,						UpperCAmelCase						,						smooth_method=UpperCAmelCase						,						smooth_value=UpperCAmelCase						,						force=UpperCAmelCase						,						lowercase=UpperCAmelCase						,						use_effective_order=UpperCAmelCase						,						**({"""tokenize""": tokenize} if tokenize else {})						,						)
														_snake_case     =						{
														    """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

'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_xglm import XGLMTokenizer
else:
				__lowerCAmelCase  							= None


__lowerCAmelCase  							= logging.get_logger(__name__)

__lowerCAmelCase  							= {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase  							= {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase  							= {
    'facebook/xglm-564M': 2_048,
}


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		VOCAB_FILES_NAMES
							lowerCAmelCase_      		=		PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase_      		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase_      		=		["input_ids", "attention_mask"]
							lowerCAmelCase_      		=		XGLMTokenizer

							def __init__(self						,						UpperCAmelCase=None						,						UpperCAmelCase=None						,						UpperCAmelCase="<s>"						,						UpperCAmelCase="</s>"						,						UpperCAmelCase="</s>"						,						UpperCAmelCase="<s>"						,						UpperCAmelCase="<unk>"						,						UpperCAmelCase="<pad>"						,						**UpperCAmelCase						,						)  ->     List[Any]:
														# Compatibility with the original tokenizer
														_snake_case     =						7
														_snake_case     =						[f"""<madeupword{i}>""" for i in range(self.num_madeup_words						)]

														_snake_case     =						kwargs.get("""additional_special_tokens"""						,						[]						)
														kwargs["additional_special_tokens"] += [
														    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
														]

														super().__init__(
														    UpperCAmelCase						,						tokenizer_file=UpperCAmelCase						,						bos_token=UpperCAmelCase						,						eos_token=UpperCAmelCase						,						sep_token=UpperCAmelCase						,						cls_token=UpperCAmelCase						,						unk_token=UpperCAmelCase						,						pad_token=UpperCAmelCase						,						**UpperCAmelCase						,						)

														_snake_case     =						vocab_file
														_snake_case     =						False if not self.vocab_file else True

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     List[int]:

														if token_ids_a is None:
																					return [self.sep_token_id] + token_ids_a
														_snake_case     =						[self.sep_token_id]
														return sep + token_ids_a + sep + sep + token_ids_a

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     List[int]:
														_snake_case     =						[self.sep_token_id]

														if token_ids_a is None:
																					return len(sep + token_ids_a						) * [0]
														return len(sep + token_ids_a + sep + sep + token_ids_a						) * [0]





							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase = None						)  ->     Tuple[str]:
														if not self.can_save_slow_tokenizer:
																					raise ValueError(
																					    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																					    """tokenizer."""						)

														if not os.path.isdir(UpperCAmelCase						):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""						)
																					return
														_snake_case     =						os.path.join(
														    UpperCAmelCase						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

														if os.path.abspath(self.vocab_file						) != os.path.abspath(UpperCAmelCase						):
																					copyfile(self.vocab_file						,						UpperCAmelCase						)

														return (out_vocab_file,)