


"""simple docstring"""






import sacrebleu as scb
from packaging import version

import datasets


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

UpperCamelCase__							:str       =  """\
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.
Inspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.
It also knows all the standard test sets and handles downloading, processing, and tokenization for you.

See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.
"""

UpperCamelCase__							:Dict       =  """
Produces BLEU scores along with its sufficient statistics
from a source against one or more references.

Args:
    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.
    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).
    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:
        - `'none'`: no smoothing
        - `'floor'`: increment zero counts
        - `'add-k'`: increment num/denom by k for n>1
        - `'exp'`: exponential decay
    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`).
    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:
        - `'none'`: No tokenization.
        - `'zh'`: Chinese tokenization.
        - `'13a'`: mimics the `mteval-v13a` script from Moses.
        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses
        - `'char'`: Language-agnostic character-level tokenization.
        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).
    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.
    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.
    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`.

Returns:
    'score': BLEU score,
    'counts': Counts,
    'totals': Totals,
    'precisions': Precisions,
    'bp': Brevity penalty,
    'sys_len': predictions length,
    'ref_len': reference length,

Examples:

    Example 1:
        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions, references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        100.0

    Example 2:
        >>> predictions = [\"hello there general kenobi\",
        ...                 \"on our way to ankh morpork\"]
        >>> references = [[\"hello there general kenobi\", \"hello there !\"],
        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]
        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")
        >>> results = sacrebleu.compute(predictions=predictions,
        ...                             references=references)
        >>> print(list(results.keys()))
        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']
        >>> print(round(results[\"score\"], 1))
        39.8
"""






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

		"""simple docstring"""





		def      _UpperCamelCase(   self      )			->  Optional[int]:

					"""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      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__="exp"				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=False				,							SCREAMING_SNAKE_CASE__=False				,							SCREAMING_SNAKE_CASE__=None				,							SCREAMING_SNAKE_CASE__=False				,							)			->  str:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  len(references[0]      )
					if any(len(SCREAMING_SNAKE_CASE__      ) != references_per_prediction for refs in references      ):
								raise ValueError('''Sacrebleu requires the same number of references for each prediction'''      )
					_UpperCamelCase				:Optional[int]				    =  [[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE__      )]
					_UpperCamelCase				:Union[str, Any]				    =  scb.corpus_bleu(
					    SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							smooth_method=SCREAMING_SNAKE_CASE__				,							smooth_value=SCREAMING_SNAKE_CASE__				,							force=SCREAMING_SNAKE_CASE__				,							lowercase=SCREAMING_SNAKE_CASE__				,							use_effective_order=SCREAMING_SNAKE_CASE__				,							**({'''tokenize''': tokenize} if tokenize else {})				,							)
					_UpperCamelCase				: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





"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available






@dataclass
class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       42


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
					from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
					from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline

