

import sacrebleu as scb
from packaging import version

import datasets


lowerCAmelCase_	     =     '''\
@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",
}
'''

lowerCAmelCase_	     =     '''\
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.
'''

lowerCAmelCase_	     =     '''
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      snake_case_       (		datasets.Metric ):







			'''simple docstring'''

			def     snake_case__(					self			:    Any							)				->str:
									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     snake_case__(					self			:    Optional[int]							,       _UpperCamelCase			:    List[str]							,       _UpperCamelCase			:    Tuple							,       _UpperCamelCase			:    Tuple="exp"							,       _UpperCamelCase			:    Optional[int]=None							,       _UpperCamelCase			:    Optional[int]=False							,       _UpperCamelCase			:    List[Any]=False							,       _UpperCamelCase			:    Union[str, Any]=None							,       _UpperCamelCase			:    List[str]=False							,       )				->Tuple:
									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


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[str]						      =    ["image_processor", "tokenizer"]
			SCREAMING_SNAKE_CASE					:       List[Any]						      =    "AutoImageProcessor"
			SCREAMING_SNAKE_CASE					:       Optional[int]						      =    "AutoTokenizer"







			def __init__(					self			:    List[str]							,       _UpperCamelCase			:    List[str]=None							,       _UpperCamelCase			:    Any=None							,       **_UpperCamelCase			:    Tuple							)				->str:
									snake_case_       =     None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.'''							,       _UpperCamelCase							,       )
															snake_case_       =     kwargs.pop('''feature_extractor'''							)

									snake_case_       =     image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError('''You need to specify an `image_processor`.'''							)
									if tokenizer is None:
															raise ValueError('''You need to specify a `tokenizer`.'''							)

									super().__init__(_UpperCamelCase							,       _UpperCamelCase							)
									snake_case_       =     self.image_processor
									snake_case_       =     False







			def __call__(					self			:    int							,       *_UpperCamelCase			:    Tuple							,       **_UpperCamelCase			:    Any							)				->Tuple:
									# For backward compatibility
									if self._in_target_context_manager:
															return self.current_processor(*_UpperCamelCase							,       **_UpperCamelCase							)

									snake_case_       =     kwargs.pop('''images'''							,       _UpperCamelCase							)
									snake_case_       =     kwargs.pop('''text'''							,       _UpperCamelCase							)
									if len(_UpperCamelCase							) > 0:
															snake_case_       =     args[0]
															snake_case_       =     args[1:]

									if images is None and text is None:
															raise ValueError('''You need to specify either an `images` or `text` input to process.'''							)

									if images is not None:
															snake_case_       =     self.image_processor(_UpperCamelCase							,       *_UpperCamelCase							,       **_UpperCamelCase							)
									if text is not None:
															snake_case_       =     self.tokenizer(_UpperCamelCase							,       **_UpperCamelCase							)

									if text is None:
															return inputs
									elif images is None:
															return encodings
									else:
															snake_case_       =     encodings['''input_ids''']
															return inputs







			def     snake_case__(					self			:    List[Any]							,       *_UpperCamelCase			:    Union[str, Any]							,       **_UpperCamelCase			:    Tuple							)				->Any:
									return self.tokenizer.batch_decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			def     snake_case__(					self			:    Optional[Any]							,       *_UpperCamelCase			:    List[Any]							,       **_UpperCamelCase			:    Union[str, Any]							)				->List[str]:
									return self.tokenizer.decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			@contextmanager
			def     snake_case__(					self			:    Any							)				->Any:
									warnings.warn(
									    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
									    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
									    '''your images inputs, or in a separate call.'''							)
									snake_case_       =     True
									snake_case_       =     self.tokenizer
									yield
									snake_case_       =     self.image_processor
									snake_case_       =     False







			@property
			def     snake_case__(					self			:    Optional[int]							)				->int:
									warnings.warn(
									    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       _UpperCamelCase							,       )
									return self.image_processor_class







			@property
			def     snake_case__(					self			:    Union[str, Any]							)				->List[Any]:
									warnings.warn(
									    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,       _UpperCamelCase							,       )
									return self.image_processor