




import sacrebleu as scb
from packaging import version

import datasets


SCREAMING_SNAKE_CASE:    List[Any]											=   '''\
@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",
}
'''

SCREAMING_SNAKE_CASE:    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.
'''

SCREAMING_SNAKE_CASE:    Tuple											=   '''
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       lowercase_		(datasets.Metric   ):



						def   __a				( self  :      List[Any]     ):

								"""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   __a				( self  :      int				,       snake_case__  :      List[Any]				,       snake_case__  :      Optional[Any]				,       snake_case__  :      Any="exp"				,       snake_case__  :      int=None				,       snake_case__  :      int=False				,       snake_case__  :      List[Any]=False				,       snake_case__  :      List[str]=None				,       snake_case__  :      int=False				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       len(references[0]     )
								if any(len(snake_case__     ) != references_per_prediction for refs in references     ):
										raise ValueError('Sacrebleu requires the same number of references for each prediction'     )
								SCREAMING_SNAKE_CASE_							=       [[refs[i] for refs in references] for i in range(snake_case__     )]
								SCREAMING_SNAKE_CASE_							=       scb.corpus_bleu(
								    snake_case__				,       snake_case__				,       smooth_method=snake_case__				,       smooth_value=snake_case__				,       force=snake_case__				,       lowercase=snake_case__				,       use_effective_order=snake_case__				,       **({'tokenize': tokenize} if tokenize else {})				,       )
								SCREAMING_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





from __future__ import annotations


def 				_a       (				lowerCAmelCase   )->  list[int]:
		if not list_of_ints:
				return []

		if min(lowerCAmelCase   ) < 0:
				raise ValueError('All numbers must be positive'   )

		SCREAMING_SNAKE_CASE_							=       max(len(bin(lowerCAmelCase   )[2:]   ) for x in list_of_ints   )
		return _msd_radix_sort(lowerCAmelCase						,	lowerCAmelCase   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  list[int]:
		if bit_position == 0 or len(lowerCAmelCase   ) in [0, 1]:
				return list_of_ints

		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       []
		# Split numbers based on bit at bit_position from the right
		for number in list_of_ints:
				if (number >> (bit_position - 1)) & 1:
						# number has a one at bit bit_position
						ones.append(lowerCAmelCase   )
				else:
						# number has a zero at bit bit_position
						zeros.append(lowerCAmelCase   )

    # recursively split both lists further
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )
		SCREAMING_SNAKE_CASE_							=       _msd_radix_sort(lowerCAmelCase						,	bit_position - 1   )

		# recombine lists
		SCREAMING_SNAKE_CASE_							=       zeros
		res.extend(lowerCAmelCase   )

		return res


def 				_a       (				lowerCAmelCase   )->  Dict:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if not list_of_ints or length == 1:
				return

		if min(lowerCAmelCase   ) < 0:
				raise ValueError('All numbers must be positive'   )

		SCREAMING_SNAKE_CASE_							=       max(len(bin(lowerCAmelCase   )[2:]   ) for x in list_of_ints   )
		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	0						,	lowerCAmelCase   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Optional[int]:
		if bit_position == 0 or end_index - begin_index <= 1:
				return

		bit_position -= 1

		SCREAMING_SNAKE_CASE_							=       begin_index
		SCREAMING_SNAKE_CASE_							=       end_index - 1
		while i <= j:
				SCREAMING_SNAKE_CASE_							=       False
				if not (list_of_ints[i] >> bit_position) & 1:
						# found zero at the beginning
						i += 1
						SCREAMING_SNAKE_CASE_							=       True
				if (list_of_ints[j] >> bit_position) & 1:
						# found one at the end
						j -= 1
						SCREAMING_SNAKE_CASE_							=       True

				if changed:
						continue

				SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       list_of_ints[j], list_of_ints[i]
				j -= 1
				if j != i:
						i += 1

		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )
		_msd_radix_sort_inplace(lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()