





"""simple docstring"""




import sacrebleu as scb
from packaging import version

import datasets


UpperCamelCase_     ="""\
@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_     ="""\
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_     ="""
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       ):






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




										'''simple docstring'''



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










"""simple docstring"""




def  a_       (							_lowercase     ):
						if len(_lowercase     ) > 1:
												_UpperCamelCase    :      List[str]						     =					len(_lowercase     ) // 2  # Finds the middle of the array
												_UpperCamelCase    :      Dict						     =					arr[
												    :middle_length
												]  # Creates an array of the elements in the first half.
												_UpperCamelCase    :      Union[str, Any]						     =					arr[
												    middle_length:
												]  # Creates an array of the elements in the second half.
												_UpperCamelCase    :      str						     =					len(_lowercase     )
												_UpperCamelCase    :      List[str]						     =					len(_lowercase     )
												merge(_lowercase     )  # Starts sorting the left.
												merge(_lowercase     )  # Starts sorting the right
												_UpperCamelCase    :      Dict						     =					0  # Left Counter
												_UpperCamelCase    :      Union[str, Any]						     =					0  # Right Counter
												_UpperCamelCase    :      Optional[int]						     =					0  # Position Counter
												while (
												    left_index < left_size and right_index < right_size
												):  # Runs until the lowers size of the left and right are sorted.
																		if left_array[left_index] < right_array[right_index]:
																								_UpperCamelCase    :      Dict						     =					left_array[left_index]
																								left_index += 1
																		else:
																								_UpperCamelCase    :      Tuple						     =					right_array[right_index]
																								right_index += 1
																		index += 1
												while (
												    left_index < left_size
												):  # Adds the left over elements in the left half of the array
																		_UpperCamelCase    :      int						     =					left_array[left_index]
																		left_index += 1
																		index += 1
												while (
												    right_index < right_size
												):  # Adds the left over elements in the right half of the array
																		_UpperCamelCase    :      Any						     =					right_array[right_index]
																		right_index += 1
																		index += 1
						return arr


if __name__ == "__main__":
						import doctest

						doctest.testmod()



