
import sacrebleu as scb
from packaging import version

import datasets


lowercase__							:							Any													=     "\\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"

lowercase__							:							Dict													=     "\\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"

lowercase__							:							Optional[Any]													=     "\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     SCREAMING_SNAKE_CASE__      (							datasets.Metric      ):

							"""simple docstring"""






							def       A__						(					self     )->				Tuple:





													'''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       ,		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       ,		)->				Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       					=     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       					=     [[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE_     )]
													__UpperCamelCase       					=     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       					=     {
													    '''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






def 		A_	(							snake_case							:				Union[str, Any]	, snake_case							:				Tuple	, snake_case							:				Optional[Any]	) ->      Union[str, Any]:



						'''simple docstring'''

						assert b != 0, "This cannot accept modulo that is == 0"
						if n == 0:
												return 1

						if n % 2 == 1:
												return (bin_exp_mod(snake_case	, n - 1	, snake_case	) * a) % b

						__UpperCamelCase       					=     bin_exp_mod(snake_case	, n / 2	, snake_case	)
						return (r * r) % b


if __name__ == "__main__":
				try:
								lowercase__							:							Dict													=     int(input("Enter Base : ").strip())
								lowercase__							:							Dict													=     int(input("Enter Power : ").strip())
								lowercase__							:							Dict													=     int(input("Enter Modulo : ").strip())
				except ValueError:
								print("Invalid literal for integer")

				print(bin_exp_mod(BASE, POWER, MODULO))




