






from __future__ import annotations

import sys

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						if n < 0:
												raise ValueError("""Negative arguments are not supported"""				)
						return _fib(_A				)[0]

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						if n == 0:  # (F(0), F(1))
												return (0, 1)

						# F(2n) = F(n)[2F(n+1) − F(n)]
						# F(2n+1) = F(n+1)^2+F(n)^2
						__magic_name__		,__magic_name__    :     str     		=      _fib(n // 2				)
						__magic_name__    :     Dict     		=      a * (b * 2 - a)
						__magic_name__    :     List[Any]     		=      a * a + b * b
						return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		__magic_name__:  Optional[Any]  	=			int(sys.argv[1])
		print(F"""fibonacci({n}) is {fibonacci(n)}""")








import sacrebleu as scb
from packaging import version

import datasets


__magic_name__:  Optional[int]  	=			"\\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"

__magic_name__:  int  	=			"\\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"

__magic_name__:  List[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  snake_case__      (							datasets.Metric   ):
	def     __magic_name__       (       self				)							->   Optional[int]:
							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     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__="exp"    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=None    ,     lowerCAmelCase__=False    ,     )							->   Optional[Any]:
							__magic_name__    :     Optional[int]     		=      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"""				)
							__magic_name__    :     Dict     		=      [[refs[i] for refs in references] for i in range(lowerCAmelCase__				)]
							__magic_name__    :     List[str]     		=      scb.corpus_bleu(
							    lowerCAmelCase__    ,     lowerCAmelCase__    ,     smooth_method=lowerCAmelCase__    ,     smooth_value=lowerCAmelCase__    ,     force=lowerCAmelCase__    ,     lowercase=lowerCAmelCase__    ,     use_effective_order=lowerCAmelCase__    ,     **({"""tokenize""": tokenize} if tokenize else {})    ,     )
							__magic_name__    :     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
