
"""simple docstring"""


import sacrebleu as scb
from packaging import version

import datasets


lowerCAmelCase			=		"""\\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"""

lowerCAmelCase			=		"""\\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"""

lowerCAmelCase			=		"""\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 					A_			(  datasets.Metric     ):



							"""simple docstring"""






							def    UpperCAmelCase__							(						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    UpperCAmelCase__							(						self						:Optional[Any]  ,							lowerCamelCase_						:int  ,							lowerCamelCase_						:Dict  ,							lowerCamelCase_						:Optional[Any]="exp"  ,							lowerCamelCase_						:Optional[int]=None  ,							lowerCamelCase_						:List[str]=False  ,							lowerCamelCase_						:int=False  ,							lowerCamelCase_						:List[str]=None  ,							lowerCamelCase_						:Optional[Any]=False  ,							):





										"""simple docstring"""

										lowerCamelCase__			:  Tuple  =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' )
										lowerCamelCase__			:  Dict  =[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE_ )]
										lowerCamelCase__			:  Dict  =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 {})  ,							)
										lowerCamelCase__			:  Optional[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"""


# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue






def      lowerCAmelCase_     (	snake_case_	:    List[Any]    )     ->Optional[int]:
			lowerCamelCase__			:  Optional[int]  =Queue()
			lowerCamelCase__			:  Any  =[False] * len(snake_case_    )
			lowerCamelCase__			:  Optional[Any]  =[-1] * len(snake_case_    )

			def bfs():
						while not queue.empty():
									lowerCamelCase__			:  List[Any]  =queue.get()
									lowerCamelCase__			:  int  =True

									for neighbour in graph[u]:
												if neighbour == u:
															return False

												if color[neighbour] == -1:
															lowerCamelCase__			:  Union[str, Any]  =1 - color[u]
															queue.put(snake_case_    )

												elif color[neighbour] == color[u]:
															return False

						return True

			for i in range(len(snake_case_    )    ):
						if not visited[i]:
									queue.put(snake_case_    )
									lowerCamelCase__			:  Tuple  =0
									if bfs() is False:
												return False

			return True


if __name__ == "__main__":
							# Adjacency List of graph
							print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))