






import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

UpperCAmelCase_ :						Dict      =      (
    '''4S 3H 2C 7S 5H''',
    '''9D 8H 2C 6S 7H''',
    '''2D 6D 9D TH 7D''',
    '''TC 8C 2S JH 6C''',
    '''JH 8S TH AH QH''',
    '''TS KS 5S 9S AC''',
    '''KD 6S 9D TH AD''',
    '''KS 8D 4D 9S 4S''',  # pair
    '''8C 4S KH JS 4D''',  # pair
    '''QH 8H KD JH 8S''',  # pair
    '''KC 4H KS 2H 8D''',  # pair
    '''KD 4S KC 3H 8S''',  # pair
    '''AH 8S AS KC JH''',  # pair
    '''3H 4C 4H 3S 2H''',  # 2 pairs
    '''5S 5D 2C KH KH''',  # 2 pairs
    '''3C KH 5D 5S KH''',  # 2 pairs
    '''AS 3C KH AD KH''',  # 2 pairs
    '''7C 7S 3S 7H 5S''',  # 3 of a kind
    '''7C 7S KH 2H 7H''',  # 3 of a kind
    '''AC KH QH AH AS''',  # 3 of a kind
    '''2H 4D 3C AS 5S''',  # straight (low ace)
    '''3C 5C 4C 2C 6H''',  # straight
    '''6S 8S 7S 5H 9H''',  # straight
    '''JS QS 9H TS KH''',  # straight
    '''QC KH TS JS AH''',  # straight (high ace)
    '''8C 9C 5C 3C TC''',  # flush
    '''3S 8S 9S 5S KS''',  # flush
    '''4C 5C 9C 8C KC''',  # flush
    '''JH 8H AH KH QH''',  # flush
    '''3D 2H 3H 2C 2D''',  # full house
    '''2H 2C 3S 3H 3D''',  # full house
    '''KH KC 3S 3H 3D''',  # full house
    '''JC 6H JS JD JH''',  # 4 of a kind
    '''JC 7H JS JD JH''',  # 4 of a kind
    '''JC KH JS JD JH''',  # 4 of a kind
    '''2S AS 4S 5S 3S''',  # straight flush (low ace)
    '''2D 6D 3D 4D 5D''',  # straight flush
    '''5C 6C 3C 7C 4C''',  # straight flush
    '''JH 9H TH KH QH''',  # straight flush
    '''JH AH TH KH QH''',  # royal flush (high ace straight flush)
)

UpperCAmelCase_ :						List[Any]      =      (
    ('''2H 3H 4H 5H 6H''', '''KS AS TS QS JS''', '''Loss'''),
    ('''2H 3H 4H 5H 6H''', '''AS AD AC AH JD''', '''Win'''),
    ('''AS AH 2H AD AC''', '''JS JD JC JH 3D''', '''Win'''),
    ('''2S AH 2H AS AC''', '''JS JD JC JH AD''', '''Loss'''),
    ('''2S AH 2H AS AC''', '''2H 3H 5H 6H 7H''', '''Win'''),
    ('''AS 3S 4S 8S 2S''', '''2H 3H 5H 6H 7H''', '''Win'''),
    ('''2H 3H 5H 6H 7H''', '''2S 3H 4H 5S 6C''', '''Win'''),
    ('''2S 3H 4H 5S 6C''', '''3D 4C 5H 6H 2S''', '''Tie'''),
    ('''2S 3H 4H 5S 6C''', '''AH AC 5H 6H AS''', '''Win'''),
    ('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H AS''', '''Loss'''),
    ('''2S 2H 4H 5S 4C''', '''AH AC 5H 6H 7S''', '''Win'''),
    ('''6S AD 7H 4S AS''', '''AH AC 5H 6H 7S''', '''Loss'''),
    ('''2S AH 4H 5S KC''', '''AH AC 5H 6H 7S''', '''Loss'''),
    ('''2S 3H 6H 7S 9C''', '''7H 3C TH 6H 9S''', '''Loss'''),
    ('''4S 5H 6H TS AC''', '''3S 5H 6H TS AC''', '''Win'''),
    ('''2S AH 4H 5S 6C''', '''AD 4C 5H 6H 2C''', '''Tie'''),
    ('''AS AH 3H AD AC''', '''AS AH 2H AD AC''', '''Win'''),
    ('''AH AC 5H 5C QS''', '''AH AC 5H 5C KS''', '''Loss'''),
    ('''AH AC 5H 5C QS''', '''KH KC 5H 5C QS''', '''Win'''),
    ('''7C 7S KH 2H 7H''', '''3C 3S AH 2H 3H''', '''Win'''),
    ('''3C 3S AH 2H 3H''', '''7C 7S KH 2H 7H''', '''Loss'''),
    ('''6H 5H 4H 3H 2H''', '''5H 4H 3H 2H AH''', '''Win'''),
    ('''5H 4H 3H 2H AH''', '''5H 4H 3H 2H AH''', '''Tie'''),
    ('''5H 4H 3H 2H AH''', '''6H 5H 4H 3H 2H''', '''Loss'''),
    ('''AH AD KS KC AC''', '''AH KD KH AC KC''', '''Win'''),
    ('''2H 4D 3C AS 5S''', '''2H 4D 3C 6S 5S''', '''Loss'''),
    ('''2H 3S 3C 3H 2S''', '''3S 3C 2S 2H 2D''', '''Win'''),
    ('''4D 6D 5D 2D JH''', '''3S 8S 3H TC KH''', '''Loss'''),
    ('''4S 6C 8S 3S 7S''', '''AD KS 2D 7D 7C''', '''Loss'''),
    ('''6S 4C 7H 8C 3H''', '''5H JC AH 9D 9C''', '''Loss'''),
    ('''9D 9H JH TC QH''', '''3C 2S JS 5C 7H''', '''Win'''),
    ('''2H TC 8S AD 9S''', '''4H TS 7H 2C 5C''', '''Win'''),
    ('''9D 3S 2C 7S 7C''', '''JC TD 3C TC 9H''', '''Loss'''),
)

UpperCAmelCase_ :						Tuple      =      (
    ('''2H 3H 4H 5H 6H''', True),
    ('''AS AH 2H AD AC''', False),
    ('''2H 3H 5H 6H 7H''', True),
    ('''KS AS TS QS JS''', True),
    ('''8H 9H QS JS TH''', False),
    ('''AS 3S 4S 8S 2S''', True),
)

UpperCAmelCase_ :						Union[str, Any]      =      (
    ('''2H 3H 4H 5H 6H''', True),
    ('''AS AH 2H AD AC''', False),
    ('''2H 3H 5H 6H 7H''', False),
    ('''KS AS TS QS JS''', True),
    ('''8H 9H QS JS TH''', True),
)

UpperCAmelCase_ :						Optional[int]      =      (
    ('''2H 4D 3C AS 5S''', True, [5, 4, 3, 2, 14]),
    ('''2H 5D 3C AS 5S''', False, [14, 5, 5, 3, 2]),
    ('''JH QD KC AS TS''', False, [14, 13, 12, 11, 10]),
    ('''9D 3S 2C 7S 7C''', False, [9, 7, 7, 3, 2]),
)

UpperCAmelCase_ :						int      =      (
    ('''JH AH TH KH QH''', 0),
    ('''JH 9H TH KH QH''', 0),
    ('''JC KH JS JD JH''', 7),
    ('''KH KC 3S 3H 3D''', 6),
    ('''8C 9C 5C 3C TC''', 0),
    ('''JS QS 9H TS KH''', 0),
    ('''7C 7S KH 2H 7H''', 3),
    ('''3C KH 5D 5S KH''', 2),
    ('''QH 8H KD JH 8S''', 1),
    ('''2D 6D 9D TH 7D''', 0),
)

UpperCAmelCase_ :						str      =      (
    ('''JH AH TH KH QH''', 23),
    ('''JH 9H TH KH QH''', 22),
    ('''JC KH JS JD JH''', 21),
    ('''KH KC 3S 3H 3D''', 20),
    ('''8C 9C 5C 3C TC''', 19),
    ('''JS QS 9H TS KH''', 18),
    ('''7C 7S KH 2H 7H''', 17),
    ('''3C KH 5D 5S KH''', 16),
    ('''QH 8H KD JH 8S''', 15),
    ('''2D 6D 9D TH 7D''', 14),
)

def    SCREAMING_SNAKE_CASE_							(							)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     ,      UpperCamelCase     :Optional[int]          =	randrange(len(__magic_name__					)					), randrange(len(__magic_name__					)					)
	UpperCamelCase     :List[Any]          =	["""Loss""", """Tie""", """Win"""][(play >= oppo) + (play > oppo)]
	UpperCamelCase     ,      UpperCamelCase     :Any          =	SORTED_HANDS[play], SORTED_HANDS[oppo]
	return hand, other, expected

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int = 100					)		->			Tuple:






	"""simple docstring"""



	return (generate_random_hand() for _ in range(__magic_name__					))

@pytest.mark.parametrize("""hand, expected"""	,			__magic_name__					)
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]	,			__magic_name__					:  int					)		->			Any:






	"""simple docstring"""



	assert PokerHand(__magic_name__					)._is_flush() == expected

@pytest.mark.parametrize("""hand, expected"""	,			__magic_name__					)
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int	,			__magic_name__					:  str					)		->			str:






	"""simple docstring"""



	assert PokerHand(__magic_name__					)._is_straight() == expected

@pytest.mark.parametrize("""hand, expected, card_values"""	,			__magic_name__					)
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Tuple	,			__magic_name__					:  Optional[Any]	,			__magic_name__					:  Union[str, Any]					)		->			int:






	"""simple docstring"""



	UpperCamelCase     :str          =	PokerHand(__magic_name__					)
	assert player._is_five_high_straight() == expected
	assert player._card_values == card_values

@pytest.mark.parametrize("""hand, expected"""	,			__magic_name__					)
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Tuple	,			__magic_name__					:  Optional[Any]					)		->			List[str]:






	"""simple docstring"""



	assert PokerHand(__magic_name__					)._is_same_kind() == expected

@pytest.mark.parametrize("""hand, expected"""	,			__magic_name__					)
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Dict					)		->			int:






	"""simple docstring"""



	assert PokerHand(__magic_name__					)._hand_type == expected

@pytest.mark.parametrize("""hand, other, expected"""	,			__magic_name__					)
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]	,			__magic_name__					:  str	,			__magic_name__					:  int					)		->			Optional[Any]:






	"""simple docstring"""



	assert PokerHand(__magic_name__					).compare_with(PokerHand(__magic_name__					)					) == expected

@pytest.mark.parametrize("""hand, other, expected"""	,			generate_random_hands()					)
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str	,			__magic_name__					:  Tuple	,			__magic_name__					:  Dict					)		->			int:






	"""simple docstring"""



	assert PokerHand(__magic_name__					).compare_with(PokerHand(__magic_name__					)					) == expected

def    SCREAMING_SNAKE_CASE_							(							)		->			Any:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	[PokerHand(__magic_name__					) for hand in SORTED_HANDS]
	UpperCamelCase     :Dict          =	poker_hands.copy()
	shuffle(__magic_name__					)
	UpperCamelCase     :str          =	chain(sorted(__magic_name__					)					)
	for index, hand in enumerate(__magic_name__					):
		assert hand == poker_hands[index]

def    SCREAMING_SNAKE_CASE_							(							)		->			Optional[int]:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	[PokerHand("""2D AC 3H 4H 5S"""					), PokerHand("""2S 3H 4H 5S 6C"""					)]
	pokerhands.sort(reverse=__magic_name__					)
	assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"

def    SCREAMING_SNAKE_CASE_							(							)		->			Tuple:






	"""simple docstring"""



	UpperCamelCase     :Any          =	PokerHand("""2C 4S AS 3D 5C"""					)
	UpperCamelCase     :Dict          =	True
	UpperCamelCase     :int          =	[5, 4, 3, 2, 14]
	for _ in range(10					):
		assert pokerhand._is_five_high_straight() == expected
		assert pokerhand._card_values == expected_card_values

def    SCREAMING_SNAKE_CASE_							(							)		->			str:






	"""simple docstring"""



	UpperCamelCase     :List[Any]          =	0
	UpperCamelCase     :Dict          =	os.path.abspath(os.path.dirname(__magic_name__					)					)
	UpperCamelCase     :str          =	os.path.join(__magic_name__	,			"""poker_hands.txt"""					)
	with open(__magic_name__					) as file_hand:
		for line in file_hand:
			UpperCamelCase     :str          =	line[:14].strip()
			UpperCamelCase     :Dict          =	line[15:].strip()
			UpperCamelCase     ,      UpperCamelCase     :Any          =	PokerHand(__magic_name__					), PokerHand(__magic_name__					)
			UpperCamelCase     :Optional[Any]          =	player.compare_with(__magic_name__					)
			if output == "Win":
				answer += 1
	assert answer == 376








from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCAmelCase_ :						int      =      '''\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
'''

UpperCAmelCase_ :						Optional[Any]      =      '''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''

UpperCAmelCase_ :						int      =      '''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for \'record\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'prediction_text\': the predicted answer text
        - for \'multirc\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question-answer pair as specified by the dataset
            - \'prediction\': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for \'record\': list of question-answers dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'answers\': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for \'record\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1\': F1 score
    - for \'multirc\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1_m\': Per-question macro-F1 score
        - \'f1_a\': Average F1 score over all answers
    - for \'axb\':
        \'matthews_correlation\': Matthew Correlation
    - for \'cb\':
        - \'accuracy\': Accuracy
        - \'f1\': F1 score
    - for all others:
        - \'accuracy\': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  Optional[Any]					)		->			Union[str, Any]:






	"""simple docstring"""



	return float((preds == labels).mean()					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  int	,			__magic_name__					:  Any="binary"					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	simple_accuracy(__magic_name__	,			__magic_name__					)
	UpperCamelCase     :Dict          =	float(fa_score(y_true=__magic_name__	,			y_pred=__magic_name__	,			average=__magic_name__					)					)
	return {
	    "accuracy": acc,
	    "f1": fa,
	}

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]	,			__magic_name__					:  Optional[Any]					)		->			Optional[Any]:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	{}
	for id_pred, label in zip(__magic_name__	,			__magic_name__					):
		UpperCamelCase     :str          =	f"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
		UpperCamelCase     :Union[str, Any]          =	id_pred["""prediction"""]
		if question_id in question_map:
			question_map[question_id].append((pred, label)					)
		else:
			UpperCamelCase     :Dict          =	[(pred, label)]
	UpperCamelCase     ,      UpperCamelCase     :Optional[int]          =	[], []
	for question, preds_labels in question_map.items():
		UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	zip(*__magic_name__					)
		UpperCamelCase     :Optional[int]          =	fa_score(y_true=__magic_name__	,			y_pred=__magic_name__	,			average="""macro"""					)
		fas.append(__magic_name__					)
		UpperCamelCase     :int          =	int(sum(pred == label for pred, label in preds_labels					) == len(__magic_name__					)					)
		ems.append(__magic_name__					)
	UpperCamelCase     :Optional[int]          =	float(sum(__magic_name__					) / len(__magic_name__					)					)
	UpperCamelCase     :str          =	sum(__magic_name__					) / len(__magic_name__					)
	UpperCamelCase     :Tuple          =	float(fa_score(y_true=__magic_name__	,			y_pred=[id_pred["""prediction"""] for id_pred in ids_preds]					)					)
	return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,   _KWARGS_DESCRIPTION      )
class     _SCREAMING_SNAKE_CASE       (			datasets.Metric      ):





	def 						_A (		self				:					str	):
		if self.config_name not in [
		    "boolq",
		    "cb",
		    "copa",
		    "multirc",
		    "record",
		    "rte",
		    "wic",
		    "wsc",
		    "wsc.fixed",
		    "axb",
		    "axg",
		]:
			raise KeyError(
			    """You should supply a configuration name selected in """
			    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""	)
		return datasets.MetricInfo(
		    description=_DESCRIPTION ,  citation=_CITATION ,  inputs_description=_KWARGS_DESCRIPTION ,  features=datasets.Features(self._get_feature_types()	) ,  codebase_urls=[] ,  reference_urls=[] ,  format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None ,  )





	def 						_A (		self				:					Optional[Any]	):
		if self.config_name == "record":
			return {
			    "predictions": {
			        "idx": {
			            "passage": datasets.Value("""int64"""	),
			            "query": datasets.Value("""int64"""	),
			        },
			        "prediction_text": datasets.Value("""string"""	),
			    },
			    "references": {
			        "idx": {
			            "passage": datasets.Value("""int64"""	),
			            "query": datasets.Value("""int64"""	),
			        },
			        "answers": datasets.Sequence(datasets.Value("""string"""	)	),
			    },
			}
		elif self.config_name == "multirc":
			return {
			    "predictions": {
			        "idx": {
			            "answer": datasets.Value("""int64"""	),
			            "paragraph": datasets.Value("""int64"""	),
			            "question": datasets.Value("""int64"""	),
			        },
			        "prediction": datasets.Value("""int64"""	),
			    },
			    "references": datasets.Value("""int64"""	),
			}
		else:
			return {
			    "predictions": datasets.Value("""int64"""	),
			    "references": datasets.Value("""int64"""	),
			}





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Tuple ,  __lowerCamelCase				:					str	):
		if self.config_name == "axb":
			return {"matthews_correlation": matthews_corrcoef(__lowerCamelCase ,  __lowerCamelCase	)}
		elif self.config_name == "cb":
			return acc_and_fa(__lowerCamelCase ,  __lowerCamelCase ,  fa_avg="""macro"""	)
		elif self.config_name == "record":
			UpperCamelCase     :Optional[Any]          =	[
			    {
			        """qas""": [
			            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
			            for ref in references
			        ]
			    }
			]
			UpperCamelCase     :Tuple          =	{pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
			return evaluate_record(__lowerCamelCase ,  __lowerCamelCase	)[0]
		elif self.config_name == "multirc":
			return evaluate_multirc(__lowerCamelCase ,  __lowerCamelCase	)
		elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
			return {"accuracy": simple_accuracy(__lowerCamelCase ,  __lowerCamelCase	)}
		else:
			raise KeyError(
			    """You should supply a configuration name selected in """
			    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""	)
