






from sklearn.metrics import precision_score

import datasets


__UpperCAmelCase      =				"\nPrecision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:\nPrecision = TP / (TP + FP)\nwhere TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).\n"


__UpperCAmelCase      =				"\nArgs:\n    predictions (`list` of `int`): Predicted class labels.\n    references (`list` of `int`): Actual class labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`. If `average` is `None`, it should be the label order. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to 'warn'.\n\n        - 0: Returns 0 when there is a zero division.\n        - 1: Returns 1 when there is a zero division.\n        - 'warn': Raises warnings and then returns 0 when there is a zero division.\n\nReturns:\n    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> precision_metric = datasets.load_metric(\"precision\")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {'precision': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> precision_metric = datasets.load_metric(\"precision\")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results['precision'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> precision_metric = datasets.load_metric(\"precision\")\n        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(results)\n        {'precision': 0.23529411764705882}\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average='macro')\n        >>> print(results)\n        {'precision': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average='micro')\n        >>> print(results)\n        {'precision': 0.3333333333333333}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average='weighted')\n        >>> print(results)\n        {'precision': 0.2222222222222222}\n        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print([round(res, 2) for res in results['precision']])\n        [0.67, 0.0, 0.0]\n"


__UpperCAmelCase      =				"\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n    and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n    and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n    Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,							_KWARGS_DESCRIPTION	)
class 	a_(		datasets.Metric	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	List[str])							->	Optional[Any]:

      """simple docstring"""
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,  citation=_CITATION  ,  inputs_description=_KWARGS_DESCRIPTION  ,  features=datasets.Features(
              {
                  'predictions': datasets.Sequence(datasets.Value('int32')),
                  'references': datasets.Sequence(datasets.Value('int32')),
              }
              if self.config_name == 'multilabel'
              else {
                  'predictions': datasets.Value('int32'),
                  'references': datasets.Value('int32'),
              })  ,  reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html']  ,  )





     def     __UpperCamelCase						(		self					:	int  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	Any  ,  lowerCAmelCase__					:	Tuple=None  ,  lowerCAmelCase__					:	List[Any]=1  ,  lowerCAmelCase__					:	Dict="binary"  ,  lowerCAmelCase__					:	int=None  ,  lowerCAmelCase__					:	Tuple="warn"  ,  )							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		precision_score(
          lowerCAmelCase__  ,  lowerCAmelCase__  ,  labels=lowerCAmelCase__  ,  pos_label=lowerCAmelCase__  ,  average=lowerCAmelCase__  ,  sample_weight=lowerCAmelCase__  ,  zero_division=lowerCAmelCase__  ,  )
      return {"precision": float(lowerCAmelCase__) if score.size == 1 else score}






'''simple docstring'''
from __future__ import annotations

import os






class  UpperCAmelCase_					:
 UpperCAmelCase_		     =    (
     """High card""",
     """One pair""",
     """Two pairs""",
     """Three of a kind""",
     """Straight""",
     """Flush""",
     """Full house""",
     """Four of a kind""",
     """Straight flush""",
     """Royal flush""",
 )

 UpperCAmelCase_		     =    (
     """""",  # placeholder as tuples are zero-indexed
     """One""",
     """Two""",
     """Three""",
     """Four""",
     """Five""",
     """Six""",
     """Seven""",
     """Eight""",
     """Nine""",
     """Ten""",
     """Jack""",
     """Queen""",
     """King""",
     """Ace""",
 )





 def __init__(     self			,							lowercase_):
     if not isinstance(lowercase_			,							lowercase_):
         snake_case_			:     Union[str, Any]        =    F'Hand should be of type \'str\': {hand!r}'
         raise TypeError(lowercase_)
     # split removes duplicate whitespaces so no need of strip
     if len(hand.split(" ")) != 5:
         snake_case_			:     Optional[int]        =    F'Hand should contain only 5 cards: {hand!r}'
         raise ValueError(lowercase_)
     snake_case_			:     str        =    hand
     snake_case_			:     str        =    0
     snake_case_			:     List[str]        =    0
     snake_case_      ,			snake_case_			:     Union[str, Any]        =    self._internal_state()
     snake_case_			:     List[Any]        =    self._get_hand_type()
     snake_case_			:     Optional[int]        =    self._card_values[0]





 @property
 def 							snake_case__		(     self):
     return self._hand





 def 							snake_case__		(     self			,							lowercase_):
     # Breaking the tie works on the following order of precedence:
     # 1. First pair (default 0)
     # 2. Second pair (default 0)
     # 3. Compare all cards in reverse order because they are sorted.

     # First pair and second pair will only be a non-zero value if the card
     # type is either from the following:
     # 21: Four of a kind
     # 20: Full house
     # 17: Three of a kind
     # 16: Two pairs
     # 15: One pair
     if self._hand_type > other._hand_type:
         return "Win"
     elif self._hand_type < other._hand_type:
         return "Loss"
     elif self._first_pair == other._first_pair:
         if self._second_pair == other._second_pair:
             return self._compare_cards(lowercase_)
         else:
             return "Win" if self._second_pair > other._second_pair else "Loss"
     return "Win" if self._first_pair > other._first_pair else "Loss"





 def 							snake_case__		(     self):
     snake_case_			:     Tuple        =    PokerHand._HAND_NAME[self._hand_type - 14]
     snake_case_			:     List[Any]        =    PokerHand._CARD_NAME[self._high_card]
     snake_case_			:     Any        =    PokerHand._CARD_NAME[self._first_pair]
     snake_case_			:     str        =    PokerHand._CARD_NAME[self._second_pair]
     if self._hand_type in [22, 19, 18]:
         return name + F', {high}-high'
     elif self._hand_type in [21, 17, 15]:
         return name + F', {paira}s'
     elif self._hand_type in [20, 16]:
         snake_case_			:     List[str]        =    "over" if self._hand_type == 20 else "and"
         return name + F', {paira}s {join} {paira}s'
     elif self._hand_type == 23:
         return name
     else:
         return name + F', {high}'





 def 							snake_case__		(     self			,							lowercase_):
     # Enumerate gives us the index as well as the element of a list
     for index, card_value in enumerate(self._card_values):
         if card_value != other._card_values[index]:
             return "Win" if card_value > other._card_values[index] else "Loss"
     return "Tie"





 def 							snake_case__		(     self):
     # Number representing the type of hand internally:
     # 23: Royal flush
     # 22: Straight flush
     # 21: Four of a kind
     # 20: Full house
     # 19: Flush
     # 18: Straight
     # 17: Three of a kind
     # 16: Two pairs
     # 15: One pair
     # 14: High card
     if self._is_flush():
         if self._is_five_high_straight() or self._is_straight():
             return 23 if sum(self._card_values) == 60 else 22
         return 19
     elif self._is_five_high_straight() or self._is_straight():
         return 18
     return 14 + self._is_same_kind()





 def 							snake_case__		(     self):
     return len(self._card_suit) == 1





 def 							snake_case__		(     self):
     # If a card is a five high straight (low ace) change the location of
     # ace from the start of the list to the end. Check whether the first
     # element is ace or not. (Don't want to change again)
     # Five high straight (low ace): AH 2H 3S 4C 5D
     # Why use sorted here? One call to this function will mutate the list to
     # [5, 4, 3, 2, 14] and so for subsequent calls (which will be rare) we
     # need to compare the sorted version.
     # Refer test_multiple_calls_five_high_straight in test_poker_hand.py
     if sorted(self._card_values) == [2, 3, 4, 5, 14]:
         if self._card_values[0] == 14:
             # Remember, our list is sorted in reverse order
             snake_case_			:     Optional[int]        =    self._card_values.pop(0)
             self._card_values.append(lowercase_)
         return True
     return False





 def 							snake_case__		(     self):
     for i in range(4):
         if self._card_values[i] - self._card_values[i + 1] != 1:
             return False
     return True





 def 							snake_case__		(     self):
     # Kind Values for internal use:
     # 7: Four of a kind
     # 6: Full house
     # 3: Three of a kind
     # 2: Two pairs
     # 1: One pair
     # 0: False
     snake_case_			:     Dict        =    0
     for i in range(4):
         # Compare two cards at a time, if they are same increase 'kind',
         # add the value of the card to val1, if it is repeating again we
         # will add 2 to 'kind' as there are now 3 cards with same value.
         # If we get card of different value than val1, we will do the same
         # thing with val2
         if self._card_values[i] == self._card_values[i + 1]:
             if not vala:
                 snake_case_			:     Tuple        =    self._card_values[i]
                 kind += 1
             elif vala == self._card_values[i]:
                 kind += 2
             elif not vala:
                 snake_case_			:     Optional[int]        =    self._card_values[i]
                 kind += 1
             elif vala == self._card_values[i]:
                 kind += 2
        # For consistency in hand type (look at note in _get_hand_type function)
     snake_case_			:     Dict        =    kind + 2 if kind in [4, 5] else kind
     # first meaning first pair to compare in 'compare_with'
     snake_case_			:     Union[str, Any]        =    max(lowercase_			,							lowercase_)
     snake_case_			:     Optional[Any]        =    min(lowercase_			,							lowercase_)
     # If it's full house (three count pair + two count pair), make sure
     # first pair is three count and if not then switch them both.
     if kind == 6 and self._card_values.count(lowercase_) != 3:
         snake_case_      ,			snake_case_			:     Dict        =    second, first
     snake_case_			:     Optional[int]        =    first
     snake_case_			:     Dict        =    second
     return kind





 def 							snake_case__		(     self):
     # Internal representation of hand as a list of card values and
     # a set of card suit
     snake_case_			:     dict        =    {"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
     snake_case_			:     Tuple        =    self._hand.translate(str.maketrans(lowercase_)).split()
     snake_case_			:     Dict        =    [int(card[:-1]) for card in new_hand]
     snake_case_			:     Tuple        =    {card[-1] for card in new_hand}
     return sorted(lowercase_			,							reverse=lowercase_), card_suit





 def __repr__(     self):
     return F'{self.__class__}("{self._hand}")'





 def __str__(     self):
     return self._hand





 def __eq__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return self.compare_with(lowercase_) == "Tie"
     return NotImplemented





 def __lt__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return self.compare_with(lowercase_) == "Loss"
     return NotImplemented





 def __le__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return self < other or self == other
     return NotImplemented





 def __gt__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return not self < other and self != other
     return NotImplemented





 def __ge__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return not self < other
     return NotImplemented





 def __hash__(     self):
     return object.__hash__(self)


def     UpperCamelCase_  (						):
    """simple docstring"""






    snake_case_			:     Dict        =    0
    snake_case_			:     Tuple        =    os.path.abspath(os.path.dirname(__SCREAMING_SNAKE_CASE							)							)
    snake_case_			:     List[Any]        =    os.path.join(__SCREAMING_SNAKE_CASE,		"poker_hands.txt"							)
    with open(__SCREAMING_SNAKE_CASE							) as file_hand:
        for line in file_hand:
            snake_case_			:     int        =    line[:1_4].strip()
            snake_case_			:     Optional[Any]        =    line[1_5:].strip()
            snake_case_      ,			snake_case_			:     Union[str, Any]        =    PokerHand(__SCREAMING_SNAKE_CASE							), PokerHand(__SCREAMING_SNAKE_CASE							)
            snake_case_			:     Optional[int]        =    player.compare_with(__SCREAMING_SNAKE_CASE							)
            if output == "Win":
                answer += 1
    return answer


if __name__ == "__main__":
  solution()




