



from __future__ import annotations

import os


class 	snake_case_			:




       """simple docstring"""






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

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



       def __init__(  self  ,lowercase):

             """simple docstring"""





             if not isinstance(lowercase  ,lowercase):
                   UpperCAmelCase_	:						List[str]					   =		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:
                   UpperCAmelCase_	:						Any					   =		F"""Hand should contain only 5 cards: {hand!r}"""
                   raise ValueError(lowercase)
             UpperCAmelCase_	:						Union[str, Any]					   =		hand
             UpperCAmelCase_	:						List[str]					   =		0
             UpperCAmelCase_	:						List[str]					   =		0
             UpperCAmelCase_   ,   UpperCAmelCase_	:						str					   =		self._internal_state()
             UpperCAmelCase_	:						List[str]					   =		self._get_hand_type()
             UpperCAmelCase_	:						str					   =		self._card_values[0]



       @property
       def 			A_ (  self):

             """simple docstring"""





             return self._hand



       def 			A_ (  self  ,lowercase):

             """simple docstring"""





             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 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						Optional[int]					   =		PokerHand._HAND_NAME[self._hand_type - 14]
             UpperCAmelCase_	:						List[Any]					   =		PokerHand._CARD_NAME[self._high_card]
             UpperCAmelCase_	:						Union[str, Any]					   =		PokerHand._CARD_NAME[self._first_pair]
             UpperCAmelCase_	:						List[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]:
                   UpperCAmelCase_	:						Tuple					   =		"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 			A_ (  self  ,lowercase):

             """simple docstring"""





             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 			A_ (  self):

             """simple docstring"""





             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 			A_ (  self):

             """simple docstring"""





             return len(self._card_suit) == 1



       def 			A_ (  self):

             """simple docstring"""





             if sorted(self._card_values) == [2, 3, 4, 5, 14]:
                   if self._card_values[0] == 14:
                         # Remember, our list is sorted in reverse order
                         UpperCAmelCase_	:						Optional[int]					   =		self._card_values.pop(0)
                         self._card_values.append(lowercase)
                   return True
             return False



       def 			A_ (  self):

             """simple docstring"""





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



       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						List[str]					   =		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:
                               UpperCAmelCase_	:						str					   =		self._card_values[i]
                               kind += 1
                         elif vala == self._card_values[i]:
                               kind += 2
                         elif not vala:
                               UpperCAmelCase_	:						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)
             UpperCAmelCase_	:						Tuple					   =		kind + 2 if kind in [4, 5] else kind
             # first meaning first pair to compare in 'compare_with'
             UpperCAmelCase_	:						Tuple					   =		max(lowercase  ,lowercase)
             UpperCAmelCase_	:						Dict					   =		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:
                   UpperCAmelCase_   ,   UpperCAmelCase_	:						List[str]					   =		second, first
             UpperCAmelCase_	:						Tuple					   =		first
             UpperCAmelCase_	:						Union[str, Any]					   =		second
             return kind



       def 			A_ (  self):

             """simple docstring"""





             UpperCAmelCase_	:						dict					   =		{"T": "10", "J": "11", "Q": "12", "K": "13", "A": "14"}
             UpperCAmelCase_	:						Dict					   =		self._hand.translate(str.maketrans(lowercase)).split()
             UpperCAmelCase_	:						Any					   =		[int(card[:-1]) for card in new_hand]
             UpperCAmelCase_	:						Any					   =		{card[-1] for card in new_hand}
             return sorted(lowercase  ,reverse=lowercase), card_suit



       def __repr__(  self):

             """simple docstring"""





             return F"""{self.__class__}(\"{self._hand}\")"""



       def __str__(  self):

             """simple docstring"""





             return self._hand



       def __eq__(  self  ,lowercase):

             """simple docstring"""





             if isinstance(lowercase  ,lowercase):
                   return self.compare_with(lowercase) == "Tie"
             return NotImplemented



       def __lt__(  self  ,lowercase):

             """simple docstring"""





             if isinstance(lowercase  ,lowercase):
                   return self.compare_with(lowercase) == "Loss"
             return NotImplemented



       def __le__(  self  ,lowercase):

             """simple docstring"""





             if isinstance(lowercase  ,lowercase):
                   return self < other or self == other
             return NotImplemented



       def __gt__(  self  ,lowercase):

             """simple docstring"""





             if isinstance(lowercase  ,lowercase):
                   return not self < other and self != other
             return NotImplemented



       def __ge__(  self  ,lowercase):

             """simple docstring"""





             if isinstance(lowercase  ,lowercase):
                   return not self < other
             return NotImplemented



       def __hash__(  self):

             """simple docstring"""





             return object.__hash__(self)







def  _snake_case			(							)  ->  int:






      '''simple docstring'''







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


if __name__ == "__main__":
   solution()





import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


__lowerCamelCase								=      '''\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
'''

__lowerCamelCase								=      '''\
FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
'''


__lowerCamelCase								=      '''
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric("frugalscore")
    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
    >>> print([round(s, 3) for s in results["scores"]])
    [0.631, 0.645]
'''


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




       """simple docstring"""






       def 			A_ (  self):

             """simple docstring"""





             return datasets.MetricInfo(
                 description=_DESCRIPTION  ,citation=_CITATION  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(
                     {
                         "predictions": datasets.Value("string"),
                         "references": datasets.Value("string"),
                     })  ,homepage="https://github.com/moussaKam/FrugalScore"  ,)



       def 			A_ (  self  ,lowercase):

             """simple docstring"""





             if self.config_name == "default":
                   UpperCAmelCase_	:						str					   =		"moussaKam/frugalscore_tiny_bert-base_bert-score"
             else:
                   UpperCAmelCase_	:						Optional[int]					   =		self.config_name
             UpperCAmelCase_	:						Dict					   =		AutoModelForSequenceClassification.from_pretrained(lowercase)
             UpperCAmelCase_	:						str					   =		AutoTokenizer.from_pretrained(lowercase)



       def 			A_ (  self  ,lowercase  ,lowercase  ,lowercase=32  ,lowercase=128  ,lowercase=None  ,):

             """simple docstring"""





             assert len(lowercase) == len(
                 lowercase), "predictions and references should have the same number of sentences."
             if device is not None:
                   assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
             else:
                   UpperCAmelCase_	:						Union[str, Any]					   =		"gpu" if torch.cuda.is_available() else "cpu"
             UpperCAmelCase_	:						Optional[Any]					   =		TrainingArguments(
                 "trainer"  ,fpaa=(device == "gpu")  ,per_device_eval_batch_size=lowercase  ,report_to="all"  ,no_cuda=(device == "cpu")  ,log_level="warning"  ,)
             UpperCAmelCase_	:						List[str]					   =		{"sentence1": predictions, "sentence2": references}
             UpperCAmelCase_	:						List[Any]					   =		datasets.Dataset.from_dict(lowercase)

             def tokenize_function(lowercase):
                   return self.tokenizer(
                       data["sentence1"]  ,data["sentence2"]  ,max_length=lowercase  ,truncation=lowercase  ,padding=lowercase)

             UpperCAmelCase_	:						int					   =		raw_datasets.map(lowercase  ,batched=lowercase)
             tokenized_datasets.remove_columns(["sentence1", "sentence2"])
             UpperCAmelCase_	:						Union[str, Any]					   =		Trainer(self.model  ,lowercase  ,tokenizer=self.tokenizer)
             UpperCAmelCase_	:						Tuple					   =		trainer.predict(lowercase)
             return {"scores": list(predictions.predictions.squeeze(-1))}
