


from __future__ import annotations

import os







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

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





     def __init__(self						,	UpperCAmelCase			)	->  None:
      if not isinstance(UpperCAmelCase						,	UpperCAmelCase			):
       _lowercase				 =f"Hand should be of type 'str': {hand!r}"
       raise TypeError(UpperCAmelCase			)
      # split removes duplicate whitespaces so no need of strip
      if len(hand.split(''' '''			)			) != 5:
       _lowercase				 =f"Hand should contain only 5 cards: {hand!r}"
       raise ValueError(UpperCAmelCase			)
      _lowercase				 =hand
      _lowercase				 =0
      _lowercase				 =0
      _lowercase ,       _lowercase				 =self._internal_state()
      _lowercase				 =self._get_hand_type()
      _lowercase				 =self._card_values[0]





     @property
     def 	__A   (self			)	->  Optional[Any]:
      return self._hand





     def 	__A   (self						,	UpperCAmelCase			)	->  str:
      # 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(UpperCAmelCase			)
       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			)	->  str:
      _lowercase				 =PokerHand._HAND_NAME[self._hand_type - 1_4]
      _lowercase				 =PokerHand._CARD_NAME[self._high_card]
      _lowercase				 =PokerHand._CARD_NAME[self._first_pair]
      _lowercase				 =PokerHand._CARD_NAME[self._second_pair]
      if self._hand_type in [2_2, 1_9, 1_8]:
       return name + f", {high}-high"
      elif self._hand_type in [2_1, 1_7, 1_5]:
       return name + f", {paira}s"
      elif self._hand_type in [2_0, 1_6]:
       _lowercase				 ='''over''' if self._hand_type == 2_0 else '''and'''
       return name + f", {paira}s {join} {paira}s"
      elif self._hand_type == 2_3:
       return name
      else:
       return name + f", {high}"





     def 	__A   (self						,	UpperCAmelCase			)	->  str:
      # 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 	__A   (self			)	->  int:
      # 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 2_3 if sum(self._card_values			) == 6_0 else 2_2
       return 1_9
      elif self._is_five_high_straight() or self._is_straight():
       return 1_8
      return 1_4 + self._is_same_kind()





     def 	__A   (self			)	->  bool:
      return len(self._card_suit			) == 1





     def 	__A   (self			)	->  bool:
      # 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, 1_4]:
       if self._card_values[0] == 1_4:
        # Remember, our list is sorted in reverse order
        _lowercase				 =self._card_values.pop(0			)
        self._card_values.append(UpperCAmelCase			)
       return True
      return False





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





     def 	__A   (self			)	->  int:
      # 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
      _lowercase				 =_lowercase				 =_lowercase				 =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:
         _lowercase				 =self._card_values[i]
         kind += 1
        elif vala == self._card_values[i]:
         kind += 2
        elif not vala:
         _lowercase				 =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)
      _lowercase				 =kind + 2 if kind in [4, 5] else kind
      # first meaning first pair to compare in 'compare_with'
      _lowercase				 =max(UpperCAmelCase						,	UpperCAmelCase			)
      _lowercase				 =min(UpperCAmelCase						,	UpperCAmelCase			)
      # 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(UpperCAmelCase			) != 3:
       _lowercase ,       _lowercase				 =second, first
      _lowercase				 =first
      _lowercase				 =second
      return kind





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





     def __repr__(self			)	->  str:
      return f"{self.__class__}(\"{self._hand}\")"





     def __str__(self			)	->  Any:
      return self._hand





     def __eq__(self						,	UpperCAmelCase			)	->  Optional[Any]:
      if isinstance(UpperCAmelCase						,	UpperCAmelCase			):
       return self.compare_with(UpperCAmelCase			) == "Tie"
      return NotImplemented





     def __lt__(self						,	UpperCAmelCase			)	->  Dict:
      if isinstance(UpperCAmelCase						,	UpperCAmelCase			):
       return self.compare_with(UpperCAmelCase			) == "Loss"
      return NotImplemented





     def __le__(self						,	UpperCAmelCase			)	->  List[Any]:
      if isinstance(UpperCAmelCase						,	UpperCAmelCase			):
       return self < other or self == other
      return NotImplemented





     def __gt__(self						,	UpperCAmelCase			)	->  Dict:
      if isinstance(UpperCAmelCase						,	UpperCAmelCase			):
       return not self < other and self != other
      return NotImplemented





     def __ge__(self						,	UpperCAmelCase			)	->  Dict:
      if isinstance(UpperCAmelCase						,	UpperCAmelCase			):
       return not self < other
      return NotImplemented





     def __hash__(self			)	->  Optional[int]:
      return object.__hash__(self			)



def 							UpperCAmelCase_   (				)	->     int:







 """simple docstring"""


 _lowercase				 =0
 _lowercase				 =os.path.abspath(os.path.dirname(__snake_case		)		)
 _lowercase				 =os.path.join(__snake_case	,		'''poker_hands.txt'''		)
 with open(__snake_case		) as file_hand:
  for line in file_hand:
   _lowercase				 =line[:14].strip()
   _lowercase				 =line[15:].strip()
   _lowercase ,       _lowercase				 =PokerHand(__snake_case		), PokerHand(__snake_case		)
   _lowercase				 =player.compare_with(__snake_case		)
   if output == "Win":
    answer += 1
 return answer


if __name__ == "__main__":
  solution()





from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.mpnet.modeling_tf_mpnet import (
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetModel,
  )







class  lowerCamelCase__  :





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase=1_3						,	UpperCAmelCase=7						,	UpperCAmelCase=True						,	UpperCAmelCase=True						,	UpperCAmelCase=False						,	UpperCAmelCase=True						,	UpperCAmelCase=9_9						,	UpperCAmelCase=6_4						,	UpperCAmelCase=2						,	UpperCAmelCase=4						,	UpperCAmelCase=6_4						,	UpperCAmelCase="gelu"						,	UpperCAmelCase=0.1						,	UpperCAmelCase=0.1						,	UpperCAmelCase=5_1_2						,	UpperCAmelCase=1_6						,	UpperCAmelCase=2						,	UpperCAmelCase=0.02						,	UpperCAmelCase=3						,	UpperCAmelCase=4						,	UpperCAmelCase=None						,	)	->  Optional[Any]:
      _lowercase				 =parent
      _lowercase				 =batch_size
      _lowercase				 =seq_length
      _lowercase				 =is_training
      _lowercase				 =use_input_mask
      _lowercase				 =use_token_type_ids
      _lowercase				 =use_labels
      _lowercase				 =vocab_size
      _lowercase				 =hidden_size
      _lowercase				 =num_hidden_layers
      _lowercase				 =num_attention_heads
      _lowercase				 =intermediate_size
      _lowercase				 =hidden_act
      _lowercase				 =hidden_dropout_prob
      _lowercase				 =attention_probs_dropout_prob
      _lowercase				 =max_position_embeddings
      _lowercase				 =type_vocab_size
      _lowercase				 =type_sequence_label_size
      _lowercase				 =initializer_range
      _lowercase				 =num_labels
      _lowercase				 =num_choices
      _lowercase				 =scope





     def 	__A   (self			)	->  Union[str, Any]:
      _lowercase				 =ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size			)

      _lowercase				 =None
      if self.use_input_mask:
       _lowercase				 =random_attention_mask([self.batch_size, self.seq_length]			)

      _lowercase				 =None
      _lowercase				 =None
      _lowercase				 =None
      if self.use_labels:
       _lowercase				 =ids_tensor([self.batch_size]						,	self.type_sequence_label_size			)
       _lowercase				 =ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels			)
       _lowercase				 =ids_tensor([self.batch_size]						,	self.num_choices			)

      _lowercase				 =MPNetConfig(
          vocab_size=self.vocab_size						,	hidden_size=self.hidden_size						,	num_hidden_layers=self.num_hidden_layers						,	num_attention_heads=self.num_attention_heads						,	intermediate_size=self.intermediate_size						,	hidden_act=self.hidden_act						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	initializer_range=self.initializer_range						,	)
      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Tuple:
      _lowercase				 =TFMPNetModel(config=UpperCAmelCase			)
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _lowercase				 =model(UpperCAmelCase			)
      _lowercase				 =[input_ids, input_mask]
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  int:
      _lowercase				 =TFMPNetForMaskedLM(config=UpperCAmelCase			)
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Union[str, Any]:
      _lowercase				 =TFMPNetForQuestionAnswering(config=UpperCAmelCase			)
      _lowercase				 ={
          '''input_ids''': input_ids,
          '''attention_mask''': input_mask,
      }
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.start_logits.shape						,	(self.batch_size, self.seq_length)			)
      self.parent.assertEqual(result.end_logits.shape						,	(self.batch_size, self.seq_length)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Dict:
      _lowercase				 =self.num_labels
      _lowercase				 =TFMPNetForSequenceClassification(UpperCAmelCase			)
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  List[Any]:
      _lowercase				 =self.num_choices
      _lowercase				 =TFMPNetForMultipleChoice(UpperCAmelCase			)
      _lowercase				 =tf.tile(tf.expand_dims(UpperCAmelCase						,	1			)						,	(1, self.num_choices, 1)			)
      _lowercase				 =tf.tile(tf.expand_dims(UpperCAmelCase						,	1			)						,	(1, self.num_choices, 1)			)
      _lowercase				 ={
          '''input_ids''': multiple_choice_inputs_ids,
          '''attention_mask''': multiple_choice_input_mask,
      }
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)			)





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase			)	->  Dict:
      _lowercase				 =self.num_labels
      _lowercase				 =TFMPNetForTokenClassification(UpperCAmelCase			)
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      _lowercase				 =model(UpperCAmelCase			)
      self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)			)





     def 	__A   (self			)	->  List[Any]:
      _lowercase				 =self.prepare_config_and_inputs()
      ((_lowercase) ,       (_lowercase) ,       (_lowercase) ,       (_lowercase) ,       (_lowercase) ,       (_lowercase))				 =config_and_inputs
      _lowercase				 ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict







@require_tf
class  lowerCamelCase__  (			lowerCAmelCase  ,				lowerCAmelCase  ,				unittest.TestCase):
     SCREAMING_SNAKE_CASE__      =     (
         (
             TFMPNetForMaskedLM,
             TFMPNetForMultipleChoice,
             TFMPNetForQuestionAnswering,
             TFMPNetForSequenceClassification,
             TFMPNetForTokenClassification,
             TFMPNetModel,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__      =     (
         {
             '''feature-extraction''': TFMPNetModel,
             '''fill-mask''': TFMPNetForMaskedLM,
             '''question-answering''': TFMPNetForQuestionAnswering,
             '''text-classification''': TFMPNetForSequenceClassification,
             '''token-classification''': TFMPNetForTokenClassification,
             '''zero-shot''': TFMPNetForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     SCREAMING_SNAKE_CASE__      =     False
     SCREAMING_SNAKE_CASE__      =     False





     def 	__A   (self			)	->  Union[str, Any]:
      _lowercase				 =TFMPNetModelTester(self			)
      _lowercase				 =ConfigTester(self						,	config_class=UpperCAmelCase						,	hidden_size=3_7			)





     def 	__A   (self			)	->  Optional[Any]:
      self.config_tester.run_common_tests()





     def 	__A   (self			)	->  Tuple:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_model(*UpperCAmelCase			)





     def 	__A   (self			)	->  Tuple:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_masked_lm(*UpperCAmelCase			)





     def 	__A   (self			)	->  str:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_question_answering(*UpperCAmelCase			)





     def 	__A   (self			)	->  List[str]:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_sequence_classification(*UpperCAmelCase			)





     def 	__A   (self			)	->  Any:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_multiple_choice(*UpperCAmelCase			)





     def 	__A   (self			)	->  List[str]:
      _lowercase				 =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_token_classification(*UpperCAmelCase			)





     @slow
     def 	__A   (self			)	->  Tuple:
      for model_name in ["microsoft/mpnet-base"]:
       _lowercase				 =TFMPNetModel.from_pretrained(UpperCAmelCase			)
       self.assertIsNotNone(UpperCAmelCase			)







@require_tf
class  lowerCamelCase__  (			unittest.TestCase):





     @slow
     def 	__A   (self			)	->  Dict:
      _lowercase				 =TFMPNetModel.from_pretrained('''microsoft/mpnet-base'''			)
      _lowercase				 =tf.constant([[0, 1, 2, 3, 4, 5]]			)
      _lowercase				 =model(UpperCAmelCase			)[0]

      _lowercase				 =[1, 6, 7_6_8]
      self.assertEqual(output.shape						,	UpperCAmelCase			)

      _lowercase				 =tf.constant(
          [
              [
                  [-0.106_7172, 0.0821_6473, 0.002_4543],
                  [-0.0346_5879, 0.835_4118, -0.0325_2288],
                  [-0.0656_9476, -0.1242_4111, -0.049_4436],
              ]
          ]			)
      tf.debugging.assert_near(output[:, :3, :3]						,	UpperCAmelCase						,	atol=1e-4			)

