



'''simple docstring'''






from __future__ import annotations

import os



class 				lowercase			:


 """simple docstring"""


 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    ,a_				)						->  None:
        if not isinstance(a_    ,a_				):
               _UpperCAmelCase    :		Any     	=					f'''Hand should be of type \'str\': {hand!r}'''
               raise TypeError(a_				)
        # split removes duplicate whitespaces so no need of strip
        if len(hand.split(""" """				)				) != 5:
               _UpperCAmelCase    :		List[str]     	=					f'''Hand should contain only 5 cards: {hand!r}'''
               raise ValueError(a_				)
        _UpperCAmelCase    :		int     	=					hand
        _UpperCAmelCase    :		str     	=					0
        _UpperCAmelCase    :		Optional[int]     	=					0
        _UpperCAmelCase	,_UpperCAmelCase    :		str     	=					self._internal_state()
        _UpperCAmelCase    :		Tuple     	=					self._get_hand_type()
        _UpperCAmelCase    :		Union[str, Any]     	=					self._card_values[0]







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







 def _snake_case					(				self    ,a_				)						->  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(a_				)
               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				)						->  str:
        _UpperCAmelCase    :		Any     	=					PokerHand._HAND_NAME[self._hand_type - 14]
        _UpperCAmelCase    :		List[str]     	=					PokerHand._CARD_NAME[self._high_card]
        _UpperCAmelCase    :		Optional[int]     	=					PokerHand._CARD_NAME[self._first_pair]
        _UpperCAmelCase    :		Any     	=					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    :		Dict     	=					"""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    ,a_				)						->  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 _snake_case					(				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 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				)						->  bool:
        return len(self._card_suit				) == 1







 def _snake_case					(				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, 14]:
               if self._card_values[0] == 14:
                      # Remember, our list is sorted in reverse order
                      _UpperCAmelCase    :		List[str]     	=					self._card_values.pop(0				)
                      self._card_values.append(a_				)
               return True
        return False







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







 def _snake_case					(				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
        _UpperCAmelCase    :		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:
                             _UpperCAmelCase    :		Any     	=					self._card_values[i]
                             kind += 1
                      elif vala == self._card_values[i]:
                             kind += 2
                      elif not vala:
                             _UpperCAmelCase    :		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    :		List[str]     	=					kind + 2 if kind in [4, 5] else kind
        # first meaning first pair to compare in 'compare_with'
        _UpperCAmelCase    :		Union[str, Any]     	=					max(a_    ,a_				)
        _UpperCAmelCase    :		Any     	=					min(a_    ,a_				)
        # 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(a_				) != 3:
               _UpperCAmelCase	,_UpperCAmelCase    :		Tuple     	=					second, first
        _UpperCAmelCase    :		List[str]     	=					first
        _UpperCAmelCase    :		Dict     	=					second
        return kind







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







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







 def __str__(				self				)						->  Optional[int]:
        return self._hand







 def __eq__(				self    ,a_				)						->  Tuple:
        if isinstance(a_    ,a_				):
               return self.compare_with(a_				) == "Tie"
        return NotImplemented







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







 def __le__(				self    ,a_				)						->  Optional[int]:
        if isinstance(a_    ,a_				):
               return self < other or self == other
        return NotImplemented







 def __gt__(				self    ,a_				)						->  Optional[Any]:
        if isinstance(a_    ,a_				):
               return not self < other and self != other
        return NotImplemented







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







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







def        snake_case_   (	)->						int:





       '''simple docstring'''


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


if __name__ == "__main__":
   solution()






import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
      from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import PoolFormerImageProcessor


class      lowerCamelCase_ (			_lowerCAmelCase):




 def     a       (			self							):
   __UpperCAmelCase	:  Union[str, Any]		=     self.config_class(**self.inputs_dict							)
   self.parent.assertTrue(hasattr(_UpperCAmelCase  ,  'hidden_sizes'							)							)
   self.parent.assertTrue(hasattr(_UpperCAmelCase  ,  'num_encoder_blocks'							)							)



class      lowerCamelCase_ :




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase=13  ,  _UpperCAmelCase=64  ,  _UpperCAmelCase=3  ,  _UpperCAmelCase=4  ,  _UpperCAmelCase=[2, 2, 2, 2]  ,  _UpperCAmelCase=[8, 4, 2, 1]  ,  _UpperCAmelCase=[16, 32, 64, 128]  ,  _UpperCAmelCase=[1, 4, 8, 16]  ,  _UpperCAmelCase=False  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase="gelu"  ,  _UpperCAmelCase=0.1  ,  _UpperCAmelCase=0.0_2  ,  _UpperCAmelCase=3  ,  _UpperCAmelCase=None  ,  ):
   __UpperCAmelCase	:  Optional[int]		=     parent
   __UpperCAmelCase	:  str		=     batch_size
   __UpperCAmelCase	:  Optional[int]		=     image_size
   __UpperCAmelCase	:  Dict		=     num_channels
   __UpperCAmelCase	:  int		=     num_encoder_blocks
   __UpperCAmelCase	:  str		=     sr_ratios
   __UpperCAmelCase	:  List[Any]		=     depths
   __UpperCAmelCase	:  Optional[int]		=     hidden_sizes
   __UpperCAmelCase	:  List[str]		=     downsampling_rates
   __UpperCAmelCase	:  Dict		=     is_training
   __UpperCAmelCase	:  Optional[int]		=     use_labels
   __UpperCAmelCase	:  Optional[Any]		=     hidden_act
   __UpperCAmelCase	:  int		=     hidden_dropout_prob
   __UpperCAmelCase	:  Tuple		=     initializer_range
   __UpperCAmelCase	:  str		=     num_labels
   __UpperCAmelCase	:  Union[str, Any]		=     scope




 def     a       (			self							):
   __UpperCAmelCase	:  Optional[int]		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

   __UpperCAmelCase	:  Optional[Any]		=     None
   if self.use_labels:
     __UpperCAmelCase	:  Optional[int]		=     ids_tensor([self.batch_size, self.image_size, self.image_size]  ,  self.num_labels							)

   __UpperCAmelCase	:  Tuple		=     PoolFormerConfig(
       image_size=self.image_size  ,  num_channels=self.num_channels  ,  num_encoder_blocks=self.num_encoder_blocks  ,  depths=self.depths  ,  hidden_sizes=self.hidden_sizes  ,  hidden_act=self.hidden_act  ,  hidden_dropout_prob=self.hidden_dropout_prob  ,  initializer_range=self.initializer_range  ,  )

   return config, pixel_values, labels




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Any		=     PoolFormerModel(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  int		=     model(_UpperCAmelCase							)
   __UpperCAmelCase	:  Dict		=     self.image_size // 3_2.0
   self.parent.assertEqual(
       result.last_hidden_state.shape  ,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)




 def     a       (			self							):
   __UpperCAmelCase	:  Tuple		=     self.prepare_config_and_inputs()
   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  Union[str, Any]		=     config_and_inputs
   __UpperCAmelCase	:  str		=     {'pixel_values': pixel_values}
   return config, inputs_dict



@require_torch
class      lowerCamelCase_ (			_lowerCAmelCase	,				_lowerCAmelCase	,				unittest.TestCase):
 _lowerCAmelCase									=	(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
 _lowerCAmelCase									=	(
     {'''feature-extraction''': PoolFormerModel, '''image-classification''': PoolFormerForImageClassification}
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False




 def     a       (			self							):
   __UpperCAmelCase	:  str		=     PoolFormerModelTester(self							)
   __UpperCAmelCase	:  List[str]		=     PoolFormerConfigTester(self  ,  config_class=_UpperCAmelCase							)




 def     a       (			self							):
   self.config_tester.run_common_tests()




 def     a       (			self							):
   __UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_UpperCAmelCase							)




 @unittest.skip('PoolFormer does not use inputs_embeds'							)
 def     a       (			self							):
   pass




 @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'							)
 def     a       (			self							):
   pass




 def     a       (			self							):
   def check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
     __UpperCAmelCase	:  Union[str, Any]		=     model_class(_UpperCAmelCase							)
     model.to(_UpperCAmelCase							)
     model.eval()

     with torch.no_grad():
       __UpperCAmelCase	:  Dict		=     model(**self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)							)

     __UpperCAmelCase	:  Tuple		=     outputs.hidden_states

     __UpperCAmelCase	:  Tuple		=     self.model_tester.num_encoder_blocks
     self.assertEqual(len(_UpperCAmelCase							)  ,  _UpperCAmelCase							)

     # verify the first hidden states (first block)
     self.assertListEqual(
         list(hidden_states[0].shape[-3:]							)  ,  [
             self.model_tester.hidden_sizes[0],
             self.model_tester.image_size // 4,
             self.model_tester.image_size // 4,
         ]  ,  )

   __UpperCAmelCase,			__UpperCAmelCase	:  int		=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __UpperCAmelCase	:  Optional[int]		=     True
     check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     __UpperCAmelCase	:  int		=     True

     check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							)




 def     a       (			self							):
   if not self.model_tester.is_training:
     return

   __UpperCAmelCase,			__UpperCAmelCase	:  int		=     self.model_tester.prepare_config_and_inputs_for_common()
   __UpperCAmelCase	:  Dict		=     True

   for model_class in self.all_model_classes:
     if model_class in get_values(_UpperCAmelCase							):
       continue
     __UpperCAmelCase	:  Union[str, Any]		=     model_class(_UpperCAmelCase							)
     model.to(_UpperCAmelCase							)
     model.train()
     __UpperCAmelCase	:  Any		=     self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase  ,  return_labels=_UpperCAmelCase							)
     __UpperCAmelCase	:  Optional[Any]		=     model(**_UpperCAmelCase							).loss
     loss.backward()




 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Dict		=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __UpperCAmelCase	:  List[Any]		=     model_class(_UpperCAmelCase							)
     __UpperCAmelCase	:  Dict		=     inspect.signature(model.forward							)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __UpperCAmelCase	:  Dict		=     [*signature.parameters.keys()]

     __UpperCAmelCase	:  Any		=     ['pixel_values']
     self.assertListEqual(arg_names[:1]  ,  _UpperCAmelCase							)




 @slow
 def     a       (			self							):
   for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __UpperCAmelCase	:  Dict		=     PoolFormerModel.from_pretrained(_UpperCAmelCase							)
     self.assertIsNotNone(_UpperCAmelCase							)





def  _snake_case		()							->		str:
  __UpperCAmelCase	:  Union[str, Any]		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
  return image







@require_torch
class      lowerCamelCase_ (			unittest.TestCase):




 @slow
 def     a       (			self							):
   __UpperCAmelCase	:  Optional[Any]		=     PoolFormerImageProcessor()
   __UpperCAmelCase	:  Tuple		=     PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'							).to(_UpperCAmelCase							)

   __UpperCAmelCase	:  Optional[Any]		=     image_processor(images=prepare_img()  ,  return_tensors='pt'							).to(_UpperCAmelCase							)

   # forward pass
   with torch.no_grad():
     __UpperCAmelCase	:  Tuple		=     model(**_UpperCAmelCase							)

   # verify the logits
   __UpperCAmelCase	:  Optional[Any]		=     torch.Size((1, 1_000)							)
   self.assertEqual(outputs.logits.shape  ,  _UpperCAmelCase							)

   __UpperCAmelCase	:  Dict		=     torch.tensor([-0.6_1_1_3, 0.1_6_8_5, -0.0_4_9_2]							).to(_UpperCAmelCase							)
   self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,  _UpperCAmelCase  ,  atol=1E-4							)							)





