

"""simple docstring"""

from __future__ import annotations

import os






class  __a				:





       """simple docstring"""


       __UpperCamelCase				:			Union[str, Any]          =	(
           'High card',
           'One pair',
           'Two pairs',
           'Three of a kind',
           'Straight',
           'Flush',
           'Full house',
           'Four of a kind',
           'Straight flush',
           'Royal flush',
       )

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






       def __init__(      self ,				snake_case      ):





        """simple docstring"""

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






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return self._hand






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """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(snake_case      )
         else:
          return "Win" if self._second_pair > other._second_pair else "Loss"
        return "Win" if self._first_pair > other._first_pair else "Loss"






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							PokerHand._HAND_NAME[self._hand_type - 14]
        lowerCAmelCase__      :							str    =							PokerHand._CARD_NAME[self._high_card]
        lowerCAmelCase__      :							int    =							PokerHand._CARD_NAME[self._first_pair]
        lowerCAmelCase__      :							Optional[int]    =							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]:
         lowerCAmelCase__      :							int    =							"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  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """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  SCREAMING_SNAKE_CASE_  (      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  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return len(self._card_suit      ) == 1






       def  SCREAMING_SNAKE_CASE_  (      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
          lowerCAmelCase__      :							Any    =							self._card_values.pop(0      )
          self._card_values.append(snake_case      )
         return True
        return False






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

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






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

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






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

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






       def __repr__(      self      ):





        """simple docstring"""

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






       def __str__(      self      ):





        """simple docstring"""

        return self._hand






       def __eq__(      self ,				snake_case      ):





        """simple docstring"""

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






       def __lt__(      self ,				snake_case      ):





        """simple docstring"""

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






       def __le__(      self ,				snake_case      ):





        """simple docstring"""

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






       def __gt__(      self ,				snake_case      ):





        """simple docstring"""

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






       def __ge__(      self ,				snake_case      ):





        """simple docstring"""

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






       def __hash__(      self      ):





        """simple docstring"""

        return object.__hash__(self      )




def 			SCREAMING_SNAKE_CASE       (   ) ->	int:
 # Solution for problem number 54 from Project Euler
 # Input from poker_hands.txt file
 lowerCAmelCase__      :							Tuple    =							0
 lowerCAmelCase__      :							Tuple    =							os.path.abspath(os.path.dirname(lowercase__      )      )
 lowerCAmelCase__      :							Tuple    =							os.path.join(lowercase__				,				"poker_hands.txt"      )
 with open(lowercase__      ) as file_hand:
  for line in file_hand:
   lowerCAmelCase__      :							Optional[Any]    =							line[:1_4].strip()
   lowerCAmelCase__      :							Optional[int]    =							line[1_5:].strip()
   lowerCAmelCase__ ,	lowerCAmelCase__      :							int    =							PokerHand(lowercase__      ), PokerHand(lowercase__      )
   lowerCAmelCase__      :							Union[str, Any]    =							player.compare_with(lowercase__      )
   if output == "Win":
    answer += 1
 return answer


if __name__ == "__main__":
    solution()









"""simple docstring"""

import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 EfficientNetForImageClassification, EfficientNetModel
    from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor






class  __a				:





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case=13 ,				snake_case=32 ,				snake_case=3 ,				snake_case=[3, 3, 5] ,				snake_case=[32, 16, 24] ,				snake_case=[16, 24, 40] ,				snake_case=[1, 1, 2] ,				snake_case=[1, 1, 2] ,				snake_case=[1, 6, 6] ,				snake_case=True ,				snake_case=True ,				snake_case=37 ,				snake_case="gelu" ,				snake_case=10 ,				):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							parent
        lowerCAmelCase__      :							int    =							batch_size
        lowerCAmelCase__      :							Dict    =							image_size
        lowerCAmelCase__      :							str    =							num_channels
        lowerCAmelCase__      :							Union[str, Any]    =							kernel_sizes
        lowerCAmelCase__      :							int    =							in_channels
        lowerCAmelCase__      :							Dict    =							out_channels
        lowerCAmelCase__      :							str    =							strides
        lowerCAmelCase__      :							int    =							num_block_repeats
        lowerCAmelCase__      :							str    =							expand_ratios
        lowerCAmelCase__      :							List[Any]    =							is_training
        lowerCAmelCase__      :							Any    =							hidden_act
        lowerCAmelCase__      :							Any    =							num_labels
        lowerCAmelCase__      :							int    =							use_labels






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

        lowerCAmelCase__      :							List[str]    =							None
        if self.use_labels:
         lowerCAmelCase__      :							int    =							ids_tensor([self.batch_size] ,				self.num_labels      )

        lowerCAmelCase__      :							List[str]    =							self.get_config()
        return config, pixel_values, labels






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return EfficientNetConfig(
            num_channels=self.num_channels ,				kernel_sizes=self.kernel_sizes ,				in_channels=self.in_channels ,				out_channels=self.out_channels ,				strides=self.strides ,				num_block_repeats=self.num_block_repeats ,				expand_ratios=self.expand_ratios ,				hidden_act=self.hidden_act ,				num_labels=self.num_labels ,				)






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							EfficientNetModel(config=snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							Tuple    =							model(snake_case      )
        # expected last hidden states: B, C, H // 4, W // 4
        self.parent.assertEqual(
            result.last_hidden_state.shape ,				(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4) ,				)






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							EfficientNetForImageClassification(snake_case      )
        model.to(snake_case      )
        model.eval()
        lowerCAmelCase__      :							str    =							model(snake_case ,				labels=snake_case      )
        self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							str    =							self.prepare_config_and_inputs()
        lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Dict    =							config_and_inputs
        lowerCAmelCase__      :							Dict    =							{"pixel_values": pixel_values}
        return config, inputs_dict






@require_torch
class  __a				(	__magic_name__					,  __magic_name__					,  unittest.TestCase					):





       """simple docstring"""


       __UpperCamelCase				:			Any          =	(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
       __UpperCamelCase				:			Dict          =	(
           {'feature-extraction': EfficientNetModel, 'image-classification': EfficientNetForImageClassification}
           if is_torch_available()
           else {}
       )

       __UpperCamelCase				:			str          =	False
       __UpperCamelCase				:			List[Any]          =	False
       __UpperCamelCase				:			Dict          =	False
       __UpperCamelCase				:			Optional[int]          =	False
       __UpperCamelCase				:			List[Any]          =	False






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							EfficientNetModelTester(self      )
        lowerCAmelCase__      :							Tuple    =							ConfigTester(
            self ,				config_class=snake_case ,				has_text_modality=snake_case ,				hidden_size=37      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return






       @unittest.skip(reason="EfficientNet does not use inputs_embeds"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @unittest.skip(reason="EfficientNet does not support input and output embeddings"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @unittest.skip(reason="EfficientNet does not use feedforward chunking"      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__      :							Union[str, Any]    =							model_class(snake_case      )
         lowerCAmelCase__      :							Union[str, Any]    =							inspect.signature(model.forward      )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowerCAmelCase__      :							Union[str, Any]    =							[*signature.parameters.keys()]

         lowerCAmelCase__      :							List[str]    =							["pixel_values"]
         self.assertListEqual(arg_names[:1] ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        def check_hidden_states_output(snake_case ,				snake_case ,				snake_case      ):
         lowerCAmelCase__      :							Tuple    =							model_class(snake_case      )
         model.to(snake_case      )
         model.eval()

         with torch.no_grad():
          lowerCAmelCase__      :							List[str]    =							model(**self._prepare_for_class(snake_case ,				snake_case      )      )

         lowerCAmelCase__      :							Tuple    =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
         lowerCAmelCase__      :							Any    =							sum(config.num_block_repeats      ) * 4
         self.assertEqual(len(snake_case      ) ,				snake_case      )

         # EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]      ) ,				[self.model_tester.image_size // 2, self.model_tester.image_size // 2] ,				)

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Optional[int]    =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowerCAmelCase__      :							str    =							True
         check_hidden_states_output(snake_case ,				snake_case ,				snake_case      )

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

         check_hidden_states_output(snake_case ,				snake_case ,				snake_case      )






       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case      )






       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."      )
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        pass






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCAmelCase__      :							str    =							EfficientNetModel.from_pretrained(snake_case      )
         self.assertIsNotNone(snake_case      )






       @is_pipeline_test
       @require_vision
       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        super().test_pipeline_image_classification()




def 			SCREAMING_SNAKE_CASE       (   ) ->	Dict:
 lowerCAmelCase__      :							List[Any]    =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
 return image






@require_torch
@require_vision
class  __a				(	unittest.TestCase					):





       """simple docstring"""


       @cached_property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        return AutoImageProcessor.from_pretrained("google/efficientnet-b7"      ) if is_vision_available() else None






       @slow
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							EfficientNetForImageClassification.from_pretrained("google/efficientnet-b7"      ).to(snake_case      )

        lowerCAmelCase__      :							Union[str, Any]    =							self.default_image_processor
        lowerCAmelCase__      :							Tuple    =							prepare_img()
        lowerCAmelCase__      :							Dict    =							image_processor(images=snake_case ,				return_tensors="pt"      ).to(snake_case      )

        # forward pass
        with torch.no_grad():
         lowerCAmelCase__      :							str    =							model(**snake_case      )

        # verify the logits
        lowerCAmelCase__      :							Any    =							torch.Size((1, 1_000)      )
        self.assertEqual(outputs.logits.shape ,				snake_case      )

        lowerCAmelCase__      :							Union[str, Any]    =							torch.tensor([0.0_001, 0.0_002, 0.0_002]      ).to(snake_case      )
        self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				snake_case ,				atol=1e-4      )      )






