"""simple docstring"""







from __future__ import annotations

import os



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

				_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     :							str      ,       lowerCamelCase_     :							str    ):

					"""simple docstring"""






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







				@property
				def 				__UpperCAmelCase       (			self     :							Dict    ):

					"""simple docstring"""






					return self._hand







				def 				__UpperCAmelCase       (			self     :							Optional[int]      ,       lowerCamelCase_     :							PokerHand    ):

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







				def 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					_lowercase			:     int      					=				PokerHand._HAND_NAME[self._hand_type - 1_4]
					_lowercase			:     Tuple      					=				PokerHand._CARD_NAME[self._high_card]
					_lowercase			:     Union[str, Any]      					=				PokerHand._CARD_NAME[self._first_pair]
					_lowercase			:     Optional[int]      					=				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			:     Tuple      					=				'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 				__UpperCAmelCase       (			self     :							Any      ,       lowerCamelCase_     :							PokerHand    ):

					"""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 				__UpperCAmelCase       (			self     :							int    ):

					"""simple docstring"""






					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 				__UpperCAmelCase       (			self     :							Tuple    ):

					"""simple docstring"""






					return len(self._card_suit    ) == 1







				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






					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			:     Union[str, Any]      					=				self._card_values.pop(0    )
							self._card_values.append(lowerCamelCase_    )
						return True
					return False







				def 				__UpperCAmelCase       (			self     :							List[Any]    ):

					"""simple docstring"""






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







				def 				__UpperCAmelCase       (			self     :							Optional[int]    ):

					"""simple docstring"""






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







				def 				__UpperCAmelCase       (			self     :							Tuple    ):

					"""simple docstring"""






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







				def __repr__(			self     :							Any    ):

					"""simple docstring"""






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







				def __str__(			self     :							str    ):

					"""simple docstring"""






					return self._hand







				def __eq__(			self     :							str      ,       lowerCamelCase_     :							Any    ):

					"""simple docstring"""






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







				def __lt__(			self     :							Union[str, Any]      ,       lowerCamelCase_     :							Tuple    ):

					"""simple docstring"""






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







				def __le__(			self     :							List[str]      ,       lowerCamelCase_     :							Union[str, Any]    ):

					"""simple docstring"""






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







				def __gt__(			self     :							List[str]      ,       lowerCamelCase_     :							List[str]    ):

					"""simple docstring"""






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







				def __ge__(			self     :							Optional[Any]      ,       lowerCamelCase_     :							int    ):

					"""simple docstring"""






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







				def __hash__(			self     :							Union[str, Any]    ):

					"""simple docstring"""






					return object.__hash__(self    )



def   __lowerCAmelCase(		):






	"""simple docstring"""





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


if __name__ == "__main__":
						solution()




"""simple docstring"""







from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin



@dataclass
class 			_lowerCamelCase      (__lowerCamelCase  ):
				_snake_case  =	42



class 			_lowerCamelCase      (__lowerCamelCase    ,				__lowerCamelCase  ):







				@register_to_config
				def __init__(			self     :							Dict      ,       lowerCamelCase_     :							int = 1_6      ,       lowerCamelCase_     :							int = 8_8      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							int = 1      ,       lowerCamelCase_     :							float = 0.0      ,       lowerCamelCase_     :							int = 3_2      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							bool = False      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							str = "geglu"      ,       lowerCamelCase_     :							Optional[int] = None      ,       lowerCamelCase_     :							bool = False      ,       lowerCamelCase_     :							bool = False      ,       lowerCamelCase_     :							bool = False      ,       lowerCamelCase_     :							str = "layer_norm"      ,       lowerCamelCase_     :							bool = True      ,       ):

					"""simple docstring"""






					super().__init__()
					_lowercase			:     Tuple      					=				use_linear_projection
					_lowercase			:     List[Any]      					=				num_attention_heads
					_lowercase			:     Optional[int]      					=				attention_head_dim
					_lowercase			:     Union[str, Any]      					=				num_attention_heads * attention_head_dim

					# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
					# Define whether input is continuous or discrete depending on configuration
					_lowercase			:     Dict      					=				(in_channels is not None) and (patch_size is None)
					_lowercase			:     Any      					=				num_vector_embeds is not None
					_lowercase			:     List[Any]      					=				in_channels is not None and patch_size is not None

					if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
						_lowercase			:     Tuple      					=				(
						    F'''The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'''
						    ' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'
						    ' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'
						    ' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'
						    ' would be very nice if you could open a Pull request for the `transformer/config.json` file'
						)
						deprecate('norm_type!=num_embeds_ada_norm'      ,       '1.0.0'      ,       lowerCamelCase_      ,       standard_warn=lowerCamelCase_    )
						_lowercase			:     Dict      					=				'ada_norm'

					if self.is_input_continuous and self.is_input_vectorized:
						raise ValueError(
						    F'''Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'''
						    ' sure that either `in_channels` or `num_vector_embeds` is None.'    )
					elif self.is_input_vectorized and self.is_input_patches:
						raise ValueError(
						    F'''Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'''
						    ' sure that either `num_vector_embeds` or `num_patches` is None.'    )
					elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
						raise ValueError(
						    F'''Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'''
						    F''' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.'''    )

					# 2. Define input layers
					if self.is_input_continuous:
						_lowercase			:     int      					=				in_channels

						_lowercase			:     List[Any]      					=				torch.nn.GroupNorm(num_groups=lowerCamelCase_      ,       num_channels=lowerCamelCase_      ,       eps=1E-6      ,       affine=lowerCamelCase_    )
						if use_linear_projection:
							_lowercase			:     Any      					=				nn.Linear(lowerCamelCase_      ,       lowerCamelCase_    )
						else:
							_lowercase			:     List[str]      					=				nn.Convad(lowerCamelCase_      ,       lowerCamelCase_      ,       kernel_size=1      ,       stride=1      ,       padding=0    )
					elif self.is_input_vectorized:
						assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
						assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

						_lowercase			:     int      					=				sample_size
						_lowercase			:     str      					=				sample_size
						_lowercase			:     int      					=				num_vector_embeds
						_lowercase			:     List[str]      					=				self.height * self.width

						_lowercase			:     Optional[int]      					=				ImagePositionalEmbeddings(
						    num_embed=lowerCamelCase_      ,       embed_dim=lowerCamelCase_      ,       height=self.height      ,       width=self.width    )
					elif self.is_input_patches:
						assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

						_lowercase			:     Optional[Any]      					=				sample_size
						_lowercase			:     Optional[int]      					=				sample_size

						_lowercase			:     str      					=				patch_size
						_lowercase			:     Union[str, Any]      					=				PatchEmbed(
						    height=lowerCamelCase_      ,       width=lowerCamelCase_      ,       patch_size=lowerCamelCase_      ,       in_channels=lowerCamelCase_      ,       embed_dim=lowerCamelCase_      ,       )

					# 3. Define transformers blocks
					_lowercase			:     Any      					=				nn.ModuleList(
					    [
					        BasicTransformerBlock(
					            lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       dropout=lowerCamelCase_      ,       cross_attention_dim=lowerCamelCase_      ,       activation_fn=lowerCamelCase_      ,       num_embeds_ada_norm=lowerCamelCase_      ,       attention_bias=lowerCamelCase_      ,       only_cross_attention=lowerCamelCase_      ,       upcast_attention=lowerCamelCase_      ,       norm_type=lowerCamelCase_      ,       norm_elementwise_affine=lowerCamelCase_      ,       )
					        for d in range(lowerCamelCase_    )
					    ]    )

					# 4. Define output layers
					_lowercase			:     Dict      					=				in_channels if out_channels is None else out_channels
					if self.is_input_continuous:
						# TODO: should use out_channels for continuous projections
						if use_linear_projection:
							_lowercase			:     str      					=				nn.Linear(lowerCamelCase_      ,       lowerCamelCase_    )
						else:
							_lowercase			:     List[Any]      					=				nn.Convad(lowerCamelCase_      ,       lowerCamelCase_      ,       kernel_size=1      ,       stride=1      ,       padding=0    )
					elif self.is_input_vectorized:
						_lowercase			:     Dict      					=				nn.LayerNorm(lowerCamelCase_    )
						_lowercase			:     Union[str, Any]      					=				nn.Linear(lowerCamelCase_      ,       self.num_vector_embeds - 1    )
					elif self.is_input_patches:
						_lowercase			:     Dict      					=				nn.LayerNorm(lowerCamelCase_      ,       elementwise_affine=lowerCamelCase_      ,       eps=1E-6    )
						_lowercase			:     Union[str, Any]      					=				nn.Linear(lowerCamelCase_      ,       2 * inner_dim    )
						_lowercase			:     List[Any]      					=				nn.Linear(lowerCamelCase_      ,       patch_size * patch_size * self.out_channels    )







				def 				__UpperCAmelCase       (			self     :							Optional[Any]      ,       lowerCamelCase_     :							torch.Tensor      ,       lowerCamelCase_     :							Optional[torch.Tensor] = None      ,       lowerCamelCase_     :							Optional[torch.LongTensor] = None      ,       lowerCamelCase_     :							Optional[torch.LongTensor] = None      ,       lowerCamelCase_     :							Dict[str, Any] = None      ,       lowerCamelCase_     :							Optional[torch.Tensor] = None      ,       lowerCamelCase_     :							Optional[torch.Tensor] = None      ,       lowerCamelCase_     :							bool = True      ,       ):

					"""simple docstring"""






					if attention_mask is not None and attention_mask.ndim == 2:
						# assume that mask is expressed as:
						#   (1 = keep,      0 = discard)
						# convert mask into a bias that can be added to attention scores:
						#       (keep = +0,     discard = -10000.0)
						_lowercase			:     Optional[Any]      					=				(1 - attention_mask.to(hidden_states.dtype    )) * -1_0000.0
						_lowercase			:     Optional[Any]      					=				attention_mask.unsqueeze(1    )

					# convert encoder_attention_mask to a bias the same way we do for attention_mask
					if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
						_lowercase			:     List[Any]      					=				(1 - encoder_attention_mask.to(hidden_states.dtype    )) * -1_0000.0
						_lowercase			:     Any      					=				encoder_attention_mask.unsqueeze(1    )

					# 1. Input
					if self.is_input_continuous:
						_lowercase					,   _lowercase					,   _lowercase					,   _lowercase			:     List[Any]      					=				hidden_states.shape
						_lowercase			:     int      					=				hidden_states

						_lowercase			:     Any      					=				self.norm(lowerCamelCase_    )
						if not self.use_linear_projection:
							_lowercase			:     Any      					=				self.proj_in(lowerCamelCase_    )
							_lowercase			:     Any      					=				hidden_states.shape[1]
							_lowercase			:     List[Any]      					=				hidden_states.permute(0      ,       2      ,       3      ,       1    ).reshape(lowerCamelCase_      ,       height * width      ,       lowerCamelCase_    )
						else:
							_lowercase			:     Any      					=				hidden_states.shape[1]
							_lowercase			:     int      					=				hidden_states.permute(0      ,       2      ,       3      ,       1    ).reshape(lowerCamelCase_      ,       height * width      ,       lowerCamelCase_    )
							_lowercase			:     Any      					=				self.proj_in(lowerCamelCase_    )
					elif self.is_input_vectorized:
						_lowercase			:     Optional[Any]      					=				self.latent_image_embedding(lowerCamelCase_    )
					elif self.is_input_patches:
						_lowercase			:     Union[str, Any]      					=				self.pos_embed(lowerCamelCase_    )

					# 2. Blocks
					for block in self.transformer_blocks:
						_lowercase			:     Tuple      					=				block(
						    lowerCamelCase_      ,       attention_mask=lowerCamelCase_      ,       encoder_hidden_states=lowerCamelCase_      ,       encoder_attention_mask=lowerCamelCase_      ,       timestep=lowerCamelCase_      ,       cross_attention_kwargs=lowerCamelCase_      ,       class_labels=lowerCamelCase_      ,       )

					# 3. Output
					if self.is_input_continuous:
						if not self.use_linear_projection:
							_lowercase			:     Union[str, Any]      					=				hidden_states.reshape(lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_    ).permute(0      ,       3      ,       1      ,       2    ).contiguous()
							_lowercase			:     Tuple      					=				self.proj_out(lowerCamelCase_    )
						else:
							_lowercase			:     int      					=				self.proj_out(lowerCamelCase_    )
							_lowercase			:     List[Any]      					=				hidden_states.reshape(lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_      ,       lowerCamelCase_    ).permute(0      ,       3      ,       1      ,       2    ).contiguous()

						_lowercase			:     List[str]      					=				hidden_states + residual
					elif self.is_input_vectorized:
						_lowercase			:     Dict      					=				self.norm_out(lowerCamelCase_    )
						_lowercase			:     Dict      					=				self.out(lowerCamelCase_    )
						# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
						_lowercase			:     Dict      					=				logits.permute(0      ,       2      ,       1    )

						# log(p(x_0))
						_lowercase			:     int      					=				F.log_softmax(logits.double()      ,       dim=1    ).float()
					elif self.is_input_patches:
						# TODO: cleanup!
						_lowercase			:     Tuple      					=				self.transformer_blocks[0].norma.emb(
						    lowerCamelCase_      ,       lowerCamelCase_      ,       hidden_dtype=hidden_states.dtype    )
						_lowercase					,   _lowercase			:     List[Any]      					=				self.proj_out_a(F.silu(lowerCamelCase_    )    ).chunk(2      ,       dim=1    )
						_lowercase			:     Optional[int]      					=				self.norm_out(lowerCamelCase_    ) * (1 + scale[:, None]) + shift[:, None]
						_lowercase			:     Optional[Any]      					=				self.proj_out_a(lowerCamelCase_    )

						# unpatchify
						_lowercase			:     Union[str, Any]      					=				int(hidden_states.shape[1] ** 0.5    )
						_lowercase			:     int      					=				hidden_states.reshape(
						    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)    )
						_lowercase			:     str      					=				torch.einsum('nhwpqc->nchpwq'      ,       lowerCamelCase_    )
						_lowercase			:     List[str]      					=				hidden_states.reshape(
						    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)    )

					if not return_dict:
						return (output,)

					return TransformeraDModelOutput(sample=lowerCamelCase_    )



