





'''simple docstring'''






from __future__ import annotations

import os




class    _A   :
		lowercase__:      Optional[int]							    =      (
		    "High card",
		    "One pair",
		    "Two pairs",
		    "Three of a kind",
		    "Straight",
		    "Flush",
		    "Full house",
		    "Four of a kind",
		    "Straight flush",
		    "Royal flush",
		)

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

		def __init__(      self				:      Tuple     ,			__magic_name__				:      Optional[int] )				->						None:


					"""simple docstring"""

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

		@property
		def   lowercase__    (      self				:      Optional[Any] )				->						Optional[Any]:


					"""simple docstring"""

					return self._hand

		def   lowercase__    (      self				:      List[str]     ,			__magic_name__				:      Union[str, Any] )				->						str:


					"""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(SCREAMING_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   lowercase__    (      self				:      Optional[int] )				->						str:


					"""simple docstring"""

					__snake_case				:			str       			=    PokerHand._HAND_NAME[self._hand_type - 14]
					__snake_case				:			Union[str, Any]       			=    PokerHand._CARD_NAME[self._high_card]
					__snake_case				:			str       			=    PokerHand._CARD_NAME[self._first_pair]
					__snake_case				:			Dict       			=    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]:
								__snake_case				:			Union[str, Any]       			=    '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   lowercase__    (      self				:      Any     ,			__magic_name__				:      List[Any] )				->						str:


					"""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   lowercase__    (      self				:      List[Any] )				->						int:


					"""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   lowercase__    (      self				:      Union[str, Any] )				->						bool:


					"""simple docstring"""

					return len(self._card_suit ) == 1

		def   lowercase__    (      self				:      Dict )				->						bool:


					"""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
											__snake_case				:			Optional[Any]       			=    self._card_values.pop(0 )
											self._card_values.append(SCREAMING_SNAKE_CASE_ )
								return True
					return False

		def   lowercase__    (      self				:      int )				->						bool:


					"""simple docstring"""

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

		def   lowercase__    (      self				:      Optional[int] )				->						int:


					"""simple docstring"""

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

		def   lowercase__    (      self				:      int )				->						tuple[list[int], set[str]]:


					"""simple docstring"""

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

		def __repr__(      self				:      Any )				->						Dict:


					"""simple docstring"""

					return f'''{self.__class__}(\"{self._hand}\")'''

		def __str__(      self				:      Tuple )				->						int:


					"""simple docstring"""

					return self._hand

		def __eq__(      self				:      Dict     ,			__magic_name__				:      Dict )				->						str:


					"""simple docstring"""

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

		def __lt__(      self				:      Tuple     ,			__magic_name__				:      Any )				->						Optional[int]:


					"""simple docstring"""

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

		def __le__(      self				:      str     ,			__magic_name__				:      int )				->						Union[str, Any]:


					"""simple docstring"""

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

		def __gt__(      self				:      Optional[int]     ,			__magic_name__				:      List[str] )				->						Tuple:


					"""simple docstring"""

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

		def __ge__(      self				:      int     ,			__magic_name__				:      Dict )				->						Any:


					"""simple docstring"""

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

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


					"""simple docstring"""

					return object.__hash__(self )







def 				_a	(					):



			"""simple docstring"""


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


if __name__ == "__main__":
	solution()









'''simple docstring'''






import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class    _A   (				__lowercase		):
		lowercase__:      torch.FloatTensor
		lowercase__:      Optional[torch.FloatTensor]							    =      None







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase=0.9_99						,  _lowerCamelCase="cosine"						,  )   ->							int:



			"""simple docstring"""


			if alpha_transform_type == "cosine":

						def alpha_bar_fn(_lowerCamelCase   ):
									return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2   ) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(_lowerCamelCase   ):
									return math.exp(t * -12.0   )

			else:
						raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''   )

			__snake_case				:			int       			=    []
			for i in range(_lowerCamelCase   ):
						__snake_case				:			int       			=    i / num_diffusion_timesteps
						__snake_case				:			Any       			=    (i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(_lowerCamelCase   ) / alpha_bar_fn(_lowerCamelCase   )						,  _lowerCamelCase   )   )
			return torch.tensor(_lowerCamelCase						,  dtype=torch.floataa   )




class    _A   (				__lowercase	,				__lowercase		):
		lowercase__:      Tuple							    =      [e.name for e in KarrasDiffusionSchedulers]
		lowercase__:      List[Any]							    =      1

		@register_to_config
		def __init__(      self				:      Optional[int]     ,			__magic_name__				:      int = 10_00     ,			__magic_name__				:      float = 0.0001     ,			__magic_name__				:      float = 0.02     ,			__magic_name__				:      str = "linear"     ,			__magic_name__				:      Optional[Union[np.ndarray, List[float]]] = None     ,			__magic_name__				:      Optional[bool] = False     ,			__magic_name__				:      str = "epsilon"     ,			__magic_name__				:      str = "linspace"     ,			__magic_name__				:      int = 0     ,			)				->						List[Any]:


					"""simple docstring"""

					if trained_betas is not None:
								__snake_case				:			Union[str, Any]       			=    torch.tensor(__magic_name__     ,			dtype=torch.floataa )
					elif beta_schedule == "linear":
								__snake_case				:			List[str]       			=    torch.linspace(__magic_name__     ,			__magic_name__     ,			__magic_name__     ,			dtype=torch.floataa )
					elif beta_schedule == "scaled_linear":
								# this schedule is very specific to the latent diffusion model.
								__snake_case				:			Any       			=    (
								    torch.linspace(beta_start**0.5     ,			beta_end**0.5     ,			__magic_name__     ,			dtype=torch.floataa ) ** 2
								)
					elif beta_schedule == "squaredcos_cap_v2":
								# Glide cosine schedule
								__snake_case				:			Optional[Any]       			=    betas_for_alpha_bar(__magic_name__ )
					else:
								raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

					__snake_case				:			Tuple       			=    1.0 - self.betas
					__snake_case				:			Optional[Any]       			=    torch.cumprod(self.alphas     ,			dim=0 )

					__snake_case				:			Any       			=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
					__snake_case				:			Tuple       			=    np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
					__snake_case				:			Optional[Any]       			=    torch.from_numpy(__magic_name__ )

					# setable values
					__snake_case				:			str       			=    None
					__snake_case				:			Union[str, Any]       			=    use_karras_sigmas
					self.set_timesteps(__magic_name__     ,			__magic_name__ )
					__snake_case				:			str       			=    []
					__snake_case				:			str       			=    False

		@property
		def   lowercase__    (      self				:      Optional[Any] )				->						Optional[int]:


					"""simple docstring"""

					if self.config.timestep_spacing in ["linspace", "trailing"]:
								return self.sigmas.max()

					return (self.sigmas.max() ** 2 + 1) ** 0.5

		def   lowercase__    (      self				:      str     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      Union[float, torch.FloatTensor] )				->						torch.FloatTensor:


					"""simple docstring"""

					if isinstance(__magic_name__     ,			torch.Tensor ):
								__snake_case				:			int       			=    timestep.to(self.timesteps.device )
					__snake_case				:			str       			=    (self.timesteps == timestep).nonzero().item()
					__snake_case				:			str       			=    self.sigmas[step_index]
					__snake_case				:			Optional[int]       			=    sample / ((sigma**2 + 1) ** 0.5)
					__snake_case				:			List[Any]       			=    True
					return sample

		def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      Optional[Any]     ,			__magic_name__				:      Tuple     ,			__magic_name__				:      int )				->						List[str]:


					"""simple docstring"""

					def lms_derivative(__magic_name__				:      Optional[int] ):
								__snake_case				:			Any       			=    1.0
								for k in range(__magic_name__ ):
											if current_order == k:
														continue
											prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
								return prod

					__snake_case				:			str       			=    integrate.quad(__magic_name__     ,			self.sigmas[t]     ,			self.sigmas[t + 1]     ,			epsrel=1E-4 )[0]

					return integrated_coeff

		def   lowercase__    (      self				:      List[Any]     ,			__magic_name__				:      int     ,			__magic_name__				:      Union[str, torch.device] = None )				->						Tuple:


					"""simple docstring"""

					__snake_case				:			Any       			=    num_inference_steps

					# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
					if self.config.timestep_spacing == "linspace":
								__snake_case				:			Any       			=    np.linspace(0     ,			self.config.num_train_timesteps - 1     ,			__magic_name__     ,			dtype=__magic_name__ )[
								    ::-1
								].copy()
					elif self.config.timestep_spacing == "leading":
								__snake_case				:			List[str]       			=    self.config.num_train_timesteps // self.num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# casting to int to avoid issues when num_inference_step is power of 3
								__snake_case				:			List[Any]       			=    (np.arange(0     ,			__magic_name__ ) * step_ratio).round()[::-1].copy().astype(__magic_name__ )
								timesteps += self.config.steps_offset
					elif self.config.timestep_spacing == "trailing":
								__snake_case				:			Dict       			=    self.config.num_train_timesteps / self.num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# casting to int to avoid issues when num_inference_step is power of 3
								__snake_case				:			Any       			=    (np.arange(self.config.num_train_timesteps     ,			0     ,			-step_ratio )).round().copy().astype(__magic_name__ )
								timesteps -= 1
					else:
								raise ValueError(
								    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''' )

					__snake_case				:			List[Any]       			=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
					__snake_case				:			int       			=    np.log(__magic_name__ )
					__snake_case				:			Union[str, Any]       			=    np.interp(__magic_name__     ,			np.arange(0     ,			len(__magic_name__ ) )     ,			__magic_name__ )

					if self.use_karras_sigmas:
								__snake_case				:			Optional[Any]       			=    self._convert_to_karras(in_sigmas=__magic_name__ )
								__snake_case				:			Union[str, Any]       			=    np.array([self._sigma_to_t(__magic_name__     ,			__magic_name__ ) for sigma in sigmas] )

					__snake_case				:			Tuple       			=    np.concatenate([sigmas, [0.0]] ).astype(np.floataa )

					__snake_case				:			Optional[int]       			=    torch.from_numpy(__magic_name__ ).to(device=__magic_name__ )
					if str(__magic_name__ ).startswith("""mps""" ):
								# mps does not support float64
								__snake_case				:			int       			=    torch.from_numpy(__magic_name__ ).to(__magic_name__     ,			dtype=torch.floataa )
					else:
								__snake_case				:			Dict       			=    torch.from_numpy(__magic_name__ ).to(device=__magic_name__ )

					__snake_case				:			Optional[int]       			=    []

		def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      Optional[Any]     ,			__magic_name__				:      List[Any] )				->						List[Any]:


					"""simple docstring"""

					__snake_case				:			List[str]       			=    np.log(__magic_name__ )

					# get distribution
					__snake_case				:			int       			=    log_sigma - log_sigmas[:, np.newaxis]

					# get sigmas range
					__snake_case				:			Optional[int]       			=    np.cumsum((dists >= 0)     ,			axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
					__snake_case				:			List[str]       			=    low_idx + 1

					__snake_case				:			Dict       			=    log_sigmas[low_idx]
					__snake_case				:			Any       			=    log_sigmas[high_idx]

					# interpolate sigmas
					__snake_case				:			List[Any]       			=    (low - log_sigma) / (low - high)
					__snake_case				:			Optional[int]       			=    np.clip(__magic_name__     ,			0     ,			1 )

					# transform interpolation to time range
					__snake_case				:			Optional[Any]       			=    (1 - w) * low_idx + w * high_idx
					__snake_case				:			Optional[Any]       			=    t.reshape(sigma.shape )
					return t

		def   lowercase__    (      self				:      Optional[int]     ,			__magic_name__				:      torch.FloatTensor )				->						torch.FloatTensor:


					"""simple docstring"""

					__snake_case				:			float       			=    in_sigmas[-1].item()
					__snake_case				:			float       			=    in_sigmas[0].item()

					__snake_case				:			Any       			=    7.0  # 7.0 is the value used in the paper
					__snake_case				:			Optional[Any]       			=    np.linspace(0     ,			1     ,			self.num_inference_steps )
					__snake_case				:			List[str]       			=    sigma_min ** (1 / rho)
					__snake_case				:			Optional[int]       			=    sigma_max ** (1 / rho)
					__snake_case				:			Any       			=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
					return sigmas

		def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      Union[float, torch.FloatTensor]     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      int = 4     ,			__magic_name__				:      bool = True     ,			)				->						Union[LMSDiscreteSchedulerOutput, Tuple]:


					"""simple docstring"""

					if not self.is_scale_input_called:
								warnings.warn(
								    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
								    """See `StableDiffusionPipeline` for a usage example.""" )

					if isinstance(__magic_name__     ,			torch.Tensor ):
								__snake_case				:			Tuple       			=    timestep.to(self.timesteps.device )
					__snake_case				:			Any       			=    (self.timesteps == timestep).nonzero().item()
					__snake_case				:			Optional[Any]       			=    self.sigmas[step_index]

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
								__snake_case				:			int       			=    sample - sigma * model_output
					elif self.config.prediction_type == "v_prediction":
								# * c_out + input * c_skip
								__snake_case				:			str       			=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
					elif self.config.prediction_type == "sample":
								__snake_case				:			int       			=    model_output
					else:
								raise ValueError(
								    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''' )

					# 2. Convert to an ODE derivative
					__snake_case				:			Union[str, Any]       			=    (sample - pred_original_sample) / sigma
					self.derivatives.append(__magic_name__ )
					if len(self.derivatives ) > order:
								self.derivatives.pop(0 )

					# 3. Compute linear multistep coefficients
					__snake_case				:			List[Any]       			=    min(step_index + 1     ,			__magic_name__ )
					__snake_case				:			List[str]       			=    [self.get_lms_coefficient(__magic_name__     ,			__magic_name__     ,			__magic_name__ ) for curr_order in range(__magic_name__ )]

					# 4. Compute previous sample based on the derivatives path
					__snake_case				:			Any       			=    sample + sum(
					    coeff * derivative for coeff, derivative in zip(__magic_name__     ,			reversed(self.derivatives ) ) )

					if not return_dict:
								return (prev_sample,)

					return LMSDiscreteSchedulerOutput(prev_sample=__magic_name__     ,			pred_original_sample=__magic_name__ )

		def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      torch.FloatTensor     ,			__magic_name__				:      torch.FloatTensor     ,			)				->						torch.FloatTensor:


					"""simple docstring"""

					__snake_case				:			List[str]       			=    self.sigmas.to(device=original_samples.device     ,			dtype=original_samples.dtype )
					if original_samples.device.type == "mps" and torch.is_floating_point(__magic_name__ ):
								# mps does not support float64
								__snake_case				:			str       			=    self.timesteps.to(original_samples.device     ,			dtype=torch.floataa )
								__snake_case				:			List[Any]       			=    timesteps.to(original_samples.device     ,			dtype=torch.floataa )
					else:
								__snake_case				:			Tuple       			=    self.timesteps.to(original_samples.device )
								__snake_case				:			Union[str, Any]       			=    timesteps.to(original_samples.device )

					__snake_case				:			int       			=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

					__snake_case				:			Tuple       			=    sigmas[step_indices].flatten()
					while len(sigma.shape ) < len(original_samples.shape ):
								__snake_case				:			int       			=    sigma.unsqueeze(-1 )

					__snake_case				:			List[str]       			=    original_samples + noise * sigma
					return noisy_samples

		def __len__(      self				:      Union[str, Any] )				->						Union[str, Any]:


					"""simple docstring"""

					return self.config.num_train_timesteps


