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

import numpy as np
import torch

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


__snake_case					:							int           =							logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 	UpperCamelCase__       (  snake_case__):

						'''simple docstring'''



						__a     :    Any												=			4_2
						__a     :    Any												=			None




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=0.9_99     ,						SCREAMING_SNAKE_CASE="cosine"     ,						):




			"""simple docstring"""


			if alpha_transform_type == "cosine":

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

			elif alpha_transform_type == "exp":

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

			else:
						raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""							)

			UpperCAmelCase__		:Optional[Any] 		=    []
			for i in range(__SCREAMING_SNAKE_CASE							):
						UpperCAmelCase__		:Optional[Any] 		=    i / num_diffusion_timesteps
						UpperCAmelCase__		:Dict 		=    (i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(__SCREAMING_SNAKE_CASE							) / alpha_bar_fn(__SCREAMING_SNAKE_CASE							)     ,						__SCREAMING_SNAKE_CASE							)							)
			return torch.tensor(__SCREAMING_SNAKE_CASE     ,						dtype=torch.floataa							)




class 	UpperCamelCase__       (  snake_case__						,			snake_case__):

						'''simple docstring'''



						__a     :    List[Any]												=			[e.name for e in KarrasDiffusionSchedulers]
						__a     :    Any												=			1


						@register_to_config
						def __init__(  self  , A = 10_00  , A = 0.0001  , A = 0.02  , A = "linear"  , A = None  , A = "epsilon"  , A = "linear"  , A = False  , A = "linspace"  , A = 0  , )  ->List[str]:
									if trained_betas is not None:
												UpperCAmelCase__		:str 		=    torch.tensor(lowercase_  , dtype=torch.floataa	)
									elif beta_schedule == "linear":
												UpperCAmelCase__		:int 		=    torch.linspace(lowercase_  , lowercase_  , lowercase_  , dtype=torch.floataa	)
									elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												UpperCAmelCase__		:List[str] 		=    (
												    torch.linspace(beta_start**0.5  , beta_end**0.5  , lowercase_  , dtype=torch.floataa	) ** 2
												)
									elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												UpperCAmelCase__		:Optional[Any] 		=    betas_for_alpha_bar(lowercase_	)
									else:
												raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""	)

									UpperCAmelCase__		:List[Any] 		=    1.0 - self.betas
									UpperCAmelCase__		:str 		=    torch.cumprod(self.alphas  , dim=0	)

									UpperCAmelCase__		:str 		=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
									UpperCAmelCase__		:List[Any] 		=    np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
									UpperCAmelCase__		:Dict 		=    torch.from_numpy(lowercase_	)

									# setable values
									UpperCAmelCase__		:int 		=    None
									UpperCAmelCase__		:Optional[int] 		=    np.linspace(0  , num_train_timesteps - 1  , lowercase_  , dtype=lowercase_	)[::-1].copy()
									UpperCAmelCase__		:Dict 		=    torch.from_numpy(lowercase_	)
									UpperCAmelCase__		:Optional[Any] 		=    False
									UpperCAmelCase__		:int 		=    use_karras_sigmas


						@property
						def    A__       (  self	)  ->Tuple:
									# standard deviation of the initial noise distribution
									if self.config.timestep_spacing in ["linspace", "trailing"]:
												return self.sigmas.max()

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


						def    A__       (  self  , A  , A	)  ->str:
									if isinstance(lowercase_  , torch.Tensor	):
												UpperCAmelCase__		:List[Any] 		=    timestep.to(self.timesteps.device	)
									UpperCAmelCase__		:Optional[Any] 		=    (self.timesteps == timestep).nonzero().item()
									UpperCAmelCase__		:str 		=    self.sigmas[step_index]

									UpperCAmelCase__		:List[Any] 		=    sample / ((sigma**2 + 1) ** 0.5)

									UpperCAmelCase__		:Optional[int] 		=    True
									return sample


						def    A__       (  self  , A  , A = None	)  ->Tuple:
									UpperCAmelCase__		:List[str] 		=    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":
												UpperCAmelCase__		:List[Any] 		=    np.linspace(0  , self.config.num_train_timesteps - 1  , lowercase_  , dtype=lowercase_	)[
												    ::-1
												].copy()
									elif self.config.timestep_spacing == "leading":
												UpperCAmelCase__		:int 		=    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
												UpperCAmelCase__		:Union[str, Any] 		=    (np.arange(0  , lowercase_	) * step_ratio).round()[::-1].copy().astype(lowercase_	)
												timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
												UpperCAmelCase__		:Union[str, Any] 		=    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
												UpperCAmelCase__		:Union[str, Any] 		=    (np.arange(self.config.num_train_timesteps  , 0  , -step_ratio	)).round().copy().astype(lowercase_	)
												timesteps -= 1
									else:
												raise ValueError(
												    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."""	)

									UpperCAmelCase__		:Dict 		=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
									UpperCAmelCase__		:List[Any] 		=    np.log(lowercase_	)

									if self.config.interpolation_type == "linear":
												UpperCAmelCase__		:str 		=    np.interp(lowercase_  , np.arange(0  , len(lowercase_	)	)  , lowercase_	)
									elif self.config.interpolation_type == "log_linear":
												UpperCAmelCase__		:Tuple 		=    torch.linspace(np.log(sigmas[-1]	)  , np.log(sigmas[0]	)  , num_inference_steps + 1	).exp()
									else:
												raise ValueError(
												    F"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
												    ' \'linear\' or \'log_linear\''	)

									if self.use_karras_sigmas:
												UpperCAmelCase__		:Tuple 		=    self._convert_to_karras(in_sigmas=lowercase_  , num_inference_steps=self.num_inference_steps	)
												UpperCAmelCase__		:Dict 		=    np.array([self._sigma_to_t(lowercase_  , lowercase_	) for sigma in sigmas]	)

									UpperCAmelCase__		:Dict 		=    np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
									UpperCAmelCase__		:Union[str, Any] 		=    torch.from_numpy(lowercase_	).to(device=lowercase_	)
									if str(lowercase_	).startswith('mps'	):
												# mps does not support float64
												UpperCAmelCase__		:Union[str, Any] 		=    torch.from_numpy(lowercase_	).to(lowercase_  , dtype=torch.floataa	)
									else:
												UpperCAmelCase__		:Optional[Any] 		=    torch.from_numpy(lowercase_	).to(device=lowercase_	)


						def    A__       (  self  , A  , A	)  ->List[str]:
									# get log sigma
									UpperCAmelCase__		:Optional[int] 		=    np.log(lowercase_	)

									# get distribution
									UpperCAmelCase__		:Union[str, Any] 		=    log_sigma - log_sigmas[:, np.newaxis]

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

									UpperCAmelCase__		:Tuple 		=    log_sigmas[low_idx]
									UpperCAmelCase__		:str 		=    log_sigmas[high_idx]

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

									# transform interpolation to time range
									UpperCAmelCase__		:str 		=    (1 - w) * low_idx + w * high_idx
									UpperCAmelCase__		:int 		=    t.reshape(sigma.shape	)
									return t


						def    A__       (  self  , A  , A	)  ->int:
									UpperCAmelCase__		:float 		=    in_sigmas[-1].item()
									UpperCAmelCase__		:float 		=    in_sigmas[0].item()

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


						def    A__       (  self  , A  , A  , A  , A = 0.0  , A = 0.0  , A = float('inf'	)  , A = 1.0  , A = None  , A = True  , )  ->int:

									if (
									    isinstance(lowercase_  , lowercase_	)
									    or isinstance(lowercase_  , torch.IntTensor	)
									    or isinstance(lowercase_  , torch.LongTensor	)
									):
												raise ValueError(
												    (
												        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
												        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
												        ' one of the `scheduler.timesteps` as a timestep.'
												    )  , )

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

									if isinstance(lowercase_  , torch.Tensor	):
												UpperCAmelCase__		:int 		=    timestep.to(self.timesteps.device	)

									UpperCAmelCase__		:Dict 		=    (self.timesteps == timestep).nonzero().item()
									UpperCAmelCase__		:str 		=    self.sigmas[step_index]

									UpperCAmelCase__		:str 		=    min(s_churn / (len(self.sigmas	) - 1)  , 2**0.5 - 1	) if s_tmin <= sigma <= s_tmax else 0.0

									UpperCAmelCase__		:Dict 		=    randn_tensor(
									    model_output.shape  , dtype=model_output.dtype  , device=model_output.device  , generator=lowercase_	)

									UpperCAmelCase__		:Dict 		=    noise * s_noise
									UpperCAmelCase__		:Optional[int] 		=    sigma * (gamma + 1)

									if gamma > 0:
												UpperCAmelCase__		:Any 		=    sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									# NOTE: "original_sample" should not be an expected prediction_type but is left in for
									# backwards compatibility
									if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
												UpperCAmelCase__		:Optional[int] 		=    model_output
									elif self.config.prediction_type == "epsilon":
												UpperCAmelCase__		:Dict 		=    sample - sigma_hat * model_output
									elif self.config.prediction_type == "v_prediction":
												# * c_out + input * c_skip
												UpperCAmelCase__		:List[Any] 		=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									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
									UpperCAmelCase__		:Optional[int] 		=    (sample - pred_original_sample) / sigma_hat

									UpperCAmelCase__		:Optional[Any] 		=    self.sigmas[step_index + 1] - sigma_hat

									UpperCAmelCase__		:List[Any] 		=    sample + derivative * dt

									if not return_dict:
												return (prev_sample,)

									return EulerDiscreteSchedulerOutput(prev_sample=lowercase_  , pred_original_sample=lowercase_	)


						def    A__       (  self  , A  , A  , A  , )  ->Union[str, Any]:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									UpperCAmelCase__		:Tuple 		=    self.sigmas.to(device=original_samples.device  , dtype=original_samples.dtype	)
									if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_	):
												# mps does not support float64
												UpperCAmelCase__		:Any 		=    self.timesteps.to(original_samples.device  , dtype=torch.floataa	)
												UpperCAmelCase__		:Tuple 		=    timesteps.to(original_samples.device  , dtype=torch.floataa	)
									else:
												UpperCAmelCase__		:Optional[Any] 		=    self.timesteps.to(original_samples.device	)
												UpperCAmelCase__		:int 		=    timesteps.to(original_samples.device	)

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

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

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


						def __len__(  self	)  ->Union[str, Any]:
									return self.config.num_train_timesteps







'''simple docstring'''
from __future__ import annotations

import os






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





 @property
 def 							snake_case__		(     self):
     return self._hand





 def 							snake_case__		(     self			,							lowercase_):
     # 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(lowercase_)
         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):
     snake_case_			:     Tuple        =    PokerHand._HAND_NAME[self._hand_type - 14]
     snake_case_			:     List[Any]        =    PokerHand._CARD_NAME[self._high_card]
     snake_case_			:     Any        =    PokerHand._CARD_NAME[self._first_pair]
     snake_case_			:     str        =    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_			:     List[str]        =    "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			,							lowercase_):
     # 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):
     # 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):
     return len(self._card_suit) == 1





 def 							snake_case__		(     self):
     # 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
             snake_case_			:     Optional[int]        =    self._card_values.pop(0)
             self._card_values.append(lowercase_)
         return True
     return False





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





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





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





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





 def __str__(     self):
     return self._hand





 def __eq__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return self.compare_with(lowercase_) == "Tie"
     return NotImplemented





 def __lt__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return self.compare_with(lowercase_) == "Loss"
     return NotImplemented





 def __le__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return self < other or self == other
     return NotImplemented





 def __gt__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return not self < other and self != other
     return NotImplemented





 def __ge__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return not self < other
     return NotImplemented





 def __hash__(     self):
     return object.__hash__(self)


def     UpperCamelCase_  (						):
    """simple docstring"""






    snake_case_			:     Dict        =    0
    snake_case_			:     Tuple        =    os.path.abspath(os.path.dirname(__SCREAMING_SNAKE_CASE							)							)
    snake_case_			:     List[Any]        =    os.path.join(__SCREAMING_SNAKE_CASE,		"poker_hands.txt"							)
    with open(__SCREAMING_SNAKE_CASE							) as file_hand:
        for line in file_hand:
            snake_case_			:     int        =    line[:1_4].strip()
            snake_case_			:     Optional[Any]        =    line[1_5:].strip()
            snake_case_      ,			snake_case_			:     Union[str, Any]        =    PokerHand(__SCREAMING_SNAKE_CASE							), PokerHand(__SCREAMING_SNAKE_CASE							)
            snake_case_			:     Optional[int]        =    player.compare_with(__SCREAMING_SNAKE_CASE							)
            if output == "Win":
                answer += 1
    return answer


if __name__ == "__main__":
  solution()




