






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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class  _UpperCAmelCase   (			UpperCamelCase__      ):

				"""simple docstring"""




				lowercase__											=			42
				lowercase__											=			42
				lowercase__											=			None



class  _UpperCAmelCase   (			UpperCamelCase__						,UpperCamelCase__      ):

				"""simple docstring"""




				lowercase__											=			2




				@register_to_config
				def __init__(	self		:       Any,    lowerCamelCase		:       float = 0.02,    lowerCamelCase		:       float = 100,    lowerCamelCase		:       float = 1.007,    lowerCamelCase		:       float = 80,    lowerCamelCase		:       float = 0.05,    lowerCamelCase		:       float = 50,    ):

								'''simple docstring'''
								# standard deviation of the initial noise distribution
								lowercase__  					=     sigma_max

								# setable values
								lowercase__  					=     None
								lowercase__  					=     None
								lowercase__  					=     None  # sigma(t_i)




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       Optional[int] = None    ):

								'''simple docstring'''
								return sample




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       int,    lowerCamelCase		:       Union[str, torch.device] = None    ):

								'''simple docstring'''
								lowercase__  					=     num_inference_steps
								lowercase__  					=     np.arange(0,    self.num_inference_steps    )[::-1].copy()
								lowercase__  					=     torch.from_numpy(lowercase_    ).to(lowercase_    )
								lowercase__  					=     [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								lowercase__  					=     torch.tensor(lowercase_,    dtype=torch.floataa,    device=lowercase_    )




				def 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       float,    lowerCamelCase		:       Optional[torch.Generator] = None    ):

								'''simple docstring'''
								if self.config.s_min <= sigma <= self.config.s_max:
												lowercase__  					=     min(self.config.s_churn / self.num_inference_steps,    2**0.5 - 1    )
								else:
												lowercase__  					=     0

								# sample eps ~ N(0, S_noise^2 * I)
								lowercase__  					=     self.config.s_noise * randn_tensor(sample.shape,    generator=lowercase_    ).to(sample.device    )
								lowercase__  					=     sigma + gamma * sigma
								lowercase__  					=     sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat




				def 							lowercase__  (	self		:       List[str],    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       float,    lowerCamelCase		:       float,    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       bool = True,    ):

								'''simple docstring'''
								lowercase__  					=     sample_hat + sigma_hat * model_output
								lowercase__  					=     (sample_hat - pred_original_sample) / sigma_hat
								lowercase__  					=     sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
												return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowercase_,    derivative=lowercase_,    pred_original_sample=lowercase_    )




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       float,    lowerCamelCase		:       float,    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       bool = True,    ):

								'''simple docstring'''
								lowercase__  					=     sample_prev + sigma_prev * model_output
								lowercase__  					=     (sample_prev - pred_original_sample) / sigma_prev
								lowercase__  					=     sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
												return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowercase_,    derivative=lowercase_,    pred_original_sample=lowercase_    )




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       List[str],    lowerCamelCase		:       int,    lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								raise NotImplementedError()











'''simple docstring'''

from __future__ import annotations

import math





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   bool		,			_lowerCamelCase   :   list[int]		,			_lowerCamelCase   :   float			):

    if depth < 0:
        raise ValueError("Depth cannot be less than 0"			)

    if len(_lowerCamelCase			) == 0:
        raise ValueError("Scores cannot be empty"			)

    if depth == height:
        return scores[node_index]

    if is_max:
        return max(
            minimax(depth + 1		,			node_index * 2		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			minimax(depth + 1		,			node_index * 2 + 1		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			)

    return min(
        minimax(depth + 1		,			node_index * 2		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			minimax(depth + 1		,			node_index * 2 + 1		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)		,			)





def 		UpperCamelCase							(				):
    A__							=     [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
    A__							=     math.log(len(_lowerCamelCase			)		,			2			)
    print("Optimal value : "		,			end=""			)
    print(minimax(0		,			0		,			_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



