





"""simple docstring"""





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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






@dataclass
class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:torch.FloatTensor
       __magic_name__	:torch.FloatTensor






class    _lowerCAmelCase  (	a    ,  a			):
       """simple docstring"""




       __magic_name__	:str						    =   1






       @register_to_config
       def __init__(       self       ,					__UpperCAmelCase = 2_0_0_0       ,					__UpperCAmelCase = 0.15       ,					__UpperCAmelCase = 0.01       ,					__UpperCAmelCase = 13_48.0       ,					__UpperCAmelCase = 1E-5       ,					__UpperCAmelCase = 1       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				sigma_max

         # setable values
         lowerCAmelCase__     :str											=				None

         self.set_sigmas(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         return sample






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

         lowerCAmelCase__     :str											=				torch.linspace(1       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					device=__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None ):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				sigma_min if sigma_min is not None else self.config.sigma_min
         lowerCAmelCase__     :Any											=				sigma_max if sigma_max is not None else self.config.sigma_max
         lowerCAmelCase__     :Dict											=				sampling_eps if sampling_eps is not None else self.config.sampling_eps
         if self.timesteps is None:
           self.set_timesteps(__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
         lowerCAmelCase__     :str											=				torch.exp(torch.linspace(math.log(__UpperCAmelCase )       ,					math.log(__UpperCAmelCase )       ,					__UpperCAmelCase ) )
         lowerCAmelCase__     :Optional[int]											=				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase ):



         '''simple docstring'''

         return torch.where(
             timesteps == 0       ,					torch.zeros_like(t.to(timesteps.device ) )       ,					self.discrete_sigmas[timesteps - 1].to(timesteps.device )       ,					)






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         if self.timesteps is None:
           raise ValueError(
               '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler' )

         lowerCAmelCase__     :Dict											=				timestep * torch.ones(
             sample.shape[0]       ,					device=sample.device )  # torch.repeat_interleave(timestep, sample.shape[0])
         lowerCAmelCase__     :Tuple											=				(timestep * (len(self.timesteps ) - 1)).long()

         # mps requires indices to be in the same device, so we use cpu as is the default with cuda
         lowerCAmelCase__     :List[str]											=				timesteps.to(self.discrete_sigmas.device )

         lowerCAmelCase__     :Union[str, Any]											=				self.discrete_sigmas[timesteps].to(sample.device )
         lowerCAmelCase__     :Optional[Any]											=				self.get_adjacent_sigma(__UpperCAmelCase       ,					__UpperCAmelCase ).to(sample.device )
         lowerCAmelCase__     :Optional[Any]											=				torch.zeros_like(__UpperCAmelCase )
         lowerCAmelCase__     :str											=				(sigma**2 - adjacent_sigma**2) ** 0.5

         # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
         # also equation 47 shows the analog from SDE models to ancestral sampling methods
         lowerCAmelCase__     :int											=				diffusion.flatten()
         while len(diffusion.shape ) < len(sample.shape ):
           lowerCAmelCase__     :List[Any]											=				diffusion.unsqueeze(-1 )
         lowerCAmelCase__     :Any											=				drift - diffusion**2 * model_output

         #  equation 6: sample noise for the diffusion term of
         lowerCAmelCase__     :Optional[int]											=				randn_tensor(
             sample.shape       ,					layout=sample.layout       ,					generator=__UpperCAmelCase       ,					device=sample.device       ,					dtype=sample.dtype )
         lowerCAmelCase__     :Union[str, Any]											=				sample - drift  # subtract because `dt` is a small negative timestep
         # TODO is the variable diffusion the correct scaling term for the noise?
         lowerCAmelCase__     :List[str]											=				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

         if not return_dict:
           return (prev_sample, prev_sample_mean)

         return SdeVeOutput(prev_sample=__UpperCAmelCase       ,					prev_sample_mean=__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         if self.timesteps is None:
           raise ValueError(
               '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler' )

         # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
         # sample noise for correction
         lowerCAmelCase__     :str											=				randn_tensor(sample.shape       ,					layout=sample.layout       ,					generator=__UpperCAmelCase ).to(sample.device )

         # compute step size from the model_output, the noise, and the snr
         lowerCAmelCase__     :Optional[Any]											=				torch.norm(model_output.reshape(model_output.shape[0]       ,					-1 )       ,					dim=-1 ).mean()
         lowerCAmelCase__     :Any											=				torch.norm(noise.reshape(noise.shape[0]       ,					-1 )       ,					dim=-1 ).mean()
         lowerCAmelCase__     :Tuple											=				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
         lowerCAmelCase__     :Dict											=				step_size * torch.ones(sample.shape[0] ).to(sample.device )
         # self.repeat_scalar(step_size, sample.shape[0])

         # compute corrected sample: model_output term and noise term
         lowerCAmelCase__     :Tuple											=				step_size.flatten()
         while len(step_size.shape ) < len(sample.shape ):
           lowerCAmelCase__     :Optional[int]											=				step_size.unsqueeze(-1 )
         lowerCAmelCase__     :List[str]											=				sample + step_size * model_output
         lowerCAmelCase__     :List[str]											=				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

         if not return_dict:
           return (prev_sample,)

         return SchedulerOutput(prev_sample=__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					):



         '''simple docstring'''

         lowerCAmelCase__     :Tuple											=				timesteps.to(original_samples.device )
         lowerCAmelCase__     :Optional[Any]											=				self.discrete_sigmas.to(original_samples.device )[timesteps]
         lowerCAmelCase__     :Dict											=				(
             noise * sigmas[:, None, None, None]
             if noise is not None
             else torch.randn_like(__UpperCAmelCase ) * sigmas[:, None, None, None]
         )
         lowerCAmelCase__     :List[Any]											=				noise + original_samples
         return noisy_samples






       def __len__(       self ):



         '''simple docstring'''

         return self.config.num_train_timesteps







'''simple docstring'''






from __future__ import annotations

import math







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				int:



			"""simple docstring"""


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

			if not scores:
						raise ValueError("""Scores cannot be empty""" )

			if depth == height:
						return scores[node_index]

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







def 					_a						(       ) ->				None:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     [90, 23, 6, 33, 21, 65, 123, 3_4423]
			__snake_case					:      Optional[int]							      =     math.log(len(_lowerCamelCase )    ,	2 )
			print(F'''Optimal value : {minimax(0    ,	0    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )}''' )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()


