


# 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   __a			(    __UpperCamelCase      ):
 __lowercase :							torch.FloatTensor
 __lowercase :							torch.FloatTensor





class   __a			(    __UpperCamelCase       ,       __UpperCamelCase      ):
 __lowercase :							str        = 1
 @register_to_config
 def __init__(   self				, lowerCAmelCase__ = 2_000				, lowerCAmelCase__ = 0.1_5				, lowerCAmelCase__ = 0.0_1				, lowerCAmelCase__ = 1_3_4_8.0				, lowerCAmelCase__ = 1E-5				, lowerCAmelCase__ = 1				, )					->				int:




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

        # setable values
        lowercase__:   List[Any]  			=   None

        self.set_sigmas(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = None )					->				torch.FloatTensor:




        '''simple docstring'''
        return sample
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None )					->				str:




        '''simple docstring'''
        lowercase__:   str  			=   sampling_eps if sampling_eps is not None else self.config.sampling_eps

        lowercase__:   str  			=   torch.linspace(1				, lowerCAmelCase__				, lowerCAmelCase__				, device=lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None				, lowerCAmelCase__ = None )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   sigma_min if sigma_min is not None else self.config.sigma_min
        lowercase__:   Optional[int]  			=   sigma_max if sigma_max is not None else self.config.sigma_max
        lowercase__:   Optional[int]  			=   sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if self.timesteps is None:
               self.set_timesteps(lowerCAmelCase__				, lowerCAmelCase__ )

        lowercase__:   Any  			=   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
        lowercase__:   Any  			=   torch.exp(torch.linspace(math.log(lowerCAmelCase__ )				, math.log(lowerCAmelCase__ )				, lowerCAmelCase__ ) )
        lowercase__:   Union[str, Any]  			=   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__ )					->				Tuple:




        '''simple docstring'''
        return torch.where(
            timesteps == 0				, torch.zeros_like(t.to(timesteps.device ) )				, self.discrete_sigmas[timesteps - 1].to(timesteps.device )				, )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = None				, lowerCAmelCase__ = True				, )					->				Union[SdeVeOutput, Tuple]:




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

        lowercase__:   Tuple  			=   timestep * torch.ones(
            sample.shape[0]				, device=sample.device )  # torch.repeat_interleave(timestep, sample.shape[0])
        lowercase__:   List[str]  			=   (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
        lowercase__:   Any  			=   timesteps.to(self.discrete_sigmas.device )

        lowercase__:   int  			=   self.discrete_sigmas[timesteps].to(sample.device )
        lowercase__:   Optional[int]  			=   self.get_adjacent_sigma(lowerCAmelCase__				, lowerCAmelCase__ ).to(sample.device )
        lowercase__:   Optional[Any]  			=   torch.zeros_like(lowerCAmelCase__ )
        lowercase__:   Optional[Any]  			=   (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
        lowercase__:   Union[str, Any]  			=   diffusion.flatten()
        while len(diffusion.shape ) < len(sample.shape ):
               lowercase__:   List[str]  			=   diffusion.unsqueeze(-1 )
        lowercase__:   List[Any]  			=   drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        lowercase__:   Union[str, Any]  			=   randn_tensor(
            sample.shape				, layout=sample.layout				, generator=lowerCAmelCase__				, device=sample.device				, dtype=sample.dtype )
        lowercase__:   int  			=   sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        lowercase__:   Any  			=   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=lowerCAmelCase__				, prev_sample_mean=lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = None				, lowerCAmelCase__ = True				, )					->				Union[SchedulerOutput, Tuple]:




        '''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
        lowercase__:   List[str]  			=   randn_tensor(sample.shape				, layout=sample.layout				, generator=lowerCAmelCase__ ).to(sample.device )

        # compute step size from the model_output, the noise, and the snr
        lowercase__:   Tuple  			=   torch.norm(model_output.reshape(model_output.shape[0]				, -1 )				, dim=-1 ).mean()
        lowercase__:   Dict  			=   torch.norm(noise.reshape(noise.shape[0]				, -1 )				, dim=-1 ).mean()
        lowercase__:   List[Any]  			=   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        lowercase__:   int  			=   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
        lowercase__:   int  			=   step_size.flatten()
        while len(step_size.shape ) < len(sample.shape ):
               lowercase__:   Tuple  			=   step_size.unsqueeze(-1 )
        lowercase__:   List[str]  			=   sample + step_size * model_output
        lowercase__:   Dict  			=   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
               return (prev_sample,)

        return SchedulerOutput(prev_sample=lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, )					->				torch.FloatTensor:




        '''simple docstring'''
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        lowercase__:   List[str]  			=   timesteps.to(original_samples.device )
        lowercase__:   List[Any]  			=   self.discrete_sigmas.to(original_samples.device )[timesteps]
        lowercase__:   int  			=   (
            noise * sigmas[:, None, None, None]
            if noise is not None
            else torch.randn_like(lowerCAmelCase__ ) * sigmas[:, None, None, None]
        )
        lowercase__:   str  			=   noise + original_samples
        return noisy_samples
 def __len__(   self )					->				Any:




        '''simple docstring'''
        return self.config.num_train_timesteps






import unittest

from knapsack import knapsack as k





class   __a			(    unittest.TestCase      ):
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   0
        lowercase__:   List[Any]  			=   [0]
        lowercase__:   str  			=   [0]
        lowercase__:   Tuple  			=   len(lowerCAmelCase__ )
        self.assertEqual(k.knapsack(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )				, 0 )

        lowercase__:   Optional[Any]  			=   [60]
        lowercase__:   Dict  			=   [10]
        lowercase__:   str  			=   len(lowerCAmelCase__ )
        self.assertEqual(k.knapsack(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )				, 0 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   3
        lowercase__:   List[str]  			=   [1, 2, 3]
        lowercase__:   Union[str, Any]  			=   [3, 2, 1]
        lowercase__:   Union[str, Any]  			=   len(lowerCAmelCase__ )
        self.assertEqual(k.knapsack(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )				, 5 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   50
        lowercase__:   str  			=   [60, 100, 120]
        lowercase__:   Any  			=   [10, 20, 30]
        lowercase__:   List[Any]  			=   len(lowerCAmelCase__ )
        self.assertEqual(k.knapsack(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )				, 220 )


if __name__ == "__main__":
 unittest.main()


