
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 		a_						( _lowercase	):
    a      :				torch.FloatTensor
    a      :				torch.FloatTensor
    a      :				Optional[torch.FloatTensor]     =				None







class 		a_						( _lowercase     ,						_lowercase	):
    a      :				Any     =				2







    @register_to_config
    def __init__(			self			:       Optional[int]     ,					__UpperCamelCase			:       float = 0.0_2     ,					__UpperCamelCase			:       float = 1_00     ,					__UpperCamelCase			:       float = 1.0_0_7     ,					__UpperCamelCase			:       float = 80     ,					__UpperCamelCase			:       float = 0.0_5     ,					__UpperCamelCase			:       float = 50     ,					)		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   sigma_max

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







    def      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       torch.FloatTensor     ,					__UpperCamelCase			:       Optional[int] = None     )		->torch.FloatTensor:






        '''simple docstring'''






        return sample







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Union[str, torch.device] = None     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   num_inference_steps
        _UpperCAmelCase          =   np.arange(0     ,					self.num_inference_steps     )[::-1].copy()
        _UpperCAmelCase          =   torch.from_numpy(A_     ).to(A_     )
        _UpperCAmelCase          =   [
            (
                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
        ]
        _UpperCAmelCase          =   torch.tensor(A_     ,					dtype=torch.floataa     ,					device=A_     )







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       torch.FloatTensor     ,					__UpperCamelCase			:       float     ,					__UpperCamelCase			:       Optional[torch.Generator] = None     )		->Tuple[torch.FloatTensor, float]:






        '''simple docstring'''






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

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

        return sample_hat, sigma_hat







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       torch.FloatTensor     ,					__UpperCamelCase			:       float     ,					__UpperCamelCase			:       float     ,					__UpperCamelCase			:       torch.FloatTensor     ,					__UpperCamelCase			:       bool = True     ,					)		->Union[KarrasVeOutput, Tuple]:






        '''simple docstring'''






        _UpperCAmelCase          =   sample_hat + sigma_hat * model_output
        _UpperCAmelCase          =   (sample_hat - pred_original_sample) / sigma_hat
        _UpperCAmelCase          =   sample_hat + (sigma_prev - sigma_hat) * derivative

        if not return_dict:
            return (sample_prev, derivative)

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







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       torch.FloatTensor     ,					__UpperCamelCase			:       float     ,					__UpperCamelCase			:       float     ,					__UpperCamelCase			:       torch.FloatTensor     ,					__UpperCamelCase			:       torch.FloatTensor     ,					__UpperCamelCase			:       torch.FloatTensor     ,					__UpperCamelCase			:       bool = True     ,					)		->Union[KarrasVeOutput, Tuple]:






        '''simple docstring'''






        _UpperCAmelCase          =   sample_prev + sigma_prev * model_output
        _UpperCAmelCase          =   (sample_prev - pred_original_sample) / sigma_prev
        _UpperCAmelCase          =   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=A_     ,					derivative=A_     ,					pred_original_sample=A_     )







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Optional[Any]     )		->str:






        '''simple docstring'''






        raise NotImplementedError()

"""simple docstring"""



from collections.abc import Callable

import numpy as np


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      _A      )    ->  np.array:





    """simple docstring"""

    _UpperCAmelCase          =   int(np.ceil((x_end - xa) / step_size      )      )
    _UpperCAmelCase          =   np.zeros((n + 1,)      )
    _UpperCAmelCase          =   ya
    _UpperCAmelCase          =   xa

    for k in range(_A      ):
        _UpperCAmelCase          =   y[k] + step_size * ode_func(_A					,      y[k]      )
        _UpperCAmelCase          =   y[k] + (
            (step_size / 2) * (ode_func(_A					,      y[k]      ) + ode_func(x + step_size					,      _A      ))
        )
        x += step_size

    return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()