





'''simple docstring'''




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 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   42
     snake_case_						      =   42
     snake_case_						      =   None




class 			_lowerCAmelCase		(			A__							,     A__	):






     """simple docstring"""






     snake_case_						      =   2



     @register_to_config
     def __init__(   self							:			Optional[int]   ,       __snake_case							:			float = 0.02   ,       __snake_case							:			float = 1_00   ,       __snake_case							:			float = 1.0_07   ,       __snake_case							:			float = 80   ,       __snake_case							:			float = 0.05   ,       __snake_case							:			float = 50   ,       )->	Dict:
        # standard deviation of the initial noise distribution
        snake_case 						=   sigma_max

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



     def      lowerCAmelCase			(   self							:			Optional[int]   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			Optional[int] = None						)->	torch.FloatTensor:
        return sample



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			int   ,       __snake_case							:			Union[str, torch.device] = None						)->	List[Any]:
        snake_case 						=   num_inference_steps
        snake_case 						=   np.arange(0   ,       self.num_inference_steps						)[::-1].copy()
        snake_case 						=   torch.from_numpy(__snake_case						).to(__snake_case						)
        snake_case 						=   [
            (
                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
        ]
        snake_case 						=   torch.tensor(__snake_case   ,       dtype=torch.floataa   ,       device=__snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			float   ,       __snake_case							:			Optional[torch.Generator] = None						)->	Tuple[torch.FloatTensor, float]:
        if self.config.s_min <= sigma <= self.config.s_max:
           snake_case 						=   min(self.config.s_churn / self.num_inference_steps   ,       2**0.5 - 1						)
        else:
           snake_case 						=   0

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

        return sample_hat, sigma_hat



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			float   ,       __snake_case							:			float   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			bool = True   ,       )->	Union[KarrasVeOutput, Tuple]:
        snake_case 						=   sample_hat + sigma_hat * model_output
        snake_case 						=   (sample_hat - pred_original_sample) / sigma_hat
        snake_case 						=   sample_hat + (sigma_prev - sigma_hat) * derivative

        if not return_dict:
           return (sample_prev, derivative)

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



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			float   ,       __snake_case							:			float   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			torch.FloatTensor   ,       __snake_case							:			bool = True   ,       )->	Union[KarrasVeOutput, Tuple]:
        snake_case 						=   sample_prev + sigma_prev * model_output
        snake_case 						=   (sample_prev - pred_original_sample) / sigma_prev
        snake_case 						=   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=__snake_case   ,       derivative=__snake_case   ,       pred_original_sample=__snake_case						)



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Any						)->	int:
        raise NotImplementedError()








'''simple docstring'''




from . import (
    albert,
    align,
    altclip,
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    auto,
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    yolos,
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)

