




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  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					42
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					42
				SCREAMING_SNAKE_CASE__					:							Dict	      =					None






class  __magic_name__						(  __UpperCAmelCase    ,  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							str	      =					2


				@register_to_config
				def __init__(							self:   Tuple		,					_lowerCamelCase:   float = 0.02		,					_lowerCamelCase:   float = 1_00		,					_lowerCamelCase:   float = 1.0_07		,					_lowerCamelCase:   float = 80		,					_lowerCamelCase:   float = 0.05		,					_lowerCamelCase:   float = 50		,					):
											SCREAMING_SNAKE_CASE_   	=    sigma_max

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


				def     _A							(							self:   Any		,					_lowerCamelCase:   torch.FloatTensor		,					_lowerCamelCase:   Optional[int] = None		):
											return sample


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   int		,					_lowerCamelCase:   Union[str, torch.device] = None		):
											SCREAMING_SNAKE_CASE_   	=    num_inference_steps
											SCREAMING_SNAKE_CASE_   	=    np.arange(0		,					self.num_inference_steps		)[::-1].copy()
											SCREAMING_SNAKE_CASE_   	=    torch.from_numpy(lowercase_		).to(lowercase_		)
											SCREAMING_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
											]
											SCREAMING_SNAKE_CASE_   	=    torch.tensor(lowercase_		,					dtype=torch.floataa		,					device=lowercase_		)


				def     _A							(							self:   Dict		,					_lowerCamelCase:   torch.FloatTensor		,					_lowerCamelCase:   float		,					_lowerCamelCase:   Optional[torch.Generator] = None		):







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

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

											return sample_hat, sigma_hat


				def     _A							(							self:   str		,					_lowerCamelCase:   torch.FloatTensor		,					_lowerCamelCase:   float		,					_lowerCamelCase:   float		,					_lowerCamelCase:   torch.FloatTensor		,					_lowerCamelCase:   bool = True		,					):
											SCREAMING_SNAKE_CASE_   	=    sample_hat + sigma_hat * model_output
											SCREAMING_SNAKE_CASE_   	=    (sample_hat - pred_original_sample) / sigma_hat
											SCREAMING_SNAKE_CASE_   	=    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     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   torch.FloatTensor		,					_lowerCamelCase:   float		,					_lowerCamelCase:   float		,					_lowerCamelCase:   torch.FloatTensor		,					_lowerCamelCase:   torch.FloatTensor		,					_lowerCamelCase:   torch.FloatTensor		,					_lowerCamelCase:   bool = True		,					):
											SCREAMING_SNAKE_CASE_   	=    sample_prev + sigma_prev * model_output
											SCREAMING_SNAKE_CASE_   	=    (sample_prev - pred_original_sample) / sigma_prev
											SCREAMING_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=lowercase_		,					derivative=lowercase_		,					pred_original_sample=lowercase_		)





				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   List[str]		,					_lowerCamelCase:   Any		,					_lowerCamelCase:   Any		):
											raise NotImplementedError()






def 			a   (_lowerCAmelCase     ):
							if number > 0:
														raise ValueError('''input must be a negative integer'''     )
							SCREAMING_SNAKE_CASE_   	=    len(bin(_lowerCAmelCase     )[3:]     )
							SCREAMING_SNAKE_CASE_   	=    bin(abs(_lowerCAmelCase     ) - (1 << binary_number_length)     )[3:]
							SCREAMING_SNAKE_CASE_   	=    (
							    (
							        '''1'''
							        + '''0''' * (binary_number_length - len(_lowerCAmelCase     ))
							        + twos_complement_number
							    )
							    if number < 0
							    else '''0'''
							)
							return "0b" + twos_complement_number


if __name__ == "__main__":
			import doctest

			doctest.testmod()


