





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       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   42
							UpperCAmelCase_      						=   42
							UpperCAmelCase_      						=   None


class 						lowerCamelCase       (__lowerCamelCase							,     __lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   2






							@register_to_config
							def __init__( self     :					Optional[Any],			_UpperCAmelCase     :					float = 0.02,			_UpperCAmelCase     :					float = 1_0_0,			_UpperCAmelCase     :					float = 1.007,			_UpperCAmelCase     :					float = 8_0,			_UpperCAmelCase     :					float = 0.05,			_UpperCAmelCase     :					float = 5_0,			)				->			List[str]:





														"""simple docstring"""
														# standard deviation of the initial noise distribution
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						sigma_max

														# setable values
														SCREAMING_SNAKE_CASE__       :      int     =						None
														SCREAMING_SNAKE_CASE__       :      np.IntTensor     =						None
														SCREAMING_SNAKE_CASE__       :      torch.FloatTensor     =						None  # sigma(t_i)






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					Optional[int] = None						)				->			torch.FloatTensor:





														"""simple docstring"""
														return sample






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Union[str, torch.device] = None						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						num_inference_steps
														SCREAMING_SNAKE_CASE__       :      int     =						np.arange(0,			self.num_inference_steps						)[::-1].copy()
														SCREAMING_SNAKE_CASE__       :      str     =						torch.from_numpy(_UpperCAmelCase						).to(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						[
														    (
														        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__       :      Tuple     =						torch.tensor(_UpperCAmelCase,			dtype=torch.floataa,			device=_UpperCAmelCase						)






							def  A_     ( self     :					Any,			_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:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						min(self.config.s_churn / self.num_inference_steps,			2**0.5 - 1						)
														else:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						0

														# sample eps ~ N(0, S_noise^2 * I)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.config.s_noise * randn_tensor(sample.shape,			generator=_UpperCAmelCase						).to(sample.device						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						sigma + gamma * sigma
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

														return sample_hat, sigma_hat






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						sample_hat + sigma_hat * model_output
														SCREAMING_SNAKE_CASE__       :      Dict     =						(sample_hat - pred_original_sample) / sigma_hat
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						sample_hat + (sigma_prev - sigma_hat) * derivative

														if not return_dict:
																					return (sample_prev, derivative)

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






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					float,			_UpperCAmelCase     :					float,			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					torch.FloatTensor,			_UpperCAmelCase     :					bool = True,			)				->			Union[KarrasVeOutput, Tuple]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						sample_prev + sigma_prev * model_output
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						(sample_prev - pred_original_sample) / sigma_prev
														SCREAMING_SNAKE_CASE__       :      str     =						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=_UpperCAmelCase,			derivative=_UpperCAmelCase,			pred_original_sample=_UpperCAmelCase						)






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Optional[int]						)				->			str:





														"""simple docstring"""
														raise NotImplementedError()













def _a (  SCREAMING_SNAKE_CASE__      :  int       )	->    str:

							'''simple docstring'''






							if isinstance(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														raise TypeError("'float' object cannot be interpreted as an integer"       )
							if isinstance(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														raise TypeError("'str' object cannot be interpreted as an integer"       )

							if num == 0:
														return "0b0"

							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						False

							if num < 0:
														SCREAMING_SNAKE_CASE__       :      Any     =						True
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						-num

							SCREAMING_SNAKE_CASE__       :      list[int]     =						[]
							while num > 0:
														binary.insert(0			,				num % 2       )
														num >>= 1

							if negative:
														return "-0b" + "".join(str(SCREAMING_SNAKE_CASE__       ) for e in binary       )

							return "0b" + "".join(str(SCREAMING_SNAKE_CASE__       ) for e in binary       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()






