





"""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 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							torch.FloatTensor
				__snake_case :							torch.FloatTensor
				__snake_case :							Optional[torch.FloatTensor]									=		None

class 		SCREAMING_SNAKE_CASE_				(     snake_case__							,	snake_case__ ):
				"""simple docstring"""
				__snake_case :							Optional[int]									=		2




				@register_to_config
				def __init__(  self	:List[str]    ,     __lowercase	:float = 0.02    ,     __lowercase	:float = 100    ,     __lowercase	:float = 1.007    ,     __lowercase	:float = 80    ,     __lowercase	:float = 0.05    ,     __lowercase	:float = 50    ,     ):
											# standard deviation of the initial noise distribution
											__lowerCamelCase   :   Tuple       =sigma_max

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




				def        __lowercase	(  self	:Union[str, Any]    ,     __lowercase	:torch.FloatTensor    ,     __lowercase	:Optional[int] = None      ):
											return sample




				def        __lowercase	(  self	:int    ,     __lowercase	:int    ,     __lowercase	:Union[str, torch.device] = None      ):
											__lowerCamelCase   :   Any       =num_inference_steps
											__lowerCamelCase   :   Dict       =np.arange(0    ,     self.num_inference_steps      )[::-1].copy()
											__lowerCamelCase   :   Any       =torch.from_numpy(__lowercase      ).to(__lowercase      )
											__lowerCamelCase   :   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
											]
											__lowerCamelCase   :   Optional[Any]       =torch.tensor(__lowercase    ,     dtype=torch.floataa    ,     device=__lowercase      )




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:torch.FloatTensor    ,     __lowercase	:float    ,     __lowercase	:Optional[torch.Generator] = None      ):
											if self.config.s_min <= sigma <= self.config.s_max:
																		__lowerCamelCase   :   List[Any]       =min(self.config.s_churn / self.num_inference_steps    ,     2**0.5 - 1      )
											else:
																		__lowerCamelCase   :   List[str]       =0

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

											return sample_hat, sigma_hat




				def        __lowercase	(  self	:Any    ,     __lowercase	:torch.FloatTensor    ,     __lowercase	:float    ,     __lowercase	:float    ,     __lowercase	:torch.FloatTensor    ,     __lowercase	:bool = True    ,     ):
											__lowerCamelCase   :   Any       =sample_hat + sigma_hat * model_output
											__lowerCamelCase   :   Optional[int]       =(sample_hat - pred_original_sample) / sigma_hat
											__lowerCamelCase   :   List[Any]       =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        __lowercase	(  self	:Any    ,     __lowercase	:torch.FloatTensor    ,     __lowercase	:float    ,     __lowercase	:float    ,     __lowercase	:torch.FloatTensor    ,     __lowercase	:torch.FloatTensor    ,     __lowercase	:torch.FloatTensor    ,     __lowercase	:bool = True    ,     ):
											__lowerCamelCase   :   Dict       =sample_prev + sigma_prev * model_output
											__lowerCamelCase   :   Optional[int]       =(sample_prev - pred_original_sample) / sigma_prev
											__lowerCamelCase   :   List[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=__lowercase    ,     derivative=__lowercase    ,     pred_original_sample=__lowercase      )




				def        __lowercase	(  self	:str    ,     __lowercase	:List[str]    ,     __lowercase	:Optional[Any]    ,     __lowercase	:int      ):
											raise NotImplementedError()











"""simple docstring"""





from __future__ import annotations



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				list[int | str] ):


							'''simple docstring'''







							create_state_space_tree(SCREAMING_SNAKE_CASE     ,    []     ,    0     ,    [0 for i in range(len(SCREAMING_SNAKE_CASE ) )] )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				list[int | str]     ,    SCREAMING_SNAKE_CASE							:				list[int | str]     ,    SCREAMING_SNAKE_CASE							:				int     ,    SCREAMING_SNAKE_CASE							:				list[int]     ,    ):


							'''simple docstring'''







							if index == len(SCREAMING_SNAKE_CASE ):
														print(SCREAMING_SNAKE_CASE )
														return

							for i in range(len(SCREAMING_SNAKE_CASE ) ):
														if not index_used[i]:
																					current_sequence.append(sequence[i] )
																					__lowerCamelCase   :   List[Any]       =True
																					create_state_space_tree(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    index + 1     ,    SCREAMING_SNAKE_CASE )
																					current_sequence.pop()
																					__lowerCamelCase   :   List[Any]       =False
_UpperCamelCase					      =   [3, 1, 2, 4]
generate_all_permutations(sequence)

_UpperCamelCase					      =   ["A", "B", "C"]
generate_all_permutations(sequence_a)




