"""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_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      torch.FloatTensor
				_lowerCAmelCase       :      torch.FloatTensor
				_lowerCAmelCase       :      Optional[torch.FloatTensor]     					=  None





class 					lowerCAmelCase_      (     lowerCAmelCase       ,      lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Tuple     					=  2

				@register_to_config
				def __init__(			self						,      lowerCAmelCase = 0.02						,      lowerCAmelCase = 1_00						,      lowerCAmelCase = 1.0_07						,      lowerCAmelCase = 80						,      lowerCAmelCase = 0.05						,      lowerCAmelCase = 50						,      ):






										"""simple docstring"""

										snake_case        =     sigma_max

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None      ):






										"""simple docstring"""

										return sample

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None      ):






										"""simple docstring"""

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

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = None      ):






										"""simple docstring"""

										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=lowerCAmelCase      ).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 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = True						,      ):






										"""simple docstring"""

										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=lowerCAmelCase						,      derivative=lowerCAmelCase						,      pred_original_sample=lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase = True						,      ):






										"""simple docstring"""

										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=lowerCAmelCase						,      derivative=lowerCAmelCase						,      pred_original_sample=lowerCAmelCase      )

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase						,      lowerCAmelCase      ):






										"""simple docstring"""

										raise NotImplementedError()



"""simple docstring"""
from __future__ import annotations

from typing import Any



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				pass





class 					lowerCAmelCase_      :



				"""simple docstring"""

				def __init__(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										snake_case        =     data
										snake_case        =     None

				def __iter__(			self      ):






										"""simple docstring"""

										snake_case        =     self
										snake_case        =     []
										while node:
																if node in visited:
																						raise ContainsLoopError
																visited.append(lowerCAmelCase      )
																yield node.data
																snake_case        =     node.next_node

				@property
				def 						snake_case	(			self      ):






										"""simple docstring"""

										try:
																list(self      )
																return False
										except ContainsLoopError:
																return True


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   =					Node(1)
					SCREAMING_SNAKE_CASE__   =					Node(2)
					SCREAMING_SNAKE_CASE__   =					Node(3)
					SCREAMING_SNAKE_CASE__   =					Node(4)
					print(root_node.has_loop)  # False
					SCREAMING_SNAKE_CASE__   =					root_node.next_node
					print(root_node.has_loop)  # True

					SCREAMING_SNAKE_CASE__   =					Node(5)
					SCREAMING_SNAKE_CASE__   =					Node(6)
					SCREAMING_SNAKE_CASE__   =					Node(5)
					SCREAMING_SNAKE_CASE__   =					Node(6)
					print(root_node.has_loop)  # False

					SCREAMING_SNAKE_CASE__   =					Node(1)
					print(root_node.has_loop)  # False


