


"""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__    (		lowercase    ):
       """simple docstring"""


       a  :    torch.FloatTensor
       a  :    torch.FloatTensor
       a  :    Optional[torch.FloatTensor]       =None






class 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   lowercase    ):
       """simple docstring"""


       a  :    Optional[int]       =2

       @register_to_config
       def __init__(						self		,					snake_case__ = 0.02		,					snake_case__ = 100		,					snake_case__ = 1.007		,					snake_case__ = 80		,					snake_case__ = 0.05		,					snake_case__ = 50		,					):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							sigma_max

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

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None						):






         """simple docstring"""





         return sample

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__ = None						):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							num_inference_steps
         lowerCAmelCase					:					Dict								=							np.arange(0		,					self.num_inference_steps						)[::-1].copy()
         lowerCAmelCase					:					Any								=							torch.from_numpy(snake_case__						).to(snake_case__						)
         lowerCAmelCase					:					Optional[Any]								=							[
             (
                 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					:					Dict								=							torch.tensor(snake_case__		,					dtype=torch.floataa		,					device=snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__ = None						):






         """simple docstring"""





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

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

         return sample_hat, sigma_hat

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__ = True		,					):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							sample_hat + sigma_hat * model_output
         lowerCAmelCase					:					Dict								=							(sample_hat - pred_original_sample) / sigma_hat
         lowerCAmelCase					:					Any								=							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 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__ = True		,					):






         """simple docstring"""





         lowerCAmelCase					:					Tuple								=							sample_prev + sigma_prev * model_output
         lowerCAmelCase					:					Tuple								=							(sample_prev - pred_original_sample) / sigma_prev
         lowerCAmelCase					:					Tuple								=							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 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         raise NotImplementedError()









"""simple docstring"""







from typing import Any


class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							data
         lowerCAmelCase					:					Tuple								=							None


class 				SCREAMING_SNAKE_CASE__    :
       """simple docstring"""


       def __init__(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							None

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.head
         while temp is not None:
           print(temp.data		,					end=" "						)
           lowerCAmelCase					:					List[str]								=							temp.next
         print()

       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							Node(snake_case__						)
         lowerCAmelCase					:					Union[str, Any]								=							self.head
         lowerCAmelCase					:					Optional[Any]								=							new_node







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         if node_data_a == node_data_a:
           return
         else:
           lowerCAmelCase					:					str								=							self.head
           while node_a is not None and node_a.data != node_data_a:
             lowerCAmelCase					:					Union[str, Any]								=							node_a.next

           lowerCAmelCase					:					Any								=							self.head
           while node_a is not None and node_a.data != node_data_a:
             lowerCAmelCase					:					List[Any]								=							node_a.next

           if node_a is None or node_a is None:
             return

           lowerCAmelCase						,  lowerCAmelCase					:					str								=							node_a.data, node_a.data


if __name__ == "__main__":
     lowerCAmelCase__				    =       LinkedList()
     for i in range(5, 0, -1):
          ll.push(i)

     ll.print_list()

     ll.swap_nodes(1, 4)
     print('''After swapping''')
     ll.print_list()





