



"""simple docstring"""
import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__				):
   __lowerCAmelCase						:     Optional[int]		=		(CMStochasticIterativeScheduler,)
   __lowerCAmelCase						:     Dict		=		10

   def 			SCREAMING_SNAKE_CASE  (     self   ,  **_SCREAMING_SNAKE_CASE		)						->   List[str]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			{
        """num_train_timesteps""": 201,
        """sigma_min""": 0.002,
        """sigma_max""": 80.0,
    }

    config.update(**_SCREAMING_SNAKE_CASE		)
    return config

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Dict:





    '''simple docstring'''




    UpperCAmelCase			:      str			=			10

    UpperCAmelCase			:      Optional[Any]			=			self.get_scheduler_config()
    UpperCAmelCase			:      Union[str, Any]			=			self.scheduler_classes[0](**_SCREAMING_SNAKE_CASE		)

    scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      str			=			scheduler.timesteps[0]
    UpperCAmelCase			:      Any			=			scheduler.timesteps[1]

    UpperCAmelCase			:      Optional[Any]			=			self.dummy_sample
    UpperCAmelCase			:      List[Any]			=			0.1 * sample

    UpperCAmelCase			:      List[str]			=			scheduler.step(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		).prev_sample
    UpperCAmelCase			:      Any			=			scheduler.step(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		).prev_sample

    self.assertEqual(output_a.shape   ,  sample.shape		)
    self.assertEqual(output_a.shape   ,  output_a.shape		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    for timesteps in [10, 50, 100, 1000]:
     self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[str]:





    '''simple docstring'''




    for clip_denoised in [True, False]:
     self.check_over_configs(clip_denoised=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			self.scheduler_classes[0]
    UpperCAmelCase			:      Tuple			=			self.get_scheduler_config()
    UpperCAmelCase			:      Union[str, Any]			=			scheduler_class(**_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			1
    scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Optional[Any]			=			scheduler.timesteps

    UpperCAmelCase			:      List[str]			=			torch.manual_seed(0		)

    UpperCAmelCase			:      Any			=			self.dummy_model()
    UpperCAmelCase			:      int			=			self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(_SCREAMING_SNAKE_CASE		):
     # 1. scale model input
     UpperCAmelCase			:      str			=			scheduler.scale_model_input(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     # 2. predict noise residual
     UpperCAmelCase			:      str			=			model(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     # 3. predict previous sample x_t-1
     UpperCAmelCase			:      str			=			scheduler.step(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE		).prev_sample

     UpperCAmelCase			:      Tuple			=			pred_prev_sample

    UpperCAmelCase			:      Union[str, Any]			=			torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
    UpperCAmelCase			:      Optional[int]			=			torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

    assert abs(result_sum.item() - 192.7614		) < 1E-2
    assert abs(result_mean.item() - 0.2510		) < 1E-3

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.scheduler_classes[0]
    UpperCAmelCase			:      Union[str, Any]			=			self.get_scheduler_config()
    UpperCAmelCase			:      Tuple			=			scheduler_class(**_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      List[Any]			=			[106, 0]
    scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      Any			=			scheduler.timesteps

    UpperCAmelCase			:      Optional[Any]			=			torch.manual_seed(0		)

    UpperCAmelCase			:      int			=			self.dummy_model()
    UpperCAmelCase			:      int			=			self.dummy_sample_deter * scheduler.init_noise_sigma

    for t in timesteps:
     # 1. scale model input
     UpperCAmelCase			:      int			=			scheduler.scale_model_input(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     # 2. predict noise residual
     UpperCAmelCase			:      List[str]			=			model(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

     # 3. predict previous sample x_t-1
     UpperCAmelCase			:      Tuple			=			scheduler.step(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  generator=_SCREAMING_SNAKE_CASE		).prev_sample

     UpperCAmelCase			:      List[str]			=			pred_prev_sample

    UpperCAmelCase			:      Tuple			=			torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
    UpperCAmelCase			:      Union[str, Any]			=			torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

    assert abs(result_sum.item() - 347.6357		) < 1E-2
    assert abs(result_mean.item() - 0.4527		) < 1E-3

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Any			=			self.scheduler_classes[0]
    UpperCAmelCase			:      Tuple			=			self.get_scheduler_config()
    UpperCAmelCase			:      Tuple			=			scheduler_class(**_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			[39, 30, 12, 15, 0]

    with self.assertRaises(_SCREAMING_SNAKE_CASE   ,  msg="""`timesteps` must be in descending order."""		):
     scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      int			=			self.scheduler_classes[0]
    UpperCAmelCase			:      Dict			=			self.get_scheduler_config()
    UpperCAmelCase			:      Optional[int]			=			scheduler_class(**_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			[39, 30, 12, 1, 0]
    UpperCAmelCase			:      List[Any]			=			len(_SCREAMING_SNAKE_CASE		)

    with self.assertRaises(_SCREAMING_SNAKE_CASE   ,  msg="""Can only pass one of `num_inference_steps` or `timesteps`."""		):
     scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE   ,  timesteps=_SCREAMING_SNAKE_CASE		)




   def 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.scheduler_classes[0]
    UpperCAmelCase			:      Dict			=			self.get_scheduler_config()
    UpperCAmelCase			:      List[Any]			=			scheduler_class(**_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      int			=			[scheduler.config.num_train_timesteps]

    with self.assertRaises(
        _SCREAMING_SNAKE_CASE   ,  msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""   ,  ):
     scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE		)





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

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

A:     List[Any]										=	(3, 9, -1_1, 0, 7, 5, 1, -1)
A:     Union[str, Any]										=	(4, 6, 2, 0, 8, 1_0, 3, -2)



@dataclass
class    SCREAMING_SNAKE_CASE__						:
   __lowerCAmelCase						:     int
   __lowerCAmelCase						:     Node | None



class    SCREAMING_SNAKE_CASE__						:

   def __init__(     self   ,  _SCREAMING_SNAKE_CASE		)						->   None:





    '''simple docstring'''




    UpperCAmelCase			:      Node | None			=			None
    for i in sorted(_SCREAMING_SNAKE_CASE   ,  reverse=_SCREAMING_SNAKE_CASE		):
     UpperCAmelCase			:      Union[str, Any]			=			Node(_SCREAMING_SNAKE_CASE   ,  self.head		)

   def __iter__(     self		)						->   Iterator[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[Any]			=			self.head
    while node:
     yield node.data
     UpperCAmelCase			:      List[str]			=			node.next_node

   def __len__(     self		)						->   int:





    '''simple docstring'''




    return sum(1 for _ in self		)




   def __str__(     self		)						->   str:





    '''simple docstring'''




    return " -> ".join([str(_SCREAMING_SNAKE_CASE		) for node in self]		)

def    _snake_case		(		UpperCamelCase		:	SortedLinkedList    ,	UpperCamelCase		:	SortedLinkedList		):
 return SortedLinkedList(list(UpperCamelCase		) + list(UpperCamelCase		)		)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      A:     Dict										=	SortedLinkedList
      print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
