



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			(EulerDiscreteScheduler,)
   _UpperCAmelCase     	=			10

   def 		A							(      self			:       Union[str, Any]							,   **__lowerCAmelCase			:       str							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		{
        "num_train_timesteps": 1100,
        "beta_start": 0.0_0_0_1,
        "beta_end": 0.0_2,
        "beta_schedule": "linear",
    }

    config.update(**__lowerCAmelCase							)
    return config

   def 		A							(      self			:       Dict							)       ->				Optional[int]:


    """simple docstring"""






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

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


    """simple docstring"""






    for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]							,   [0.0_0_0_2, 0.0_0_2, 0.0_2]							):
     self.check_over_configs(beta_start=__lowerCAmelCase							,   beta_end=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[int]							)       ->				int:


    """simple docstring"""






    for schedule in ["linear", "scaled_linear"]:
     self.check_over_configs(beta_schedule=__lowerCAmelCase							)

   def 		A							(      self			:       Dict							)       ->				Optional[Any]:


    """simple docstring"""






    for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=__lowerCAmelCase							)

   def 		A							(      self			:       Optional[Any]							)       ->				List[str]:


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config()
    a						=		scheduler_class(**__lowerCAmelCase							)

    scheduler.set_timesteps(self.num_inference_steps							)

    a						=		torch.manual_seed(0							)

    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter * scheduler.init_noise_sigma
    a						=		sample.to(__lowerCAmelCase							)

    for i, t in enumerate(scheduler.timesteps							):
     a						=		scheduler.scale_model_input(__lowerCAmelCase							,   __lowerCAmelCase							)

     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							)
     a						=		output.prev_sample

    a						=		torch.sum(torch.abs(__lowerCAmelCase							)							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_sum.item() - 1_0.0_8_0_7							) < 1E-2
    assert abs(result_mean.item() - 0.0_1_3_1							) < 1E-3

   def 		A							(      self			:       Optional[Any]							)       ->				List[Any]:


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config(prediction_type="v_prediction"							)
    a						=		scheduler_class(**__lowerCAmelCase							)

    scheduler.set_timesteps(self.num_inference_steps							)

    a						=		torch.manual_seed(0							)

    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter * scheduler.init_noise_sigma
    a						=		sample.to(__lowerCAmelCase							)

    for i, t in enumerate(scheduler.timesteps							):
     a						=		scheduler.scale_model_input(__lowerCAmelCase							,   __lowerCAmelCase							)

     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							)
     a						=		output.prev_sample

    a						=		torch.sum(torch.abs(__lowerCAmelCase							)							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_sum.item() - 0.0_0_0_2							) < 1E-2
    assert abs(result_mean.item() - 2.26_76E-06							) < 1E-3

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


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config()
    a						=		scheduler_class(**__lowerCAmelCase							)

    scheduler.set_timesteps(self.num_inference_steps							,   device=__lowerCAmelCase							)

    a						=		torch.manual_seed(0							)

    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    a						=		sample.to(__lowerCAmelCase							)

    for t in scheduler.timesteps:
     a						=		scheduler.scale_model_input(__lowerCAmelCase							,   __lowerCAmelCase							)

     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							)
     a						=		output.prev_sample

    a						=		torch.sum(torch.abs(__lowerCAmelCase							)							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_sum.item() - 1_0.0_8_0_7							) < 1E-2
    assert abs(result_mean.item() - 0.0_1_3_1							) < 1E-3




   def 		A							(      self			:       List[str]							)       ->				int:


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config()
    a						=		scheduler_class(**__lowerCAmelCase							,   use_karras_sigmas=__lowerCAmelCase							)

    scheduler.set_timesteps(self.num_inference_steps							,   device=__lowerCAmelCase							)

    a						=		torch.manual_seed(0							)

    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
    a						=		sample.to(__lowerCAmelCase							)

    for t in scheduler.timesteps:
     a						=		scheduler.scale_model_input(__lowerCAmelCase							,   __lowerCAmelCase							)

     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   generator=__lowerCAmelCase							)
     a						=		output.prev_sample

    a						=		torch.sum(torch.abs(__lowerCAmelCase							)							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

    assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9							) < 1E-2
    assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3							) < 1E-3






from __future__ import annotations

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

A_		:     List[str]        =     (3, 9, -11, 0, 7, 5, 1, -1)
A_		:     Optional[int]        =     (4, 6, 2, 0, 8, 10, 3, -2)



@dataclass
class 		_lowercase		:
   _UpperCAmelCase     	=			42
   _UpperCAmelCase     	=			42



class 		_lowercase		:

   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       Iterable[int]							)       ->				None:


    """simple docstring"""






    a						=		None
    for i in sorted(__lowerCAmelCase							,   reverse=__lowerCAmelCase							):
     a						=		Node(__lowerCAmelCase							,   self.head							)

   def __iter__(      self			:       Union[str, Any]							)       ->				Iterator[int]:


    """simple docstring"""






    a						=		self.head
    while node:
     yield node.data
     a						=		node.next_node

   def __len__(      self			:       Tuple							)       ->				int:


    """simple docstring"""






    return sum(1 for _ in self							)




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


    """simple docstring"""






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


def 	UpperCAmelCase__      (      UpperCAmelCase__ :SortedLinkedList ,					UpperCAmelCase__ :SortedLinkedList					):






 '''simple docstring'''

 return SortedLinkedList(list(UpperCAmelCase__					) + list(UpperCAmelCase__					)					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     A_		:     Optional[Any]        =     SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

