




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      (DDIMParallelScheduler,)
    UpperCAmelCase__							  =      (('''eta''', 0.0), ('''num_inference_steps''', 50))


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





           '''simple docstring'''

           A__       =    {
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''clip_sample''': True,
           }

           config.update(**UpperCAmelCase__)
           return config


    def        SCREAMING_SNAKE_CASE							(			self      :	int					, **UpperCAmelCase__      :	List[Any])						->Tuple:





           '''simple docstring'''

           A__       =    self.scheduler_classes[0]
           A__       =    self.get_scheduler_config(**UpperCAmelCase__)
           A__       =    scheduler_class(**UpperCAmelCase__)

           A__ ,							A__       =    10, 0.0

           A__       =    self.dummy_model()
           A__       =    self.dummy_sample_deter

           scheduler.set_timesteps(UpperCAmelCase__)

           for t in scheduler.timesteps:
                  A__       =    model(UpperCAmelCase__					, UpperCAmelCase__)
                  A__       =    scheduler.step(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__).prev_sample

           return sample


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





           '''simple docstring'''

           for timesteps in [100, 500, 1_000]:
                  self.check_over_configs(num_train_timesteps=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->Tuple:





           '''simple docstring'''

           for steps_offset in [0, 1]:
                  self.check_over_configs(steps_offset=UpperCAmelCase__)

           A__       =    self.scheduler_classes[0]
           A__       =    self.get_scheduler_config(steps_offset=1)
           A__       =    scheduler_class(**UpperCAmelCase__)
           scheduler.set_timesteps(5)
           assert torch.equal(scheduler.timesteps					, torch.LongTensor([801, 601, 401, 201, 1]))


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->int:





           '''simple docstring'''

           for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					, [0.002, 0.02, 0.2, 2]):
                  self.check_over_configs(beta_start=UpperCAmelCase__					, beta_end=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->int:





           '''simple docstring'''

           for schedule in ["linear", "squaredcos_cap_v2"]:
                  self.check_over_configs(beta_schedule=UpperCAmelCase__)


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





           '''simple docstring'''

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


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





           '''simple docstring'''

           for clip_sample in [True, False]:
                  self.check_over_configs(clip_sample=UpperCAmelCase__)


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





           '''simple docstring'''

           for timestep_spacing in ["trailing", "leading"]:
                  self.check_over_configs(timestep_spacing=UpperCAmelCase__)


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





           '''simple docstring'''

           for rescale_betas_zero_snr in [True, False]:
                  self.check_over_configs(rescale_betas_zero_snr=UpperCAmelCase__)


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





           '''simple docstring'''

           self.check_over_configs(thresholding=UpperCAmelCase__)
           for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "v_prediction"]:
                         self.check_over_configs(
                             thresholding=UpperCAmelCase__					, prediction_type=UpperCAmelCase__					, sample_max_value=UpperCAmelCase__					, )


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Optional[int]:





           '''simple docstring'''

           for t in [1, 10, 49]:
                  self.check_over_forward(time_step=UpperCAmelCase__)


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





           '''simple docstring'''

           for t, num_inference_steps in zip([1, 10, 50]					, [10, 50, 500]):
                  self.check_over_forward(time_step=UpperCAmelCase__					, num_inference_steps=UpperCAmelCase__)


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





           '''simple docstring'''

           for t, eta in zip([1, 10, 49]					, [0.0, 0.5, 1.0]):
                  self.check_over_forward(time_step=UpperCAmelCase__					, eta=UpperCAmelCase__)


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





           '''simple docstring'''

           A__       =    self.scheduler_classes[0]
           A__       =    self.get_scheduler_config()
           A__       =    scheduler_class(**UpperCAmelCase__)

           assert torch.sum(torch.abs(scheduler._get_variance(0					, 0) - 0.0)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(420					, 400) - 0.14771)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(980					, 960) - 0.32460)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(0					, 0) - 0.0)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(487					, 486) - 0.00979)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(999					, 998) - 0.02)) < 1e-5


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





           '''simple docstring'''

           A__       =    self.scheduler_classes[0]
           A__       =    self.get_scheduler_config()
           A__       =    scheduler_class(**UpperCAmelCase__)

           A__ ,							A__       =    10, 0.0
           scheduler.set_timesteps(UpperCAmelCase__)

           A__       =    self.dummy_model()
           A__       =    self.dummy_sample_deter
           A__       =    self.dummy_sample_deter + 0.1
           A__       =    self.dummy_sample_deter - 0.1

           A__       =    samplea.shape[0]
           A__       =    torch.stack([samplea, samplea, samplea]					, dim=0)
           A__       =    torch.arange(UpperCAmelCase__)[0:3, None].repeat(1					, UpperCAmelCase__)

           A__       =    model(samples.flatten(0					, 1)					, timesteps.flatten(0					, 1))
           A__       =    scheduler.batch_step_no_noise(UpperCAmelCase__					, timesteps.flatten(0					, 1)					, samples.flatten(0					, 1)					, UpperCAmelCase__)

           A__       =    torch.sum(torch.abs(UpperCAmelCase__))
           A__       =    torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 1147.7904) < 1e-2
           assert abs(result_mean.item() - 0.4982) < 1e-3


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





           '''simple docstring'''

           A__       =    self.full_loop()

           A__       =    torch.sum(torch.abs(UpperCAmelCase__))
           A__       =    torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 172.0067) < 1e-2
           assert abs(result_mean.item() - 0.223967) < 1e-3


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





           '''simple docstring'''

           A__       =    self.full_loop(prediction_type='''v_prediction''')

           A__       =    torch.sum(torch.abs(UpperCAmelCase__))
           A__       =    torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 52.5302) < 1e-2
           assert abs(result_mean.item() - 0.0684) < 1e-3


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





           '''simple docstring'''

           A__       =    self.full_loop(set_alpha_to_one=UpperCAmelCase__					, beta_start=0.01)
           A__       =    torch.sum(torch.abs(UpperCAmelCase__))
           A__       =    torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 149.8295) < 1e-2
           assert abs(result_mean.item() - 0.1951) < 1e-3





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





           '''simple docstring'''

           A__       =    self.full_loop(set_alpha_to_one=UpperCAmelCase__					, beta_start=0.01)
           A__       =    torch.sum(torch.abs(UpperCAmelCase__))
           A__       =    torch.mean(torch.abs(UpperCAmelCase__))

           assert abs(result_sum.item() - 149.0784) < 1e-2
           assert abs(result_mean.item() - 0.1941) < 1e-3








from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase	:		str							       =   typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase	:		Tuple							       =   typing.Union[np.floataa, int, float]  # noqa: UP007







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       VectorOut:



       """simple docstring"""

       return np.sqrt(np.sum((np.asarray(lowercase_						) - np.asarray(lowercase_						)) ** 2						)						)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       VectorOut:



       """simple docstring"""

       return sum((va - va) ** 2 for va, va in zip(lowercase_ ,				lowercase_						)						) ** (1 / 2)


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE				(       )   ->       None:



         """simple docstring"""

         from timeit import timeit

         print('''Without Numpy'''						)
         print(
             timeit(
                 '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])''' ,				number=10_000 ,				globals=globals() ,				)						)
         print('''With Numpy'''						)
         print(
             timeit(
                 '''euclidean_distance([1, 2, 3], [4, 5, 6])''' ,				number=10_000 ,				globals=globals() ,				)						)
  benchmark()


