
'''simple docstring'''







import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 						__UpperCAmelCase      (					lowerCamelCase_						):
							__lowercase      		=       (DDIMParallelScheduler,)
							__lowercase      		=       (("""eta""", 0.0), ("""num_inference_steps""", 50))


							def  lowerCamelCase				(       self    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		{
								    'num_train_timesteps': 10_00,
								    'beta_start': 0.0001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'clip_sample': True,
								}

								config.update(**_lowercase  )
								return config


							def  lowerCamelCase				(       self    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		self.scheduler_classes[0]
								_snake_case   						=		self.get_scheduler_config(**_lowercase  )
								_snake_case   						=		scheduler_class(**_lowercase  )

								_snake_case						, _snake_case   						=		10, 0.0

								_snake_case   						=		self.dummy_model()
								_snake_case   						=		self.dummy_sample_deter

								scheduler.set_timesteps(_lowercase  )

								for t in scheduler.timesteps:
									_snake_case   						=		model(_lowercase    ,   _lowercase  )
									_snake_case   						=		scheduler.step(_lowercase    ,   _lowercase    ,   _lowercase    ,   _lowercase  ).prev_sample

								return sample


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for timesteps in [1_00, 5_00, 10_00]:
									self.check_over_configs(num_train_timesteps=_lowercase  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

								_snake_case   						=		self.scheduler_classes[0]
								_snake_case   						=		self.get_scheduler_config(steps_offset=1  )
								_snake_case   						=		scheduler_class(**_lowercase  )
								scheduler.set_timesteps(5  )
								assert torch.equal(scheduler.timesteps    ,   torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]  )  )


							def  lowerCamelCase				(       self  ):





								"""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=_lowercase    ,   beta_end=_lowercase  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for t, num_inference_steps in zip([1, 10, 50]    ,   [10, 50, 5_00]  ):
									self.check_over_forward(time_step=_lowercase    ,   num_inference_steps=_lowercase  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.scheduler_classes[0]
								_snake_case   						=		self.get_scheduler_config()
								_snake_case   						=		scheduler_class(**_lowercase  )

								assert torch.sum(torch.abs(scheduler._get_variance(0    ,   0  ) - 0.0  )  ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,   4_00  ) - 0.14771  )  ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,   9_60  ) - 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(4_87    ,   4_86  ) - 0.00979  )  ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,   9_98  ) - 0.02  )  ) < 1E-5


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.scheduler_classes[0]
								_snake_case   						=		self.get_scheduler_config()
								_snake_case   						=		scheduler_class(**_lowercase  )

								_snake_case						, _snake_case   						=		10, 0.0
								scheduler.set_timesteps(_lowercase  )

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

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

								_snake_case   						=		model(samples.flatten(0    ,   1  )    ,   timesteps.flatten(0    ,   1  )  )
								_snake_case   						=		scheduler.batch_step_no_noise(_lowercase    ,   timesteps.flatten(0    ,   1  )    ,   samples.flatten(0    ,   1  )    ,   _lowercase  )

								_snake_case   						=		torch.sum(torch.abs(_lowercase  )  )
								_snake_case   						=		torch.mean(torch.abs(_lowercase  )  )

								assert abs(result_sum.item() - 11_47.79_04  ) < 1E-2
								assert abs(result_mean.item() - 0.4982  ) < 1E-3


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.full_loop()

								_snake_case   						=		torch.sum(torch.abs(_lowercase  )  )
								_snake_case   						=		torch.mean(torch.abs(_lowercase  )  )

								assert abs(result_sum.item() - 1_72.00_67  ) < 1E-2
								assert abs(result_mean.item() - 0.223967  ) < 1E-3


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

								_snake_case   						=		torch.sum(torch.abs(_lowercase  )  )
								_snake_case   						=		torch.mean(torch.abs(_lowercase  )  )

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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.full_loop(set_alpha_to_one=_lowercase    ,   beta_start=0.01  )
								_snake_case   						=		torch.sum(torch.abs(_lowercase  )  )
								_snake_case   						=		torch.mean(torch.abs(_lowercase  )  )

								assert abs(result_sum.item() - 1_49.82_95  ) < 1E-2
								assert abs(result_mean.item() - 0.1951  ) < 1E-3




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.full_loop(set_alpha_to_one=_lowercase    ,   beta_start=0.01  )
								_snake_case   						=		torch.sum(torch.abs(_lowercase  )  )
								_snake_case   						=		torch.mean(torch.abs(_lowercase  )  )

								assert abs(result_sum.item() - 1_49.07_84  ) < 1E-2
								assert abs(result_mean.item() - 0.1941  ) < 1E-3


# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowerCamelCase			: Any							   =		abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int]	)							->					Any:







  """simple docstring"""





  from diffusers.utils.testing_utils import pytest_addoption_shared

  pytest_addoption_shared(__UpperCamelCase	)






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict	)							->					Optional[Any]:







  """simple docstring"""





  from diffusers.utils.testing_utils import pytest_terminal_summary_main

  SCREAMING_SNAKE_CASE__										=      terminalreporter.config.getoption("""--make-reports"""	)
  if make_reports:
    pytest_terminal_summary_main(__UpperCamelCase ,     id=__UpperCamelCase	)
