


from collections import namedtuple

lowerCamelCase						=		namedtuple('''from_to''', '''from_ to''')

lowerCamelCase						=		{
    '''cubicmeter''': from_to(1, 1),
    '''litre''': from_to(0.0_0_1, 1000),
    '''kilolitre''': from_to(1, 1),
    '''gallon''': from_to(0.0_0_4_5_4, 2_6_4.1_7_2),
    '''cubicyard''': from_to(0.7_6_4_5_5, 1.3_0_7_9_5),
    '''cubicfoot''': from_to(0.0_2_8, 3_5.3_1_4_7),
    '''cup''': from_to(0.0_0_0_2_3_6_5_8_8, 4_2_2_6.7_5),
}






def      lowerCamelCase_    (       _a	,   _a	,   _a       ):




		"""simple docstring"""






		if from_type not in METRIC_CONVERSION:
				raise ValueError(
				    f'Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'
				    + ''', '''.join(__lowerCamelCase       )       )
		if to_type not in METRIC_CONVERSION:
				raise ValueError(
				    f'Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'
				    + ''', '''.join(__lowerCamelCase       )       )
		return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
		import doctest

		doctest.testmod()






"""simple docstring"""





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		(DDIMParallelScheduler,)
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		(("""eta""", 0.0), ("""num_inference_steps""", 50))







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    {
         "num_train_timesteps": 1000,
         "beta_start": 0.00_01,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "clip_sample": True,
     }

     config.update(**lowercase_  )
     return config







  def        UpperCamelCase__					(		self				,   **lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					Union[str, Any]			     =    self.get_scheduler_config(**lowercase_  )
     UpperCAmelCase_					:					int			     =    scheduler_class(**lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    10, 0.0

     UpperCAmelCase_					:					Optional[int]			     =    self.dummy_model()
     UpperCAmelCase_					:					str			     =    self.dummy_sample_deter

     scheduler.set_timesteps(lowercase_  )

     for t in scheduler.timesteps:
        UpperCAmelCase_					:					Dict			     =    model(lowercase_				,   lowercase_  )
        UpperCAmelCase_					:					Dict			     =    scheduler.step(lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  ).prev_sample

     return sample







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for timesteps in [100, 500, 1000]:
        self.check_over_configs(num_train_timesteps=lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for steps_offset in [0, 1]:
        self.check_over_configs(steps_offset=lowercase_  )

     UpperCAmelCase_					:					str			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config(steps_offset=1  )
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )
     scheduler.set_timesteps(5  )
     assert torch.equal(scheduler.timesteps				,   torch.LongTensor([801, 601, 401, 201, 1]  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,   [0.0_02, 0.02, 0.2, 2]  ):
        self.check_over_configs(beta_start=lowercase_				,   beta_end=lowercase_  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=lowercase_  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for rescale_betas_zero_snr in [True, False]:
        self.check_over_configs(rescale_betas_zero_snr=lowercase_  )







  def        UpperCamelCase__					(		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        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for t in [1, 10, 49]:
        self.check_over_forward(time_step=lowercase_  )







  def        UpperCamelCase__					(		self  ):

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







  def        UpperCamelCase__					(		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        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					List[str]			     =    self.get_scheduler_config()
     UpperCAmelCase_					:					List[str]			     =    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(420				,   400  ) - 0.1_47_71  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(980				,   960  ) - 0.3_24_60  )  ) < 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  ) - 0.0_09_79  )  ) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999				,   998  ) - 0.02  )  ) < 1E-5







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.scheduler_classes[0]
     UpperCAmelCase_					:					Optional[int]			     =    self.get_scheduler_config()
     UpperCAmelCase_					:					List[str]			     =    scheduler_class(**lowercase_  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    10, 0.0
     scheduler.set_timesteps(lowercase_  )

     UpperCAmelCase_					:					Union[str, Any]			     =    self.dummy_model()
     UpperCAmelCase_					:					List[str]			     =    self.dummy_sample_deter
     UpperCAmelCase_					:					Any			     =    self.dummy_sample_deter + 0.1
     UpperCAmelCase_					:					int			     =    self.dummy_sample_deter - 0.1

     UpperCAmelCase_					:					List[Any]			     =    samplea.shape[0]
     UpperCAmelCase_					:					int			     =    torch.stack([samplea, samplea, samplea]				,   dim=0  )
     UpperCAmelCase_					:					int			     =    torch.arange(lowercase_  )[0:3, None].repeat(1				,   lowercase_  )

     UpperCAmelCase_					:					int			     =    model(samples.flatten(0				,   1  )				,   timesteps.flatten(0				,   1  )  )
     UpperCAmelCase_					:					Optional[Any]			     =    scheduler.batch_step_no_noise(lowercase_				,   timesteps.flatten(0				,   1  )				,   samples.flatten(0				,   1  )				,   lowercase_  )

     UpperCAmelCase_					:					List[Any]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					str			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 11_47.79_04  ) < 1E-2
     assert abs(result_mean.item() - 0.49_82  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    self.full_loop()

     UpperCAmelCase_					:					int			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					List[str]			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_72.00_67  ) < 1E-2
     assert abs(result_mean.item() - 0.22_39_67  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.full_loop(prediction_type="v_prediction"  )

     UpperCAmelCase_					:					str			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Dict			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 52.53_02  ) < 1E-2
     assert abs(result_mean.item() - 0.06_84  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					List[str]			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					Dict			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Tuple			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_49.82_95  ) < 1E-2
     assert abs(result_mean.item() - 0.19_51  ) < 1E-3







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # We specify different beta, so that the first alpha is 0.99
     UpperCAmelCase_					:					int			     =    self.full_loop(set_alpha_to_one=lowercase_				,   beta_start=0.01  )
     UpperCAmelCase_					:					List[Any]			     =    torch.sum(torch.abs(lowercase_  )  )
     UpperCAmelCase_					:					Dict			     =    torch.mean(torch.abs(lowercase_  )  )

     assert abs(result_sum.item() - 1_49.07_84  ) < 1E-2
     assert abs(result_mean.item() - 0.19_41  ) < 1E-3


