
'''simple docstring'''


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		Union[str, Any]	      =			(DDPMScheduler,)

			def        UpperCAmelCase__ (self			,   **A      ):
								lowerCamelCase_					:      List[str]     =		{
								    '''num_train_timesteps''': 1_0_0_0,
								    '''beta_start''': 0.00_01,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								    '''variance_type''': '''fixed_small''',
								    '''clip_sample''': True,
								}

								config.update(**A      )
								return config

			def        UpperCAmelCase__ (self      ):
								for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=A      )

			def        UpperCAmelCase__ (self      ):
								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=A			,   beta_end=A      )

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

			def        UpperCAmelCase__ (self      ):
								for variance in ["fixed_small", "fixed_large", "other"]:
													self.check_over_configs(variance_type=A      )

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

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

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

			def        UpperCAmelCase__ (self      ):
								for t in [0, 5_0_0, 9_9_9]:
													self.check_over_forward(time_step=A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Dict     =		self.scheduler_classes[0]
								lowerCamelCase_					:      Any     =		self.get_scheduler_config()
								lowerCamelCase_					:      List[str]     =		scheduler_class(**A      )

								assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_8_7      ) - 0.0_09_79      )      ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_9_9      ) - 0.02      )      ) < 1E-5

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Union[str, Any]     =		self.scheduler_classes[0]
								lowerCamelCase_					:      Tuple     =		self.get_scheduler_config()
								lowerCamelCase_					:      Optional[int]     =		scheduler_class(**A      )

								lowerCamelCase_					:      int     =		len(A      )

								lowerCamelCase_					:      Optional[int]     =		self.dummy_model()
								lowerCamelCase_					:      Dict     =		self.dummy_sample_deter
								lowerCamelCase_					:      Tuple     =		torch.manual_seed(0      )

								for t in reversed(range(A      )      ):
													# 1. predict noise residual
													lowerCamelCase_					:      Any     =		model(A			,   A      )

													# 2. predict previous mean of sample x_t-1
													lowerCamelCase_					:      Optional[int]     =		scheduler.step(A			,   A			,   A			,   generator=A      ).prev_sample

													# if t > 0:
													#     noise = self.dummy_sample_deter
													#     variance = scheduler.get_variance(t) ** (0.5) * noise
													#
													# sample = pred_prev_sample + variance
													lowerCamelCase_					:      Optional[Any]     =		pred_prev_sample

								lowerCamelCase_					:      Tuple     =		torch.sum(torch.abs(A      )      )
								lowerCamelCase_					:      Any     =		torch.mean(torch.abs(A      )      )

								assert abs(result_sum.item() - 2_58.96_06      ) < 1E-2
								assert abs(result_mean.item() - 0.33_72      ) < 1E-3

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Dict     =		self.scheduler_classes[0]
								lowerCamelCase_					:      str     =		self.get_scheduler_config(prediction_type='''v_prediction'''      )
								lowerCamelCase_					:      Optional[int]     =		scheduler_class(**A      )

								lowerCamelCase_					:      List[Any]     =		len(A      )

								lowerCamelCase_					:      Dict     =		self.dummy_model()
								lowerCamelCase_					:      Tuple     =		self.dummy_sample_deter
								lowerCamelCase_					:      Dict     =		torch.manual_seed(0      )

								for t in reversed(range(A      )      ):
													# 1. predict noise residual
													lowerCamelCase_					:      Dict     =		model(A			,   A      )

													# 2. predict previous mean of sample x_t-1
													lowerCamelCase_					:      Any     =		scheduler.step(A			,   A			,   A			,   generator=A      ).prev_sample

													# if t > 0:
													#     noise = self.dummy_sample_deter
													#     variance = scheduler.get_variance(t) ** (0.5) * noise
													#
													# sample = pred_prev_sample + variance
													lowerCamelCase_					:      List[str]     =		pred_prev_sample

								lowerCamelCase_					:      int     =		torch.sum(torch.abs(A      )      )
								lowerCamelCase_					:      Dict     =		torch.mean(torch.abs(A      )      )

								assert abs(result_sum.item() - 2_02.02_96      ) < 1E-2
								assert abs(result_mean.item() - 0.26_31      ) < 1E-3

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		self.scheduler_classes[0]
								lowerCamelCase_					:      Optional[int]     =		self.get_scheduler_config()
								lowerCamelCase_					:      List[Any]     =		scheduler_class(**A      )

								lowerCamelCase_					:      int     =		[1_0_0, 8_7, 5_0, 1, 0]

								scheduler.set_timesteps(timesteps=A      )

								lowerCamelCase_					:      str     =		scheduler.timesteps

								for i, timestep in enumerate(A      ):
													if i == len(A      ) - 1:
																		lowerCamelCase_					:      int     =		-1
													else:
																		lowerCamelCase_					:      Any     =		timesteps[i + 1]

													lowerCamelCase_					:      List[Any]     =		scheduler.previous_timestep(A      )
													lowerCamelCase_					:      List[str]     =		prev_t.item()

													self.assertEqual(A			,   A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      int     =		self.scheduler_classes[0]
								lowerCamelCase_					:      Optional[int]     =		self.get_scheduler_config()
								lowerCamelCase_					:      List[str]     =		scheduler_class(**A      )

								lowerCamelCase_					:      List[str]     =		[1_0_0, 8_7, 5_0, 5_1, 0]

								with self.assertRaises(A			,   msg='''`custom_timesteps` must be in descending order.'''      ):
													scheduler.set_timesteps(timesteps=A      )

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		self.scheduler_classes[0]
								lowerCamelCase_					:      List[str]     =		self.get_scheduler_config()
								lowerCamelCase_					:      List[str]     =		scheduler_class(**A      )

								lowerCamelCase_					:      Optional[Any]     =		[1_0_0, 8_7, 5_0, 1, 0]
								lowerCamelCase_					:      Optional[int]     =		len(A      )

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






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[int]     =		self.scheduler_classes[0]
								lowerCamelCase_					:      Optional[int]     =		self.get_scheduler_config()
								lowerCamelCase_					:      Optional[int]     =		scheduler_class(**A      )

								lowerCamelCase_					:      Dict     =		[scheduler.config.num_train_timesteps]

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




'''simple docstring'''


from collections import namedtuple

__lowercase							:   List[Any]					=     namedtuple('''from_to''', '''from_ to''')

__lowercase							:   Optional[Any]					=     {
    '''cubicmeter''': from_to(1, 1),
    '''litre''': from_to(0.0_01, 1000),
    '''kilolitre''': from_to(1, 1),
    '''gallon''': from_to(0.0_04_54, 2_64.1_72),
    '''cubicyard''': from_to(0.7_64_55, 1.3_07_95),
    '''cubicfoot''': from_to(0.0_28, 35.31_47),
    '''cup''': from_to(0.0_00_23_65_88, 42_26.75),
}




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							float:
					'''simple docstring'''

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


if __name__ == "__main__":
					import doctest

					doctest.testmod()


