


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
a					  =     2_9_9_7_9_2_4_5_8

# Symbols
a  ,					a  ,					a  ,					a					  =     symbols('ct x y z')


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					if velocity > c:
										raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""  )
					elif velocity < 1:
										# Usually the speed should be much higher than 1 (c order of magnitude)
										raise ValueError("""Speed must be greater than or equal to 1!"""  )

					return velocity / c


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					return 1 / sqrt(1 - beta(UpperCAmelCase__  ) ** 2  )


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					return np.array(
					    [
					        [gamma(UpperCAmelCase__  ), -gamma(UpperCAmelCase__  ) * beta(UpperCAmelCase__  ), 0, 0],
					        [-gamma(UpperCAmelCase__  ) * beta(UpperCAmelCase__  ), gamma(UpperCAmelCase__  ), 0, 0],
					        [0, 0, 1, 0],
					        [0, 0, 0, 1],
					    ]  )


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__ = None  ):
					# Ensure event is not empty
					if event is None:
										lowercase_  							=			np.array([ct, x, y, z]  )  # Symbolic four vector
					else:
										event[0] *= c  # x0 is ct (speed of light * time)

					return transformation_matrix(UpperCAmelCase__  ) @ event


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Example of symbolic vector:
							a					  =     transform(2_9_9_7_9_2_4_5)
							print('Example of four vector: ')
							print(F'''ct\' = {four_vector[0]}''')
							print(F'''x\' = {four_vector[1]}''')
							print(F'''y\' = {four_vector[2]}''')
							print(F'''z\' = {four_vector[3]}''')

							# Substitute symbols with numerical values
							a					  =     {ct: c, x: 1, y: 1, z: 1}
							a					  =     [four_vector[i].subs(sub_dict) for i in range(4)]

							print(F'''\n{numerical_vector}''')






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   (UnCLIPScheduler,)

					def   UpperCAmelCase__       (     self       :   int							,					**UpperCamelCase__       :   int       ):






										'''simple docstring'''






										lowercase_  							=			{
										    """num_train_timesteps""": 1_000,
										    """variance_type""": """fixed_small_log""",
										    """clip_sample""": True,
										    """clip_sample_range""": 1.0,
										    """prediction_type""": """epsilon""",
										}

										config.update(**UpperCamelCase__       )
										return config

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										for timesteps in [1, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(prediction_type=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																				if prev_timestep is not None and prev_timestep >= time_step:
																									continue

																				self.check_over_forward(time_step=UpperCamelCase__							,					prev_timestep=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config(variance_type="""fixed_small_log"""       )
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0000e-10       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_549_625       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9_994_987       )       ) < 1e-5

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config(variance_type="""learned_range"""       )
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										lowercase_  							=			0.5

										assert scheduler._get_variance(1							,					predicted_variance=UpperCamelCase__       ) - -10.1_712_790 < 1e-5
										assert scheduler._get_variance(487							,					predicted_variance=UpperCamelCase__       ) - -5.7_998_052 < 1e-5
										assert scheduler._get_variance(999							,					predicted_variance=UpperCamelCase__       ) - -0.0_010_011 < 1e-5

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config()
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										lowercase_  							=			scheduler.timesteps

										lowercase_  							=			self.dummy_model()
										lowercase_  							=			self.dummy_sample_deter
										lowercase_  							=			torch.manual_seed(0       )

										for i, t in enumerate(UpperCamelCase__       ):
															# 1. predict noise residual
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

															# 2. predict previous mean of sample x_t-1
															lowercase_  							=			scheduler.step(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					generator=UpperCamelCase__       ).prev_sample

															lowercase_  							=			pred_prev_sample

										lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
										lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

										assert abs(result_sum.item() - 252.2_682_495       ) < 1e-2
										assert abs(result_mean.item() - 0.3_284_743       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config()
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										scheduler.set_timesteps(25       )

										lowercase_  							=			scheduler.timesteps

										lowercase_  							=			self.dummy_model()
										lowercase_  							=			self.dummy_sample_deter
										lowercase_  							=			torch.manual_seed(0       )

										for i, t in enumerate(UpperCamelCase__       ):
															# 1. predict noise residual
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

															if i + 1 == timesteps.shape[0]:
																				lowercase_  							=			None
															else:
																				lowercase_  							=			timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															lowercase_  							=			scheduler.step(
															    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					prev_timestep=UpperCamelCase__							,					generator=UpperCamelCase__       ).prev_sample

															lowercase_  							=			pred_prev_sample

										lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
										lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

										assert abs(result_sum.item() - 258.2_044_983       ) < 1e-2
										assert abs(result_mean.item() - 0.3_362_038       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										pass







					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										pass


