

'''simple docstring'''





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						(DDPMScheduler,)







				def    lowerCamelCase_  (					self    :		Optional[int]		,				**UpperCAmelCase__    :		str      ):
										'''simple docstring'''
										lowercase   :       List[str]								={
										    '''num_train_timesteps''': 1000,
										    '''beta_start''': 0.00_01,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										    '''variance_type''': '''fixed_small''',
										    '''clip_sample''': True,
										}

										config.update(**UpperCAmelCase__      )
										return config







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										for timesteps in [1, 5, 100, 1000]:
																self.check_over_configs(num_train_timesteps=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''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=UpperCAmelCase__		,				beta_end=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										for schedule in ["linear", "squaredcos_cap_v2"]:
																self.check_over_configs(beta_schedule=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										for variance in ["fixed_small", "fixed_large", "other"]:
																self.check_over_configs(variance_type=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=UpperCAmelCase__      )







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







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(prediction_type=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										for t in [0, 500, 999]:
																self.check_over_forward(time_step=UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       str								=self.scheduler_classes[0]
										lowercase   :       Dict								=self.get_scheduler_config()
										lowercase   :       Union[str, Any]								=scheduler_class(**UpperCAmelCase__      )

										assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(487      ) - 0.0_09_79      )      ) < 1E-5
										assert torch.sum(torch.abs(scheduler._get_variance(999      ) - 0.02      )      ) < 1E-5







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										lowercase   :       Any								=self.scheduler_classes[0]
										lowercase   :       Optional[Any]								=self.get_scheduler_config()
										lowercase   :       int								=scheduler_class(**UpperCAmelCase__      )

										lowercase   :       Dict								=len(UpperCAmelCase__      )

										lowercase   :       Optional[int]								=self.dummy_model()
										lowercase   :       Optional[Any]								=self.dummy_sample_deter
										lowercase   :       Union[str, Any]								=torch.manual_seed(0      )

										for t in reversed(range(UpperCAmelCase__      )      ):
																# 1. predict noise residual
																lowercase   :       List[str]								=model(UpperCAmelCase__		,				UpperCAmelCase__      )

																# 2. predict previous mean of sample x_t-1
																lowercase   :       List[Any]								=scheduler.step(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

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

										lowercase   :       Any								=torch.sum(torch.abs(UpperCAmelCase__      )      )
										lowercase   :       List[str]								=torch.mean(torch.abs(UpperCAmelCase__      )      )

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







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       List[str]								=self.scheduler_classes[0]
										lowercase   :       Any								=self.get_scheduler_config(prediction_type='''v_prediction'''      )
										lowercase   :       Dict								=scheduler_class(**UpperCAmelCase__      )

										lowercase   :       int								=len(UpperCAmelCase__      )

										lowercase   :       int								=self.dummy_model()
										lowercase   :       int								=self.dummy_sample_deter
										lowercase   :       int								=torch.manual_seed(0      )

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

																# 2. predict previous mean of sample x_t-1
																lowercase   :       List[str]								=scheduler.step(UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				generator=UpperCAmelCase__      ).prev_sample

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

										lowercase   :       int								=torch.sum(torch.abs(UpperCAmelCase__      )      )
										lowercase   :       Tuple								=torch.mean(torch.abs(UpperCAmelCase__      )      )

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







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Any								=self.scheduler_classes[0]
										lowercase   :       Tuple								=self.get_scheduler_config()
										lowercase   :       Optional[Any]								=scheduler_class(**UpperCAmelCase__      )

										lowercase   :       Tuple								=[100, 87, 50, 1, 0]

										scheduler.set_timesteps(timesteps=UpperCAmelCase__      )

										lowercase   :       Any								=scheduler.timesteps

										for i, timestep in enumerate(UpperCAmelCase__      ):
																if i == len(UpperCAmelCase__      ) - 1:
																						lowercase   :       List[Any]								=-1
																else:
																						lowercase   :       Optional[int]								=timesteps[i + 1]

																lowercase   :       Dict								=scheduler.previous_timestep(UpperCAmelCase__      )
																lowercase   :       Optional[int]								=prev_t.item()

																self.assertEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.scheduler_classes[0]
										lowercase   :       List[Any]								=self.get_scheduler_config()
										lowercase   :       int								=scheduler_class(**UpperCAmelCase__      )

										lowercase   :       Optional[int]								=[100, 87, 50, 51, 0]

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







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.scheduler_classes[0]
										lowercase   :       Tuple								=self.get_scheduler_config()
										lowercase   :       Optional[int]								=scheduler_class(**UpperCAmelCase__      )

										lowercase   :       Optional[int]								=[100, 87, 50, 1, 0]
										lowercase   :       Any								=len(UpperCAmelCase__      )

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







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.scheduler_classes[0]
										lowercase   :       str								=self.get_scheduler_config()
										lowercase   :       List[Any]								=scheduler_class(**UpperCAmelCase__      )

										lowercase   :       str								=[scheduler.config.num_train_timesteps]

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








'''simple docstring'''





import mpmath  # for roots of unity
import numpy as np





class 		__SCREAMING_SNAKE_CASE       :







				def __init__(					self    :		Union[str, Any]		,				UpperCAmelCase__    :		List[Any]=None		,				UpperCAmelCase__    :		Optional[Any]=None      ):
										'''simple docstring'''
										# Input as list
										lowercase   :       Optional[int]								=list(poly_a or [0]      )[:]
										lowercase   :       Optional[Any]								=list(poly_b or [0]      )[:]

										# Remove leading zero coefficients
										while self.polyA[-1] == 0:
																self.polyA.pop()
										lowercase   :       Any								=len(self.polyA      )

										while self.polyB[-1] == 0:
																self.polyB.pop()
										lowercase   :       Dict								=len(self.polyB      )

										# Add 0 to make lengths equal a power of 2
										lowercase   :       int								=int(
										    2 ** np.ceil(np.loga(len(self.polyA      ) + len(self.polyB      ) - 1      )      )      )

										while len(self.polyA      ) < self.c_max_length:
																self.polyA.append(0      )
										while len(self.polyB      ) < self.c_max_length:
																self.polyB.append(0      )
										# A complex root used for the fourier transform
										lowercase   :       Union[str, Any]								=complex(mpmath.root(x=1		,				n=self.c_max_length		,				k=1      )      )

										# The product
										lowercase   :       Tuple								=self.__multiply()







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
										# Corner case
										if len(UpperCAmelCase__      ) <= 1:
																return dft[0]
										#
										lowercase   :       Any								=self.c_max_length // 2
										while next_ncol > 0:
																lowercase   :       Optional[int]								=[[] for i in range(UpperCAmelCase__      )]
																lowercase   :       Tuple								=self.root**next_ncol

																# First half of next step
																lowercase   :       str								=1
																for j in range(self.c_max_length // (next_ncol * 2)      ):
																						for i in range(UpperCAmelCase__      ):
																												new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]      )
																						current_root *= root
																# Second half of next step
																lowercase   :       int								=1
																for j in range(self.c_max_length // (next_ncol * 2)      ):
																						for i in range(UpperCAmelCase__      ):
																												new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]      )
																						current_root *= root
																# Update
																lowercase   :       Dict								=new_dft
																lowercase   :       Tuple								=next_ncol // 2
										return dft[0]







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Any								=self.__dft('''A'''      )
										lowercase   :       Any								=self.__dft('''B'''      )
										lowercase   :       Optional[int]								=[[dft_a[i] * dft_b[i] for i in range(self.c_max_length      )]]
										del dft_a
										del dft_b

										# Corner Case
										if len(inverce_c[0]      ) <= 1:
																return inverce_c[0]
										# Inverse DFT
										lowercase   :       Optional[int]								=2
										while next_ncol <= self.c_max_length:
																lowercase   :       Optional[int]								=[[] for i in range(UpperCAmelCase__      )]
																lowercase   :       List[str]								=self.root ** (next_ncol // 2)
																lowercase   :       Optional[int]								=1
																# First half of next step
																for j in range(self.c_max_length // next_ncol      ):
																						for i in range(next_ncol // 2      ):
																												# Even positions
																												new_inverse_c[i].append(
																												    (
																												        inverce_c[i][j]
																												        + inverce_c[i][j + self.c_max_length // next_ncol]
																												    )
																												    / 2      )
																												# Odd positions
																												new_inverse_c[i + next_ncol // 2].append(
																												    (
																												        inverce_c[i][j]
																												        - inverce_c[i][j + self.c_max_length // next_ncol]
																												    )
																												    / (2 * current_root)      )
																						current_root *= root
																# Update
																lowercase   :       List[Any]								=new_inverse_c
																next_ncol *= 2
										# Unpack
										lowercase   :       Tuple								=[round(x[0].real		,				8      ) + round(x[0].imag		,				8      ) * 1J for x in inverce_c]

										# Remove leading 0's
										while inverce_c[-1] == 0:
																inverce_c.pop()
										return inverce_c







				def __str__(					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Any								='''A = ''' + ''' + '''.join(
										    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]      )      )
										lowercase   :       Tuple								='''B = ''' + ''' + '''.join(
										    F'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]      )      )
										lowercase   :       List[str]								='''A*B = ''' + ''' + '''.join(
										    F'''{coef}*x^{i}''' for coef, i in enumerate(self.product      )      )

										return F'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
							import doctest

							doctest.testmod()





