






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class   __SCREAMING_SNAKE_CASE(	_snake_case	):

							'''simple docstring'''





							_UpperCAmelCase     =       (DDPMScheduler,)
							def lowerCAmelCase_ (     self:      List[str]       ,	**UpperCamelCase:      List[Any]						)		->		Optional[int]:
											snake_case__									=     {
											    "num_train_timesteps": 10_00,
											    "beta_start": 0.0_001,
											    "beta_end": 0.02,
											    "beta_schedule": "linear",
											    "variance_type": "fixed_small",
											    "clip_sample": True,
											}

											config.update(**snake_case_						)
											return config
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		int:
											for timesteps in [1, 5, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=snake_case_						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		str:
											for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]       ,	[0.002, 0.02, 0.2, 2]						):
															self.check_over_configs(beta_start=snake_case_       ,	beta_end=snake_case_						)
							def lowerCAmelCase_ (     self:      Dict						)		->		Dict:
											for schedule in ["linear", "squaredcos_cap_v2"]:
															self.check_over_configs(beta_schedule=snake_case_						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		List[Any]:
											for variance in ["fixed_small", "fixed_large", "other"]:
															self.check_over_configs(variance_type=snake_case_						)
							def lowerCAmelCase_ (     self:      str						)		->		Optional[Any]:
											for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=snake_case_						)
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Tuple:
											self.check_over_configs(thresholding=snake_case_						)
											for threshold in [0.5, 1.0, 2.0]:
															for prediction_type in ["epsilon", "sample", "v_prediction"]:
																			self.check_over_configs(
																			    thresholding=snake_case_       ,	prediction_type=snake_case_       ,	sample_max_value=snake_case_       ,	)
							def lowerCAmelCase_ (     self:      List[str]						)		->		Union[str, Any]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
															self.check_over_configs(prediction_type=snake_case_						)
							def lowerCAmelCase_ (     self:      Any						)		->		int:
											for t in [0, 5_00, 9_99]:
															self.check_over_forward(time_step=snake_case_						)
							def lowerCAmelCase_ (     self:      str						)		->		List[Any]:
											snake_case__									=     self.scheduler_classes[0]
											snake_case__									=     self.get_scheduler_config()
											snake_case__									=     scheduler_class(**snake_case_						)

											assert torch.sum(torch.abs(scheduler._get_variance(0						) - 0.0						)						) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(4_87						) - 0.00_979						)						) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.02						)						) < 1e-5
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Optional[int]:
											snake_case__									=     self.scheduler_classes[0]
											snake_case__									=     self.get_scheduler_config()
											snake_case__									=     scheduler_class(**snake_case_						)

											snake_case__									=     len(snake_case_						)

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

											for t in reversed(range(snake_case_						)						):
															# 1. predict noise residual
															snake_case__									=     model(snake_case_       ,	snake_case_						)

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

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

											snake_case__									=     torch.sum(torch.abs(snake_case_						)						)
											snake_case__									=     torch.mean(torch.abs(snake_case_						)						)

											assert abs(result_sum.item() - 258.9_606						) < 1e-2
											assert abs(result_mean.item() - 0.3_372						) < 1e-3
							def lowerCAmelCase_ (     self:      str						)		->		Dict:
											snake_case__									=     self.scheduler_classes[0]
											snake_case__									=     self.get_scheduler_config(prediction_type='v_prediction'						)
											snake_case__									=     scheduler_class(**snake_case_						)

											snake_case__									=     len(snake_case_						)

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

											for t in reversed(range(snake_case_						)						):
															# 1. predict noise residual
															snake_case__									=     model(snake_case_       ,	snake_case_						)

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

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

											snake_case__									=     torch.sum(torch.abs(snake_case_						)						)
											snake_case__									=     torch.mean(torch.abs(snake_case_						)						)

											assert abs(result_sum.item() - 202.0_296						) < 1e-2
											assert abs(result_mean.item() - 0.2_631						) < 1e-3
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict:
											snake_case__									=     self.scheduler_classes[0]
											snake_case__									=     self.get_scheduler_config()
											snake_case__									=     scheduler_class(**snake_case_						)

											snake_case__									=     [1_00, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=snake_case_						)

											snake_case__									=     scheduler.timesteps

											for i, timestep in enumerate(snake_case_						):
															if i == len(snake_case_						) - 1:
																			snake_case__									=     -1
															else:
																			snake_case__									=     timesteps[i + 1]

															snake_case__									=     scheduler.previous_timestep(snake_case_						)
															snake_case__									=     prev_t.item()

															self.assertEqual(snake_case_       ,	snake_case_						)
							def lowerCAmelCase_ (     self:      str						)		->		Any:
											snake_case__									=     self.scheduler_classes[0]
											snake_case__									=     self.get_scheduler_config()
											snake_case__									=     scheduler_class(**snake_case_						)

											snake_case__									=     [1_00, 87, 50, 51, 0]

											with self.assertRaises(snake_case_       ,	msg='`custom_timesteps` must be in descending order.'						):
															scheduler.set_timesteps(timesteps=snake_case_						)
							def lowerCAmelCase_ (     self:      Dict						)		->		Dict:
											snake_case__									=     self.scheduler_classes[0]
											snake_case__									=     self.get_scheduler_config()
											snake_case__									=     scheduler_class(**snake_case_						)

											snake_case__									=     [1_00, 87, 50, 1, 0]
											snake_case__									=     len(snake_case_						)

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


							def lowerCAmelCase_ (     self:      str						)		->		List[Any]:
											snake_case__									=     self.scheduler_classes[0]
											snake_case__									=     self.get_scheduler_config()
											snake_case__									=     scheduler_class(**snake_case_						)

											snake_case__									=     [scheduler.config.num_train_timesteps]

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












def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )
				binary_number += "0" * shift_amount
				return binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number < 0 or shift_amount < 0:
								raise ValueError('both inputs must be positive integers'  )

				snake_case__									=     str(bin(_A  )  )[2:]
				if shift_amount >= len(_A  ):
								return "0b0"
				snake_case__									=     binary_number[: len(_A  ) - shift_amount]
				return "0b" + shifted_binary_number
def      a_ (      _A      ,   _A  )			->			str:







				"""simple docstring"""
				if number >= 0:  # Get binary representation of positive number
								snake_case__									=     '0' + str(bin(_A  )  ).strip('-'  )[2:]
				else:  # Get binary (2's complement) representation of negative number
								snake_case__									=     len(bin(_A  )[3:]  )  # Find 2's complement of number
								snake_case__									=     bin(abs(_A  ) - (1 << binary_number_length)  )[3:]
								snake_case__									=     (
								    '1' + '0' * (binary_number_length - len(_A  )) + binary_number
								)

				if shift_amount >= len(_A  ):
								return "0b" + binary_number[0] * len(_A  )
				return (
				    "0b"
				    + binary_number[0] * shift_amount
				    + binary_number[: len(_A  ) - shift_amount]
				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




