





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 						lowerCamelCase       (lowerCamelCase__      ):







       """simple docstring"""




       UpperCAmelCase_      						=   (DDPMScheduler,)






       def  A_     ( self     :					Optional[int],			**_UpperCAmelCase     :					str						)				->			Dict:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{
                  'num_train_timesteps': 1_0_0_0,
                  'beta_start': 0.0001,
                  'beta_end': 0.02,
                  'beta_schedule': 'linear',
                  'variance_type': 'fixed_small',
                  'clip_sample': True,
              }

              config.update(**__snake_case						)
              return config






       def  A_     ( self     :					Optional[Any]						)				->			Optional[int]:





              """simple docstring"""
              for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                     self.check_over_configs(num_train_timesteps=__snake_case						)






       def  A_     ( self     :					int						)				->			List[Any]:





              """simple docstring"""
              for beta_start, beta_end in zip([0.0001, 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  A_     ( self     :					Optional[Any]						)				->			str:





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






       def  A_     ( self     :					Optional[Any]						)				->			Any:





              """simple docstring"""
              for variance in ["fixed_small", "fixed_large", "other"]:
                     self.check_over_configs(variance_type=__snake_case						)






       def  A_     ( self     :					Optional[Any]						)				->			Any:





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






       def  A_     ( self     :					List[Any]						)				->			Any:





              """simple docstring"""
              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  A_     ( self     :					Dict						)				->			Tuple:





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






       def  A_     ( self     :					int						)				->			List[str]:





              """simple docstring"""
              for t in [0, 5_0_0, 9_9_9]:
                     self.check_over_forward(time_step=__snake_case						)






       def  A_     ( self     :					Dict						)				->			Optional[int]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[str]     =						self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE__       :      str     =						self.get_scheduler_config()
              SCREAMING_SNAKE_CASE__       :      int     =						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_8_7						) - 0.00979						)						) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_9_9						) - 0.02						)						) < 1E-5






       def  A_     ( self     :					Tuple						)				->			Optional[int]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[str]     =						self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_scheduler_config()
              SCREAMING_SNAKE_CASE__       :      List[str]     =						scheduler_class(**__snake_case						)

              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						len(__snake_case						)

              SCREAMING_SNAKE_CASE__       :      Dict     =						self.dummy_model()
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.dummy_sample_deter
              SCREAMING_SNAKE_CASE__       :      int     =						torch.manual_seed(0						)

              for t in reversed(range(__snake_case						)						):
                     # 1. predict noise residual
                     SCREAMING_SNAKE_CASE__       :      Optional[int]     =						model(__snake_case,			__snake_case						)

                     # 2. predict previous mean of sample x_t-1
                     SCREAMING_SNAKE_CASE__       :      Dict     =						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
                     SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						pred_prev_sample

              SCREAMING_SNAKE_CASE__       :      Dict     =						torch.sum(torch.abs(__snake_case						)						)
              SCREAMING_SNAKE_CASE__       :      Any     =						torch.mean(torch.abs(__snake_case						)						)

              assert abs(result_sum.item() - 258.9606						) < 1E-2
              assert abs(result_mean.item() - 0.3372						) < 1E-3






       def  A_     ( self     :					List[str]						)				->			str:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      int     =						self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_scheduler_config(prediction_type="v_prediction"						)
              SCREAMING_SNAKE_CASE__       :      int     =						scheduler_class(**__snake_case						)

              SCREAMING_SNAKE_CASE__       :      Dict     =						len(__snake_case						)

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

              for t in reversed(range(__snake_case						)						):
                     # 1. predict noise residual
                     SCREAMING_SNAKE_CASE__       :      List[Any]     =						model(__snake_case,			__snake_case						)

                     # 2. predict previous mean of sample x_t-1
                     SCREAMING_SNAKE_CASE__       :      Tuple     =						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
                     SCREAMING_SNAKE_CASE__       :      Dict     =						pred_prev_sample

              SCREAMING_SNAKE_CASE__       :      int     =						torch.sum(torch.abs(__snake_case						)						)
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						torch.mean(torch.abs(__snake_case						)						)

              assert abs(result_sum.item() - 202.0296						) < 1E-2
              assert abs(result_mean.item() - 0.2631						) < 1E-3






       def  A_     ( self     :					List[Any]						)				->			Optional[Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Any     =						self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.get_scheduler_config()
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						scheduler_class(**__snake_case						)

              SCREAMING_SNAKE_CASE__       :      Dict     =						[1_0_0, 8_7, 5_0, 1, 0]

              scheduler.set_timesteps(timesteps=__snake_case						)

              SCREAMING_SNAKE_CASE__       :      Any     =						scheduler.timesteps

              for i, timestep in enumerate(__snake_case						):
                     if i == len(__snake_case						) - 1:
                            SCREAMING_SNAKE_CASE__       :      Optional[int]     =						-1
                     else:
                            SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						timesteps[i + 1]

                     SCREAMING_SNAKE_CASE__       :      List[str]     =						scheduler.previous_timestep(__snake_case						)
                     SCREAMING_SNAKE_CASE__       :      Any     =						prev_t.item()

                     self.assertEqual(__snake_case,			__snake_case						)






       def  A_     ( self     :					int						)				->			str:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_scheduler_config()
              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						scheduler_class(**__snake_case						)

              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[1_0_0, 8_7, 5_0, 5_1, 0]

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






       def  A_     ( self     :					Optional[Any]						)				->			List[str]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Dict     =						self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE__       :      int     =						self.get_scheduler_config()
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						scheduler_class(**__snake_case						)

              SCREAMING_SNAKE_CASE__       :      List[str]     =						[1_0_0, 8_7, 5_0, 1, 0]
              SCREAMING_SNAKE_CASE__       :      Dict     =						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  A_     ( self     :					Optional[int]						)				->			Union[str, Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Tuple     =						self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE__       :      Dict     =						self.get_scheduler_config()
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						scheduler_class(**__snake_case						)

              SCREAMING_SNAKE_CASE__       :      Dict     =						[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 (  SCREAMING_SNAKE_CASE__      :  str       )	->    str:

							'''simple docstring'''






							if not all(char in "01" for char in bin_string       ):
														raise ValueError("Non-binary value was passed to the function"       )
							if not bin_string:
														raise ValueError("Empty string was passed to the function"       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						""
							while len(SCREAMING_SNAKE_CASE__       ) % 3 != 0:
														SCREAMING_SNAKE_CASE__       :      str     =						"0" + bin_string
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						[
							    bin_string[index : index + 3]
							    for index in range(len(SCREAMING_SNAKE_CASE__       )       )
							    if index % 3 == 0
							]
							for bin_group in bin_string_in_3_list:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						0
														for index, val in enumerate(SCREAMING_SNAKE_CASE__       ):
																					oct_val += int(2 ** (2 - index) * int(SCREAMING_SNAKE_CASE__       )       )
														oct_string += str(SCREAMING_SNAKE_CASE__       )
							return oct_string


if __name__ == "__main__":
		from doctest import testmod

		testmod()






