
"""simple docstring"""

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	(UnCLIPScheduler,)




      def     a_      (							self   ,       **lowercase_							)						->					int:
          UpperCAmelCase									=					{
              'num_train_timesteps': 1_0_0_0,
              'variance_type': 'fixed_small_log',
              'clip_sample': True,
              'clip_sample_range': 1.0,
              'prediction_type': 'epsilon',
          }

          config.update(**lowercase_							)
          return config




      def     a_      (							self							)						->					Optional[Any]:
          for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=lowercase_							)




      def     a_      (							self							)						->					Dict:
          for variance in ["fixed_small_log", "learned_range"]:
              self.check_over_configs(variance_type=lowercase_							)




      def     a_      (							self							)						->					List[str]:
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase_							)




      def     a_      (							self							)						->					str:
          for clip_sample_range in [1, 5, 1_0, 2_0]:
              self.check_over_configs(clip_sample_range=lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          for prediction_type in ["epsilon", "sample"]:
              self.check_over_configs(prediction_type=lowercase_							)




      def     a_      (							self							)						->					Union[str, Any]:
          for time_step in [0, 5_0_0, 9_9_9]:
              for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
                  if prev_timestep is not None and prev_timestep >= time_step:
                      continue

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




      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config(variance_type='fixed_small_log'							)
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0000E-10							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.0_5_4_9_6_2_5							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.9_9_9_4_9_8_7							)							) < 1E-5




      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config(variance_type='learned_range'							)
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					0.5

          assert scheduler._get_variance(1   ,       predicted_variance=lowercase_							) - -1_0.1_7_1_2_7_9_0 < 1E-5
          assert scheduler._get_variance(4_8_7   ,       predicted_variance=lowercase_							) - -5.7_9_9_8_0_5_2 < 1E-5
          assert scheduler._get_variance(9_9_9   ,       predicted_variance=lowercase_							) - -0.0_0_1_0_0_1_1 < 1E-5




      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					scheduler.timesteps

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

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

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

              UpperCAmelCase									=					pred_prev_sample

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

          assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5							) < 1E-2
          assert abs(result_mean.item() - 0.3_2_8_4_7_4_3							) < 1E-3




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          scheduler.set_timesteps(2_5							)

          UpperCAmelCase									=					scheduler.timesteps

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

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

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

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

              UpperCAmelCase									=					pred_prev_sample

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

          assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3							) < 1E-2
          assert abs(result_mean.item() - 0.3_3_6_2_0_3_8							) < 1E-3




      def     a_      (							self							)						->					Tuple:
          pass




      def     a_      (							self							)						->					Tuple:
          pass





"""simple docstring"""

from abc import ABC, abstractmethod
from argparse import ArgumentParser
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):




      @staticmethod
      @abstractmethod
      def     a_      (							lowercase_							)						->					Optional[Any]:
          raise NotImplementedError()




      @abstractmethod
      def     a_      (							self							)						->					Any:
          raise NotImplementedError()



