



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				(EulerDiscreteScheduler,)
     UpperCamelCase__		    =				10




     def 		snake_case_		(				self ,						**a__):
         A__		 =					{
             '''num_train_timesteps''': 1_1_0_0,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
         }

         config.update(**a__)
         return config




     def 		snake_case_		(				self):
         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=a__)




     def 		snake_case_		(				self):
         for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]):
             self.check_over_configs(beta_start=a__ ,						beta_end=a__)




     def 		snake_case_		(				self):
         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=a__)




     def 		snake_case_		(				self):
         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         scheduler.set_timesteps(self.num_inference_steps)

         A__		 =					torch.manual_seed(0)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter * scheduler.init_noise_sigma
         A__		 =					sample.to(a__)

         for i, t in enumerate(scheduler.timesteps):
             A__		 =					scheduler.scale_model_input(a__ ,						a__)

             A__		 =					model(a__ ,						a__)

             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__)
             A__		 =					output.prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 1_0.0_8_0_7) < 1e-2
         assert abs(result_mean.item() - 0.0_1_3_1) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config(prediction_type='''v_prediction''')
         A__		 =					scheduler_class(**a__)

         scheduler.set_timesteps(self.num_inference_steps)

         A__		 =					torch.manual_seed(0)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter * scheduler.init_noise_sigma
         A__		 =					sample.to(a__)

         for i, t in enumerate(scheduler.timesteps):
             A__		 =					scheduler.scale_model_input(a__ ,						a__)

             A__		 =					model(a__ ,						a__)

             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__)
             A__		 =					output.prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 0.0_0_0_2) < 1e-2
         assert abs(result_mean.item() - 2.2_676e-06) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         scheduler.set_timesteps(self.num_inference_steps ,						device=a__)

         A__		 =					torch.manual_seed(0)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         A__		 =					sample.to(a__)

         for t in scheduler.timesteps:
             A__		 =					scheduler.scale_model_input(a__ ,						a__)

             A__		 =					model(a__ ,						a__)

             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__)
             A__		 =					output.prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 1_0.0_8_0_7) < 1e-2
         assert abs(result_mean.item() - 0.0_1_3_1) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__ ,						use_karras_sigmas=a__)

         scheduler.set_timesteps(self.num_inference_steps ,						device=a__)

         A__		 =					torch.manual_seed(0)

         A__		 =					self.dummy_model()
         A__		 =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         A__		 =					sample.to(a__)

         for t in scheduler.timesteps:
             A__		 =					scheduler.scale_model_input(a__ ,						a__)

             A__		 =					model(a__ ,						a__)

             A__		 =					scheduler.step(a__ ,						a__ ,						a__ ,						generator=a__)
             A__		 =					output.prev_sample

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9) < 1e-2
         assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3) < 1e-3





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				(DDPMScheduler,)




     def 		snake_case_		(				self ,						**a__):
         A__		 =					{
             '''num_train_timesteps''': 1_0_0_0,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
             '''variance_type''': '''fixed_small''',
             '''clip_sample''': True,
         }

         config.update(**a__)
         return config




     def 		snake_case_		(				self):
         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=a__)




     def 		snake_case_		(				self):
         for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] ,						[0.0_0_2, 0.0_2, 0.2, 2]):
             self.check_over_configs(beta_start=a__ ,						beta_end=a__)




     def 		snake_case_		(				self):
         for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=a__)




     def 		snake_case_		(				self):
         for variance in ["fixed_small", "fixed_large", "other"]:
             self.check_over_configs(variance_type=a__)




     def 		snake_case_		(				self):
         for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=a__)




     def 		snake_case_		(				self):
         self.check_over_configs(thresholding=a__)
         for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(
                     thresholding=a__ ,						prediction_type=a__ ,						sample_max_value=a__ ,						)




     def 		snake_case_		(				self):
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(prediction_type=a__)




     def 		snake_case_		(				self):
         for t in [0, 5_0_0, 9_9_9]:
             self.check_over_forward(time_step=a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7) - 0.0_0_9_7_9)) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9) - 0.0_2)) < 1e-5




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					len(a__)

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

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

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

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

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 2_5_8.9_6_0_6) < 1e-2
         assert abs(result_mean.item() - 0.3_3_7_2) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config(prediction_type='''v_prediction''')
         A__		 =					scheduler_class(**a__)

         A__		 =					len(a__)

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

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

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

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

         A__		 =					torch.sum(torch.abs(a__))
         A__		 =					torch.mean(torch.abs(a__))

         assert abs(result_sum.item() - 2_0_2.0_2_9_6) < 1e-2
         assert abs(result_mean.item() - 0.2_6_3_1) < 1e-3




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 1, 0]

         scheduler.set_timesteps(timesteps=a__)

         A__		 =					scheduler.timesteps

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

             A__		 =					scheduler.previous_timestep(a__)
             A__		 =					prev_t.item()

             self.assertEqual(a__ ,						a__)




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 5_1, 0]

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




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[1_0_0, 8_7, 5_0, 1, 0]
         A__		 =					len(a__)

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




     def 		snake_case_		(				self):
         A__		 =					self.scheduler_classes[0]
         A__		 =					self.get_scheduler_config()
         A__		 =					scheduler_class(**a__)

         A__		 =					[scheduler.config.num_train_timesteps]

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