"""simple docstring"""




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class     UpperCamelCase__(			__A ):
      lowerCAmelCase__  :						Optional[Any]													=		(DDPMParallelScheduler,)





      def 				snake_case__	(       self		,**__UpperCAmelCase     )					->				Any:
             A__	     =							{
                 'num_train_timesteps': 10_00,
                 'beta_start': 0.0_0_0_1,
                 'beta_end': 0.0_2,
                 'beta_schedule': 'linear',
                 'variance_type': 'fixed_small',
                 'clip_sample': True,
             }

             config.update(**__UpperCAmelCase     )
             return config





      def 				snake_case__	(       self     )					->				Optional[int]:
             for timesteps in [1, 5, 1_00, 10_00]:
                    self.check_over_configs(num_train_timesteps=__UpperCAmelCase     )





      def 				snake_case__	(       self     )					->				Optional[Any]:
             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=__UpperCAmelCase		,beta_end=__UpperCAmelCase     )





      def 				snake_case__	(       self     )					->				List[Any]:
             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=__UpperCAmelCase     )





      def 				snake_case__	(       self     )					->				Tuple:
             for variance in ["fixed_small", "fixed_large", "other"]:
                    self.check_over_configs(variance_type=__UpperCAmelCase     )





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





      def 				snake_case__	(       self     )					->				str:
             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 				snake_case__	(       self     )					->				List[Any]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                    self.check_over_configs(prediction_type=__UpperCAmelCase     )





      def 				snake_case__	(       self     )					->				Tuple:
             for t in [0, 5_00, 9_99]:
                    self.check_over_forward(time_step=__UpperCAmelCase     )





      def 				snake_case__	(       self     )					->				str:
             A__	     =							self.scheduler_classes[0]
             A__	     =							self.get_scheduler_config()
             A__	     =							scheduler_class(**__UpperCAmelCase     )

             assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 0.0     )     ) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_0_9_7_9     )     ) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.0_2     )     ) < 1e-5





      def 				snake_case__	(       self     )					->				Optional[int]:
             A__	     =							self.scheduler_classes[0]
             A__	     =							self.get_scheduler_config()
             A__	     =							scheduler_class(**__UpperCAmelCase     )

             A__	     =							len(__UpperCAmelCase     )

             A__	     =							self.dummy_model()
             A__	     =							self.dummy_sample_deter
             A__	     =							self.dummy_sample_deter + 0.1
             A__	     =							self.dummy_sample_deter - 0.1

             A__	     =							samplea.shape[0]
             A__	     =							torch.stack([samplea, samplea, samplea]		,dim=0     )
             A__	     =							torch.arange(__UpperCAmelCase     )[0:3, None].repeat(1		,__UpperCAmelCase     )

             A__	     =							model(samples.flatten(0		,1     )		,timesteps.flatten(0		,1     )     )
             A__	     =							scheduler.batch_step_no_noise(__UpperCAmelCase		,timesteps.flatten(0		,1     )		,samples.flatten(0		,1     )     )

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

             assert abs(result_sum.item() - 11_53.18_33     ) < 1e-2
             assert abs(result_mean.item() - 0.5_0_0_5     ) < 1e-3





      def 				snake_case__	(       self     )					->				Union[str, Any]:
             A__	     =							self.scheduler_classes[0]
             A__	     =							self.get_scheduler_config()
             A__	     =							scheduler_class(**__UpperCAmelCase     )

             A__	     =							len(__UpperCAmelCase     )

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

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

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

                    A__	     =							pred_prev_sample

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

             assert abs(result_sum.item() - 2_58.96_06     ) < 1e-2
             assert abs(result_mean.item() - 0.3_3_7_2     ) < 1e-3





      def 				snake_case__	(       self     )					->				Any:
             A__	     =							self.scheduler_classes[0]
             A__	     =							self.get_scheduler_config(prediction_type='v_prediction'     )
             A__	     =							scheduler_class(**__UpperCAmelCase     )

             A__	     =							len(__UpperCAmelCase     )

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

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

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

                    A__	     =							pred_prev_sample

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

             assert abs(result_sum.item() - 2_02.02_96     ) < 1e-2
             assert abs(result_mean.item() - 0.2_6_3_1     ) < 1e-3





      def 				snake_case__	(       self     )					->				Optional[Any]:
             A__	     =							self.scheduler_classes[0]
             A__	     =							self.get_scheduler_config()
             A__	     =							scheduler_class(**__UpperCAmelCase     )

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

             scheduler.set_timesteps(timesteps=__UpperCAmelCase     )

             A__	     =							scheduler.timesteps

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

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

                    self.assertEqual(__UpperCAmelCase		,__UpperCAmelCase     )





      def 				snake_case__	(       self     )					->				List[str]:
             A__	     =							self.scheduler_classes[0]
             A__	     =							self.get_scheduler_config()
             A__	     =							scheduler_class(**__UpperCAmelCase     )

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

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





      def 				snake_case__	(       self     )					->				Union[str, Any]:
             A__	     =							self.scheduler_classes[0]
             A__	     =							self.get_scheduler_config()
             A__	     =							scheduler_class(**__UpperCAmelCase     )

             A__	     =							[1_00, 87, 50, 1, 0]
             A__	     =							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 				snake_case__	(       self     )					->				Union[str, Any]:
             A__	     =							self.scheduler_classes[0]
             A__	     =							self.get_scheduler_config()
             A__	     =							scheduler_class(**__UpperCAmelCase     )

             A__	     =							[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 unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation
class     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self     )					->				str:
													A__	     =							get_activation('swish'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.SiLU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							get_activation('silu'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.SiLU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							get_activation('mish'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.Mish     )

													self.assertEqual(act(torch.tensor(-2_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							get_activation('gelu'     )

													self.assertIsInstance(__UpperCAmelCase		,nn.GELU     )

													self.assertEqual(act(torch.tensor(-1_00		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertNotEqual(act(torch.tensor(-1		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(0		,dtype=torch.floataa     )     ).item()		,0     )
													self.assertEqual(act(torch.tensor(20		,dtype=torch.floataa     )     ).item()		,20     )





