





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			(CMStochasticIterativeScheduler,)
     __magic_name__   =			10




     def        lowerCAmelCase__		(    self				,     **snake_case_							):
         _A     =							{
             'num_train_timesteps': 201,
             'sigma_min': 0.002,
             'sigma_max': 80.0,
         }

         config.update(**snake_case_							)
         return config




     def        lowerCAmelCase__		(    self							):
         _A     =							10

         _A     =							self.get_scheduler_config()
         _A     =							self.scheduler_classes[0](**snake_case_							)

         scheduler.set_timesteps(snake_case_							)

         _A     =							scheduler.timesteps[0]
         _A     =							scheduler.timesteps[1]

         _A     =							self.dummy_sample
         _A     =							0.1 * sample

         _A     =							scheduler.step(snake_case_				,     snake_case_				,     snake_case_							).prev_sample
         _A     =							scheduler.step(snake_case_				,     snake_case_				,     snake_case_							).prev_sample

         self.assertEqual(output_a.shape				,     sample.shape							)
         self.assertEqual(output_a.shape				,     output_a.shape							)




     def        lowerCAmelCase__		(    self							):
         for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=snake_case_							)




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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.scheduler_classes[0]
         _A     =							self.get_scheduler_config()
         _A     =							scheduler_class(**snake_case_							)

         _A     =							1
         scheduler.set_timesteps(snake_case_							)
         _A     =							scheduler.timesteps

         _A     =							torch.manual_seed(0							)

         _A     =							self.dummy_model()
         _A     =							self.dummy_sample_deter * scheduler.init_noise_sigma

         for i, t in enumerate(snake_case_							):
             # 1. scale model input
             _A     =							scheduler.scale_model_input(snake_case_				,     snake_case_							)

             # 2. predict noise residual
             _A     =							model(snake_case_				,     snake_case_							)

             # 3. predict previous sample x_t-1
             _A     =							scheduler.step(snake_case_				,     snake_case_				,     snake_case_				,     generator=snake_case_							).prev_sample

             _A     =							pred_prev_sample

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

         assert abs(result_sum.item() - 192.7614							) < 1E-2
         assert abs(result_mean.item() - 0.2510							) < 1E-3




     def        lowerCAmelCase__		(    self							):
         _A     =							self.scheduler_classes[0]
         _A     =							self.get_scheduler_config()
         _A     =							scheduler_class(**snake_case_							)

         _A     =							[106, 0]
         scheduler.set_timesteps(timesteps=snake_case_							)
         _A     =							scheduler.timesteps

         _A     =							torch.manual_seed(0							)

         _A     =							self.dummy_model()
         _A     =							self.dummy_sample_deter * scheduler.init_noise_sigma

         for t in timesteps:
             # 1. scale model input
             _A     =							scheduler.scale_model_input(snake_case_				,     snake_case_							)

             # 2. predict noise residual
             _A     =							model(snake_case_				,     snake_case_							)

             # 3. predict previous sample x_t-1
             _A     =							scheduler.step(snake_case_				,     snake_case_				,     snake_case_				,     generator=snake_case_							).prev_sample

             _A     =							pred_prev_sample

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

         assert abs(result_sum.item() - 347.6357							) < 1E-2
         assert abs(result_mean.item() - 0.4527							) < 1E-3




     def        lowerCAmelCase__		(    self							):
         _A     =							self.scheduler_classes[0]
         _A     =							self.get_scheduler_config()
         _A     =							scheduler_class(**snake_case_							)

         _A     =							[39, 30, 12, 15, 0]

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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.scheduler_classes[0]
         _A     =							self.get_scheduler_config()
         _A     =							scheduler_class(**snake_case_							)

         _A     =							[39, 30, 12, 1, 0]
         _A     =							len(snake_case_							)

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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.scheduler_classes[0]
         _A     =							self.get_scheduler_config()
         _A     =							scheduler_class(**snake_case_							)

         _A     =							[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_							)












import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         torch.manual_seed(0							)
         _A     =							UNetaDModel(
             block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=3				,     out_channels=3				,     down_block_types=('DownBlock2D', 'AttnDownBlock2D')				,     up_block_types=('AttnUpBlock2D', 'UpBlock2D')				,     )
         return model




     def        lowerCAmelCase__		(    self							):
         _A     =							self.dummy_uncond_unet
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=2				,     generator=snake_case_				,     output_type='numpy'							).images

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=2				,     generator=snake_case_				,     output_type='numpy'				,     return_dict=snake_case_							)[0]

         _A     =							image[0, -3:, -3:, -1]
         _A     =							image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         _A     =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2







@slow
@require_torch
class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def        lowerCAmelCase__		(    self							):
         _A     =							'google/ncsnpp-celebahq-256'
         _A     =							UNetaDModel.from_pretrained(snake_case_							)
         _A     =							KarrasVeScheduler()

         _A     =							KarrasVePipeline(unet=snake_case_				,     scheduler=snake_case_							)
         pipe.to(snake_case_							)
         pipe.set_progress_bar_config(disable=snake_case_							)

         _A     =							torch.manual_seed(0							)
         _A     =							pipe(num_inference_steps=20				,     generator=snake_case_				,     output_type='numpy'							).images

         _A     =							image[0, -3:, -3:, -1]
         assert image.shape == (1, 256, 256, 3)
         _A     =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2





