"""simple docstring"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    Optional[Any]   =  (DDPMScheduler,)


      def 		_UpperCAmelCase       (   self		,			**a							)       ->					str:
             lowercase__			:		int       =	{
                 'num_train_timesteps': 1_0_0_0,
                 'beta_start': 0.0_001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'variance_type': 'fixed_small',
                 'clip_sample': True,
             }

             config.update(**a							)
             return config


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


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]		,			[0.002, 0.02, 0.2, 2]							):
                    self.check_over_configs(beta_start=a		,			beta_end=a							)


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


      def 		_UpperCAmelCase       (   self							)       ->					str:
             for variance in ["fixed_small", "fixed_large", "other"]:
                    self.check_over_configs(variance_type=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             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 		_UpperCAmelCase       (   self							)       ->					List[str]:
             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                    self.check_over_configs(prediction_type=a							)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             for t in [0, 5_0_0, 9_9_9]:
                    self.check_over_forward(time_step=a							)


      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		Optional[Any]       =	self.scheduler_classes[0]
             lowercase__			:		List[Any]       =	self.get_scheduler_config()
             lowercase__			:		Union[str, Any]       =	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							) - 0.00_979							)							) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.02							)							) < 1e-5


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             lowercase__			:		Optional[Any]       =	self.scheduler_classes[0]
             lowercase__			:		Optional[Any]       =	self.get_scheduler_config()
             lowercase__			:		Union[str, Any]       =	scheduler_class(**a							)

             lowercase__			:		List[Any]       =	len(a							)

             lowercase__			:		List[Any]       =	self.dummy_model()
             lowercase__			:		str       =	self.dummy_sample_deter
             lowercase__			:		List[Any]       =	torch.manual_seed(0							)

             for t in reversed(range(a							)							):
                    # 1. predict noise residual
                    lowercase__			:		Dict       =	model(a		,			a							)

                    # 2. predict previous mean of sample x_t-1
                    lowercase__			:		Optional[int]       =	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
                    lowercase__			:		List[str]       =	pred_prev_sample

             lowercase__			:		Any       =	torch.sum(torch.abs(a							)							)
             lowercase__			:		str       =	torch.mean(torch.abs(a							)							)

             assert abs(result_sum.item() - 258.9_606							) < 1e-2
             assert abs(result_mean.item() - 0.3_372							) < 1e-3


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		Any       =	self.scheduler_classes[0]
             lowercase__			:		Optional[int]       =	self.get_scheduler_config(prediction_type='v_prediction'							)
             lowercase__			:		Optional[Any]       =	scheduler_class(**a							)

             lowercase__			:		int       =	len(a							)

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

             for t in reversed(range(a							)							):
                    # 1. predict noise residual
                    lowercase__			:		Optional[Any]       =	model(a		,			a							)

                    # 2. predict previous mean of sample x_t-1
                    lowercase__			:		Optional[int]       =	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
                    lowercase__			:		List[Any]       =	pred_prev_sample

             lowercase__			:		Dict       =	torch.sum(torch.abs(a							)							)
             lowercase__			:		List[str]       =	torch.mean(torch.abs(a							)							)

             assert abs(result_sum.item() - 202.0_296							) < 1e-2
             assert abs(result_mean.item() - 0.2_631							) < 1e-3


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		Optional[Any]       =	self.scheduler_classes[0]
             lowercase__			:		Any       =	self.get_scheduler_config()
             lowercase__			:		List[Any]       =	scheduler_class(**a							)

             lowercase__			:		Any       =	[1_0_0, 8_7, 5_0, 1, 0]

             scheduler.set_timesteps(timesteps=a							)

             lowercase__			:		List[Any]       =	scheduler.timesteps

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

                    lowercase__			:		Tuple       =	scheduler.previous_timestep(a							)
                    lowercase__			:		List[Any]       =	prev_t.item()

                    self.assertEqual(a		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__			:		Union[str, Any]       =	self.scheduler_classes[0]
             lowercase__			:		List[str]       =	self.get_scheduler_config()
             lowercase__			:		Any       =	scheduler_class(**a							)

             lowercase__			:		Optional[Any]       =	[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 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		Tuple       =	self.scheduler_classes[0]
             lowercase__			:		int       =	self.get_scheduler_config()
             lowercase__			:		int       =	scheduler_class(**a							)

             lowercase__			:		Dict       =	[1_0_0, 8_7, 5_0, 1, 0]
             lowercase__			:		Dict       =	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 		_UpperCAmelCase       (   self							)       ->					Tuple:
             lowercase__			:		Union[str, Any]       =	self.scheduler_classes[0]
             lowercase__			:		str       =	self.get_scheduler_config()
             lowercase__			:		List[Any]       =	scheduler_class(**a							)

             lowercase__			:		Union[str, Any]       =	[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							)





'''simple docstring'''




import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

@slow
@require_torch_gpu
class 		UpperCAmelCase_      (  unittest.TestCase		):





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  int:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Any:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_euler'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe([prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339]    )

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





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Dict:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_euler'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe([prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=9.0    ,      num_inference_steps=2_0    ,      output_type='np'    )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-1





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     =			StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
        lowerCAmelCase     =			sd_pipe.to(UpperCAmelCase__    )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__    )

        sd_pipe.set_scheduler('sample_dpmpp_2m'    )

        lowerCAmelCase     =			'A painting of a squirrel eating a burger'
        lowerCAmelCase     =			torch.manual_seed(0    )
        lowerCAmelCase     =			sd_pipe(
            [prompt]    ,      generator=UpperCAmelCase__    ,      guidance_scale=7.5    ,      num_inference_steps=1_5    ,      output_type='np'    ,      use_karras_sigmas=UpperCAmelCase__    ,      )

        lowerCAmelCase     =			output.images

        lowerCAmelCase     =			image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        lowerCAmelCase     =			np.array(
            [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048]    )

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





