





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class    __lowerCAmelCase			(     _a	):
			lowerCamelCase_	:	int			     = (DDIMParallelScheduler,)
			lowerCamelCase_	:	Tuple			     = (('''eta''', 0.0), ('''num_inference_steps''', 50))






			def      lowerCamelCase    (self   ,				**__magic_name__       )	->  List[str]:


							'''simple docstring'''



							snake_case_		:       List[str]           =    {
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0_001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''clip_sample''': True,
							}

							config.update(**__magic_name__       )
							return config






			def      lowerCamelCase    (self   ,				**__magic_name__       )	->  Union[str, Any]:


							'''simple docstring'''



							snake_case_		:       Any           =    self.scheduler_classes[0]
							snake_case_		:       Tuple           =    self.get_scheduler_config(**__magic_name__       )
							snake_case_		:       List[Any]           =    scheduler_class(**__magic_name__       )

							snake_case_     ,				snake_case_		:       Optional[Any]           =    10, 0.0

							snake_case_		:       Union[str, Any]           =    self.dummy_model()
							snake_case_		:       Tuple           =    self.dummy_sample_deter

							scheduler.set_timesteps(__magic_name__       )

							for t in scheduler.timesteps:
											snake_case_		:       Optional[int]           =    model(__magic_name__   ,				__magic_name__       )
											snake_case_		:       Tuple           =    scheduler.step(__magic_name__   ,				__magic_name__   ,				__magic_name__   ,				__magic_name__       ).prev_sample

							return sample






			def      lowerCamelCase    (self       )	->  Optional[int]:


							'''simple docstring'''



							for timesteps in [100, 500, 1000]:
											self.check_over_configs(num_train_timesteps=__magic_name__       )






			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=__magic_name__       )

							snake_case_		:       int           =    self.scheduler_classes[0]
							snake_case_		:       List[str]           =    self.get_scheduler_config(steps_offset=1       )
							snake_case_		:       Optional[int]           =    scheduler_class(**__magic_name__       )
							scheduler.set_timesteps(5       )
							assert torch.equal(scheduler.timesteps   ,				torch.LongTensor([801, 601, 401, 201, 1]       )       )






			def      lowerCamelCase    (self       )	->  Union[str, Any]:


							'''simple docstring'''



							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=__magic_name__   ,				beta_end=__magic_name__       )






			def      lowerCamelCase    (self       )	->  List[Any]:


							'''simple docstring'''



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






			def      lowerCamelCase    (self       )	->  List[str]:


							'''simple docstring'''



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






			def      lowerCamelCase    (self       )	->  Tuple:


							'''simple docstring'''



							for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=__magic_name__       )






			def      lowerCamelCase    (self       )	->  Optional[Any]:


							'''simple docstring'''



							for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=__magic_name__       )






			def      lowerCamelCase    (self       )	->  Tuple:


							'''simple docstring'''



							for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=__magic_name__       )






			def      lowerCamelCase    (self       )	->  Optional[Any]:


							'''simple docstring'''



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






			def      lowerCamelCase    (self       )	->  Union[str, Any]:


							'''simple docstring'''



							for t in [1, 10, 49]:
											self.check_over_forward(time_step=__magic_name__       )






			def      lowerCamelCase    (self       )	->  Optional[Any]:


							'''simple docstring'''



							for t, num_inference_steps in zip([1, 10, 50]   ,				[10, 50, 500]       ):
											self.check_over_forward(time_step=__magic_name__   ,				num_inference_steps=__magic_name__       )






			def      lowerCamelCase    (self       )	->  str:


							'''simple docstring'''



							for t, eta in zip([1, 10, 49]   ,				[0.0, 0.5, 1.0]       ):
											self.check_over_forward(time_step=__magic_name__   ,				eta=__magic_name__       )






			def      lowerCamelCase    (self       )	->  Union[str, Any]:


							'''simple docstring'''



							snake_case_		:       Any           =    self.scheduler_classes[0]
							snake_case_		:       List[Any]           =    self.get_scheduler_config()
							snake_case_		:       Optional[Any]           =    scheduler_class(**__magic_name__       )

							assert torch.sum(torch.abs(scheduler._get_variance(0   ,				0       ) - 0.0       )       ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420   ,				400       ) - 0.14_771       )       ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980   ,				960       ) - 0.32_460       )       ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0   ,				0       ) - 0.0       )       ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487   ,				486       ) - 0.00_979       )       ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999   ,				998       ) - 0.02       )       ) < 1e-5






			def      lowerCamelCase    (self       )	->  Tuple:


							'''simple docstring'''



							snake_case_		:       Any           =    self.scheduler_classes[0]
							snake_case_		:       int           =    self.get_scheduler_config()
							snake_case_		:       List[str]           =    scheduler_class(**__magic_name__       )

							snake_case_     ,				snake_case_		:       Any           =    10, 0.0
							scheduler.set_timesteps(__magic_name__       )

							snake_case_		:       List[Any]           =    self.dummy_model()
							snake_case_		:       Tuple           =    self.dummy_sample_deter
							snake_case_		:       Optional[Any]           =    self.dummy_sample_deter + 0.1
							snake_case_		:       Optional[Any]           =    self.dummy_sample_deter - 0.1

							snake_case_		:       str           =    samplea.shape[0]
							snake_case_		:       Any           =    torch.stack([samplea, samplea, samplea]   ,				dim=0       )
							snake_case_		:       Any           =    torch.arange(__magic_name__       )[0:3, None].repeat(1   ,				__magic_name__       )

							snake_case_		:       int           =    model(samples.flatten(0   ,				1       )   ,				timesteps.flatten(0   ,				1       )       )
							snake_case_		:       List[Any]           =    scheduler.batch_step_no_noise(__magic_name__   ,				timesteps.flatten(0   ,				1       )   ,				samples.flatten(0   ,				1       )   ,				__magic_name__       )

							snake_case_		:       str           =    torch.sum(torch.abs(__magic_name__       )       )
							snake_case_		:       Optional[int]           =    torch.mean(torch.abs(__magic_name__       )       )

							assert abs(result_sum.item() - 1_147.7_904       ) < 1e-2
							assert abs(result_mean.item() - 0.4_982       ) < 1e-3






			def      lowerCamelCase    (self       )	->  List[Any]:


							'''simple docstring'''



							snake_case_		:       Any           =    self.full_loop()

							snake_case_		:       Optional[Any]           =    torch.sum(torch.abs(__magic_name__       )       )
							snake_case_		:       Tuple           =    torch.mean(torch.abs(__magic_name__       )       )

							assert abs(result_sum.item() - 172.0_067       ) < 1e-2
							assert abs(result_mean.item() - 0.223_967       ) < 1e-3






			def      lowerCamelCase    (self       )	->  Any:


							'''simple docstring'''



							snake_case_		:       Optional[Any]           =    self.full_loop(prediction_type='''v_prediction'''       )

							snake_case_		:       Optional[int]           =    torch.sum(torch.abs(__magic_name__       )       )
							snake_case_		:       List[str]           =    torch.mean(torch.abs(__magic_name__       )       )

							assert abs(result_sum.item() - 52.5_302       ) < 1e-2
							assert abs(result_mean.item() - 0.0_684       ) < 1e-3






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       Any           =    self.full_loop(set_alpha_to_one=__magic_name__   ,				beta_start=0.01       )
							snake_case_		:       Optional[Any]           =    torch.sum(torch.abs(__magic_name__       )       )
							snake_case_		:       Dict           =    torch.mean(torch.abs(__magic_name__       )       )

							assert abs(result_sum.item() - 149.8_295       ) < 1e-2
							assert abs(result_mean.item() - 0.1_951       ) < 1e-3






			def      lowerCamelCase    (self       )	->  Dict:


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    self.full_loop(set_alpha_to_one=__magic_name__   ,				beta_start=0.01       )
							snake_case_		:       int           =    torch.sum(torch.abs(__magic_name__       )       )
							snake_case_		:       List[str]           =    torch.mean(torch.abs(__magic_name__       )       )

							assert abs(result_sum.item() - 149.0_784       ) < 1e-2
							assert abs(result_mean.item() - 0.1_941       ) < 1e-3






'''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      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    int   )     ->				Any:


														"""simple docstring"""


														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				int:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_euler'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=9.0				,     num_inference_steps=20				,     output_type='''np'''   )

														_a      	=							output.images

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

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]   )

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







							def   __lowerCAmelCase     (    self			:    Any   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_euler'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=9.0				,     num_inference_steps=20				,     output_type='''np'''   )

														_a      	=							output.images

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

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]   )

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







							def   __lowerCAmelCase     (    self			:    Dict   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														sd_pipe.set_scheduler('''sample_dpmpp_2m'''   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.manual_seed(0   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=7.5				,     num_inference_steps=15				,     output_type='''np'''				,     use_karras_sigmas=lowerCAmelCase_				,     )

														_a      	=							output.images

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

														assert image.shape == (1, 5_12, 5_12, 3)
														_a      	=							np.array(
														    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]   )

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

