


"""simple docstring"""






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 			__lowerCamelCase     (    lowerCAmelCase_ ):

     '''simple docstring'''
     A_				:		Optional[int]			=						(DDIMParallelScheduler,)
     A_				:		Union[str, Any]			=						(('eta', 0.0), ('num_inference_steps', 50))


     def 							_UpperCAmelCase					(					self			, **__UpperCAmelCase							)				->							List[Any]:
          _a					= {
              'num_train_timesteps': 1000,
              'beta_start': 0.0001,
              'beta_end': 0.02,
              'beta_schedule': 'linear',
              'clip_sample': True,
          }

          config.update(**__a							)
          return config


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

          _a					= 10, 0.0

          _a					= self.dummy_model()
          _a					= self.dummy_sample_deter

          scheduler.set_timesteps(__a							)

          for t in scheduler.timesteps:
               _a					= model(__a			, __a							)
               _a					= scheduler.step(__a			, __a			, __a			, __a							).prev_sample

          return sample


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





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


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





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

          _a					= self.scheduler_classes[0]
          _a					= self.get_scheduler_config(steps_offset=1							)
          _a					= scheduler_class(**__a							)
          scheduler.set_timesteps(5							)
          assert torch.equal(scheduler.timesteps			, torch.LongTensor([801, 601, 401, 201, 1]							)							)


     def 							_UpperCAmelCase					(					self							)				->							str:





          for beta_start, beta_end in zip([0.0001, 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							)				->							Tuple:





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


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





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


     def 							_UpperCAmelCase					(					self							)				->							str:





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


     def 							_UpperCAmelCase					(					self							)				->							Any:





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


     def 							_UpperCAmelCase					(					self							)				->							Dict:





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


     def 							_UpperCAmelCase					(					self							)				->							Optional[Any]:
          self.check_over_configs(thresholding=__a							)
          for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=__a			, prediction_type=__a			, sample_max_value=__a			, )


     def 							_UpperCAmelCase					(					self							)				->							int:





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


     def 							_UpperCAmelCase					(					self							)				->							Dict:





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


     def 							_UpperCAmelCase					(					self							)				->							str:





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


     def 							_UpperCAmelCase					(					self							)				->							List[Any]:
          _a					= self.scheduler_classes[0]
          _a					= self.get_scheduler_config()
          _a					= scheduler_class(**__a							)

          assert torch.sum(torch.abs(scheduler._get_variance(0			, 0							) - 0.0							)							) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(420			, 400							) - 0.14771							)							) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(980			, 960							) - 0.32460							)							) < 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							) - 0.00979							)							) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999			, 998							) - 0.02							)							) < 1e-5


     def 							_UpperCAmelCase					(					self							)				->							int:
          _a					= self.scheduler_classes[0]
          _a					= self.get_scheduler_config()
          _a					= scheduler_class(**__a							)

          _a					= 10, 0.0
          scheduler.set_timesteps(__a							)

          _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(__a							)[0:3, None].repeat(1			, __a							)

          _a					= model(samples.flatten(0			, 1							)			, timesteps.flatten(0			, 1							)							)
          _a					= scheduler.batch_step_no_noise(__a			, timesteps.flatten(0			, 1							)			, samples.flatten(0			, 1							)			, __a							)

          _a					= torch.sum(torch.abs(__a							)							)
          _a					= torch.mean(torch.abs(__a							)							)

          assert abs(result_sum.item() - 1147.7904							) < 1e-2
          assert abs(result_mean.item() - 0.4982							) < 1e-3


     def 							_UpperCAmelCase					(					self							)				->							Any:
          _a					= self.full_loop()

          _a					= torch.sum(torch.abs(__a							)							)
          _a					= torch.mean(torch.abs(__a							)							)

          assert abs(result_sum.item() - 172.0067							) < 1e-2
          assert abs(result_mean.item() - 0.223967							) < 1e-3


     def 							_UpperCAmelCase					(					self							)				->							Tuple:
          _a					= self.full_loop(prediction_type='''v_prediction'''							)

          _a					= torch.sum(torch.abs(__a							)							)
          _a					= torch.mean(torch.abs(__a							)							)

          assert abs(result_sum.item() - 52.5302							) < 1e-2
          assert abs(result_mean.item() - 0.0684							) < 1e-3


     def 							_UpperCAmelCase					(					self							)				->							Any:
          _a					= self.full_loop(set_alpha_to_one=__a			, beta_start=0.01							)
          _a					= torch.sum(torch.abs(__a							)							)
          _a					= torch.mean(torch.abs(__a							)							)

          assert abs(result_sum.item() - 149.8295							) < 1e-2
          assert abs(result_mean.item() - 0.1951							) < 1e-3





     def 							_UpperCAmelCase					(					self							)				->							Dict:
          _a					= self.full_loop(set_alpha_to_one=__a			, beta_start=0.01							)
          _a					= torch.sum(torch.abs(__a							)							)
          _a					= torch.mean(torch.abs(__a							)							)

          assert abs(result_sum.item() - 149.0784							) < 1e-2
          assert abs(result_mean.item() - 0.1941							) < 1e-3



'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 							__UpperCamelCase				(		lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      unittest.TestCase					):
	A_				=    StableDiffusionInpaintPipeline
	A_				=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	A_				=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	A_				=    frozenset(
	    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	A_				=    frozenset([]					)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.manual_seed(0							)
						__a			:       int					    =  UNetaDConditionModel(
						    block_out_channels=(32, 64)						,				layers_per_block=2						,				sample_size=32						,				in_channels=9						,				out_channels=4						,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,				up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,				cross_attention_dim=32						,				attention_head_dim=(2, 4)						,				use_linear_projection=__a						,				)
						__a			:       str					    =  PNDMScheduler(skip_prk_steps=__a							)
						torch.manual_seed(0							)
						__a			:       Union[str, Any]					    =  AutoencoderKL(
						    block_out_channels=[32, 64]						,				in_channels=3						,				out_channels=3						,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,				latent_channels=4						,				sample_size=128						,				)
						torch.manual_seed(0							)
						__a			:       List[str]					    =  CLIPTextConfig(
						    bos_token_id=0						,				eos_token_id=2						,				hidden_size=32						,				intermediate_size=37						,				layer_norm_eps=1E-0_5						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				hidden_act='gelu'						,				projection_dim=512						,				)
						__a			:       Dict					    =  CLIPTextModel(__a							)
						__a			:       Union[str, Any]					    =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

						__a			:       Union[str, Any]					    =  {
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components




	def       __UpperCAmelCase							(   self						,				__a						,				__a=0							):

						'''simple docstring'''





						__a			:       Union[str, Any]					    =  floats_tensor((1, 3, 32, 32)						,				rng=random.Random(__a							)							).to(__a							)
						__a			:       List[Any]					    =  image.cpu().permute(0						,				2						,				3						,				1							)[0]
						__a			:       Tuple					    =  Image.fromarray(np.uinta(__a							)							).convert('RGB'							).resize((64, 64)							)
						__a			:       Tuple					    =  Image.fromarray(np.uinta(image + 4							)							).convert('RGB'							).resize((64, 64)							)
						if str(__a							).startswith('mps'							):
											__a			:       Any					    =  torch.manual_seed(__a							)
						else:
											__a			:       str					    =  torch.Generator(device=__a							).manual_seed(__a							)
						__a			:       Dict					    =  {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'image': init_image,
						    'mask_image': mask_image,
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						}
						return inputs




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  'cpu'  # ensure determinism for the device-dependent torch.Generator
						__a			:       str					    =  self.get_dummy_components()
						__a			:       Union[str, Any]					    =  StableDiffusionInpaintPipeline(**__a							)
						__a			:       List[Any]					    =  sd_pipe.to(__a							)
						sd_pipe.set_progress_bar_config(disable=__a							)

						__a			:       List[Any]					    =  self.get_dummy_inputs(__a							)
						__a			:       Dict					    =  sd_pipe(**__a							).images
						__a			:       Optional[Any]					    =  image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						__a			:       List[Any]					    =  np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]							)

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





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().test_inference_batch_single_identical(expected_max_diff=3E-3							)





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





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




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       List[str]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       List[str]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
						__a			:       str					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
						    '/yellow_cat_sitting_on_a_park_bench.npy'							)

						__a			:       Optional[int]					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       Optional[int]					    =  StableDiffusionInpaintPipeline.from_pretrained(__a						,				safety_checker=__a							)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a			:       Dict					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       Tuple					    =  torch.manual_seed(0							)
						__a			:       int					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				output_type='np'						,				)
						__a			:       Dict					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 9E-3




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
						__a			:       Any					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
						    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'							)

						__a			:       str					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       List[str]					    =  StableDiffusionInpaintPipeline.from_pretrained(
						    __a						,				torch_dtype=torch.floataa						,				safety_checker=__a						,				)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing()

						__a			:       Union[str, Any]					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       int					    =  torch.manual_seed(0							)
						__a			:       Optional[Any]					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				output_type='np'						,				)
						__a			:       int					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 5E-1





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

						__a			:       str					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/sd2-inpaint/init_image.png'							)
						__a			:       List[Any]					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)

						__a			:       str					    =  'stabilityai/stable-diffusion-2-inpainting'
						__a			:       Any					    =  PNDMScheduler.from_pretrained(__a						,				subfolder='scheduler'							)
						__a			:       str					    =  StableDiffusionInpaintPipeline.from_pretrained(
						    __a						,				safety_checker=__a						,				scheduler=__a						,				torch_dtype=torch.floataa						,				)
						pipe.to(__a							)
						pipe.set_progress_bar_config(disable=__a							)
						pipe.enable_attention_slicing(1							)
						pipe.enable_sequential_cpu_offload()

						__a			:       str					    =  'Face of a yellow cat, high resolution, sitting on a park bench'

						__a			:       Tuple					    =  torch.manual_seed(0							)
						__a			:       str					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				generator=__a						,				num_inference_steps=2						,				output_type='np'						,				)

						__a			:       List[str]					    =  torch.cuda.max_memory_allocated()
						# make sure that less than 2.65 GB is allocated
						assert mem_bytes < 2.65 * 10**9



