


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__   (	__magic_name__							,				__magic_name__							,				__magic_name__							,				unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  int						=   StableDiffusionInpaintPipeline
					__SCREAMING_SNAKE_CASE       :  int						=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					__SCREAMING_SNAKE_CASE       :  Tuple						=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					__SCREAMING_SNAKE_CASE       :  List[str]						=   frozenset(
					    []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					__SCREAMING_SNAKE_CASE       :  Optional[Any]						=   frozenset([]   )

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										torch.manual_seed(0       )
										lowercase_  							=			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=UpperCamelCase__							,					)
										lowercase_  							=			PNDMScheduler(skip_prk_steps=UpperCamelCase__       )
										torch.manual_seed(0       )
										lowercase_  							=			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       )
										lowercase_  							=			CLIPTextConfig(
										    bos_token_id=0							,					eos_token_id=2							,					hidden_size=32							,					intermediate_size=37							,					layer_norm_eps=1e-05							,					num_attention_heads=4							,					num_hidden_layers=5							,					pad_token_id=1							,					vocab_size=1_000							,					hidden_act="""gelu"""							,					projection_dim=512							,					)
										lowercase_  							=			CLIPTextModel(UpperCamelCase__       )
										lowercase_  							=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

										lowercase_  							=			{
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}
										return components

					def   UpperCAmelCase__       (     self       :   List[Any]							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   str=0       ):






										'''simple docstring'''






										lowercase_  							=			floats_tensor((1, 3, 32, 32)							,					rng=random.Random(UpperCamelCase__       )       ).to(UpperCamelCase__       )
										lowercase_  							=			image.cpu().permute(0							,					2							,					3							,					1       )[0]
										lowercase_  							=			Image.fromarray(np.uinta(UpperCamelCase__       )       ).convert("""RGB"""       ).resize((64, 64)       )
										lowercase_  							=			Image.fromarray(np.uinta(image + 4       )       ).convert("""RGB"""       ).resize((64, 64)       )
										if str(UpperCamelCase__       ).startswith("""mps"""       ):
															lowercase_  							=			torch.manual_seed(UpperCamelCase__       )
										else:
															lowercase_  							=			torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )
										lowercase_  							=			{
										    """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       :   List[Any]       ):






										'''simple docstring'''






										lowercase_  							=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
										lowercase_  							=			self.get_dummy_components()
										lowercase_  							=			StableDiffusionInpaintPipeline(**UpperCamelCase__       )
										lowercase_  							=			sd_pipe.to(UpperCamelCase__       )
										sd_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			self.get_dummy_inputs(UpperCamelCase__       )
										lowercase_  							=			sd_pipe(**UpperCamelCase__       ).images
										lowercase_  							=			image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										lowercase_  							=			np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]       )

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







					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






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







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

					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/sd2-inpaint/init_image.png"""       )
										lowercase_  							=			load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )
										lowercase_  							=			load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
										    """/yellow_cat_sitting_on_a_park_bench.npy"""       )

										lowercase_  							=			"""stabilityai/stable-diffusion-2-inpainting"""
										lowercase_  							=			StableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase__							,					safety_checker=UpperCamelCase__       )
										pipe.to(UpperCamelCase__       )
										pipe.set_progress_bar_config(disable=UpperCamelCase__       )
										pipe.enable_attention_slicing()

										lowercase_  							=			"""Face of a yellow cat, high resolution, sitting on a park bench"""

										lowercase_  							=			torch.manual_seed(0       )
										lowercase_  							=			pipe(
										    prompt=UpperCamelCase__							,					image=UpperCamelCase__							,					mask_image=UpperCamelCase__							,					generator=UpperCamelCase__							,					output_type="""np"""							,					)
										lowercase_  							=			output.images[0]

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

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/sd2-inpaint/init_image.png"""       )
										lowercase_  							=			load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )
										lowercase_  							=			load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
										    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""       )

										lowercase_  							=			"""stabilityai/stable-diffusion-2-inpainting"""
										lowercase_  							=			StableDiffusionInpaintPipeline.from_pretrained(
										    UpperCamelCase__							,					torch_dtype=torch.floataa							,					safety_checker=UpperCamelCase__							,					)
										pipe.to(UpperCamelCase__       )
										pipe.set_progress_bar_config(disable=UpperCamelCase__       )
										pipe.enable_attention_slicing()

										lowercase_  							=			"""Face of a yellow cat, high resolution, sitting on a park bench"""

										lowercase_  							=			torch.manual_seed(0       )
										lowercase_  							=			pipe(
										    prompt=UpperCamelCase__							,					image=UpperCamelCase__							,					mask_image=UpperCamelCase__							,					generator=UpperCamelCase__							,					output_type="""np"""							,					)
										lowercase_  							=			output.images[0]

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







					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






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

										lowercase_  							=			load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/sd2-inpaint/init_image.png"""       )
										lowercase_  							=			load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""       )

										lowercase_  							=			"""stabilityai/stable-diffusion-2-inpainting"""
										lowercase_  							=			PNDMScheduler.from_pretrained(UpperCamelCase__							,					subfolder="""scheduler"""       )
										lowercase_  							=			StableDiffusionInpaintPipeline.from_pretrained(
										    UpperCamelCase__							,					safety_checker=UpperCamelCase__							,					scheduler=UpperCamelCase__							,					torch_dtype=torch.floataa							,					)
										pipe.to(UpperCamelCase__       )
										pipe.set_progress_bar_config(disable=UpperCamelCase__       )
										pipe.enable_attention_slicing(1       )
										pipe.enable_sequential_cpu_offload()

										lowercase_  							=			"""Face of a yellow cat, high resolution, sitting on a park bench"""

										lowercase_  							=			torch.manual_seed(0       )
										lowercase_  							=			pipe(
										    prompt=UpperCamelCase__							,					image=UpperCamelCase__							,					mask_image=UpperCamelCase__							,					generator=UpperCamelCase__							,					num_inference_steps=2							,					output_type="""np"""							,					)

										lowercase_  							=			torch.cuda.max_memory_allocated()
										# make sure that less than 2.65 GB is allocated
										assert mem_bytes < 2.65 * 10**9






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest







class        UpperCamelCase__   (	__magic_name__   ):
					__SCREAMING_SNAKE_CASE       :  str						=   (UnCLIPScheduler,)

					def   UpperCAmelCase__       (     self       :   int							,					**UpperCamelCase__       :   int       ):






										'''simple docstring'''






										lowercase_  							=			{
										    """num_train_timesteps""": 1_000,
										    """variance_type""": """fixed_small_log""",
										    """clip_sample""": True,
										    """clip_sample_range""": 1.0,
										    """prediction_type""": """epsilon""",
										}

										config.update(**UpperCamelCase__       )
										return config

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										for timesteps in [1, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										for variance in ["fixed_small_log", "learned_range"]:
															self.check_over_configs(variance_type=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										for clip_sample_range in [1, 5, 10, 20]:
															self.check_over_configs(clip_sample_range=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






										for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(prediction_type=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										for time_step in [0, 500, 999]:
															for prev_timestep in [None, 5, 100, 250, 500, 750]:
																				if prev_timestep is not None and prev_timestep >= time_step:
																									continue

																				self.check_over_forward(time_step=UpperCamelCase__							,					prev_timestep=UpperCamelCase__       )

					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config(variance_type="""fixed_small_log"""       )
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 1.0000e-10       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.0_549_625       )       ) < 1e-5
										assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.9_994_987       )       ) < 1e-5

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config(variance_type="""learned_range"""       )
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										lowercase_  							=			0.5

										assert scheduler._get_variance(1							,					predicted_variance=UpperCamelCase__       ) - -10.1_712_790 < 1e-5
										assert scheduler._get_variance(487							,					predicted_variance=UpperCamelCase__       ) - -5.7_998_052 < 1e-5
										assert scheduler._get_variance(999							,					predicted_variance=UpperCamelCase__       ) - -0.0_010_011 < 1e-5

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config()
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										lowercase_  							=			scheduler.timesteps

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

										for i, t in enumerate(UpperCamelCase__       ):
															# 1. predict noise residual
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

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

															lowercase_  							=			pred_prev_sample

										lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
										lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

										assert abs(result_sum.item() - 252.2_682_495       ) < 1e-2
										assert abs(result_mean.item() - 0.3_284_743       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			self.scheduler_classes[0]
										lowercase_  							=			self.get_scheduler_config()
										lowercase_  							=			scheduler_class(**UpperCamelCase__       )

										scheduler.set_timesteps(25       )

										lowercase_  							=			scheduler.timesteps

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

										for i, t in enumerate(UpperCamelCase__       ):
															# 1. predict noise residual
															lowercase_  							=			model(UpperCamelCase__							,					UpperCamelCase__       )

															if i + 1 == timesteps.shape[0]:
																				lowercase_  							=			None
															else:
																				lowercase_  							=			timesteps[i + 1]

															# 2. predict previous mean of sample x_t-1
															lowercase_  							=			scheduler.step(
															    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					prev_timestep=UpperCamelCase__							,					generator=UpperCamelCase__       ).prev_sample

															lowercase_  							=			pred_prev_sample

										lowercase_  							=			torch.sum(torch.abs(UpperCamelCase__       )       )
										lowercase_  							=			torch.mean(torch.abs(UpperCamelCase__       )       )

										assert abs(result_sum.item() - 258.2_044_983       ) < 1e-2
										assert abs(result_mean.item() - 0.3_362_038       ) < 1e-3

					def   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										pass







					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										pass


