




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_      (   A		,      A		,      A		,      unittest.TestCase       ):




							'''simple docstring'''



							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       :	str			)			->					Optional[int]:
										torch.manual_seed(0			)
										SCREAMING_SNAKE_CASE           =					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       ,     )
										SCREAMING_SNAKE_CASE           =					PNDMScheduler(skip_prk_steps=a			)
										torch.manual_seed(0			)
										SCREAMING_SNAKE_CASE           =					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			)
										SCREAMING_SNAKE_CASE           =					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=1_000       ,     hidden_act="""gelu"""       ,     projection_dim=512       ,     )
										SCREAMING_SNAKE_CASE           =					CLIPTextModel(a			)
										SCREAMING_SNAKE_CASE           =					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

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





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	Union[str, Any]       ,     a       :	List[str]=0			)			->					List[str]:
										# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
										SCREAMING_SNAKE_CASE           =					floats_tensor((1, 3, 32, 32)       ,     rng=random.Random(a			)			).to(a			)
										SCREAMING_SNAKE_CASE           =					image.cpu().permute(0       ,     2       ,     3       ,     1			)[0]
										SCREAMING_SNAKE_CASE           =					Image.fromarray(np.uinta(a			)			).convert("""RGB"""			).resize((64, 64)			)
										SCREAMING_SNAKE_CASE           =					Image.fromarray(np.uinta(image + 4			)			).convert("""RGB"""			).resize((64, 64)			)
										if str(a			).startswith("""mps"""			):
													SCREAMING_SNAKE_CASE           =					torch.manual_seed(a			)
										else:
													SCREAMING_SNAKE_CASE           =					torch.Generator(device=a			).manual_seed(a			)
										SCREAMING_SNAKE_CASE           =					{
										    """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       :	Dict			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE           =					self.get_dummy_components()
										SCREAMING_SNAKE_CASE           =					StableDiffusionInpaintPipeline(**a			)
										SCREAMING_SNAKE_CASE           =					sd_pipe.to(a			)
										sd_pipe.set_progress_bar_config(disable=a			)

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

										assert image.shape == (1, 64, 64, 3)
										SCREAMING_SNAKE_CASE           =					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       :	Any			)			->					List[str]:
										super().test_inference_batch_single_identical(expected_max_diff=3E-3			)





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




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					List[str]:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					int:
										SCREAMING_SNAKE_CASE           =					load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/sd2-inpaint/init_image.png"""			)
										SCREAMING_SNAKE_CASE           =					load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
										SCREAMING_SNAKE_CASE           =					load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
										    """/yellow_cat_sitting_on_a_park_bench.npy"""			)

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

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

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

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





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					Any:
										SCREAMING_SNAKE_CASE           =					load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/sd2-inpaint/init_image.png"""			)
										SCREAMING_SNAKE_CASE           =					load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
										SCREAMING_SNAKE_CASE           =					load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
										    """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""			)

										SCREAMING_SNAKE_CASE           =					"""stabilityai/stable-diffusion-2-inpainting"""
										SCREAMING_SNAKE_CASE           =					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()

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

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

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







							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Any:
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

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

										SCREAMING_SNAKE_CASE           =					"""stabilityai/stable-diffusion-2-inpainting"""
										SCREAMING_SNAKE_CASE           =					PNDMScheduler.from_pretrained(a       ,     subfolder="""scheduler"""			)
										SCREAMING_SNAKE_CASE           =					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()

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

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

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












from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__A						:							Dict							=  typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__A						:							Tuple							=  typing.Union[np.floataa, int, float]  # noqa: UP007
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return np.sqrt(np.sum((np.asarray(SCREAMING_SNAKE_CASE   ) - np.asarray(SCREAMING_SNAKE_CASE   )) ** 2   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return sum((va - va) ** 2 for va, va in zip(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )   ) ** (1 / 2)


if __name__ == "__main__":
		def 					lowerCamelCase_    (    ):
					'''simple docstring'''
					from timeit import timeit

					print("""Without Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )
					print("""With Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )




		benchmark()






