

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'''







			lowerCamelCase__							     =StableDiffusionInpaintPipeline
			lowerCamelCase__							     =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase__							     =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase__							     =frozenset(
			    [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			lowerCamelCase__							     =frozenset([] )
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							List[str]:

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Dict					  =							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 :   List[str]					  =							PNDMScheduler(skip_prk_steps=a						)
				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Optional[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						)
				SCREAMING_SNAKE_CASE :   Dict					  =							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=1000   ,   hidden_act="gelu"   ,   projection_dim=512   ,   )
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							CLIPTextModel(a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							{
				    "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]   ,   a				:			Tuple   ,   a				:			List[Any]=0						)				->							Dict:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							"cpu"  # ensure determinism for the device-dependent torch.Generator
				SCREAMING_SNAKE_CASE :   Tuple					  =							self.get_dummy_components()
				SCREAMING_SNAKE_CASE :   Tuple					  =							StableDiffusionInpaintPipeline(**a						)
				SCREAMING_SNAKE_CASE :   int					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

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

				assert image.shape == (1, 64, 64, 3)
				SCREAMING_SNAKE_CASE :   Dict					  =							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				:			Dict						)				->							List[str]:

				"""simple docstring"""

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






@slow
@require_torch_gpu
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







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

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 				__UpperCamelCase			(      self				:			str						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/sd2-inpaint/init_image.png"						)
				SCREAMING_SNAKE_CASE :   str					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							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 :   Tuple					  =							"stabilityai/stable-diffusion-2-inpainting"
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							StableDiffusionInpaintPipeline.from_pretrained(a   ,   safety_checker=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)
				pipe.enable_attention_slicing()

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

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

				assert image.shape == (512, 512, 3)
				assert np.abs(expected_image - image						).max() < 9e-3
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/sd2-inpaint/init_image.png"						)
				SCREAMING_SNAKE_CASE :   Dict					  =							load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							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 :   Any					  =							"stabilityai/stable-diffusion-2-inpainting"
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							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 :   Dict					  =							"Face of a yellow cat, high resolution, sitting on a park bench"

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

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






			def 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

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

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

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

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							"Face of a yellow cat, high resolution, sitting on a park bench"

				SCREAMING_SNAKE_CASE :   Dict					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pipe(
				    prompt=a   ,   image=a   ,   mask_image=a   ,   generator=a   ,   num_inference_steps=2   ,   output_type="np"   ,   )

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


def 			lowerCamelCase__			(       _a):
	if not isinstance(_a				,      _a):
		SCREAMING_SNAKE_CASE :   Tuple					  =							f"Input value of [number={number}] must be an integer"
		raise TypeError(_a)
	if number < 0:
		return False
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							number * number
	while number > 0:
		if number % 10 != number_square % 10:
			return False
		number //= 10
		number_square //= 10
	return True


if __name__ == "__main__":
			import doctest

			doctest.testmod()