




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




	def   lowercase__   (       self     :  Optional[int]      ):
							torch.manual_seed(0      )
							__snake_case						 =   UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)       ,		layers_per_block=2       ,		sample_size=3_2       ,		in_channels=9       ,		out_channels=4       ,		down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,		up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,		cross_attention_dim=3_2       ,		attention_head_dim=(2, 4)       ,		use_linear_projection=__lowerCAmelCase       ,		)
							__snake_case						 =   PNDMScheduler(skip_prk_steps=__lowerCAmelCase      )
							torch.manual_seed(0      )
							__snake_case						 =   AutoencoderKL(
							    block_out_channels=[3_2, 6_4]       ,		in_channels=3       ,		out_channels=3       ,		down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,		up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,		latent_channels=4       ,		sample_size=1_2_8       ,		)
							torch.manual_seed(0      )
							__snake_case						 =   CLIPTextConfig(
							    bos_token_id=0       ,		eos_token_id=2       ,		hidden_size=3_2       ,		intermediate_size=3_7       ,		layer_norm_eps=1E-05       ,		num_attention_heads=4       ,		num_hidden_layers=5       ,		pad_token_id=1       ,		vocab_size=1_0_0_0       ,		hidden_act='gelu'       ,		projection_dim=5_1_2       ,		)
							__snake_case						 =   CLIPTextModel(__lowerCAmelCase      )
							__snake_case						 =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

							__snake_case						 =   {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}
							return components




	def   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Any=0      ):
							# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
							__snake_case						 =   floats_tensor((1, 3, 3_2, 3_2)       ,		rng=random.Random(__lowerCAmelCase      )      ).to(__lowerCAmelCase      )
							__snake_case						 =   image.cpu().permute(0       ,		2       ,		3       ,		1      )[0]
							__snake_case						 =   Image.fromarray(np.uinta(__lowerCAmelCase      )      ).convert('RGB'      ).resize((6_4, 6_4)      )
							__snake_case						 =   Image.fromarray(np.uinta(image + 4      )      ).convert('RGB'      ).resize((6_4, 6_4)      )
							if str(__lowerCAmelCase      ).startswith('mps'      ):
													__snake_case						 =   torch.manual_seed(__lowerCAmelCase      )
							else:
													__snake_case						 =   torch.Generator(device=__lowerCAmelCase      ).manual_seed(__lowerCAmelCase      )
							__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   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   'cpu'  # ensure determinism for the device-dependent torch.Generator
							__snake_case						 =   self.get_dummy_components()
							__snake_case						 =   StableDiffusionInpaintPipeline(**__lowerCAmelCase      )
							__snake_case						 =   sd_pipe.to(__lowerCAmelCase      )
							sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase      )

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

							assert image.shape == (1, 6_4, 6_4, 3)
							__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   lowercase__   (       self     :  str      ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3      )





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




	def   lowercase__   (       self     :  Optional[int]      ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




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

							__snake_case						 =   'stabilityai/stable-diffusion-2-inpainting'
							__snake_case						 =   StableDiffusionInpaintPipeline.from_pretrained(__lowerCAmelCase       ,		safety_checker=__lowerCAmelCase      )
							pipe.to(__lowerCAmelCase      )
							pipe.set_progress_bar_config(disable=__lowerCAmelCase      )
							pipe.enable_attention_slicing()

							__snake_case						 =   'Face of a yellow cat, high resolution, sitting on a park bench'

							__snake_case						 =   torch.manual_seed(0      )
							__snake_case						 =   pipe(
							    prompt=__lowerCAmelCase       ,		image=__lowerCAmelCase       ,		mask_image=__lowerCAmelCase       ,		generator=__lowerCAmelCase       ,		output_type='np'       ,		)
							__snake_case						 =   output.images[0]

							assert image.shape == (5_1_2, 5_1_2, 3)
							assert np.abs(expected_image - image      ).max() < 9E-3




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-inpaint/init_image.png'      )
							__snake_case						 =   load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'      )
							__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'      )

							__snake_case						 =   'stabilityai/stable-diffusion-2-inpainting'
							__snake_case						 =   StableDiffusionInpaintPipeline.from_pretrained(
							    __lowerCAmelCase       ,		torch_dtype=torch.floataa       ,		safety_checker=__lowerCAmelCase       ,		)
							pipe.to(__lowerCAmelCase      )
							pipe.set_progress_bar_config(disable=__lowerCAmelCase      )
							pipe.enable_attention_slicing()

							__snake_case						 =   'Face of a yellow cat, high resolution, sitting on a park bench'

							__snake_case						 =   torch.manual_seed(0      )
							__snake_case						 =   pipe(
							    prompt=__lowerCAmelCase       ,		image=__lowerCAmelCase       ,		mask_image=__lowerCAmelCase       ,		generator=__lowerCAmelCase       ,		output_type='np'       ,		)
							__snake_case						 =   output.images[0]

							assert image.shape == (5_1_2, 5_1_2, 3)
							assert np.abs(expected_image - image      ).max() < 5E-1




	def   lowercase__   (       self     :  List[Any]      ):
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

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

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

							__snake_case						 =   'Face of a yellow cat, high resolution, sitting on a park bench'

							__snake_case						 =   torch.manual_seed(0      )
							__snake_case						 =   pipe(
							    prompt=__lowerCAmelCase       ,		image=__lowerCAmelCase       ,		mask_image=__lowerCAmelCase       ,		generator=__lowerCAmelCase       ,		num_inference_steps=2       ,		output_type='np'       ,		)

							__snake_case						 =   torch.cuda.max_memory_allocated()
							# make sure that less than 2.65 GB is allocated
							assert mem_bytes < 2.65 * 1_0**9







'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						assert (
						    isinstance(a    ,	a							) and number_of_steps > 0
						), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
						if number_of_steps == 1:
												return 1
						__snake_case ,       __snake_case						 =   1, 1
						for _ in range(number_of_steps - 1							):
												__snake_case ,       __snake_case						 =   current + previous, current
						return current


if __name__ == "__main__":
	import doctest

	doctest.testmod()

