





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







		def 			__lowercase   (		self   )				->   Union[str, Any]:
				torch.manual_seed(0   )
				lowerCamelCase   : Optional[int]								=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=__lowercase			,     )
				lowerCamelCase   : Optional[Any]								=PNDMScheduler(skip_prk_steps=__lowercase   )
				torch.manual_seed(0   )
				lowerCamelCase   : List[Any]								=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   )
				lowerCamelCase   : str								=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			,     )
				lowerCamelCase   : Optional[Any]								=CLIPTextModel(__lowercase   )
				lowerCamelCase   : Union[str, Any]								=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

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







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

				lowerCamelCase   : Any								=self.get_dummy_inputs(__lowercase   )
				lowerCamelCase   : List[str]								=sd_pipe(**__lowercase   ).images
				lowerCamelCase   : Union[str, Any]								=image[0, -3:, -3:, -1]

				assert image.shape == (1, 6_4, 6_4, 3)
				lowerCamelCase   : List[str]								=np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6]   )

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







		def 			__lowercase   (		self   )				->   Optional[Any]:
				super().test_inference_batch_single_identical(expected_max_diff=3e-3   )

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







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







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

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

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

				lowerCamelCase   : str								=torch.manual_seed(0   )
				lowerCamelCase   : Optional[Any]								=pipe(
				    prompt=__lowercase			,     image=__lowercase			,     mask_image=__lowercase			,     generator=__lowercase			,     output_type='''np'''			,     )
				lowerCamelCase   : List[str]								=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   )				->   Any:
				lowerCamelCase   : Any								=load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/sd2-inpaint/init_image.png'''   )
				lowerCamelCase   : Optional[int]								=load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''   )
				lowerCamelCase   : 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'''   )

				lowerCamelCase   : Any								='''stabilityai/stable-diffusion-2-inpainting'''
				lowerCamelCase   : Optional[int]								=StableDiffusionInpaintPipeline.from_pretrained(
				    __lowercase			,     torch_dtype=torch.floataa			,     safety_checker=__lowercase			,     )
				pipe.to(__lowercase   )
				pipe.set_progress_bar_config(disable=__lowercase   )
				pipe.enable_attention_slicing()

				lowerCamelCase   : List[str]								='''Face of a yellow cat, high resolution, sitting on a park bench'''

				lowerCamelCase   : Optional[int]								=torch.manual_seed(0   )
				lowerCamelCase   : Any								=pipe(
				    prompt=__lowercase			,     image=__lowercase			,     mask_image=__lowercase			,     generator=__lowercase			,     output_type='''np'''			,     )
				lowerCamelCase   : List[str]								=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   )				->   str:
				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

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

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

				lowerCamelCase   : int								='''Face of a yellow cat, high resolution, sitting on a park bench'''

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

				lowerCamelCase   : str								=torch.cuda.max_memory_allocated()
				# make sure that less than 2.65 GB is allocated
				assert mem_bytes < 2.6_5 * 1_0**9








from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class        snake_case_		(			_A						,					_A):







		@register_to_config
		def __init__(		self			,     __lowercase = 7_6_8			,     )				->   List[Any]:
				super().__init__()

				lowerCamelCase   : str								=nn.Parameter(torch.zeros(1			,     __lowercase   )   )
				lowerCamelCase   : Union[str, Any]								=nn.Parameter(torch.ones(1			,     __lowercase   )   )







		def 			__lowercase   (		self			,     __lowercase = None			,     __lowercase = None			,     )				->   List[str]:
				lowerCamelCase   : Tuple								=nn.Parameter(self.mean.to(__lowercase   ).to(__lowercase   )   )
				lowerCamelCase   : Dict								=nn.Parameter(self.std.to(__lowercase   ).to(__lowercase   )   )
				return self







		def 			__lowercase   (		self			,     __lowercase   )				->   Optional[Any]:
				lowerCamelCase   : Any								=(embeds - self.mean) * 1.0 / self.std
				return embeds







		def 			__lowercase   (		self			,     __lowercase   )				->   Optional[int]:
				lowerCamelCase   : Any								=(embeds * self.std) + self.mean
				return embeds

