

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




				def     a							(					self       ):
											torch.manual_seed(0       )
											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      ,   )
											snake_case_							=    PNDMScheduler(skip_prk_steps=__a       )
											torch.manual_seed(0       )
											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       )
											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=1000      ,   hidden_act='gelu'      ,   projection_dim=512      ,   )
											snake_case_							=    CLIPTextModel(__a       )
											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     a							(					self      ,   snake_case      ,   snake_case=0       ):
											snake_case_							=    floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(__a       )       ).to(__a       )
											snake_case_							=    image.cpu().permute(0      ,   2      ,   3      ,   1       )[0]
											snake_case_							=    Image.fromarray(np.uinta(__a       )       ).convert('RGB'       ).resize((64, 64)       )
											snake_case_							=    Image.fromarray(np.uinta(image + 4       )       ).convert('RGB'       ).resize((64, 64)       )
											if str(__a       ).startswith('mps'       ):
																		snake_case_							=    torch.manual_seed(__a       )
											else:
																		snake_case_							=    torch.Generator(device=__a       ).manual_seed(__a       )
											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     a							(					self       ):
											snake_case_							=    'cpu'  # ensure determinism for the device-dependent torch.Generator
											snake_case_							=    self.get_dummy_components()
											snake_case_							=    StableDiffusionInpaintPipeline(**__a       )
											snake_case_							=    sd_pipe.to(__a       )
											sd_pipe.set_progress_bar_config(disable=__a       )

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

											assert image.shape == (1, 64, 64, 3)
											snake_case_							=    np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]       )

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




				def     a							(					self       ):
											super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







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




				def     a							(					self       ):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def     a							(					self       ):
											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(__a      ,   safety_checker=__a       )
											pipe.to(__a       )
											pipe.set_progress_bar_config(disable=__a       )
											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=__a      ,   image=__a      ,   mask_image=__a      ,   generator=__a      ,   output_type='np'      ,   )
											snake_case_							=    output.images[0]

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




				def     a							(					self       ):
											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(
											    __a      ,   torch_dtype=torch.floataa      ,   safety_checker=__a      ,   )
											pipe.to(__a       )
											pipe.set_progress_bar_config(disable=__a       )
											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=__a      ,   image=__a      ,   mask_image=__a      ,   generator=__a      ,   output_type='np'      ,   )
											snake_case_							=    output.images[0]

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




				def     a							(					self       ):
											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(__a      ,   subfolder='scheduler'       )
											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()

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

											snake_case_							=    torch.manual_seed(0       )
											snake_case_							=    pipe(
											    prompt=__a      ,   image=__a      ,   mask_image=__a      ,   generator=__a      ,   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 * 10**9








'''simple docstring'''



import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
	import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class 							__UpperCamelCase				(		unittest.TestCase					):




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  ort.SessionOptions()
						__a			:       Dict					    =  False
						return options





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'							)
						__a			:       Dict					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'							)

						# using the PNDM scheduler by default
						__a			:       str					    =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    'CompVis/stable-diffusion-v1-4'						,				revision='onnx'						,				safety_checker=__a						,				feature_extractor=__a						,				provider=self.gpu_provider						,				sess_options=self.gpu_options						,				)
						pipe.set_progress_bar_config(disable=__a							)

						__a			:       Tuple					    =  'A red cat sitting on a park bench'

						__a			:       int					    =  np.random.RandomState(0							)
						__a			:       Tuple					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				strength=0.75						,				guidance_scale=7.5						,				num_inference_steps=15						,				generator=__a						,				output_type='np'						,				)

						__a			:       Tuple					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 1E-2



