"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


__A	=					logging.get_logger(__name__)
class 			_snake_case  (       __UpperCAmelCase		):






						def __init__(							self   :      List[Any]				,       *UpperCAmelCase   :      List[str]				,       **UpperCAmelCase   :      Dict     ):
													warnings.warn(
													    "The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
													    " use GLPNImageProcessor instead."				,       lowerCAmelCase_				,       )
													super().__init__(*lowerCAmelCase_				,       **lowerCAmelCase_     )







"""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 _lowerCAmelCase						(    __UpperCAmelCase   ,  __UpperCAmelCase   ,  __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				StableDiffusionInpaintPipeline
						SCREAMING_SNAKE_CASE_:					List[str] 					=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						SCREAMING_SNAKE_CASE_:					Dict 					=				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_:					Any 					=				frozenset([]       )







						def 			A      (    self							)		->							Optional[Any]:
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    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_   ,				)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    PNDMScheduler(skip_prk_steps=lowerCAmelCase_							)
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    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							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    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   ,				)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    CLIPTextModel(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

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







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

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    self.get_dummy_inputs(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    sd_pipe(**lowerCAmelCase_							).images
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]							)

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







						def 			A      (    self							)		->							str:
										super().test_inference_batch_single_identical(expected_max_diff=3e-3							)







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







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







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/sd2-inpaint/init_image.png'							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    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   :		Optional[Any]       		=    'stabilityai/stable-diffusion-2-inpainting'
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    StableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase_   ,				safety_checker=lowerCAmelCase_							)
										pipe.to(lowerCAmelCase_							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
										pipe.enable_attention_slicing()

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

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    pipe(
										    prompt=lowerCAmelCase_   ,				image=lowerCAmelCase_   ,				mask_image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				output_type='np'   ,				)
										_SCREAMING_SNAKE_CASE   :		int       		=    output.images[0]

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







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    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   :		int       		=    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   :		Optional[Any]       		=    'stabilityai/stable-diffusion-2-inpainting'
										_SCREAMING_SNAKE_CASE   :		str       		=    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()

										_SCREAMING_SNAKE_CASE   :		str       		=    'Face of a yellow cat, high resolution, sitting on a park bench'

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    pipe(
										    prompt=lowerCAmelCase_   ,				image=lowerCAmelCase_   ,				mask_image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				output_type='np'   ,				)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    output.images[0]

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







						def 			A      (    self							)		->							List[str]:
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

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

										_SCREAMING_SNAKE_CASE   :		str       		=    'stabilityai/stable-diffusion-2-inpainting'
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    PNDMScheduler.from_pretrained(lowerCAmelCase_   ,				subfolder='scheduler'							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    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()

										_SCREAMING_SNAKE_CASE   :		str       		=    'Face of a yellow cat, high resolution, sitting on a park bench'

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    pipe(
										    prompt=lowerCAmelCase_   ,				image=lowerCAmelCase_   ,				mask_image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				num_inference_steps=2   ,				output_type='np'   ,				)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.cuda.max_memory_allocated()
										# make sure that less than 2.65 GB is allocated
										assert mem_bytes < 2.65 * 1_0**9

