"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor
class 			_snake_case  (       unittest.TestCase		):






						def __init__(							self   :      List[str]				,       UpperCAmelCase   :      int				,       UpperCAmelCase   :      Optional[int]=7				,       UpperCAmelCase   :      Union[str, Any]=3				,       UpperCAmelCase   :      Optional[Any]=30				,       UpperCAmelCase   :      Union[str, Any]=400				,       UpperCAmelCase   :      str=True				,       UpperCAmelCase   :      Any=None				,       UpperCAmelCase   :      List[str]=0.9				,       UpperCAmelCase   :      Optional[Any]=None				,       UpperCAmelCase   :      Optional[int]=True				,       UpperCAmelCase   :      str=[0.5, 0.5, 0.5]				,       UpperCAmelCase   :      str=[0.5, 0.5, 0.5]				,       ):
													__lowerCamelCase       :		Union[str, Any]		 =  size if size is not None else {'shortest_edge': 30}
													__lowerCamelCase       :		Optional[Any]		 =  crop_size if crop_size is not None else {'height': 30, 'width': 30}
													__lowerCamelCase       :		str		 =  parent
													__lowerCamelCase       :		int		 =  batch_size
													__lowerCamelCase       :		List[Any]		 =  num_channels
													__lowerCamelCase       :		str		 =  min_resolution
													__lowerCamelCase       :		Any		 =  max_resolution
													__lowerCamelCase       :		Dict		 =  do_resize_and_center_crop
													__lowerCamelCase       :		int		 =  size
													__lowerCamelCase       :		Any		 =  crop_pct
													__lowerCamelCase       :		Optional[Any]		 =  crop_size
													__lowerCamelCase       :		Union[str, Any]		 =  do_normalize
													__lowerCamelCase       :		int		 =  image_mean
													__lowerCamelCase       :		Any		 =  image_std







						def    lowerCamelCase__  (							self   :      Tuple     ):
													return {
													    "size": self.size,
													    "do_resize_and_center_crop": self.do_resize_and_center_crop,
													    "crop_pct": self.crop_pct,
													    "crop_size": self.crop_size,
													    "do_normalize": self.do_normalize,
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													}

@require_torch
@require_vision
class 			_snake_case  (       __UpperCAmelCase					,			unittest.TestCase		):
						snake_case__      	=      PoolFormerImageProcessor if is_vision_available() else None






						def    lowerCamelCase__  (							self   :      Optional[int]     ):
													__lowerCamelCase       :		List[Any]		 =  PoolFormerImageProcessingTester(self     )






						@property
						def    lowerCamelCase__  (							self   :      Tuple     ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def    lowerCamelCase__  (							self   :      str     ):
													__lowerCamelCase       :		str		 =  self.image_processing_class(**self.image_processor_dict     )
													self.assertTrue(hasattr(lowerCAmelCase_				,       "do_resize_and_center_crop"     )     )
													self.assertTrue(hasattr(lowerCAmelCase_				,       "size"     )     )
													self.assertTrue(hasattr(lowerCAmelCase_				,       "crop_pct"     )     )
													self.assertTrue(hasattr(lowerCAmelCase_				,       "do_normalize"     )     )
													self.assertTrue(hasattr(lowerCAmelCase_				,       "image_mean"     )     )
													self.assertTrue(hasattr(lowerCAmelCase_				,       "image_std"     )     )






						def    lowerCamelCase__  (							self   :      List[str]     ):
													__lowerCamelCase       :		Tuple		 =  self.image_processing_class.from_dict(self.image_processor_dict     )
													self.assertEqual(image_processor.size				,       {"shortest_edge": 30}     )
													self.assertEqual(image_processor.crop_size				,       {"height": 30, "width": 30}     )

													__lowerCamelCase       :		int		 =  self.image_processing_class.from_dict(self.image_processor_dict				,       size=42				,       crop_size=84     )
													self.assertEqual(image_processor.size				,       {"shortest_edge": 42}     )
													self.assertEqual(image_processor.crop_size				,       {"height": 84, "width": 84}     )






						def    lowerCamelCase__  (							self   :      int     ):
													pass






						def    lowerCamelCase__  (							self   :      int     ):
													# Initialize image_processing
													__lowerCamelCase       :		int		 =  self.image_processing_class(**self.image_processor_dict     )
													# create random PIL images
													__lowerCamelCase       :		Tuple		 =  prepare_image_inputs(self.image_processor_tester				,       equal_resolution=lowerCAmelCase_     )
													for image in image_inputs:
																				self.assertIsInstance(lowerCAmelCase_				,       Image.Image     )

													# Test not batched input
													__lowerCamelCase       :		List[str]		 =  image_processing(image_inputs[0]				,       return_tensors="pt"     ).pixel_values

													self.assertEqual(
													    encoded_images.shape				,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )				,       )

													# Test batched
													__lowerCamelCase       :		str		 =  image_processing(lowerCAmelCase_				,       return_tensors="pt"     ).pixel_values
													self.assertEqual(
													    encoded_images.shape				,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )				,       )






						def    lowerCamelCase__  (							self   :      Any     ):
													# Initialize image_processing
													__lowerCamelCase       :		Optional[int]		 =  self.image_processing_class(**self.image_processor_dict     )
													# create random numpy tensors
													__lowerCamelCase       :		Optional[int]		 =  prepare_image_inputs(self.image_processor_tester				,       equal_resolution=lowerCAmelCase_				,       numpify=lowerCAmelCase_     )
													for image in image_inputs:
																				self.assertIsInstance(lowerCAmelCase_				,       np.ndarray     )

													# Test not batched input
													__lowerCamelCase       :		Optional[int]		 =  image_processing(image_inputs[0]				,       return_tensors="pt"     ).pixel_values
													self.assertEqual(
													    encoded_images.shape				,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )				,       )

													# Test batched
													__lowerCamelCase       :		List[Any]		 =  image_processing(lowerCAmelCase_				,       return_tensors="pt"     ).pixel_values
													self.assertEqual(
													    encoded_images.shape				,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )				,       )







						def    lowerCamelCase__  (							self   :      Any     ):
													# Initialize image_processing
													__lowerCamelCase       :		Dict		 =  self.image_processing_class(**self.image_processor_dict     )
													# create random PyTorch tensors
													__lowerCamelCase       :		Any		 =  prepare_image_inputs(self.image_processor_tester				,       equal_resolution=lowerCAmelCase_				,       torchify=lowerCAmelCase_     )
													for image in image_inputs:
																				self.assertIsInstance(lowerCAmelCase_				,       torch.Tensor     )

													# Test not batched input
													__lowerCamelCase       :		Any		 =  image_processing(image_inputs[0]				,       return_tensors="pt"     ).pixel_values
													self.assertEqual(
													    encoded_images.shape				,       (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )				,       )

													# Test batched
													__lowerCamelCase       :		Optional[int]		 =  image_processing(lowerCAmelCase_				,       return_tensors="pt"     ).pixel_values
													self.assertEqual(
													    encoded_images.shape				,       (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )				,       )







"""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

