

"""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 							SCREAMING_SNAKE_CASE   (						a_       ,				a_       ,				a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			StableDiffusionInpaintPipeline
					lowercase__												=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					lowercase__												=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					lowercase__												=			frozenset(
					    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					lowercase__												=			frozenset([]			)







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						torch.manual_seed(0						)
						lowerCAmelCase__      :		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=lowercase_  ,)
						lowerCAmelCase__      :		Tuple    							=      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__      :		List[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__      :		List[str]    							=      CLIPTextModel(lowercase_						)
						lowerCAmelCase__      :		List[Any]    							=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

						lowerCAmelCase__      :		Optional[Any]    							=      {
						    '''unet''': unet,
						    '''scheduler''': scheduler,
						    '''vae''': vae,
						    '''text_encoder''': text_encoder,
						    '''tokenizer''': tokenizer,
						    '''safety_checker''': None,
						    '''feature_extractor''': None,
						}
						return components







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

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

						assert image.shape == (1, 6_4, 6_4, 3)
						lowerCAmelCase__      :		Optional[Any]    							=      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 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						super().test_inference_batch_single_identical(expected_max_diff=3E-3						)







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






					"""simple docstring"""



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







					def 			__lowerCAmelCase					(			self     :							Dict						):
						lowerCAmelCase__      :		Tuple    							=      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__      :		Union[str, Any]    							=      load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
						    '''/yellow_cat_sitting_on_a_park_bench.npy'''						)

						lowerCAmelCase__      :		List[str]    							=      '''stabilityai/stable-diffusion-2-inpainting'''
						lowerCAmelCase__      :		str    							=      StableDiffusionInpaintPipeline.from_pretrained(lowercase_  ,safety_checker=lowercase_						)
						pipe.to(lowercase_						)
						pipe.set_progress_bar_config(disable=lowercase_						)
						pipe.enable_attention_slicing()

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

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

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







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Tuple    							=      load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/sd2-inpaint/init_image.png'''						)
						lowerCAmelCase__      :		Any    							=      load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''						)
						lowerCAmelCase__      :		Dict    							=      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__      :		Dict    							=      '''stabilityai/stable-diffusion-2-inpainting'''
						lowerCAmelCase__      :		Any    							=      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__      :		Any    							=      '''Face of a yellow cat, high resolution, sitting on a park bench'''

						lowerCAmelCase__      :		Any    							=      torch.manual_seed(0						)
						lowerCAmelCase__      :		Dict    							=      pipe(
						    prompt=lowercase_  ,image=lowercase_  ,mask_image=lowercase_  ,generator=lowercase_  ,output_type='''np'''  ,)
						lowerCAmelCase__      :		Optional[Any]    							=      output.images[0]

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







					def 			__lowerCAmelCase					(			self     :							Tuple						):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

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

						lowerCAmelCase__      :		Optional[int]    							=      '''stabilityai/stable-diffusion-2-inpainting'''
						lowerCAmelCase__      :		Optional[Any]    							=      PNDMScheduler.from_pretrained(lowercase_  ,subfolder='''scheduler'''						)
						lowerCAmelCase__      :		Optional[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__      :		Any    							=      '''Face of a yellow cat, high resolution, sitting on a park bench'''

						lowerCAmelCase__      :		Dict    							=      torch.manual_seed(0						)
						lowerCAmelCase__      :		int    							=      pipe(
						    prompt=lowercase_  ,image=lowercase_  ,mask_image=lowercase_  ,generator=lowercase_  ,num_inference_steps=2  ,output_type='''np'''  ,)

						lowerCAmelCase__      :		Dict    							=      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"""





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 LevitImageProcessor







class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					def __init__(			self     :							Any  ,lowercase_     :							Dict  ,lowercase_     :							List[str]=7  ,lowercase_     :							Tuple=3  ,lowercase_     :							List[str]=1_8  ,lowercase_     :							Optional[Any]=3_0  ,lowercase_     :							List[Any]=4_0_0  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Any=None  ,lowercase_     :							Optional[Any]=True  ,lowercase_     :							str=None  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Dict=[0.5, 0.5, 0.5]  ,lowercase_     :							Dict=[0.5, 0.5, 0.5]  ,):
						lowerCAmelCase__      :		Any    							=      size if size is not None else {'''shortest_edge''': 1_8}
						lowerCAmelCase__      :		Union[str, Any]    							=      crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
						lowerCAmelCase__      :		Dict    							=      parent
						lowerCAmelCase__      :		Dict    							=      batch_size
						lowerCAmelCase__      :		List[str]    							=      num_channels
						lowerCAmelCase__      :		Any    							=      image_size
						lowerCAmelCase__      :		Union[str, Any]    							=      min_resolution
						lowerCAmelCase__      :		Dict    							=      max_resolution
						lowerCAmelCase__      :		List[str]    							=      do_resize
						lowerCAmelCase__      :		Optional[Any]    							=      size
						lowerCAmelCase__      :		Tuple    							=      do_center_crop
						lowerCAmelCase__      :		Optional[int]    							=      crop_size
						lowerCAmelCase__      :		List[str]    							=      do_normalize
						lowerCAmelCase__      :		Tuple    							=      image_mean
						lowerCAmelCase__      :		int    							=      image_std







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "do_center_crop": self.do_center_crop,
						    "size": self.size,
						    "crop_size": self.crop_size,
						}







@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE   (						a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			LevitImageProcessor if is_vision_available() else None







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Optional[Any]    							=      LevitImageProcessingTester(self						)







					@property
					def 			__lowerCAmelCase					(			self     :							Any						):
						return self.image_processor_tester.prepare_image_processor_dict()







					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		Optional[int]    							=      self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(lowercase_  ,'''image_mean'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''image_std'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_normalize'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_resize'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_center_crop'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''size'''						)						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		int    							=      self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size  ,{'''shortest_edge''': 1_8}						)
						self.assertEqual(image_processor.crop_size  ,{'''height''': 1_8, '''width''': 1_8}						)

						lowerCAmelCase__      :		List[str]    							=      self.image_processing_class.from_dict(self.image_processor_dict  ,size=4_2  ,crop_size=8_4						)
						self.assertEqual(image_processor.size  ,{'''shortest_edge''': 4_2}						)
						self.assertEqual(image_processor.crop_size  ,{'''height''': 8_4, '''width''': 8_4}						)







					def 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						pass







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						# Initialize image_processing
						lowerCAmelCase__      :		List[str]    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						lowerCAmelCase__      :		Union[str, Any]    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,Image.Image						)

						# 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(lowercase_  ,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     :							Union[str, Any]						):
						# Initialize image_processing
						lowerCAmelCase__      :		str    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						lowerCAmelCase__      :		Optional[Any]    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_  ,numpify=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,np.ndarray						)

						# Test not batched input
						lowerCAmelCase__      :		Tuple    							=      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__      :		Union[str, Any]    							=      image_processing(lowercase_  ,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     :							int						):
						# Initialize image_processing
						lowerCAmelCase__      :		Tuple    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						lowerCAmelCase__      :		Dict    							=      prepare_image_inputs(self.image_processor_tester  ,equal_resolution=lowercase_  ,torchify=lowercase_						)
						for image in image_inputs:
							self.assertIsInstance(lowercase_  ,torch.Tensor						)

						# Test not batched input
						lowerCAmelCase__      :		Dict    							=      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(lowercase_  ,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'''],
						    )  ,)




