
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 				UpperCAmelCase (    __lowercase     ,     __lowercase     ,     __lowercase     ,     unittest.TestCase    ):




	'''simple docstring'''





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






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					torch.manual_seed(0					)
					__A       =					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				,)
					__A       =					PNDMScheduler(skip_prk_steps=_a					)
					torch.manual_seed(0					)
					__A       =					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=1_28				,)
					torch.manual_seed(0					)
					__A       =					CLIPTextConfig(
					    bos_token_id=0				,eos_token_id=2				,hidden_size=32				,intermediate_size=37				,layer_norm_eps=1E-05				,num_attention_heads=4				,num_hidden_layers=5				,pad_token_id=1				,vocab_size=10_00				,hidden_act="gelu"				,projection_dim=5_12				,)
					__A       =					CLIPTextModel(_a					)
					__A       =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

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






	def 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					int				,A   :					str=0					):
					# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
					__A       =					floats_tensor((1, 3, 32, 32)				,rng=random.Random(_a					)					).to(_a					)
					__A       =					image.cpu().permute(0				,2				,3				,1					)[0]
					__A       =					Image.fromarray(np.uinta(_a					)					).convert("RGB"					).resize((64, 64)					)
					__A       =					Image.fromarray(np.uinta(image + 4					)					).convert("RGB"					).resize((64, 64)					)
					if str(_a					).startswith("mps"					):
									__A       =					torch.manual_seed(_a					)
					else:
									__A       =					torch.Generator(device=_a					).manual_seed(_a					)
					__A       =					{
					    '''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 							UpperCamelCase_    (			self   :					Dict					):
					__A       =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					__A       =					self.get_dummy_components()
					__A       =					StableDiffusionInpaintPipeline(**_a					)
					__A       =					sd_pipe.to(_a					)
					sd_pipe.set_progress_bar_config(disable=_a					)

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

					assert image.shape == (1, 64, 64, 3)
					__A       =					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 							UpperCamelCase_    (			self   :					str					):
					super().test_inference_batch_single_identical(expected_max_diff=3E-3					)





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




	'''simple docstring'''





	def 							UpperCamelCase_    (			self   :					int					):
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






	def 							UpperCamelCase_    (			self   :					str					):
					__A       =					load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/sd2-inpaint/init_image.png"					)
					__A       =					load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"					)
					__A       =					load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
					    "/yellow_cat_sitting_on_a_park_bench.npy"					)

					__A       =					'''stabilityai/stable-diffusion-2-inpainting'''
					__A       =					StableDiffusionInpaintPipeline.from_pretrained(_a				,safety_checker=_a					)
					pipe.to(_a					)
					pipe.set_progress_bar_config(disable=_a					)
					pipe.enable_attention_slicing()

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

					__A       =					torch.manual_seed(0					)
					__A       =					pipe(
					    prompt=_a				,image=_a				,mask_image=_a				,generator=_a				,output_type="np"				,)
					__A       =					output.images[0]

					assert image.shape == (5_12, 5_12, 3)
					assert np.abs(expected_image - image					).max() < 9E-3






	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/sd2-inpaint/init_image.png"					)
					__A       =					load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"					)
					__A       =					load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
					    "/yellow_cat_sitting_on_a_park_bench_fp16.npy"					)

					__A       =					'''stabilityai/stable-diffusion-2-inpainting'''
					__A       =					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()

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

					__A       =					torch.manual_seed(0					)
					__A       =					pipe(
					    prompt=_a				,image=_a				,mask_image=_a				,generator=_a				,output_type="np"				,)
					__A       =					output.images[0]

					assert image.shape == (5_12, 5_12, 3)
					assert np.abs(expected_image - image					).max() < 5E-1







	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()

					__A       =					load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/sd2-inpaint/init_image.png"					)
					__A       =					load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"					)

					__A       =					'''stabilityai/stable-diffusion-2-inpainting'''
					__A       =					PNDMScheduler.from_pretrained(_a				,subfolder="scheduler"					)
					__A       =					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()

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

					__A       =					torch.manual_seed(0					)
					__A       =					pipe(
					    prompt=_a				,image=_a				,mask_image=_a				,generator=_a				,num_inference_steps=2				,output_type="np"				,)

					__A       =					torch.cuda.max_memory_allocated()
					# make sure that less than 2.65 GB is allocated
					assert mem_bytes < 2.65 * 10**9



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		Union[str, Any]             =       KandinskyInpaintPipeline
							UpperCAmelCase__			:		Optional[int]             =       ["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
							UpperCAmelCase__			:		Optional[Any]             =       [
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							    "mask_image",
							]
							UpperCAmelCase__			:		Optional[int]             =       [
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase__			:		Any             =       False


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       int:
												return 3_2


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												return self.time_input_dim


							@property
							def 							__lowercase							(     self			)       ->       List[str]:
												return self.time_input_dim * 4


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												return 1_0_0


							@property
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Any   =						XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''			)
												return tokenizer


							@property
							def 							__lowercase							(     self			)       ->       str:
												torch.manual_seed(0			)
												_a      :       List[Any]   =						MCLIPConfig(
												    numDims=self.cross_attention_dim			,				transformerDimensions=self.text_embedder_hidden_size			,				hidden_size=self.text_embedder_hidden_size			,				intermediate_size=3_7			,				num_attention_heads=4			,				num_hidden_layers=5			,				vocab_size=1_0_0_5			,				)

												_a      :       Optional[int]   =						MultilingualCLIP(_a			)
												_a      :       Tuple   =						text_encoder.eval()

												return text_encoder


							@property
							def 							__lowercase							(     self			)       ->       str:
												torch.manual_seed(0			)

												_a      :       List[str]   =						{
												    '''in_channels''': 9,
												    # Out channels is double in channels because predicts mean and variance
												    '''out_channels''': 8,
												    '''addition_embed_type''': '''text_image''',
												    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
												    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
												    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
												    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
												    '''layers_per_block''': 1,
												    '''encoder_hid_dim''': self.text_embedder_hidden_size,
												    '''encoder_hid_dim_type''': '''text_image_proj''',
												    '''cross_attention_dim''': self.cross_attention_dim,
												    '''attention_head_dim''': 4,
												    '''resnet_time_scale_shift''': '''scale_shift''',
												    '''class_embed_type''': None,
												}

												_a      :       Dict   =						UNetaDConditionModel(**_a			)
												return model


							@property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return {
												    "block_out_channels": [3_2, 6_4],
												    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
												    "in_channels": 3,
												    "latent_channels": 4,
												    "layers_per_block": 1,
												    "norm_num_groups": 8,
												    "norm_type": "spatial",
												    "num_vq_embeddings": 1_2,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}


							@property
							def 							__lowercase							(     self			)       ->       Tuple:
												torch.manual_seed(0			)
												_a      :       List[Any]   =						VQModel(**self.dummy_movq_kwargs			)
												return model


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       List[Any]   =						self.dummy_text_encoder
												_a      :       Optional[Any]   =						self.dummy_tokenizer
												_a      :       Optional[Any]   =						self.dummy_unet
												_a      :       Union[str, Any]   =						self.dummy_movq

												_a      :       Tuple   =						DDIMScheduler(
												    num_train_timesteps=1_0_0_0			,				beta_schedule='''linear'''			,				beta_start=0.0_0085			,				beta_end=0.012			,				clip_sample=_a			,				set_alpha_to_one=_a			,				steps_offset=1			,				prediction_type='''epsilon'''			,				thresholding=_a			,				)

												_a      :       str   =						{
												    '''text_encoder''': text_encoder,
												    '''tokenizer''': tokenizer,
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}

												return components


							def 							__lowercase							(     self			,				_a			,				_a=0			)       ->       int:
												_a      :       Union[str, Any]   =						floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       List[str]   =						floats_tensor((1, self.cross_attention_dim)			,				rng=random.Random(seed + 1			)			).to(_a			)
												# create init_image
												_a      :       Tuple   =						floats_tensor((1, 3, 6_4, 6_4)			,				rng=random.Random(_a			)			).to(_a			)
												_a      :       Dict   =						image.cpu().permute(0			,				2			,				3			,				1			)[0]
												_a      :       Optional[int]   =						Image.fromarray(np.uinta(_a			)			).convert('''RGB'''			).resize((2_5_6, 2_5_6)			)
												# create mask
												_a      :       Union[str, Any]   =						np.ones((6_4, 6_4)			,				dtype=np.floataa			)
												_a      :       List[str]   =						0

												if str(_a			).startswith('''mps'''			):
																	_a      :       Tuple   =						torch.manual_seed(_a			)
												else:
																	_a      :       Any   =						torch.Generator(device=_a			).manual_seed(_a			)
												_a      :       Any   =						{
												    '''prompt''': '''horse''',
												    '''image''': init_image,
												    '''mask_image''': mask,
												    '''image_embeds''': image_embeds,
												    '''negative_image_embeds''': negative_image_embeds,
												    '''generator''': generator,
												    '''height''': 6_4,
												    '''width''': 6_4,
												    '''num_inference_steps''': 2,
												    '''guidance_scale''': 4.0,
												    '''output_type''': '''np''',
												}
												return inputs


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Optional[Any]   =						'''cpu'''

												_a      :       List[Any]   =						self.get_dummy_components()

												_a      :       Tuple   =						self.pipeline_class(**_a			)
												_a      :       int   =						pipe.to(_a			)

												pipe.set_progress_bar_config(disable=_a			)

												_a      :       Any   =						pipe(**self.get_dummy_inputs(_a			)			)
												_a      :       str   =						output.images

												_a      :       Tuple   =						pipe(
												    **self.get_dummy_inputs(_a			)			,				return_dict=_a			,				)[0]

												_a      :       Union[str, Any]   =						image[0, -3:, -3:, -1]
												_a      :       Tuple   =						image_from_tuple[0, -3:, -3:, -1]

												print(F"""image.shape {image.shape}"""			)

												assert image.shape == (1, 6_4, 6_4, 3)

												_a      :       str   =						np.array(
												    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]			)

												assert (
												    np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
												), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
												assert (
												    np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
												), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""




							def 							__lowercase							(     self			)       ->       Dict:
												super().test_inference_batch_single_identical(expected_max_diff=3e-3			)



@slow
@require_torch_gpu
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







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




							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a      :       Tuple   =						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''			)

												_a      :       str   =						load_image(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''			)
												_a      :       Tuple   =						np.ones((7_6_8, 7_6_8)			,				dtype=np.floataa			)
												_a      :       Any   =						0

												_a      :       Optional[Any]   =						'''a hat'''

												_a      :       Optional[Any]   =						KandinskyPriorPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-prior'''			,				torch_dtype=torch.floataa			)
												pipe_prior.to(_a			)

												_a      :       Tuple   =						KandinskyInpaintPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-1-inpaint'''			,				torch_dtype=torch.floataa			)
												_a      :       Union[str, Any]   =						pipeline.to(_a			)
												pipeline.set_progress_bar_config(disable=_a			)

												_a      :       Union[str, Any]   =						torch.Generator(device='''cpu'''			).manual_seed(0			)
												_a ,   _a      :       Dict   =						pipe_prior(
												    _a			,				generator=_a			,				num_inference_steps=5			,				negative_prompt=''''''			,				).to_tuple()

												_a      :       Optional[int]   =						pipeline(
												    _a			,				image=_a			,				mask_image=_a			,				image_embeds=_a			,				negative_image_embeds=_a			,				generator=_a			,				num_inference_steps=1_0_0			,				height=7_6_8			,				width=7_6_8			,				output_type='''np'''			,				)

												_a      :       Optional[int]   =						output.images[0]

												assert image.shape == (7_6_8, 7_6_8, 3)

												assert_mean_pixel_difference(_a			,				_a			)



