

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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   __lowercase    (			lowerCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							str      							=					KandinskyVaaInpaintPipeline
			_UpperCAmelCase       :							int      							=					["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
			_UpperCAmelCase       :							str      							=					[
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			    """mask_image""",
			]
			_UpperCAmelCase       :							Optional[int]      							=					[
			    """generator""",
			    """height""",
			    """width""",
			    """latents""",
			    """guidance_scale""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			_UpperCAmelCase       :							Dict      							=					False



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
										return 32



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
										return 32



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
										return self.time_input_dim



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
										return self.time_input_dim * 4



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
										return 100



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
										torch.manual_seed(0)

										SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{
										    "in_channels": 9,
										    # Out channels is double in channels because predicts mean and variance
										    "out_channels": 8,
										    "addition_embed_type": "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": "image_proj",
										    "cross_attention_dim": self.cross_attention_dim,
										    "attention_head_dim": 4,
										    "resnet_time_scale_shift": "scale_shift",
										    "class_embed_type": None,
										}

										SCREAMING_SNAKE_CASE_:			Tuple  		=	UNetaDConditionModel(**__SCREAMING_SNAKE_CASE)
										return model



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
										return {
										    "block_out_channels": [32, 64],
										    "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": 12,
										    "out_channels": 3,
										    "up_block_types": [
										        "AttnUpDecoderBlock2D",
										        "UpDecoderBlock2D",
										    ],
										    "vq_embed_dim": 4,
										}



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
										torch.manual_seed(0)
										SCREAMING_SNAKE_CASE_:			str  		=	VQModel(**self.dummy_movq_kwargs)
										return model



			def 	_SCREAMING_SNAKE_CASE      (			self  :					int):
										SCREAMING_SNAKE_CASE_:			Tuple  		=	self.dummy_unet
										SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.dummy_movq

										SCREAMING_SNAKE_CASE_:			str  		=	DDIMScheduler(
										    num_train_timesteps=1000       ,						beta_schedule="linear"       ,						beta_start=0.0_0085       ,						beta_end=0.012       ,						clip_sample=__SCREAMING_SNAKE_CASE       ,						set_alpha_to_one=__SCREAMING_SNAKE_CASE       ,						steps_offset=1       ,						prediction_type="epsilon"       ,						thresholding=__SCREAMING_SNAKE_CASE       ,						)

										SCREAMING_SNAKE_CASE_:			Tuple  		=	{
										    "unet": unet,
										    "scheduler": scheduler,
										    "movq": movq,
										}

										return components



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					Any=0):
										SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	floats_tensor((1, self.text_embedder_hidden_size)       ,						rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
										SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	floats_tensor((1, self.text_embedder_hidden_size)       ,						rng=random.Random(seed + 1)).to(
										    __SCREAMING_SNAKE_CASE)
										# create init_image
										SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	floats_tensor((1, 3, 64, 64)       ,						rng=random.Random(__SCREAMING_SNAKE_CASE)).to(__SCREAMING_SNAKE_CASE)
										SCREAMING_SNAKE_CASE_:			str  		=	image.cpu().permute(0       ,						2       ,						3       ,						1)[0]
										SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE)).convert("RGB").resize((256, 256))
										# create mask
										SCREAMING_SNAKE_CASE_:			int  		=	np.ones((64, 64)       ,						dtype=np.floataa)
										SCREAMING_SNAKE_CASE_:			Any  		=	0

										if str(__SCREAMING_SNAKE_CASE).startswith("mps"):
																	SCREAMING_SNAKE_CASE_:			List[Any]  		=	torch.manual_seed(__SCREAMING_SNAKE_CASE)
										else:
																	SCREAMING_SNAKE_CASE_:			str  		=	torch.Generator(device=__SCREAMING_SNAKE_CASE).manual_seed(__SCREAMING_SNAKE_CASE)
										SCREAMING_SNAKE_CASE_:			List[Any]  		=	{
										    "image": init_image,
										    "mask_image": mask,
										    "image_embeds": image_embeds,
										    "negative_image_embeds": negative_image_embeds,
										    "generator": generator,
										    "height": 64,
										    "width": 64,
										    "num_inference_steps": 2,
										    "guidance_scale": 4.0,
										    "output_type": "np",
										}
										return inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
										SCREAMING_SNAKE_CASE_:			int  		=	"cpu"

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

										SCREAMING_SNAKE_CASE_:			Any  		=	self.pipeline_class(**__SCREAMING_SNAKE_CASE)
										SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	pipe.to(__SCREAMING_SNAKE_CASE)

										pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

										SCREAMING_SNAKE_CASE_:			List[Any]  		=	pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE))
										SCREAMING_SNAKE_CASE_:			List[Any]  		=	output.images

										SCREAMING_SNAKE_CASE_:			int  		=	pipe(
										    **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE)       ,						return_dict=__SCREAMING_SNAKE_CASE       ,						)[0]

										SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image[0, -3:, -3:, -1]
										SCREAMING_SNAKE_CASE_:			int  		=	image_from_tuple[0, -3:, -3:, -1]

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

										assert image.shape == (1, 64, 64, 3)

										SCREAMING_SNAKE_CASE_:			List[Any]  		=	np.array(
										    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848])

										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 	_SCREAMING_SNAKE_CASE      (			self  :					int):
										super().test_inference_batch_single_identical(expected_max_diff=3E-3)







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




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
										SCREAMING_SNAKE_CASE_:			str  		=	load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy")

										SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png")
										SCREAMING_SNAKE_CASE_:			Any  		=	np.ones((768, 768)       ,						dtype=np.floataa)
										SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	0

										SCREAMING_SNAKE_CASE_:			Dict  		=	"a hat"

										SCREAMING_SNAKE_CASE_:			Any  		=	KandinskyVaaPriorPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-prior"       ,						torch_dtype=torch.floataa)
										pipe_prior.to(__SCREAMING_SNAKE_CASE)

										SCREAMING_SNAKE_CASE_:			List[Any]  		=	KandinskyVaaInpaintPipeline.from_pretrained(
										    "kandinsky-community/kandinsky-2-2-decoder-inpaint"       ,						torch_dtype=torch.floataa)
										SCREAMING_SNAKE_CASE_:			List[str]  		=	pipeline.to(__SCREAMING_SNAKE_CASE)
										pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE)

										SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.Generator(device="cpu").manual_seed(0)
										SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	pipe_prior(
										    __SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=5       ,						negative_prompt=""       ,						).to_tuple()

										SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	pipeline(
										    image=__SCREAMING_SNAKE_CASE       ,						mask_image=__SCREAMING_SNAKE_CASE       ,						image_embeds=__SCREAMING_SNAKE_CASE       ,						negative_image_embeds=__SCREAMING_SNAKE_CASE       ,						generator=__SCREAMING_SNAKE_CASE       ,						num_inference_steps=100       ,						height=768       ,						width=768       ,						output_type="np"       ,						)

										SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	output.images[0]

										assert image.shape == (768, 768, 3)

										assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE)









import torch

from diffusers import StableDiffusionPipeline


lowerCAmelCase							:  Any            =						"""path-to-your-trained-model"""
lowerCAmelCase							:  int            =						StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

lowerCAmelCase							:  Union[str, Any]            =						"""A photo of sks dog in a bucket"""
lowerCAmelCase							:  Any            =						pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






