

'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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    snake_case     (      lowercase_,						unittest.TestCase		):






				"""simple docstring"""
				_a    	=     KandinskyVaaControlnetImgaImgPipeline
				_a    	=     ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
				_a    	=     ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
				_a    	=     [
				    """generator""",
				    """height""",
				    """width""",
				    """strength""",
				    """guidance_scale""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				_a    	=     False



				@property
				def   a__					(    self       )     ->							Tuple:
											return 32



				@property
				def   a__					(    self       )     ->							int:
											return 32



				@property
				def   a__					(    self       )     ->							List[Any]:
											return self.time_input_dim



				@property
				def   a__					(    self       )     ->							Any:
											return self.time_input_dim * 4



				@property
				def   a__					(    self       )     ->							Optional[int]:
											return 100



				@property
				def   a__					(    self       )     ->							Any:
											torch.manual_seed(0       )

											SCREAMING_SNAKE_CASE_				=				{
											    'in_channels': 8,
											    # Out channels is double in channels because predicts mean and variance
											    'out_channels': 8,
											    'addition_embed_type': 'image_hint',
											    '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_				=				UNetaDConditionModel(**_lowercase       )
											return model



				@property
				def   a__					(    self       )     ->							Union[str, Any]:
											return {
											    "block_out_channels": [32, 32, 64, 64],
											    "down_block_types": [
											        "DownEncoderBlock2D",
											        "DownEncoderBlock2D",
											        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
											    "vq_embed_dim": 4,
											}



				@property
				def   a__					(    self       )     ->							Dict:
											torch.manual_seed(0       )
											SCREAMING_SNAKE_CASE_				=				VQModel(**self.dummy_movq_kwargs       )
											return model



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				self.dummy_unet
											SCREAMING_SNAKE_CASE_				=				self.dummy_movq

											SCREAMING_SNAKE_CASE_				=				{
											    'num_train_timesteps': 1000,
											    'beta_schedule': 'linear',
											    'beta_start': 0.00_085,
											    'beta_end': 0.012,
											    'clip_sample': False,
											    'set_alpha_to_one': False,
											    'steps_offset': 0,
											    'prediction_type': 'epsilon',
											    'thresholding': False,
											}

											SCREAMING_SNAKE_CASE_				=				DDIMScheduler(**_lowercase       )

											SCREAMING_SNAKE_CASE_				=				{
											    'unet': unet,
											    'scheduler': scheduler,
											    'movq': movq,
											}

											return components



				def   a__					(    self,     _lowercase,     _lowercase=0       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				floats_tensor((1, self.text_embedder_hidden_size),     rng=random.Random(_lowercase       )       ).to(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				floats_tensor((1, self.text_embedder_hidden_size),     rng=random.Random(seed + 1       )       ).to(
											    _lowercase       )
											# create init_image
											SCREAMING_SNAKE_CASE_				=				floats_tensor((1, 3, 64, 64),     rng=random.Random(_lowercase       )       ).to(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				image.cpu().permute(0,     2,     3,     1       )[0]
											SCREAMING_SNAKE_CASE_				=				Image.fromarray(np.uinta(_lowercase       )       ).convert('RGB'       ).resize((256, 256)       )
											# create hint
											SCREAMING_SNAKE_CASE_				=				floats_tensor((1, 3, 64, 64),     rng=random.Random(_lowercase       )       ).to(_lowercase       )

											if str(_lowercase       ).startswith('mps'       ):
																		SCREAMING_SNAKE_CASE_				=				torch.manual_seed(_lowercase       )
											else:
																		SCREAMING_SNAKE_CASE_				=				torch.Generator(device=_lowercase       ).manual_seed(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				{
											    'image': init_image,
											    'image_embeds': image_embeds,
											    'negative_image_embeds': negative_image_embeds,
											    'hint': hint,
											    'generator': generator,
											    'height': 64,
											    'width': 64,
											    'num_inference_steps': 10,
											    'guidance_scale': 7.0,
											    'strength': 0.2,
											    'output_type': 'np',
											}
											return inputs





				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				'cpu'

											SCREAMING_SNAKE_CASE_				=				self.get_dummy_components()

											SCREAMING_SNAKE_CASE_				=				self.pipeline_class(**_lowercase       )
											SCREAMING_SNAKE_CASE_				=				pipe.to(_lowercase       )

											pipe.set_progress_bar_config(disable=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				pipe(**self.get_dummy_inputs(_lowercase       )       )
											SCREAMING_SNAKE_CASE_				=				output.images

											SCREAMING_SNAKE_CASE_				=				pipe(
											    **self.get_dummy_inputs(_lowercase       ),     return_dict=_lowercase,     )[0]

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

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

											SCREAMING_SNAKE_CASE_				=				np.array(
											    [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]       )
											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()}"""






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






				"""simple docstring"""



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





				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'       )

											SCREAMING_SNAKE_CASE_				=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'       )
											SCREAMING_SNAKE_CASE_				=				init_image.resize((512, 512)       )

											SCREAMING_SNAKE_CASE_				=				load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/kandinskyv22/hint_image_cat.png'       )
											SCREAMING_SNAKE_CASE_				=				torch.from_numpy(np.array(_lowercase       )       ).float() / 255.0
											SCREAMING_SNAKE_CASE_				=				hint.permute(2,     0,     1       ).unsqueeze(0       )

											SCREAMING_SNAKE_CASE_				=				'A robot, 4k photo'

											SCREAMING_SNAKE_CASE_				=				KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-2-prior',     torch_dtype=torch.floataa       )
											pipe_prior.to(_lowercase       )

											SCREAMING_SNAKE_CASE_				=				KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
											    'kandinsky-community/kandinsky-2-2-controlnet-depth',     torch_dtype=torch.floataa       )
											SCREAMING_SNAKE_CASE_				=				pipeline.to(_lowercase       )

											pipeline.set_progress_bar_config(disable=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				torch.Generator(device='cpu'       ).manual_seed(0       )

											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				pipe_prior(
											    _lowercase,     image=_lowercase,     strength=0.85,     generator=_lowercase,     negative_prompt='',     ).to_tuple()

											SCREAMING_SNAKE_CASE_				=				pipeline(
											    image=_lowercase,     image_embeds=_lowercase,     negative_image_embeds=_lowercase,     hint=_lowercase,     generator=_lowercase,     num_inference_steps=100,     height=512,     width=512,     strength=0.5,     output_type='np',     )

											SCREAMING_SNAKE_CASE_				=				output.images[0]

											assert image.shape == (512, 512, 3)

											assert_mean_pixel_difference(_lowercase,     _lowercase       )








'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											super().__init__()
											self.register_modules(vqvae=_lowercase,     unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = None,     _lowercase = 0.0,     _lowercase = 50,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Union[Tuple, ImagePipelineOutput]:
											SCREAMING_SNAKE_CASE_				=				randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE_				=				latents * self.scheduler.init_noise_sigma

											self.scheduler.set_timesteps(_lowercase       )

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											SCREAMING_SNAKE_CASE_				=				'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step(_lowercase,     _lowercase,     _lowercase,     **_lowercase       ).prev_sample

											# decode the image latents with the VAE
											SCREAMING_SNAKE_CASE_				=				self.vqvae.decode(_lowercase       ).sample

											SCREAMING_SNAKE_CASE_				=				(image / 2 + 0.5).clamp(0,     1       )
											SCREAMING_SNAKE_CASE_				=				image.cpu().permute(0,     2,     3,     1       ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowercase       )





