

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 			A						(							_UpperCAmelCase   ,							unittest.TestCase			):







				"""simple docstring"""





				lowerCamelCase		     =							KandinskyVaaControlnetImgaImgPipeline
				lowerCamelCase		     =							['image_embeds', 'negative_image_embeds', 'image', 'hint']
				lowerCamelCase		     =							['image_embeds', 'negative_image_embeds', 'image', 'hint']
				lowerCamelCase		     =							[
				    'generator',
				    'height',
				    'width',
				    'strength',
				    'guidance_scale',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				lowerCamelCase		     =							False


				@property
				def 						snake_case__					(				self			:	List[str]		)->   Any:





								'''simple docstring'''







								return 3_2


				@property
				def 						snake_case__					(				self			:	Optional[int]		)->   Optional[int]:





								'''simple docstring'''







								return 3_2


				@property
				def 						snake_case__					(				self			:	Union[str, Any]		)->   int:





								'''simple docstring'''







								return self.time_input_dim


				@property
				def 						snake_case__					(				self			:	Any		)->   Optional[int]:





								'''simple docstring'''







								return self.time_input_dim * 4


				@property
				def 						snake_case__					(				self			:	Tuple		)->   Optional[int]:





								'''simple docstring'''







								return 1_0_0


				@property
				def 						snake_case__					(				self			:	Union[str, Any]		)->   str:





								'''simple docstring'''







								torch.manual_seed(0		)

								A__   				=       {
								    '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,
								}

								A__   				=       UNetaDConditionModel(**lowercase_		)
								return model


				@property
				def 						snake_case__					(				self			:	str		)->   List[Any]:





								'''simple docstring'''







								return {
								    "block_out_channels": [3_2, 3_2, 6_4, 6_4],
								    "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": 1_2,
								    "out_channels": 3,
								    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
								    "vq_embed_dim": 4,
								}


				@property
				def 						snake_case__					(				self			:	str		)->   int:





								'''simple docstring'''







								torch.manual_seed(0		)
								A__   				=       VQModel(**self.dummy_movq_kwargs		)
								return model


				def 						snake_case__					(				self			:	Optional[Any]		)->   int:





								'''simple docstring'''







								A__   				=       self.dummy_unet
								A__   				=       self.dummy_movq

								A__   				=       {
								    'num_train_timesteps': 1_0_0_0,
								    '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,
								}

								A__   				=       DDIMScheduler(**lowercase_		)

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

								return components


				def 						snake_case__					(				self			:	int,lowercase_			:	List[Any],lowercase_			:	Dict=0		)->   Any:





								'''simple docstring'''







								A__   				=       floats_tensor((1, self.text_embedder_hidden_size),rng=random.Random(lowercase_		)		).to(lowercase_		)
								A__   				=       floats_tensor((1, self.text_embedder_hidden_size),rng=random.Random(seed + 1		)		).to(
								    lowercase_		)
								# create init_image
								A__   				=       floats_tensor((1, 3, 6_4, 6_4),rng=random.Random(lowercase_		)		).to(lowercase_		)
								A__   				=       image.cpu().permute(0,2,3,1		)[0]
								A__   				=       Image.fromarray(np.uinta(lowercase_		)		).convert('RGB'		).resize((2_5_6, 2_5_6)		)
								# create hint
								A__   				=       floats_tensor((1, 3, 6_4, 6_4),rng=random.Random(lowercase_		)		).to(lowercase_		)

								if str(lowercase_		).startswith('mps'		):
												A__   				=       torch.manual_seed(lowercase_		)
								else:
												A__   				=       torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
								A__   				=       {
								    'image': init_image,
								    'image_embeds': image_embeds,
								    'negative_image_embeds': negative_image_embeds,
								    'hint': hint,
								    'generator': generator,
								    'height': 6_4,
								    'width': 6_4,
								    'num_inference_steps': 1_0,
								    'guidance_scale': 7.0,
								    'strength': 0.2,
								    'output_type': 'np',
								}
								return inputs


				def 						snake_case__					(				self			:	str		)->   Optional[Any]:





								'''simple docstring'''







								A__   				=       'cpu'

								A__   				=       self.get_dummy_components()

								A__   				=       self.pipeline_class(**lowercase_		)
								A__   				=       pipe.to(lowercase_		)

								pipe.set_progress_bar_config(disable=lowercase_		)

								A__   				=       pipe(**self.get_dummy_inputs(lowercase_		)		)
								A__   				=       output.images

								A__   				=       pipe(
								    **self.get_dummy_inputs(lowercase_		),return_dict=lowercase_,)[0]

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

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

								A__   				=       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 			A						(							unittest.TestCase			):







				"""simple docstring"""





				def 						snake_case__					(				self			:	str		)->   Optional[int]:





								'''simple docstring'''







								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


				def 						snake_case__					(				self			:	Union[str, Any]		)->   Optional[int]:





								'''simple docstring'''







								A__   				=       load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'		)

								A__   				=       load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'		)
								A__   				=       init_image.resize((5_1_2, 5_1_2)		)

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

								A__   				=       'A robot, 4k photo'

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

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

								pipeline.set_progress_bar_config(disable=lowercase_		)

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

								A__				,						A__   				=       pipe_prior(
								    lowercase_,image=lowercase_,strength=0.85,generator=lowercase_,negative_prompt='',).to_tuple()

								A__   				=       pipeline(
								    image=lowercase_,image_embeds=lowercase_,negative_image_embeds=lowercase_,hint=lowercase_,generator=lowercase_,num_inference_steps=1_0_0,height=5_1_2,width=5_1_2,strength=0.5,output_type='np',)

								A__   				=       output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)

								assert_mean_pixel_difference(lowercase_,lowercase_		)




import random
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list   ,      SCREAMING_SNAKE_CASE__      :     str				)							->				tuple:





    '''simple docstring'''






    A__				,						A__				,						A__   				=       [], [], []
    for element in data:
        if element < pivot:
            less.append(SCREAMING_SNAKE_CASE__				)
        elif element > pivot:
            greater.append(SCREAMING_SNAKE_CASE__				)
        else:
            equal.append(SCREAMING_SNAKE_CASE__				)
    return less, equal, greater

def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     list   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				str:





    '''simple docstring'''






    if index >= len(SCREAMING_SNAKE_CASE__				) or index < 0:
        return None

    A__   				=       items[random.randint(0   ,      len(SCREAMING_SNAKE_CASE__				) - 1				)]
    A__   				=       0
    A__				,						A__				,						A__   				=       _partition(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    A__   				=       len(SCREAMING_SNAKE_CASE__				)
    A__   				=       len(SCREAMING_SNAKE_CASE__				)

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    # must be in larger
    else:
        return quick_select(SCREAMING_SNAKE_CASE__   ,      index - (m + count)				)

