






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						(			_A       ,    unittest.TestCase ):
	_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    lowerCAmelCase_							(		self       )						->    Tuple:
					return 32






	@property
	def    lowerCAmelCase_							(		self       )						->    str:
					return 32






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






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






	@property
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					return 100






	@property
	def    lowerCAmelCase_							(		self       )						->    Dict:
					torch.manual_seed(0       )

					snake_case__      :Optional[Any]         =   {
					    "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,
					}

					snake_case__      :List[Any]         =   UNetaDConditionModel(**UpperCamelCase       )
					return model






	@property
	def    lowerCAmelCase_							(		self       )						->    str:
					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    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					torch.manual_seed(0       )
					snake_case__      :Dict         =   VQModel(**self.dummy_movq_kwargs       )
					return model






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :str         =   self.dummy_unet
					snake_case__      :List[Any]         =   self.dummy_movq

					snake_case__      :List[Any]         =   {
					    "num_train_timesteps": 1_000,
					    "beta_schedule": "linear",
					    "beta_start": 0.00085,
					    "beta_end": 0.012,
					    "clip_sample": False,
					    "set_alpha_to_one": False,
					    "steps_offset": 0,
					    "prediction_type": "epsilon",
					    "thresholding": False,
					}

					snake_case__      :List[str]         =   DDIMScheduler(**UpperCamelCase       )

					snake_case__      :Optional[Any]         =   {
					    "unet": unet,
					    "scheduler": scheduler,
					    "movq": movq,
					}

					return components






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase=0       )						->    Optional[int]:
					snake_case__      :Optional[int]         =   floats_tensor((1, self.text_embedder_hidden_size)					,rng=random.Random(UpperCamelCase       )       ).to(UpperCamelCase       )
					snake_case__      :int         =   floats_tensor((1, self.text_embedder_hidden_size)					,rng=random.Random(seed + 1       )       ).to(
					    UpperCamelCase       )
					# create init_image
					snake_case__      :Optional[int]         =   floats_tensor((1, 3, 64, 64)					,rng=random.Random(UpperCamelCase       )       ).to(UpperCamelCase       )
					snake_case__      :List[str]         =   image.cpu().permute(0					,2					,3					,1       )[0]
					snake_case__      :Any         =   Image.fromarray(np.uinta(UpperCamelCase       )       ).convert("RGB"       ).resize((256, 256)       )
					# create hint
					snake_case__      :int         =   floats_tensor((1, 3, 64, 64)					,rng=random.Random(UpperCamelCase       )       ).to(UpperCamelCase       )

					if str(UpperCamelCase       ).startswith("mps"       ):
									snake_case__      :int         =   torch.manual_seed(UpperCamelCase       )
					else:
									snake_case__      :Tuple         =   torch.Generator(device=UpperCamelCase       ).manual_seed(UpperCamelCase       )
					snake_case__      :List[Any]         =   {
					    "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    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :Dict         =   "cpu"

					snake_case__      :Union[str, Any]         =   self.get_dummy_components()

					snake_case__      :Dict         =   self.pipeline_class(**UpperCamelCase       )
					snake_case__      :Union[str, Any]         =   pipe.to(UpperCamelCase       )

					pipe.set_progress_bar_config(disable=UpperCamelCase       )

					snake_case__      :Optional[int]         =   pipe(**self.get_dummy_inputs(UpperCamelCase       )       )
					snake_case__      :Dict         =   output.images

					snake_case__      :List[Any]         =   pipe(
					    **self.get_dummy_inputs(UpperCamelCase       )					,return_dict=UpperCamelCase					,)[0]

					snake_case__      :Union[str, Any]         =   image[0, -3:, -3:, -1]
					snake_case__      :Dict         =   image_from_tuple[0, -3:, -3:, -1]

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

					snake_case__      :Union[str, Any]         =   np.array(
					    [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]       )
					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 ):






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :Tuple         =   load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"       )

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

					snake_case__      :int         =   load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/kandinskyv22/hint_image_cat.png"       )
					snake_case__      :Tuple         =   torch.from_numpy(np.array(UpperCamelCase       )       ).float() / 255.0
					snake_case__      :List[Any]         =   hint.permute(2					,0					,1       ).unsqueeze(0       )

					snake_case__      :Dict         =   "A robot, 4k photo"

					snake_case__      :Dict         =   KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
					    "kandinsky-community/kandinsky-2-2-prior"					,torch_dtype=torch.floataa       )
					pipe_prior.to(UpperCamelCase       )

					snake_case__      :Dict         =   KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
					    "kandinsky-community/kandinsky-2-2-controlnet-depth"					,torch_dtype=torch.floataa       )
					snake_case__      :Optional[Any]         =   pipeline.to(UpperCamelCase       )

					pipeline.set_progress_bar_config(disable=UpperCamelCase       )

					snake_case__      :Dict         =   torch.Generator(device="cpu"       ).manual_seed(0       )

					snake_case__      :Dict         =   pipe_prior(
					    UpperCamelCase					,image=UpperCamelCase					,strength=0.85					,generator=UpperCamelCase					,negative_prompt=""					,).to_tuple()

					snake_case__      :Union[str, Any]         =   pipeline(
					    image=UpperCamelCase					,image_embeds=UpperCamelCase					,negative_image_embeds=UpperCamelCase					,hint=UpperCamelCase					,generator=UpperCamelCase					,num_inference_steps=100					,height=512					,width=512					,strength=0.5					,output_type="np"					,)

					snake_case__      :Optional[Any]         =   output.images[0]

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

					assert_mean_pixel_difference(UpperCamelCase					,UpperCamelCase       )







def 					lowercase_ (    __snake_case     :							list      )     -> list:





				'''simple docstring'''


				if any(not isinstance(__snake_case	,	__snake_case      ) or x < 0 for x in sequence      ):
								raise TypeError("Sequence must be list of non-negative integers"      )
				for _ in range(len(__snake_case      )      ):
								for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case	,	sequence[1:]      )      ):
												if rod_upper > rod_lower:
																sequence[i] -= rod_upper - rod_lower
																sequence[i + 1] += rod_upper - rod_lower
				return sequence


if __name__ == "__main__":
	assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
	assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]