

"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				Any       				=  KandinskyVaaControlnetPipeline
				SCREAMING_SNAKE_CASE_       :				Any       				=  ["""image_embeds""", """negative_image_embeds""", """hint"""]
				SCREAMING_SNAKE_CASE_       :				int       				=  ["""image_embeds""", """negative_image_embeds""", """hint"""]
				SCREAMING_SNAKE_CASE_       :				Any       				=  [
				    """generator""",
				    """height""",
				    """width""",
				    """latents""",
				    """guidance_scale""",
				    """num_inference_steps""",
				    """return_dict""",
				    """guidance_scale""",
				    """num_images_per_prompt""",
				    """output_type""",
				    """return_dict""",
				]
				SCREAMING_SNAKE_CASE_       :				str       				=  False

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



						'''simple docstring'''


						return 3_2

				@property
				def 							lowercase_					(     self							:    int)->       List[Any]:



						'''simple docstring'''


						return 3_2

				@property
				def 							lowercase_					(     self							:    Any)->       int:



						'''simple docstring'''


						return self.time_input_dim

				@property
				def 							lowercase_					(     self							:    List[str])->       Tuple:



						'''simple docstring'''


						return self.time_input_dim * 4

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



						'''simple docstring'''


						return 1_0_0

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



						'''simple docstring'''


						torch.manual_seed(0)

						__lowerCAmelCase:	List[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,
						}

						__lowerCAmelCase:	str   				=      UNetaDConditionModel(**UpperCamelCase__)
						return model

				@property
				def 							lowercase_					(     self							:    List[Any])->       Optional[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 							lowercase_					(     self							:    Dict)->       Tuple:



						'''simple docstring'''


						torch.manual_seed(0)
						__lowerCAmelCase:	str   				=      VQModel(**self.dummy_movq_kwargs)
						return model

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



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      self.dummy_unet
						__lowerCAmelCase:	Any   				=      self.dummy_movq

						__lowerCAmelCase:	List[Any]   				=      DDIMScheduler(
						    num_train_timesteps=1_0_0_0					,  beta_schedule="linear"					,  beta_start=0.00085					,  beta_end=0.012					,  clip_sample=UpperCamelCase__					,  set_alpha_to_one=UpperCamelCase__					,  steps_offset=1					,  prediction_type="epsilon"					,  thresholding=UpperCamelCase__					,  )

						__lowerCAmelCase:	Optional[int]   				=      {
						    "unet": unet,
						    "scheduler": scheduler,
						    "movq": movq,
						}
						return components

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    int=0)->       str:



						'''simple docstring'''


						__lowerCAmelCase:	Any   				=      floats_tensor((1, self.text_embedder_hidden_size)					,  rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)
						__lowerCAmelCase:	int   				=      floats_tensor((1, self.text_embedder_hidden_size)					,  rng=random.Random(seed + 1)).to(
						    UpperCamelCase__)

						# create hint
						__lowerCAmelCase:	str   				=      floats_tensor((1, 3, 6_4, 6_4)					,  rng=random.Random(UpperCamelCase__)).to(UpperCamelCase__)

						if str(UpperCamelCase__).startswith("mps"):
								__lowerCAmelCase:	Optional[Any]   				=      torch.manual_seed(UpperCamelCase__)
						else:
								__lowerCAmelCase:	str   				=      torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      {
						    "image_embeds": image_embeds,
						    "negative_image_embeds": negative_image_embeds,
						    "hint": hint,
						    "generator": generator,
						    "height": 6_4,
						    "width": 6_4,
						    "guidance_scale": 4.0,
						    "num_inference_steps": 2,
						    "output_type": "np",
						}
						return inputs




				def 							lowercase_					(     self							:    int)->       List[str]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      "cpu"

						__lowerCAmelCase:	Optional[int]   				=      self.get_dummy_components()

						__lowerCAmelCase:	Optional[Any]   				=      self.pipeline_class(**UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      pipe.to(UpperCamelCase__)

						pipe.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:	Union[str, Any]   				=      pipe(**self.get_dummy_inputs(UpperCamelCase__))
						__lowerCAmelCase:	int   				=      output.images

						__lowerCAmelCase:	int   				=      pipe(
						    **self.get_dummy_inputs(UpperCamelCase__)					,  return_dict=UpperCamelCase__					,  )[0]

						__lowerCAmelCase:	str   				=      image[0, -3:, -3:, -1]
						__lowerCAmelCase:	List[str]   				=      image_from_tuple[0, -3:, -3:, -1]

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

						__lowerCAmelCase:	Any   				=      np.array(
						    [0.6959826, 0.868279, 0.7558092, 0.68769467, 0.85805804, 0.65977496, 0.44885302, 0.5959111, 0.4251595])

						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 							lowercase_					(     self							:    List[Any])->       str:



						'''simple docstring'''


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




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



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy")

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

						__lowerCAmelCase:	List[Any]   				=      KandinskyVaaPriorPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-2-prior"					,  torch_dtype=torch.floataa)
						pipe_prior.to(UpperCamelCase__)

						__lowerCAmelCase:	List[str]   				=      KandinskyVaaControlnetPipeline.from_pretrained(
						    "kandinsky-community/kandinsky-2-2-controlnet-depth"					,  torch_dtype=torch.floataa)
						__lowerCAmelCase:	Union[str, Any]   				=      pipeline.to(UpperCamelCase__)
						pipeline.set_progress_bar_config(disable=UpperCamelCase__)

						__lowerCAmelCase:	Optional[Any]   				=      "A robot, 4k photo"

						__lowerCAmelCase:	Tuple   				=      torch.Generator(device="cuda").manual_seed(0)
						__lowerCAmelCase				,			__lowerCAmelCase:	Dict   				=      pipe_prior(
						    UpperCamelCase__					,  generator=UpperCamelCase__					,  num_inference_steps=5					,  negative_prompt=""					,  ).to_tuple()

						__lowerCAmelCase:	Tuple   				=      torch.Generator(device="cuda").manual_seed(0)
						__lowerCAmelCase:	List[Any]   				=      pipeline(
						    image_embeds=UpperCamelCase__					,  negative_image_embeds=UpperCamelCase__					,  hint=UpperCamelCase__					,  generator=UpperCamelCase__					,  num_inference_steps=1_0_0					,  output_type="np"					,  )

						__lowerCAmelCase:	Tuple   				=      output.images[0]

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

						assert_mean_pixel_difference(UpperCamelCase__					,  UpperCamelCase__)





"""simple docstring"""


import math
import sys






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		if number != int(__SCREAMING_SNAKE_CASE			):
				raise ValueError("the value of input must be a natural number"			)
		if number < 0:
				raise ValueError("the value of input must not be a negative number"			)
		if number == 0:
				return 1
		__lowerCAmelCase:	int   				=      [-1] * (number + 1)
		__lowerCAmelCase:	Optional[Any]   				=      0
		for i in range(1 ,			number + 1			):
				__lowerCAmelCase:	List[str]   				=      sys.maxsize
				__lowerCAmelCase:	int   				=      int(math.sqrt(__SCREAMING_SNAKE_CASE			)			)
				for j in range(1 ,			root + 1			):
						__lowerCAmelCase:	Optional[Any]   				=      1 + answers[i - (j**2)]
						__lowerCAmelCase:	List[Any]   				=      min(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase:	Optional[Any]   				=      answer
		return answers[number]


if __name__ == "__main__":
			import doctest

			doctest.testmod()


