
"""simple docstring"""


from math import sqrt



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							bool:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and (
           number >= 0
       ), "'number' must been an int and positive"

       UpperCamelCase					 =		True

       # 0 and 1 are none primes.
       if number <= 1:
              UpperCamelCase					 =		False

       for divisor in range(2		,      int(round(sqrt(UpperCAmelCase_				)				)				) + 1				):
              # if 'number' divisible by 'divisor' then sets 'status'
              # of false and break up the loop.
              if number % divisor == 0:
                     UpperCamelCase					 =		False
                     break

    # precondition
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				), "'status' must been from type bool"

       return status



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							Union[str, Any]:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and (n > 2), "'N' must been an int and > 2"

       # beginList: contains all natural numbers from 2 up to N
       UpperCamelCase					 =		list(range(2		,      n + 1				)				)

       UpperCamelCase					 =		[]  # this list will be returns.

       # actual sieve of erathostenes
       for i in range(len(UpperCAmelCase_				)				):
              for j in range(i + 1		,      len(UpperCAmelCase_				)				):
                     if (begin_list[i] != 0) and (begin_list[j] % begin_list[i] == 0):
                            UpperCamelCase					 =		0

    # filters actual prime numbers.
       UpperCamelCase					 =		[x for x in begin_list if x != 0]

       # precondition
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				), "'ans' must been from type list"

       return ans



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							Any:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and (n > 2), "'N' must been an int and > 2"

       UpperCamelCase					 =		[]

       # iterates over all numbers between 2 up to N+1
       # if a number is prime then appends to list 'ans'
       for number in range(2		,      n + 1				):
              if is_prime(UpperCAmelCase_				):
                     ans.append(UpperCAmelCase_				)

    # precondition
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				), "'ans' must been from type list"

       return ans



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							int:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and number >= 0, "'number' must been an int and >= 0"

       UpperCamelCase					 =		[]  # this list will be returns of the function.

       # potential prime number factors.

       UpperCamelCase					 =		2

       UpperCamelCase					 =		number

       if number == 0 or number == 1:
              ans.append(UpperCAmelCase_				)

       # if 'number' not prime then builds the prime factorization of 'number'
       elif not is_prime(UpperCAmelCase_				):
              while quotient != 1:
                     if is_prime(UpperCAmelCase_				) and (quotient % factor == 0):
                            ans.append(UpperCAmelCase_				)
                            quotient /= factor
                     else:
                            factor += 1

       else:
              ans.append(UpperCAmelCase_				)

       # precondition
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				), "'ans' must been from type list"

       return ans



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							int:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and (
           number >= 0
       ), "'number' bust been an int and >= 0"

       UpperCamelCase					 =		0

       # prime factorization of 'number'
       UpperCamelCase					 =		prime_factorization(UpperCAmelCase_				)

       UpperCamelCase					 =		max(UpperCAmelCase_				)

       # precondition
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				), "'ans' must been from type int"

       return ans



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							Union[str, Any]:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and (
           number >= 0
       ), "'number' bust been an int and >= 0"

       UpperCamelCase					 =		0

       # prime factorization of 'number'
       UpperCamelCase					 =		prime_factorization(UpperCAmelCase_				)

       UpperCamelCase					 =		min(UpperCAmelCase_				)

       # precondition
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				), "'ans' must been from type int"

       return ans



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							Optional[Any]:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				), "'number' must been an int"
       assert isinstance(number % 2 == 0		,      UpperCAmelCase_				), "compare bust been from type bool"

       return number % 2 == 0



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							Dict:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				), "'number' must been an int"
       assert isinstance(number % 2 != 0		,      UpperCAmelCase_				), "compare bust been from type bool"

       return number % 2 != 0



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[str]:







       """simple docstring"""
       assert (
           isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and (number > 2) and is_even(UpperCAmelCase_				)
       ), "'number' must been an int, even and > 2"

       UpperCamelCase					 =		[]  # this list will returned

       # creates a list of prime numbers between 2 up to 'number'
       UpperCamelCase					 =		get_prime_numbers(UpperCAmelCase_				)
       UpperCamelCase					 =		len(UpperCAmelCase_				)

       # run variable for while-loops.
       UpperCamelCase					 =		0
       UpperCamelCase					 =		None

       # exit variable. for break up the loops
       UpperCamelCase					 =		True

       while i < len_pn and loop:
              UpperCamelCase					 =		i + 1

              while j < len_pn and loop:
                     if prime_numbers[i] + prime_numbers[j] == number:
                            UpperCamelCase					 =		False
                            ans.append(prime_numbers[i]				)
                            ans.append(prime_numbers[j]				)

                     j += 1

              i += 1

       # precondition
       assert (
           isinstance(UpperCAmelCase_		,      UpperCAmelCase_				)
           and (len(UpperCAmelCase_				) == 2)
           and (ans[0] + ans[1] == number)
           and is_prime(ans[0]				)
           and is_prime(ans[1]				)
       ), "'ans' must contains two primes. And sum of elements must been eq 'number'"

       return ans



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							int:







       """simple docstring"""
       assert (
           isinstance(UpperCAmelCase_		,      UpperCAmelCase_				)
           and isinstance(UpperCAmelCase_		,      UpperCAmelCase_				)
           and (numbera >= 0)
           and (numbera >= 0)
       ), "'number1' and 'number2' must been positive integer."

       UpperCamelCase					 =		0

       while numbera != 0:
              UpperCamelCase					 =		numbera % numbera
              UpperCamelCase					 =		numbera
              UpperCamelCase					 =		rest

       # precondition
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and (
           numbera >= 0
       ), "'number' must been from type int and positive"

       return numbera



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							Any:







       """simple docstring"""
       assert (
           isinstance(UpperCAmelCase_		,      UpperCAmelCase_				)
           and isinstance(UpperCAmelCase_		,      UpperCAmelCase_				)
           and (numbera >= 1)
           and (numbera >= 1)
       ), "'number1' and 'number2' must been positive integer."

       UpperCamelCase					 =		1  # actual answer that will be return.

       # for kgV (x,1)
       if numbera > 1 and numbera > 1:
              # builds the prime factorization of 'number1' and 'number2'
              UpperCamelCase					 =		prime_factorization(UpperCAmelCase_				)
              UpperCamelCase					 =		prime_factorization(UpperCAmelCase_				)

       elif numbera == 1 or numbera == 1:
              UpperCamelCase					 =		[]
              UpperCamelCase					 =		[]
              UpperCamelCase					 =		max(UpperCAmelCase_		,      UpperCAmelCase_				)

       UpperCamelCase					 =		0
       UpperCamelCase					 =		0

       UpperCamelCase					 =		[]  # captured numbers int both 'primeFac1' and 'primeFac2'

       # iterates through primeFac1
       for n in prime_fac_a:
              if n not in done:
                     if n in prime_fac_a:
                            UpperCamelCase					 =		prime_fac_a.count(UpperCAmelCase_				)
                            UpperCamelCase					 =		prime_fac_a.count(UpperCAmelCase_				)

                            for _ in range(max(UpperCAmelCase_		,      UpperCAmelCase_				)				):
                                   ans *= n

                     else:
                            UpperCamelCase					 =		prime_fac_a.count(UpperCAmelCase_				)

                            for _ in range(UpperCAmelCase_				):
                                   ans *= n

                     done.append(UpperCAmelCase_				)

    # iterates through primeFac2
       for n in prime_fac_a:
              if n not in done:
                     UpperCamelCase					 =		prime_fac_a.count(UpperCAmelCase_				)

                     for _ in range(UpperCAmelCase_				):
                            ans *= n

                     done.append(UpperCAmelCase_				)

    # precondition
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and (
           ans >= 0
       ), "'ans' must been from type int and positive"

       return ans



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[str]:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and (n >= 0), "'number' must been a positive int"

       UpperCamelCase					 =		0
       UpperCamelCase					 =		2  # this variable holds the answer

       while index < n:
              index += 1

              ans += 1  # counts to the next number

              # if ans not prime then
              # runs to the next prime number.
              while not is_prime(UpperCAmelCase_				):
                     ans += 1

    # precondition
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and is_prime(
           UpperCAmelCase_				), "'ans' must been a prime number and from type int"

       return ans



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							Dict:







       """simple docstring"""
       assert (
           is_prime(UpperCAmelCase_				) and is_prime(UpperCAmelCase_				) and (p_number_a < p_number_a)
       ), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

       UpperCamelCase					 =		p_number_a + 1  # jump to the next number

       UpperCamelCase					 =		[]  # this list will be returns.

       # if number is not prime then
       # fetch the next prime number.
       while not is_prime(UpperCAmelCase_				):
              number += 1

       while number < p_number_a:
              ans.append(UpperCAmelCase_				)

              number += 1

              # fetch the next prime number.
              while not is_prime(UpperCAmelCase_				):
                     number += 1

    # precondition
       assert (
           isinstance(UpperCAmelCase_		,      UpperCAmelCase_				)
           and ans[0] != p_number_a
           and ans[len(UpperCAmelCase_				) - 1] != p_number_a
       ), "'ans' must been a list without the arguments"

       # 'ans' contains not 'pNumber1' and 'pNumber2' !
       return ans



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[Any]:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and (n >= 1), "'n' must been int and >= 1"

       UpperCamelCase					 =		[]  # will be returned.

       for divisor in range(1		,      n + 1				):
              if n % divisor == 0:
                     ans.append(UpperCAmelCase_				)

    # precondition
       assert ans[0] == 1 and ans[len(UpperCAmelCase_				) - 1] == n, "Error in function getDivisiors(...)"

       return ans



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							Optional[int]:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and (
           number > 1
       ), "'number' must been an int and >= 1"

       UpperCamelCase					 =		get_divisors(UpperCAmelCase_				)

       # precondition
       assert (
           isinstance(UpperCAmelCase_		,      UpperCAmelCase_				)
           and (divisors[0] == 1)
           and (divisors[len(UpperCAmelCase_				) - 1] == number)
       ), "Error in help-function getDivisiors(...)"

       # summed all divisors up to 'number' (exclusive), hence [:-1]
       return sum(divisors[:-1]				) == number



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_				)->							Any:







       """simple docstring"""
       assert (
           isinstance(UpperCAmelCase_		,      UpperCAmelCase_				)
           and isinstance(UpperCAmelCase_		,      UpperCAmelCase_				)
           and (denominator != 0)
       ), "The arguments must been from type int and 'denominator' != 0"

       # build the greatest common divisor of numerator and denominator.
       UpperCamelCase					 =		gcd(abs(UpperCAmelCase_				)		,      abs(UpperCAmelCase_				)				)

       # precondition
       assert (
           isinstance(UpperCAmelCase_		,      UpperCAmelCase_				)
           and (numerator % gcd_of_fraction == 0)
           and (denominator % gcd_of_fraction == 0)
       ), "Error in function gcd(...,...)"

       return (numerator // gcd_of_fraction, denominator // gcd_of_fraction)



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							int:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and (n >= 0), "'n' must been a int and >= 0"

       UpperCamelCase					 =		1  # this will be return.

       for factor in range(1		,      n + 1				):
              ans *= factor

       return ans



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							List[Any]:







       """simple docstring"""
       assert isinstance(UpperCAmelCase_		,      UpperCAmelCase_				) and (n >= 0), "'n' must been an int and >= 0"

       UpperCamelCase					 =		0
       UpperCamelCase					 =		1
       UpperCamelCase					 =		1  # this will be return

       for _ in range(n - 1				):
              UpperCamelCase					 =		ans
              ans += fiba
              UpperCamelCase					 =		tmp

       return ans







"""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   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					List[Any]   =			KandinskyVaaControlnetImgaImgPipeline
       UpperCamelCase_		:					Optional[Any]   =			['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
       UpperCamelCase_		:					Union[str, Any]   =			['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
       UpperCamelCase_		:					List[str]   =			[
           '''generator''',
           '''height''',
           '''width''',
           '''strength''',
           '''guidance_scale''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       UpperCamelCase_		:					Optional[Any]   =			False






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Any:







              """simple docstring"""
              return 32






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







              """simple docstring"""
              return 32






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







              """simple docstring"""
              return self.time_input_dim






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







              """simple docstring"""
              return self.time_input_dim * 4






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







              """simple docstring"""
              return 100






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







              """simple docstring"""
              torch.manual_seed(0			)

              UpperCamelCase					 =		{
                  "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,
              }

              UpperCamelCase					 =		UNetaDConditionModel(**UpperCAmelCase_			)
              return model






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







              """simple docstring"""
              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 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  Any:







              """simple docstring"""
              torch.manual_seed(0			)
              UpperCamelCase					 =		VQModel(**self.dummy_movq_kwargs			)
              return model






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  Union[str, Any]:







              """simple docstring"""
              UpperCamelCase					 =		self.dummy_unet
              UpperCamelCase					 =		self.dummy_movq

              UpperCamelCase					 =		{
                  "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,
              }

              UpperCamelCase					 =		DDIMScheduler(**UpperCAmelCase_			)

              UpperCamelCase					 =		{
                  "unet": unet,
                  "scheduler": scheduler,
                  "movq": movq,
              }

              return components






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      UpperCAmelCase_ :							Dict       ,      UpperCAmelCase_ :							Any=0			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		floats_tensor((1, self.text_embedder_hidden_size)       ,      rng=random.Random(UpperCAmelCase_			)			).to(UpperCAmelCase_			)
              UpperCamelCase					 =		floats_tensor((1, self.text_embedder_hidden_size)       ,      rng=random.Random(seed + 1			)			).to(
                  UpperCAmelCase_			)
              # create init_image
              UpperCamelCase					 =		floats_tensor((1, 3, 64, 64)       ,      rng=random.Random(UpperCAmelCase_			)			).to(UpperCAmelCase_			)
              UpperCamelCase					 =		image.cpu().permute(0       ,      2       ,      3       ,      1			)[0]
              UpperCamelCase					 =		Image.fromarray(np.uinta(UpperCAmelCase_			)			).convert("RGB"			).resize((256, 256)			)
              # create hint
              UpperCamelCase					 =		floats_tensor((1, 3, 64, 64)       ,      rng=random.Random(UpperCAmelCase_			)			).to(UpperCAmelCase_			)

              if str(UpperCAmelCase_			).startswith("mps"			):
                     UpperCamelCase					 =		torch.manual_seed(UpperCAmelCase_			)
              else:
                     UpperCamelCase					 =		torch.Generator(device=UpperCAmelCase_			).manual_seed(UpperCAmelCase_			)
              UpperCamelCase					 =		{
                  "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 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		"cpu"

              UpperCamelCase					 =		self.get_dummy_components()

              UpperCamelCase					 =		self.pipeline_class(**UpperCAmelCase_			)
              UpperCamelCase					 =		pipe.to(UpperCAmelCase_			)

              pipe.set_progress_bar_config(disable=UpperCAmelCase_			)

              UpperCamelCase					 =		pipe(**self.get_dummy_inputs(UpperCAmelCase_			)			)
              UpperCamelCase					 =		output.images

              UpperCamelCase					 =		pipe(
                  **self.get_dummy_inputs(UpperCAmelCase_			)       ,      return_dict=UpperCAmelCase_       ,      )[0]

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

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

              UpperCamelCase					 =		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   __a  (      unittest.TestCase	):






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Union[str, Any]:







              """simple docstring"""
              # clean up the VRAM after each test
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		load_numpy(
                  "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                  "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"			)

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

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

              UpperCamelCase					 =		"A robot, 4k photo"

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

              UpperCamelCase					 =		KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
                  "kandinsky-community/kandinsky-2-2-controlnet-depth"       ,      torch_dtype=torch.floataa			)
              UpperCamelCase					 =		pipeline.to(UpperCAmelCase_			)

              pipeline.set_progress_bar_config(disable=UpperCAmelCase_			)

              UpperCamelCase					 =		torch.Generator(device="cpu"			).manual_seed(0			)

              UpperCamelCase ,       UpperCamelCase					 =		pipe_prior(
                  UpperCAmelCase_       ,      image=UpperCAmelCase_       ,      strength=0.85       ,      generator=UpperCAmelCase_       ,      negative_prompt=""       ,      ).to_tuple()

              UpperCamelCase					 =		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"       ,      )

              UpperCamelCase					 =		output.images[0]

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

              assert_mean_pixel_difference(UpperCAmelCase_       ,      UpperCAmelCase_			)





