





"""simple docstring"""


from math import sqrt

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and (
						    number >= 0
						), "'number' must been an int and positive"

						UpperCamelCase				:		Dict				   =			True

						# 0 and 1 are none primes.
						if number <= 1:
												UpperCamelCase				:		Optional[int]				   =			False

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

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

						return status

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and (n > 2), "'N' must been an int and > 2"

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

						UpperCamelCase				:		Optional[Any]				   =			[]  # this list will be returns.

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

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

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

						return ans

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and (n > 2), "'N' must been an int and > 2"

						UpperCamelCase				:		Dict				   =			[]

						# 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(SCREAMING_SNAKE_CASE     ):
																		ans.append(SCREAMING_SNAKE_CASE     )

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

						return ans

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and number >= 0, "'number' must been an int and >= 0"

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

						# potential prime number factors.

						UpperCamelCase				:		Union[str, Any]				   =			2

						UpperCamelCase				:		Tuple				   =			number

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

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

						else:
												ans.append(SCREAMING_SNAKE_CASE     )

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

						return ans

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and (
						    number >= 0
						), "'number' bust been an int and >= 0"

						UpperCamelCase				:		List[Any]				   =			0

						# prime factorization of 'number'
						UpperCamelCase				:		int				   =			prime_factorization(SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[Any]				   =			max(SCREAMING_SNAKE_CASE     )

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

						return ans

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and (
						    number >= 0
						), "'number' bust been an int and >= 0"

						UpperCamelCase				:		List[Any]				   =			0

						# prime factorization of 'number'
						UpperCamelCase				:		Tuple				   =			prime_factorization(SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		str				   =			min(SCREAMING_SNAKE_CASE     )

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

						return ans

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ), "'number' must been an int"
						assert isinstance(number % 2 == 0							,     SCREAMING_SNAKE_CASE     ), "compare bust been from type bool"

						return number % 2 == 0

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ), "'number' must been an int"
						assert isinstance(number % 2 != 0							,     SCREAMING_SNAKE_CASE     ), "compare bust been from type bool"

						return number % 2 != 0

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert (
						    isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and (number > 2) and is_even(SCREAMING_SNAKE_CASE     )
						), "'number' must been an int, even and > 2"

						UpperCamelCase				:		Tuple				   =			[]  # this list will returned

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

						# run variable for while-loops.
						UpperCamelCase				:		Optional[int]				   =			0
						UpperCamelCase				:		Union[str, Any]				   =			None

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

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

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

																		j += 1

												i += 1

						# precondition
						assert (
						    isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						    and (len(SCREAMING_SNAKE_CASE     ) == 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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						assert (
						    isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						    and isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						    and (numbera >= 0)
						    and (numbera >= 0)
						), "'number1' and 'number2' must been positive integer."

						UpperCamelCase				:		Optional[int]				   =			0

						while numbera != 0:
												UpperCamelCase				:		Union[str, Any]				   =			numbera % numbera
												UpperCamelCase				:		Any				   =			numbera
												UpperCamelCase				:		Optional[Any]				   =			rest

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

						return numbera

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						assert (
						    isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						    and isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						    and (numbera >= 1)
						    and (numbera >= 1)
						), "'number1' and 'number2' must been positive integer."

						UpperCamelCase				:		List[Any]				   =			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				:		Dict				   =			prime_factorization(SCREAMING_SNAKE_CASE     )
												UpperCamelCase				:		Optional[int]				   =			prime_factorization(SCREAMING_SNAKE_CASE     )

						elif numbera == 1 or numbera == 1:
												UpperCamelCase				:		Optional[Any]				   =			[]
												UpperCamelCase				:		List[str]				   =			[]
												UpperCamelCase				:		str				   =			max(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[int]				   =			0
						UpperCamelCase				:		int				   =			0

						UpperCamelCase				:		int				   =			[]  # 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				:		Optional[Any]				   =			prime_fac_a.count(SCREAMING_SNAKE_CASE     )
																								UpperCamelCase				:		str				   =			prime_fac_a.count(SCREAMING_SNAKE_CASE     )

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

																		else:
																								UpperCamelCase				:		Tuple				   =			prime_fac_a.count(SCREAMING_SNAKE_CASE     )

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

																		done.append(SCREAMING_SNAKE_CASE     )

    # iterates through primeFac2
						for n in prime_fac_a:
												if n not in done:
																		UpperCamelCase				:		Optional[Any]				   =			prime_fac_a.count(SCREAMING_SNAKE_CASE     )

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

																		done.append(SCREAMING_SNAKE_CASE     )

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

						return ans

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and (n >= 0), "'number' must been a positive int"

						UpperCamelCase				:		int				   =			0
						UpperCamelCase				:		Optional[Any]				   =			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(SCREAMING_SNAKE_CASE     ):
																		ans += 1

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

						return ans

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						assert (
						    is_prime(SCREAMING_SNAKE_CASE     ) and is_prime(SCREAMING_SNAKE_CASE     ) and (p_number_a < p_number_a)
						), "The arguments must been prime numbers and 'pNumber1' < 'pNumber2'"

						UpperCamelCase				:		int				   =			p_number_a + 1  # jump to the next number

						UpperCamelCase				:		Optional[Any]				   =			[]  # this list will be returns.

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

						while number < p_number_a:
												ans.append(SCREAMING_SNAKE_CASE     )

												number += 1

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

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

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and (n >= 1), "'n' must been int and >= 1"

						UpperCamelCase				:		Tuple				   =			[]  # will be returned.

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

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

						return ans

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and (
						    number > 1
						), "'number' must been an int and >= 1"

						UpperCamelCase				:		Any				   =			get_divisors(SCREAMING_SNAKE_CASE     )

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

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						assert (
						    isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						    and isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						    and (denominator != 0)
						), "The arguments must been from type int and 'denominator' != 0"

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

						# precondition
						assert (
						    isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						    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      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and (n >= 0), "'n' must been a int and >= 0"

						UpperCamelCase				:		Optional[int]				   =			1  # this will be return.

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

						return ans

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						assert isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) and (n >= 0), "'n' must been an int and >= 0"

						UpperCamelCase				:		List[str]				   =			0
						UpperCamelCase				:		Union[str, Any]				   =			1
						UpperCamelCase				:		Dict				   =			1  # this will be return

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

						return ans











import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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 						lowercase_   (      lowercase					,    unittest.TestCase  ):






      '''simple docstring'''




      __snake_case	    =    KandinskyVaaInpaintPipeline
      __snake_case	    =    ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      __snake_case	    =    [
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      __snake_case	    =    [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      __snake_case	    =    False







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




       """simple docstring"""


       return 32







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




       """simple docstring"""


       return 32







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




       """simple docstring"""


       return self.time_input_dim







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




       """simple docstring"""


       return self.time_input_dim * 4







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




       """simple docstring"""


       return 100







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




       """simple docstring"""


       torch.manual_seed(0    )

       a						    = {
           '''in_channels''': 9,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''image''',
           '''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(**__UpperCAmelCase    )
       return model







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




       """simple docstring"""


       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["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",
           ],
           "vq_embed_dim": 4,
       }







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




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Any:




       """simple docstring"""


       a						    = self.dummy_unet
       a						    = self.dummy_movq

       a						    = DDIMScheduler(
           num_train_timesteps=1_000					,   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					,   )

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

       return components







      def  __lowerCAmelCase   ( self :		Dict					,   __UpperCAmelCase :		List[str]					,   __UpperCAmelCase :		int=0    )       ->Optional[int]:




       """simple docstring"""


       a						    = floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
       a						    = floats_tensor((1, self.text_embedder_hidden_size)					,   rng=random.Random(seed + 1    )    ).to(
           __UpperCAmelCase    )
       # create init_image
       a						    = floats_tensor((1, 3, 64, 64)					,   rng=random.Random(__UpperCAmelCase    )    ).to(__UpperCAmelCase    )
       a						    = image.cpu().permute(0					,   2					,   3					,   1    )[0]
       a						    = Image.fromarray(np.uinta(__UpperCAmelCase    )    ).convert('''RGB'''    ).resize((256, 256)    )
       # create mask
       a						    = np.ones((64, 64)					,   dtype=np.floataa    )
       a						    = 0

       if str(__UpperCAmelCase    ).startswith('''mps'''    ):
        a						    = torch.manual_seed(__UpperCAmelCase    )
       else:
        a						    = torch.Generator(device=__UpperCAmelCase    ).manual_seed(__UpperCAmelCase    )
       a						    = {
           '''image''': init_image,
           '''mask_image''': mask,
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 4.0,
           '''output_type''': '''np''',
       }
       return inputs







      def  __lowerCAmelCase   ( self :		Dict    )       ->int:




       """simple docstring"""


       a						    = '''cpu'''

       a						    = self.get_dummy_components()

       a						    = self.pipeline_class(**__UpperCAmelCase    )
       a						    = pipe.to(__UpperCAmelCase    )

       pipe.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = pipe(**self.get_dummy_inputs(__UpperCAmelCase    )    )
       a						    = output.images

       a						    = pipe(
           **self.get_dummy_inputs(__UpperCAmelCase    )					,   return_dict=__UpperCAmelCase					,   )[0]

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

       print(F"""image.shape {image.shape}"""    )

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

       a						    = np.array(
           [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]    )

       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()}"""







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




       """simple docstring"""


       super().test_inference_batch_single_identical(expected_max_diff=3e-3    )






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






      '''simple docstring'''




      def  __lowerCAmelCase   ( self :		Any    )       ->List[Any]:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		List[str]    )       ->str:




       """simple docstring"""


       a						    = load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''    )

       a						    = load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''    )
       a						    = np.ones((768, 768)					,   dtype=np.floataa    )
       a						    = 0

       a						    = '''a hat'''

       a						    = KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''					,   torch_dtype=torch.floataa    )
       pipe_prior.to(__UpperCAmelCase    )

       a						    = KandinskyVaaInpaintPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''					,   torch_dtype=torch.floataa    )
       a						    = pipeline.to(__UpperCAmelCase    )
       pipeline.set_progress_bar_config(disable=__UpperCAmelCase    )

       a						    = torch.Generator(device='''cpu'''    ).manual_seed(0    )
       a    ,	a						    = pipe_prior(
           __UpperCAmelCase					,   generator=__UpperCAmelCase					,   num_inference_steps=5					,   negative_prompt=''''''					,   ).to_tuple()

       a						    = pipeline(
           image=__UpperCAmelCase					,   mask_image=__UpperCAmelCase					,   image_embeds=__UpperCAmelCase					,   negative_image_embeds=__UpperCAmelCase					,   generator=__UpperCAmelCase					,   num_inference_steps=100					,   height=768					,   width=768					,   output_type='''np'''					,   )

       a						    = output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(__UpperCAmelCase					,   __UpperCAmelCase    )


