


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 		__magic_name__						(lowercase_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Tuple	   =       KandinskyVaaControlnetPipeline
     __lowercase				:							List[str]	   =       ['''image_embeds''', '''negative_image_embeds''', '''hint''']
     __lowercase				:							Dict	   =       ['''image_embeds''', '''negative_image_embeds''', '''hint''']
     __lowercase				:							Union[str, Any]	   =       [
         '''generator''',
         '''height''',
         '''width''',
         '''latents''',
         '''guidance_scale''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     __lowercase				:							Union[str, Any]	   =       False

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       return 32

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       return 32

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       return self.time_input_dim

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       return self.time_input_dim * 4

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       return 1_00

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       torch.manual_seed(0   )

       snake_case__						= {
           '''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__						= UNetaDConditionModel(**_a   )
       return model

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       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:List[str]   ):
       torch.manual_seed(0   )
       snake_case__						= VQModel(**self.dummy_movq_kwargs   )
       return model

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       snake_case__						= self.dummy_unet
       snake_case__						= self.dummy_movq

       snake_case__						= DDIMScheduler(
           num_train_timesteps=10_00     ,		beta_schedule='''linear'''     ,		beta_start=0.00085     ,		beta_end=0.012     ,		clip_sample=_a     ,		set_alpha_to_one=_a     ,		steps_offset=1     ,		prediction_type='''epsilon'''     ,		thresholding=_a     ,		)

       snake_case__						= {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }
       return components

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:int     ,		_a:Dict=0   ):
       snake_case__						= floats_tensor((1, self.text_embedder_hidden_size)     ,		rng=random.Random(_a   )   ).to(_a   )
       snake_case__						= floats_tensor((1, self.text_embedder_hidden_size)     ,		rng=random.Random(seed + 1   )   ).to(
           _a   )

       # create hint
       snake_case__						= floats_tensor((1, 3, 64, 64)     ,		rng=random.Random(_a   )   ).to(_a   )

       if str(_a   ).startswith('''mps'''   ):
         snake_case__						= torch.manual_seed(_a   )
       else:
         snake_case__						= torch.Generator(device=_a   ).manual_seed(_a   )
       snake_case__						= {
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''hint''': hint,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''guidance_scale''': 4.0,
           '''num_inference_steps''': 2,
           '''output_type''': '''np''',
       }
       return inputs







     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= '''cpu'''

       snake_case__						= self.get_dummy_components()

       snake_case__						= self.pipeline_class(**_a   )
       snake_case__						= pipe.to(_a   )

       pipe.set_progress_bar_config(disable=_a   )

       snake_case__						= pipe(**self.get_dummy_inputs(_a   )   )
       snake_case__						= output.images

       snake_case__						= pipe(
           **self.get_dummy_inputs(_a   )     ,		return_dict=_a     ,		)[0]

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

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

       snake_case__						= 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 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''   )

       snake_case__						= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/hint_image_cat.png'''   )
       snake_case__						= torch.from_numpy(np.array(_a   )   ).float() / 2_55.0
       snake_case__						= hint.permute(2     ,		0     ,		1   ).unsqueeze(0   )

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

       snake_case__						= KandinskyVaaControlnetPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-controlnet-depth'''     ,		torch_dtype=torch.floataa   )
       snake_case__						= pipeline.to(_a   )
       pipeline.set_progress_bar_config(disable=_a   )

       snake_case__						= '''A robot, 4k photo'''

       snake_case__						= torch.Generator(device='''cuda'''   ).manual_seed(0   )
       snake_case__				,				snake_case__						= pipe_prior(
           _a     ,		generator=_a     ,		num_inference_steps=5     ,		negative_prompt=''''''     ,		).to_tuple()

       snake_case__						= torch.Generator(device='''cuda'''   ).manual_seed(0   )
       snake_case__						= pipeline(
           image_embeds=_a     ,		negative_image_embeds=_a     ,		hint=_a     ,		generator=_a     ,		num_inference_steps=1_00     ,		output_type='''np'''     ,		)

       snake_case__						= output.images[0]

       assert image.shape == (5_12, 5_12, 3)

       assert_mean_pixel_difference(_a     ,		_a   )




from math import sqrt





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or number % 2 == 0 or number % 3 == 0:
														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
														return False

							# All primes number are in format of 6k +/- 1
							for i in range(5  ,				int(sqrt(UpperCamelCase__ ) + 1 )  ,				6 ):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True





def    __lowerCamelCase      (					UpperCamelCase__ = 10001 ):







							'''simple docstring'''






							snake_case_     							=       0
							snake_case_     							=       1
							while count != nth and number < 3:
														number += 1
														if is_prime(UpperCamelCase__ ):
																					count += 1
							while count != nth:
														number += 2
														if is_prime(UpperCamelCase__ ):
																					count += 1
							return number


if __name__ == "__main__":
					print(F'''{solution() = }''')




