


'''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  __magic_name__							(		lowerCAmelCase ,unittest.TestCase    ):
							UpperCAmelCase										=KandinskyVaaControlnetPipeline
							UpperCAmelCase										=["image_embeds", "negative_image_embeds", "hint"]
							UpperCAmelCase										=["image_embeds", "negative_image_embeds", "hint"]
							UpperCAmelCase										=[
							    "generator",
							    "height",
							    "width",
							    "latents",
							    "guidance_scale",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							UpperCAmelCase										=False






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




										'''simple docstring'''
										return 3_2






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




										'''simple docstring'''
										return 3_2






							@property
							def       lowerCAmelCase		(      self)						->  int:




										'''simple docstring'''
										return self.time_input_dim






							@property
							def       lowerCAmelCase		(      self)						->  int:




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






							@property
							def       lowerCAmelCase		(      self)						->  str:




										'''simple docstring'''
										return 1_0_0






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




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

										_UpperCAmelCase       :       int							   ={
										    '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       :       Dict							   =UNetaDConditionModel(**snake_case)
										return model






							@property
							def       lowerCAmelCase		(      self)						->  int:




										'''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       lowerCAmelCase		(      self)						->  str:




										'''simple docstring'''
										torch.manual_seed(0)
										_UpperCAmelCase       :       List[Any]							   =VQModel(**self.dummy_movq_kwargs)
										return model






							def       lowerCAmelCase		(      self)						->  str:




										'''simple docstring'''
										_UpperCAmelCase       :       int							   =self.dummy_unet
										_UpperCAmelCase       :       Optional[Any]							   =self.dummy_movq

										_UpperCAmelCase       :       Tuple							   =DDIMScheduler(
										    num_train_timesteps=1_0_0_0  ,							beta_schedule='linear'  ,							beta_start=0.0_00_85  ,							beta_end=0.0_12  ,							clip_sample=snake_case  ,							set_alpha_to_one=snake_case  ,							steps_offset=1  ,							prediction_type='epsilon'  ,							thresholding=snake_case  ,							)

										_UpperCAmelCase       :       str							   ={
										    'unet': unet,
										    'scheduler': scheduler,
										    'movq': movq,
										}
										return components






							def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case=0)						->  Union[str, Any]:




										'''simple docstring'''
										_UpperCAmelCase       :       List[str]							   =floats_tensor((1, self.text_embedder_hidden_size)  ,							rng=random.Random(snake_case)).to(snake_case)
										_UpperCAmelCase       :       int							   =floats_tensor((1, self.text_embedder_hidden_size)  ,							rng=random.Random(seed + 1)).to(
										    snake_case)

										# create hint
										_UpperCAmelCase       :       Any							   =floats_tensor((1, 3, 6_4, 6_4)  ,							rng=random.Random(snake_case)).to(snake_case)

										if str(snake_case).startswith('mps'):
													_UpperCAmelCase       :       Tuple							   =torch.manual_seed(snake_case)
										else:
													_UpperCAmelCase       :       int							   =torch.Generator(device=snake_case).manual_seed(snake_case)
										_UpperCAmelCase       :       Tuple							   ={
										    '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       lowerCAmelCase		(      self)						->  str:




										'''simple docstring'''
										_UpperCAmelCase       :       Dict							   ='cpu'

										_UpperCAmelCase       :       List[Any]							   =self.get_dummy_components()

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

										pipe.set_progress_bar_config(disable=snake_case)

										_UpperCAmelCase       :       Any							   =pipe(**self.get_dummy_inputs(snake_case))
										_UpperCAmelCase       :       Any							   =output.images

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

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

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

										_UpperCAmelCase       :       Optional[int]							   =np.array(
										    [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95])

										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    ):






							def       lowerCAmelCase		(      self)						->  str:




										'''simple docstring'''
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






							def       lowerCAmelCase		(      self)						->  Any:




										'''simple docstring'''
										_UpperCAmelCase       :       Dict							   =load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy')

										_UpperCAmelCase       :       str							   =load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/kandinskyv22/hint_image_cat.png')
										_UpperCAmelCase       :       Tuple							   =torch.from_numpy(np.array(snake_case)).float() / 2_5_5.0
										_UpperCAmelCase       :       Tuple							   =hint.permute(2  ,							0  ,							1).unsqueeze(0)

										_UpperCAmelCase       :       Any							   =KandinskyVaaPriorPipeline.from_pretrained(
										    'kandinsky-community/kandinsky-2-2-prior'  ,							torch_dtype=torch.floataa)
										pipe_prior.to(snake_case)

										_UpperCAmelCase       :       Any							   =KandinskyVaaControlnetPipeline.from_pretrained(
										    'kandinsky-community/kandinsky-2-2-controlnet-depth'  ,							torch_dtype=torch.floataa)
										_UpperCAmelCase       :       Tuple							   =pipeline.to(snake_case)
										pipeline.set_progress_bar_config(disable=snake_case)

										_UpperCAmelCase       :       int							   ='A robot, 4k photo'

										_UpperCAmelCase       :       List[str]							   =torch.Generator(device='cuda').manual_seed(0)
										_UpperCAmelCase       :       Union[str, Any]							   =pipe_prior(
										    snake_case  ,							generator=snake_case  ,							num_inference_steps=5  ,							negative_prompt=''  ,							).to_tuple()

										_UpperCAmelCase       :       Union[str, Any]							   =torch.Generator(device='cuda').manual_seed(0)
										_UpperCAmelCase       :       str							   =pipeline(
										    image_embeds=snake_case  ,							negative_image_embeds=snake_case  ,							hint=snake_case  ,							generator=snake_case  ,							num_inference_steps=1_0_0  ,							output_type='np'  ,							)

										_UpperCAmelCase       :       Any							   =output.images[0]

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

										assert_mean_pixel_difference(snake_case  ,							snake_case)





'''simple docstring'''






from __future__ import annotations

from random import choice





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[int]							):







   '''simple docstring'''




   return choice(__lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =random_pivot(__lowerCamelCase							)

   # partition based on pivot
   # linear time
   _UpperCAmelCase       :       str							   =[e for e in lst if e < pivot]
   _UpperCAmelCase       :       Dict							   =[e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__lowerCamelCase							) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__lowerCamelCase							) < k - 1:
      return kth_number(__lowerCamelCase	,   k - len(__lowerCamelCase							) - 1							)
   # pivot is in elements smaller than k
   else:
      return kth_number(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

