


'''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'''
          # clean up the VRAM after each test
          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_55.0
          _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   ,	_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()

