


'''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   __snake_case			(  _SCREAMING_SNAKE_CASE      ,unittest.TestCase):







    """simple docstring"""



    lowercase  					=   KandinskyVaaControlnetImgaImgPipeline
    lowercase  					=   ['image_embeds', 'negative_image_embeds', 'image', 'hint']
    lowercase  					=   ['image_embeds', 'negative_image_embeds', 'image', 'hint']
    lowercase  					=   [
        'generator',
        'height',
        'width',
        'strength',
        'guidance_scale',
        'num_inference_steps',
        'return_dict',
        'guidance_scale',
        'num_images_per_prompt',
        'output_type',
        'return_dict',
    ]
    lowercase  					=   False

    @property
    def __lowercase  (  self       :    int						)  ->    Optional[int]:
      return 32

    @property
    def __lowercase  (  self       :    int						)  ->    int:
      return 32

    @property
    def __lowercase  (  self       :    Optional[Any]						)  ->    str:
      return self.time_input_dim

    @property
    def __lowercase  (  self       :    str						)  ->    Tuple:
      return self.time_input_dim * 4

    @property
    def __lowercase  (  self       :    Union[str, Any]						)  ->    Tuple:
      return 1_00

    @property
    def __lowercase  (  self       :    Union[str, Any]						)  ->    List[str]:
      torch.manual_seed(0						)

      lowerCAmelCase_     : Union[str, Any]     =  {
          """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,
      }

      lowerCAmelCase_     : Union[str, Any]     =  UNetaDConditionModel(**lowerCamelCase						)
      return model

    @property
    def __lowercase  (  self       :    Any						)  ->    Union[str, Any]:
      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 __lowercase  (  self       :    List[str]						)  ->    Tuple:
      torch.manual_seed(0						)
      lowerCAmelCase_     : List[Any]     =  VQModel(**self.dummy_movq_kwargs						)
      return model

    def __lowercase  (  self       :    Optional[Any]						)  ->    Optional[int]:
      lowerCAmelCase_     : Optional[Any]     =  self.dummy_unet
      lowerCAmelCase_     : List[str]     =  self.dummy_movq

      lowerCAmelCase_     : int     =  {
          """num_train_timesteps""": 10_00,
          """beta_schedule""": """linear""",
          """beta_start""": 0.00_085,
          """beta_end""": 0.012,
          """clip_sample""": False,
          """set_alpha_to_one""": False,
          """steps_offset""": 0,
          """prediction_type""": """epsilon""",
          """thresholding""": False,
      }

      lowerCAmelCase_     : Dict     =  DDIMScheduler(**lowerCamelCase						)

      lowerCAmelCase_     : Optional[Any]     =  {
          """unet""": unet,
          """scheduler""": scheduler,
          """movq""": movq,
      }

      return components

    def __lowercase  (  self       :    Union[str, Any]       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    int=0						)  ->    Optional[int]:
      lowerCAmelCase_     : List[str]     =  floats_tensor((1, self.text_embedder_hidden_size)       ,		rng=random.Random(lowerCamelCase						)						).to(lowerCamelCase						)
      lowerCAmelCase_     : Union[str, Any]     =  floats_tensor((1, self.text_embedder_hidden_size)       ,		rng=random.Random(seed + 1						)						).to(
          lowerCamelCase						)
      # create init_image
      lowerCAmelCase_     : Dict     =  floats_tensor((1, 3, 64, 64)       ,		rng=random.Random(lowerCamelCase						)						).to(lowerCamelCase						)
      lowerCAmelCase_     : List[str]     =  image.cpu().permute(0       ,		2       ,		3       ,		1						)[0]
      lowerCAmelCase_     : Dict     =  Image.fromarray(np.uinta(lowerCamelCase						)						).convert("""RGB"""						).resize((2_56, 2_56)						)
      # create hint
      lowerCAmelCase_     : Optional[int]     =  floats_tensor((1, 3, 64, 64)       ,		rng=random.Random(lowerCamelCase						)						).to(lowerCamelCase						)

      if str(lowerCamelCase						).startswith("""mps"""						):
        lowerCAmelCase_     : Dict     =  torch.manual_seed(lowerCamelCase						)
      else:
        lowerCAmelCase_     : List[Any]     =  torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
      lowerCAmelCase_     : str     =  {
          """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 __lowercase  (  self       :    Any						)  ->    List[Any]:
      lowerCAmelCase_     : int     =  """cpu"""

      lowerCAmelCase_     : Union[str, Any]     =  self.get_dummy_components()

      lowerCAmelCase_     : Any     =  self.pipeline_class(**lowerCamelCase						)
      lowerCAmelCase_     : str     =  pipe.to(lowerCamelCase						)

      pipe.set_progress_bar_config(disable=lowerCamelCase						)

      lowerCAmelCase_     : str     =  pipe(**self.get_dummy_inputs(lowerCamelCase						)						)
      lowerCAmelCase_     : Dict     =  output.images

      lowerCAmelCase_     : List[str]     =  pipe(
          **self.get_dummy_inputs(lowerCamelCase						)       ,		return_dict=lowerCamelCase       ,		)[0]

      lowerCAmelCase_     : str     =  image[0, -3:, -3:, -1]
      lowerCAmelCase_     : List[str]     =  image_from_tuple[0, -3:, -3:, -1]

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

      lowerCAmelCase_     : Dict     =  np.array(
          [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]						)
      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   __snake_case			(  unittest.TestCase):







    """simple docstring"""



    def __lowercase  (  self       :    List[Any]						)  ->    Optional[Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()





    def __lowercase  (  self       :    int						)  ->    str:
      lowerCAmelCase_     : List[str]     =  load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""						)

      lowerCAmelCase_     : Union[str, Any]     =  load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""						)
      lowerCAmelCase_     : List[Any]     =  init_image.resize((5_12, 5_12)						)

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

      lowerCAmelCase_     : Optional[Any]     =  """A robot, 4k photo"""

      lowerCAmelCase_     : Optional[int]     =  KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-prior"""       ,		torch_dtype=torch.floataa						)
      pipe_prior.to(lowerCamelCase						)

      lowerCAmelCase_     : List[Any]     =  KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-controlnet-depth"""       ,		torch_dtype=torch.floataa						)
      lowerCAmelCase_     : Any     =  pipeline.to(lowerCamelCase						)

      pipeline.set_progress_bar_config(disable=lowerCamelCase						)

      lowerCAmelCase_     : Optional[int]     =  torch.Generator(device="""cpu"""						).manual_seed(0						)

      lowerCAmelCase_,				lowerCAmelCase_     : int     =  pipe_prior(
          lowerCamelCase       ,		image=lowerCamelCase       ,		strength=0.85       ,		generator=lowerCamelCase       ,		negative_prompt=""""""       ,		).to_tuple()

      lowerCAmelCase_     : Tuple     =  pipeline(
          image=lowerCamelCase       ,		image_embeds=lowerCamelCase       ,		negative_image_embeds=lowerCamelCase       ,		hint=lowerCamelCase       ,		generator=lowerCamelCase       ,		num_inference_steps=1_00       ,		height=5_12       ,		width=5_12       ,		strength=0.5       ,		output_type="""np"""       ,		)

      lowerCAmelCase_     : Tuple     =  output.images[0]

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

      assert_mean_pixel_difference(lowerCamelCase       ,		lowerCamelCase						)







'''simple docstring'''






from collections.abc import Callable






class   __snake_case			:







    """simple docstring"""



    def __init__(  self       :    Tuple       ,		lowerCamelCase       :    Callable | None = None						)  ->    None:
      # Stores actual heap items.
      lowerCAmelCase_     : list     =  []
      # Stores indexes of each item for supporting updates and deletion.
      lowerCAmelCase_     : dict     =  {}
      # Stores current size of heap.
      lowerCAmelCase_     : List[Any]     =  0
      # Stores function used to evaluate the score of an item on which basis ordering
      # will be done.
      lowerCAmelCase_     : Tuple     =  key or (lambda lowerCamelCase						: x)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      return int((i - 1) / 2						) if i > 0 else None

    def __lowercase  (  self       :    Union[str, Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      lowerCAmelCase_     : List[str]     =  int(2 * i + 1						)
      return left if 0 < left < self.size else None

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int						)  ->    int | None:
      lowerCAmelCase_     : List[Any]     =  int(2 * i + 2						)
      return right if 0 < right < self.size else None

    def __lowercase  (  self       :    List[str]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_,				lowerCAmelCase_     : Union[str, Any]     =  (
          self.pos_map[self.arr[j][0]],
          self.pos_map[self.arr[i][0]],
      )
      # Then swap the items in the list.
      lowerCAmelCase_,				lowerCAmelCase_     : List[Any]     =  self.arr[j], self.arr[i]

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    bool:
      return self.arr[i][1] < self.arr[j][1]

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    int						)  ->    int:
      lowerCAmelCase_     : List[str]     =  self._left(lowerCamelCase						)
      lowerCAmelCase_     : Optional[Any]     =  self._right(lowerCamelCase						)
      lowerCAmelCase_     : Tuple     =  i

      if left is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        lowerCAmelCase_     : int     =  left
      if right is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        lowerCAmelCase_     : Optional[Any]     =  right

      return valid_parent

    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Tuple     =  self._parent(lowerCamelCase						)
      while parent is not None and not self._cmp(lowerCamelCase       ,		lowerCamelCase						):
        self._swap(lowerCamelCase       ,		lowerCamelCase						)
        lowerCAmelCase_,				lowerCAmelCase_     : str     =  parent, self._parent(lowerCamelCase						)

    def __lowercase  (  self       :    Tuple       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Optional[Any]     =  self._get_valid_parent(lowerCamelCase						)
      while valid_parent != index:
        self._swap(lowerCamelCase       ,		lowerCamelCase						)
        lowerCAmelCase_,				lowerCAmelCase_     : int     =  valid_parent, self._get_valid_parent(lowerCamelCase						)

    def __lowercase  (  self       :    Optional[Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      if item not in self.pos_map:
        return
      lowerCAmelCase_     : Dict     =  self.pos_map[item]
      lowerCAmelCase_     : Dict     =  [item, self.key(lowerCamelCase						)]
      # Make sure heap is right in both up and down direction.
      # Ideally only one of them will make any change.
      self._heapify_up(lowerCamelCase						)
      self._heapify_down(lowerCamelCase						)

    def __lowercase  (  self       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      if item not in self.pos_map:
        return
      lowerCAmelCase_     : List[str]     =  self.pos_map[item]
      del self.pos_map[item]
      lowerCAmelCase_     : Tuple     =  self.arr[self.size - 1]
      lowerCAmelCase_     : List[str]     =  index
      self.size -= 1
      # Make sure heap is right in both up and down direction. Ideally only one
      # of them will make any change- so no performance loss in calling both.
      if self.size > index:
        self._heapify_up(lowerCamelCase						)
        self._heapify_down(lowerCamelCase						)

    def __lowercase  (  self       :    List[Any]       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    int						)  ->    None:
      lowerCAmelCase_     : Any     =  len(self.arr						)
      if arr_len == self.size:
        self.arr.append([item, self.key(lowerCamelCase						)]						)
      else:
        lowerCAmelCase_     : str     =  [item, self.key(lowerCamelCase						)]
      lowerCAmelCase_     : Optional[Any]     =  self.size
      self.size += 1
      self._heapify_up(self.size - 1						)

    def __lowercase  (  self       :    str						)  ->    tuple | None:
      return self.arr[0] if self.size else None





    def __lowercase  (  self       :    Optional[Any]						)  ->    tuple | None:
      lowerCAmelCase_     : int     =  self.get_top()
      if top_item_tuple:
        self.delete_item(top_item_tuple[0]						)
      return top_item_tuple







def 							UpperCamelCase_     (       ):
  '''simple docstring'''







if __name__ == "__main__":
  import doctest

  doctest.testmod()



