






"""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    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				Dict 						=KandinskyVaaControlnetPipeline
   a      :				List[str] 						=["""image_embeds""", """negative_image_embeds""", """hint"""]
   a      :				str 						=["""image_embeds""", """negative_image_embeds""", """hint"""]
   a      :				Any 						=[
       """generator""",
       """height""",
       """width""",
       """latents""",
       """guidance_scale""",
       """num_inference_steps""",
       """return_dict""",
       """guidance_scale""",
       """num_images_per_prompt""",
       """output_type""",
       """return_dict""",
   ]
   a      :				Optional[Any] 						=False

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       return 32

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       return 32

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       return self.time_input_dim

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       return self.time_input_dim * 4

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       return 100

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       torch.manual_seed(0      )

       UpperCamelCase__         =    {
           '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__         =    UNetaDConditionModel(**snake_case_      )
       return model

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       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      )		->       Tuple:
       torch.manual_seed(0      )
       UpperCamelCase__         =    VQModel(**self.dummy_movq_kwargs      )
       return model

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    self.dummy_unet
       UpperCamelCase__         =    self.dummy_movq

       UpperCamelCase__         =    DDIMScheduler(
           num_train_timesteps=1000 ,				beta_schedule='linear' ,				beta_start=0.00_085 ,				beta_end=0.012 ,				clip_sample=snake_case_ ,				set_alpha_to_one=snake_case_ ,				steps_offset=1 ,				prediction_type='epsilon' ,				thresholding=snake_case_ ,				)

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_=0      )		->       int:
       UpperCamelCase__         =    floats_tensor((1, self.text_embedder_hidden_size) ,				rng=random.Random(snake_case_      )      ).to(snake_case_      )
       UpperCamelCase__         =    floats_tensor((1, self.text_embedder_hidden_size) ,				rng=random.Random(seed + 1      )      ).to(
           snake_case_      )

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

       if str(snake_case_      ).startswith('mps'      ):
           UpperCamelCase__         =    torch.manual_seed(snake_case_      )
       else:
           UpperCamelCase__         =    torch.Generator(device=snake_case_      ).manual_seed(snake_case_      )
       UpperCamelCase__         =    {
           '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]:
       UpperCamelCase__         =    'cpu'

       UpperCamelCase__         =    self.get_dummy_components()

       UpperCamelCase__         =    self.pipeline_class(**snake_case_      )
       UpperCamelCase__         =    pipe.to(snake_case_      )

       pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    pipe(**self.get_dummy_inputs(snake_case_      )      )
       UpperCamelCase__         =    output.images

       UpperCamelCase__         =    pipe(
           **self.get_dummy_inputs(snake_case_      ) ,				return_dict=snake_case_ ,				)[0]

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

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

       UpperCamelCase__         =    np.array(
           [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]      )

       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    __lowerCamelCase    (  unittest.TestCase  ):

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







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

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

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

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

       UpperCamelCase__         =    'A robot, 4k photo'

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

       UpperCamelCase__         =    torch.Generator(device='cuda'      ).manual_seed(0      )
       UpperCamelCase__         =    pipeline(
           image_embeds=snake_case_ ,				negative_image_embeds=snake_case_ ,				hint=snake_case_ ,				generator=snake_case_ ,				num_inference_steps=100 ,				output_type='np' ,				)

       UpperCamelCase__         =    output.images[0]

       assert image.shape == (512, 512, 3)

       assert_mean_pixel_difference(snake_case_ ,				snake_case_      )












"""simple docstring"""





import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  A__					: str=	{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  A__					: str=	{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    (images / 2 + 0.5).clamp(0 ,	1					)
    UpperCamelCase__         =    images.cpu().permute(0 ,	2 ,	3 ,	1					).float().numpy()
    UpperCamelCase__         =    numpy_to_pil(SCREAMING_SNAKE_CASE					)
    return images







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[int]:
    """simple docstring"""

    if images.ndim == 3:
        UpperCamelCase__         =    images[None, ...]
    UpperCamelCase__         =    (images * 2_55).round().astype('uint8'					)
    if images.shape[-1] == 1:
        # special case for grayscale (single channel) images
        UpperCamelCase__         =    [Image.fromarray(image.squeeze() ,	mode='L'					) for image in images]
    else:
        UpperCamelCase__         =    [Image.fromarray(SCREAMING_SNAKE_CASE					) for image in images]

    return pil_images




