




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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 		UpperCamelCase__	(lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	int	     =      KandinskyVaaInpaintPipeline
    lowerCamelCase_      :	int	     =      ["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
    lowerCamelCase_      :	Dict	     =      [
        """image_embeds""",
        """negative_image_embeds""",
        """image""",
        """mask_image""",
    ]
    lowerCamelCase_      :	Any	     =      [
        """generator""",
        """height""",
        """width""",
        """latents""",
        """guidance_scale""",
        """num_inference_steps""",
        """return_dict""",
        """guidance_scale""",
        """num_images_per_prompt""",
        """output_type""",
        """return_dict""",
    ]
    lowerCamelCase_      :	Tuple	     =      False







    @property
    def     _lowercase     (    self     )     ->      List[Any]:
           return 32







    @property
    def     _lowercase     (    self     )     ->      Dict:
           return 32







    @property
    def     _lowercase     (    self     )     ->      List[Any]:
           return self.time_input_dim







    @property
    def     _lowercase     (    self     )     ->      int:
           return self.time_input_dim * 4







    @property
    def     _lowercase     (    self     )     ->      int:
           return 100







    @property
    def     _lowercase     (    self     )     ->      Tuple:
           torch.manual_seed(0     )

           lowerCamelCase   :      str			 =					{
               "in_channels": 9,
               # Out channels is double in channels because predicts mean and variance
               "out_channels": 8,
               "addition_embed_type": "image",
               "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   :      Any			 =					UNetaDConditionModel(**UpperCamelCase__     )
           return model







    @property
    def     _lowercase     (    self     )     ->      Dict:
           return {
               "block_out_channels": [32, 64],
               "down_block_types": ["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",
               ],
               "vq_embed_dim": 4,
           }







    @property
    def     _lowercase     (    self     )     ->      Dict:
           torch.manual_seed(0     )
           lowerCamelCase   :      int			 =					VQModel(**self.dummy_movq_kwargs     )
           return model







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase   :      List[str]			 =					self.dummy_unet
           lowerCamelCase   :      int			 =					self.dummy_movq

           lowerCamelCase   :      Optional[int]			 =					DDIMScheduler(
               num_train_timesteps=1000     ,  beta_schedule="linear"     ,  beta_start=0.00085     ,  beta_end=0.012     ,  clip_sample=UpperCamelCase__     ,  set_alpha_to_one=UpperCamelCase__     ,  steps_offset=1     ,  prediction_type="epsilon"     ,  thresholding=UpperCamelCase__     ,  )

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

           return components







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__=0     )     ->      Any:
           lowerCamelCase   :      Optional[int]			 =					floats_tensor((1, self.text_embedder_hidden_size)     ,  rng=random.Random(UpperCamelCase__     )     ).to(UpperCamelCase__     )
           lowerCamelCase   :      int			 =					floats_tensor((1, self.text_embedder_hidden_size)     ,  rng=random.Random(seed + 1     )     ).to(
               UpperCamelCase__     )
           # create init_image
           lowerCamelCase   :      str			 =					floats_tensor((1, 3, 64, 64)     ,  rng=random.Random(UpperCamelCase__     )     ).to(UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					image.cpu().permute(0     ,  2     ,  3     ,  1     )[0]
           lowerCamelCase   :      List[str]			 =					Image.fromarray(np.uinta(UpperCamelCase__     )     ).convert("RGB"     ).resize((256, 256)     )
           # create mask
           lowerCamelCase   :      Dict			 =					np.ones((64, 64)     ,  dtype=np.floataa     )
           lowerCamelCase   :      Dict			 =					0

           if str(UpperCamelCase__     ).startswith("mps"     ):
                  lowerCamelCase   :      Optional[Any]			 =					torch.manual_seed(UpperCamelCase__     )
           else:
                  lowerCamelCase   :      str			 =					torch.Generator(device=UpperCamelCase__     ).manual_seed(UpperCamelCase__     )
           lowerCamelCase   :      Optional[Any]			 =					{
               "image": init_image,
               "mask_image": mask,
               "image_embeds": image_embeds,
               "negative_image_embeds": negative_image_embeds,
               "generator": generator,
               "height": 64,
               "width": 64,
               "num_inference_steps": 2,
               "guidance_scale": 4.0,
               "output_type": "np",
           }
           return inputs







    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      Optional[Any]			 =					"cpu"

           lowerCamelCase   :      Optional[Any]			 =					self.get_dummy_components()

           lowerCamelCase   :      Optional[Any]			 =					self.pipeline_class(**UpperCamelCase__     )
           lowerCamelCase   :      str			 =					pipe.to(UpperCamelCase__     )

           pipe.set_progress_bar_config(disable=UpperCamelCase__     )

           lowerCamelCase   :      Any			 =					pipe(**self.get_dummy_inputs(UpperCamelCase__     )     )
           lowerCamelCase   :      Dict			 =					output.images

           lowerCamelCase   :      Union[str, Any]			 =					pipe(
               **self.get_dummy_inputs(UpperCamelCase__     )     ,  return_dict=UpperCamelCase__     ,  )[0]

           lowerCamelCase   :      int			 =					image[0, -3:, -3:, -1]
           lowerCamelCase   :      Any			 =					image_from_tuple[0, -3:, -3:, -1]

           print(F'''image.shape {image.shape}'''     )

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

           lowerCamelCase   :      Optional[int]			 =					np.array(
               [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]     )

           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()}'''







    def     _lowercase     (    self     )     ->      Union[str, Any]:
           super().test_inference_batch_single_identical(expected_max_diff=3e-3     )




@slow
@require_torch_gpu
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



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







    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      Optional[Any]			 =					load_numpy(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
               "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"     )

           lowerCamelCase   :      List[str]			 =					load_image(
               "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"     )
           lowerCamelCase   :      List[Any]			 =					np.ones((768, 768)     ,  dtype=np.floataa     )
           lowerCamelCase   :      Any			 =					0

           lowerCamelCase   :      Optional[int]			 =					"a hat"

           lowerCamelCase   :      Tuple			 =					KandinskyVaaPriorPipeline.from_pretrained(
               "kandinsky-community/kandinsky-2-2-prior"     ,  torch_dtype=torch.floataa     )
           pipe_prior.to(UpperCamelCase__     )

           lowerCamelCase   :      int			 =					KandinskyVaaInpaintPipeline.from_pretrained(
               "kandinsky-community/kandinsky-2-2-decoder-inpaint"     ,  torch_dtype=torch.floataa     )
           lowerCamelCase   :      Any			 =					pipeline.to(UpperCamelCase__     )
           pipeline.set_progress_bar_config(disable=UpperCamelCase__     )

           lowerCamelCase   :      str			 =					torch.Generator(device="cpu"     ).manual_seed(0     )
           lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					pipe_prior(
               UpperCamelCase__     ,  generator=UpperCamelCase__     ,  num_inference_steps=5     ,  negative_prompt=""     ,  ).to_tuple()

           lowerCamelCase   :      Any			 =					pipeline(
               image=UpperCamelCase__     ,  mask_image=UpperCamelCase__     ,  image_embeds=UpperCamelCase__     ,  negative_image_embeds=UpperCamelCase__     ,  generator=UpperCamelCase__     ,  num_inference_steps=100     ,  height=768     ,  width=768     ,  output_type="np"     ,  )

           lowerCamelCase   :      str			 =					output.images[0]

           assert image.shape == (768, 768, 3)

           assert_mean_pixel_difference(UpperCamelCase__     ,  UpperCamelCase__     )












from math import sqrt

def      A      (      _SCREAMING_SNAKE_CASE = 100_0000    )	-> int:
       lowerCamelCase   :      int			 =					0
       lowerCamelCase   :      int			 =					0
       lowerCamelCase   :      int

       while num_cuboids <= limit:
              max_cuboid_size += 1
              for sum_shortest_sides in range(2 ,2 * max_cuboid_size + 1    ):
                     if sqrt(sum_shortest_sides**2 + max_cuboid_size**2    ).is_integer():
                            num_cuboids += (
                                min(_SCREAMING_SNAKE_CASE ,sum_shortest_sides // 2    )
                                - max(1 ,sum_shortest_sides - max_cuboid_size    )
                                + 1
                            )

       return max_cuboid_size


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






