

'''simple docstring'''

def    A_  ( snake_case = 50 ):
  SCREAMING_SNAKE_CASE:Any						      =					[[0] * 3 for _ in range(length + 1 )]

  for row_length in range(length + 1 ):
    for tile_length in range(2      ,       5 ):
      for tile_start in range(row_length - tile_length + 1 ):
        different_colour_ways_number[row_length][tile_length - 2] += (
            different_colour_ways_number[row_length - tile_start - tile_length][
                tile_length - 2
            ]
            + 1
        )

  return sum(different_colour_ways_number[length] )


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









'''simple docstring'''

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 							_snake_case (       _a	,  unittest.TestCase						):
   _A       :     str       =					KandinskyVaaInpaintPipeline
   _A       :     Tuple       =					['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
   _A       :     List[str]       =					[
       '''image_embeds''',
       '''negative_image_embeds''',
       '''image''',
       '''mask_image''',
   ]
   _A       :     Optional[Any]       =					[
       '''generator''',
       '''height''',
       '''width''',
       '''latents''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   _A       :     int       =					False
   @property
   def     __UpperCamelCase						( self			:      Any							):
     return 32
   @property
   def     __UpperCamelCase						( self			:      Tuple							):
     return 32
   @property
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     return self.time_input_dim
   @property
   def     __UpperCamelCase						( self			:      Any							):
     return self.time_input_dim * 4
   @property
   def     __UpperCamelCase						( self			:      Any							):
     return 100
   @property
   def     __UpperCamelCase						( self			:      Any							):
     torch.manual_seed(0							)

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					{
         "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,
     }

     SCREAMING_SNAKE_CASE:Dict						      =					UNetaDConditionModel(**SCREAMING_SNAKE_CASE__							)
     return model
   @property
   def     __UpperCamelCase						( 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     __UpperCamelCase						( self			:      int							):
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:str						      =					VQModel(**self.dummy_movq_kwargs							)
     return model
   def     __UpperCamelCase						( self			:      List[Any]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					self.dummy_unet
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.dummy_movq

     SCREAMING_SNAKE_CASE:Any						      =					DDIMScheduler(
         num_train_timesteps=1_000     ,beta_schedule="linear"     ,beta_start=0.00_085     ,beta_end=0.012     ,clip_sample=SCREAMING_SNAKE_CASE__     ,set_alpha_to_one=SCREAMING_SNAKE_CASE__     ,steps_offset=1     ,prediction_type="epsilon"     ,thresholding=SCREAMING_SNAKE_CASE__     ,)

     SCREAMING_SNAKE_CASE:Any						      =					{
         "unet": unet,
         "scheduler": scheduler,
         "movq": movq,
     }

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

     if str(SCREAMING_SNAKE_CASE__							).startswith("mps"							):
       SCREAMING_SNAKE_CASE:Tuple						      =					torch.manual_seed(SCREAMING_SNAKE_CASE__							)
     else:
       SCREAMING_SNAKE_CASE:List[Any]						      =					torch.Generator(device=SCREAMING_SNAKE_CASE__							).manual_seed(SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:Tuple						      =					{
         "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     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					"cpu"

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

     SCREAMING_SNAKE_CASE:List[Any]						      =					self.pipeline_class(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					pipe.to(SCREAMING_SNAKE_CASE__							)

     pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					output.images

     SCREAMING_SNAKE_CASE:int						      =					pipe(
         **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__							)     ,return_dict=SCREAMING_SNAKE_CASE__     ,)[0]

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

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

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

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					np.array(
         [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]							)

     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     __UpperCamelCase						( self			:      Any							):
     super().test_inference_batch_single_identical(expected_max_diff=3e-3							)





@slow
@require_torch_gpu
class 							_snake_case (       unittest.TestCase						):
   def     __UpperCamelCase						( self			:      Any							):
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







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

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

     SCREAMING_SNAKE_CASE:Optional[int]						      =					"a hat"

     SCREAMING_SNAKE_CASE:Optional[int]						      =					KandinskyVaaPriorPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-prior"     ,torch_dtype=torch.floataa							)
     pipe_prior.to(SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					KandinskyVaaInpaintPipeline.from_pretrained(
         "kandinsky-community/kandinsky-2-2-decoder-inpaint"     ,torch_dtype=torch.floataa							)
     SCREAMING_SNAKE_CASE:List[str]						      =					pipeline.to(SCREAMING_SNAKE_CASE__							)
     pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					torch.Generator(device="cpu"							).manual_seed(0							)
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					pipe_prior(
         SCREAMING_SNAKE_CASE__     ,generator=SCREAMING_SNAKE_CASE__     ,num_inference_steps=5     ,negative_prompt=""     ,).to_tuple()

     SCREAMING_SNAKE_CASE:Dict						      =					pipeline(
         image=SCREAMING_SNAKE_CASE__     ,mask_image=SCREAMING_SNAKE_CASE__     ,image_embeds=SCREAMING_SNAKE_CASE__     ,negative_image_embeds=SCREAMING_SNAKE_CASE__     ,generator=SCREAMING_SNAKE_CASE__     ,num_inference_steps=100     ,height=768     ,width=768     ,output_type="np"     ,)

     SCREAMING_SNAKE_CASE:List[Any]						      =					output.images[0]

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

     assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							)






