




'''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    __lowercase						(  __magic_name__       ,							unittest.TestCase ):
   _a							=      KandinskyVaaInpaintPipeline
   _a							=      ["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
   _a							=      [
       """image_embeds""",
       """negative_image_embeds""",
       """image""",
       """mask_image""",
   ]
   _a							=      [
       """generator""",
       """height""",
       """width""",
       """latents""",
       """guidance_scale""",
       """num_inference_steps""",
       """return_dict""",
       """guidance_scale""",
       """num_images_per_prompt""",
       """output_type""",
       """return_dict""",
   ]
   _a							=      False







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







   @property
   def 		UpperCamelCase__			(	self      )				->    Any:
     return 32







   @property
   def 		UpperCamelCase__			(	self      )				->    int:
     return self.time_input_dim







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







   @property
   def 		UpperCamelCase__			(	self      )				->    List[str]:
     return 100







   @property
   def 		UpperCamelCase__			(	self      )				->    int:
     torch.manual_seed(0      )

     __a			=					{
         '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,
     }

     __a			=					UNetaDConditionModel(**UpperCamelCase      )
     return model







   @property
   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     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      )				->    str:
     torch.manual_seed(0      )
     __a			=					VQModel(**self.dummy_movq_kwargs      )
     return model







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					self.dummy_unet
     __a			=					self.dummy_movq

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

     __a			=					{
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }

     return components







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=0      )				->    str:
     __a			=					floats_tensor((1, self.text_embedder_hidden_size)      ,							rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
     __a			=					floats_tensor((1, self.text_embedder_hidden_size)      ,							rng=random.Random(seed + 1      )      ).to(
         UpperCamelCase      )
     # create init_image
     __a			=					floats_tensor((1, 3, 64, 64)      ,							rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
     __a			=					image.cpu().permute(0      ,							2      ,							3      ,							1      )[0]
     __a			=					Image.fromarray(np.uinta(UpperCamelCase      )      ).convert('RGB'      ).resize((256, 256)      )
     # create mask
     __a			=					np.ones((64, 64)      ,							dtype=np.floataa      )
     __a			=					0

     if str(UpperCamelCase      ).startswith('mps'      ):
       __a			=					torch.manual_seed(UpperCamelCase      )
     else:
       __a			=					torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
     __a			=					{
         '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      )				->    str:
     __a			=					'cpu'

     __a			=					self.get_dummy_components()

     __a			=					self.pipeline_class(**UpperCamelCase      )
     __a			=					pipe.to(UpperCamelCase      )

     pipe.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					pipe(**self.get_dummy_inputs(UpperCamelCase      )      )
     __a			=					output.images

     __a			=					pipe(
         **self.get_dummy_inputs(UpperCamelCase      )      ,							return_dict=UpperCamelCase      ,							)[0]

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

     print(f"image.shape {image.shape}"      )

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

     __a			=					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      )				->    List[str]:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3      )





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







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







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'      )

     __a			=					load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'      )
     __a			=					np.ones((768, 768)      ,							dtype=np.floataa      )
     __a			=					0

     __a			=					'a hat'

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

     __a			=					KandinskyVaaInpaintPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-2-decoder-inpaint'      ,							torch_dtype=torch.floataa      )
     __a			=					pipeline.to(UpperCamelCase      )
     pipeline.set_progress_bar_config(disable=UpperCamelCase      )

     __a			=					torch.Generator(device='cpu'      ).manual_seed(0      )
     __a	,       __a			=					pipe_prior(
         UpperCamelCase      ,							generator=UpperCamelCase      ,							num_inference_steps=5      ,							negative_prompt=''      ,							).to_tuple()

     __a			=					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'      ,							)

     __a			=					output.images[0]

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

     assert_mean_pixel_difference(UpperCamelCase      ,							UpperCamelCase      )









'''simple docstring'''


import os


def 						SCREAMING_SNAKE_CASE       (      ):
  with open(os.path.dirname(a_			) + '/grid.txt'			) as f:
    __a			=					[]  # noqa: E741
    for _ in range(20			):
      l.append([int(a_			) for x in f.readline().split()]			)

    __a			=					0

    # right
    for i in range(20			):
      for j in range(17			):
        __a			=					l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
        if temp > maximum:
          __a			=					temp

        # down
    for i in range(17			):
      for j in range(20			):
        __a			=					l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
        if temp > maximum:
          __a			=					temp

        # diagonal 1
    for i in range(17			):
      for j in range(17			):
        __a			=					l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
        if temp > maximum:
          __a			=					temp

        # diagonal 2
    for i in range(17			):
      for j in range(3				,					20			):
        __a			=					l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
        if temp > maximum:
          __a			=					temp
    return maximum


if __name__ == "__main__":
  print(solution())



