



"""simple docstring"""


import os

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			len(grid[0]						)
						UpperCAmelCase					=			len(lowercase_						)

						UpperCAmelCase					=			0
						UpperCAmelCase					=			0
						UpperCAmelCase					=			0

						# Check vertically, horizontally, diagonally at the same time (only works
						# for nxn grid)
						for i in range(lowercase_						):
												for j in range(n_rows - 3						):
																		UpperCAmelCase					=			grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
																		UpperCAmelCase					=			grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]

																		# Left-to-right diagonal (\) product
																		if i < n_columns - 3:
																								UpperCAmelCase					=			(
																								    grid[i][j]
																								    * grid[i + 1][j + 1]
																								    * grid[i + 2][j + 2]
																								    * grid[i + 3][j + 3]
																								)

																		# Right-to-left diagonal(/) product
																		if i > 2:
																								UpperCAmelCase					=			(
																								    grid[i][j]
																								    * grid[i - 1][j + 1]
																								    * grid[i - 2][j + 2]
																								    * grid[i - 3][j + 3]
																								)

																		UpperCAmelCase					=			max(
																		    lowercase_		,     lowercase_		,     lowercase_		,     lowercase_						)
																		if max_product > largest:
																								UpperCAmelCase					=			max_product

						return largest

def 			_lowerCAmelCase				(       ):
						UpperCAmelCase					=			[]
						with open(os.path.dirname(lowercase_						) + '/grid.txt'						) as file:
												for line in file:
																		grid.append(line.strip('\n'						).split(' '						)						)

						UpperCAmelCase					=			[[int(lowercase_						) for i in grid[j]] for j in range(len(lowercase_						)						)]

						return largest_product(lowercase_						)


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










"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()







class 				A_      (					unittest.TestCase    ):



						"""simple docstring"""






						def      UpperCAmelCase__					( self			:Tuple )    ->      Union[str, Any]:
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()






						@property
						def      UpperCAmelCase__					( self			:int )    ->      Union[str, Any]:
												UpperCAmelCase					=			1
												UpperCAmelCase					=			3
												UpperCAmelCase					=			(32, 32)

												UpperCAmelCase					=			floats_tensor((batch_size, num_channels) + sizes       ,  rng=random.Random(0 ) ).to(lowercase_ )
												return image






						@property
						def      UpperCAmelCase__					( self			:List[Any] )    ->      Tuple:
												torch.manual_seed(0 )
												UpperCAmelCase					=			UNetaDConditionModel(
												    block_out_channels=(32, 32, 64)       ,  layers_per_block=2       ,  sample_size=32       ,  in_channels=7       ,  out_channels=4       ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')       ,  up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')       ,  cross_attention_dim=32       ,  attention_head_dim=8       ,  use_linear_projection=lowercase_       ,  only_cross_attention=(True, True, False)       ,  num_class_embeds=1_00       ,  )
												return model






						@property
						def      UpperCAmelCase__					( self			:str )    ->      Any:
												torch.manual_seed(0 )
												UpperCAmelCase					=			AutoencoderKL(
												    block_out_channels=[32, 32, 64]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']       ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']       ,  latent_channels=4       ,  )
												return model






						@property
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												torch.manual_seed(0 )
												UpperCAmelCase					=			CLIPTextConfig(
												    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=32       ,  intermediate_size=37       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=10_00       ,  hidden_act='gelu'       ,  projection_dim=5_12       ,  )
												return CLIPTextModel(lowercase_ )






						def      UpperCAmelCase__					( self			:str )    ->      Union[str, Any]:
												UpperCAmelCase					=			'cpu'  # ensure determinism for the device-dependent torch.Generator
												UpperCAmelCase					=			self.dummy_cond_unet_upscale
												UpperCAmelCase					=			DDPMScheduler()
												UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
												UpperCAmelCase					=			self.dummy_vae
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

												UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

												# make sure here that pndm scheduler skips prk
												UpperCAmelCase					=			StableDiffusionUpscalePipeline(
												    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
												UpperCAmelCase					=			sd_pipe.to(lowercase_ )
												sd_pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			'A painting of a squirrel eating a burger'
												UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )

												UpperCAmelCase					=			output.images

												UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  return_dict=lowercase_       ,  )[0]

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

												UpperCAmelCase					=			low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)
												UpperCAmelCase					=			np.array([0.3113, 0.3910, 0.4272, 0.4859, 0.5061, 0.4652, 0.5362, 0.5715, 0.5661] )

												assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
												assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2






						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      Any:
												UpperCAmelCase					=			'cpu'  # ensure determinism for the device-dependent torch.Generator
												UpperCAmelCase					=			self.dummy_cond_unet_upscale
												UpperCAmelCase					=			DDPMScheduler()
												UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
												UpperCAmelCase					=			self.dummy_vae
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

												UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

												# make sure here that pndm scheduler skips prk
												UpperCAmelCase					=			StableDiffusionUpscalePipeline(
												    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
												UpperCAmelCase					=			sd_pipe.to(lowercase_ )
												sd_pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			'A painting of a squirrel eating a burger'
												UpperCAmelCase					=			sd_pipe(
												    2 * [prompt]       ,  image=2 * [low_res_image]       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images
												assert image.shape[0] == 2

												UpperCAmelCase					=			torch.Generator(device=lowercase_ ).manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  num_images_per_prompt=2       ,  guidance_scale=6.0       ,  noise_level=20       ,  num_inference_steps=2       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images
												assert image.shape[0] == 2






						@unittest.skipIf(torch_device != 'cuda'       ,  'This test requires a GPU' )
						def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[Any]:
												UpperCAmelCase					=			self.dummy_cond_unet_upscale
												UpperCAmelCase					=			DDPMScheduler()
												UpperCAmelCase					=			DDIMScheduler(prediction_type='v_prediction' )
												UpperCAmelCase					=			self.dummy_vae
												UpperCAmelCase					=			self.dummy_text_encoder
												UpperCAmelCase					=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

												UpperCAmelCase					=			self.dummy_image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												UpperCAmelCase					=			Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )

												# put models in fp16, except vae as it overflows in fp16
												UpperCAmelCase					=			unet.half()
												UpperCAmelCase					=			text_encoder.half()

												# make sure here that pndm scheduler skips prk
												UpperCAmelCase					=			StableDiffusionUpscalePipeline(
												    unet=lowercase_       ,  low_res_scheduler=lowercase_       ,  scheduler=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  max_noise_level=3_50       ,  )
												UpperCAmelCase					=			sd_pipe.to(lowercase_ )
												sd_pipe.set_progress_bar_config(disable=lowercase_ )

												UpperCAmelCase					=			'A painting of a squirrel eating a burger'
												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			sd_pipe(
												    [prompt]       ,  image=lowercase_       ,  generator=lowercase_       ,  num_inference_steps=2       ,  output_type='np'       ,  ).images

												UpperCAmelCase					=			low_res_image.size[0] * 4
												assert image.shape == (1, expected_height_width, expected_height_width, 3)







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



						"""simple docstring"""






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






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      int:
												UpperCAmelCase					=			load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-upscale/low_res_cat.png' )
												UpperCAmelCase					=			load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
												    '/upsampled_cat.npy' )

												UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
												UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(lowercase_ )
												pipe.to(lowercase_ )
												pipe.set_progress_bar_config(disable=lowercase_ )
												pipe.enable_attention_slicing()

												UpperCAmelCase					=			'a cat sitting on a park bench'

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			pipe(
												    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image ).max() < 1E-3






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      Tuple:
												UpperCAmelCase					=			load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-upscale/low_res_cat.png' )
												UpperCAmelCase					=			load_numpy(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
												    '/upsampled_cat_fp16.npy' )

												UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
												UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(
												    lowercase_       ,  torch_dtype=torch.floataa       ,  )
												pipe.to(lowercase_ )
												pipe.set_progress_bar_config(disable=lowercase_ )
												pipe.enable_attention_slicing()

												UpperCAmelCase					=			'a cat sitting on a park bench'

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			pipe(
												    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  output_type='np'       ,  )
												UpperCAmelCase					=			output.images[0]

												assert image.shape == (5_12, 5_12, 3)
												assert np.abs(expected_image - image ).max() < 5E-1






						def      UpperCAmelCase__					( self			:Any )    ->      List[Any]:
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												UpperCAmelCase					=			load_image(
												    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
												    '/sd2-upscale/low_res_cat.png' )

												UpperCAmelCase					=			'stabilityai/stable-diffusion-x4-upscaler'
												UpperCAmelCase					=			StableDiffusionUpscalePipeline.from_pretrained(
												    lowercase_       ,  torch_dtype=torch.floataa       ,  )
												pipe.to(lowercase_ )
												pipe.set_progress_bar_config(disable=lowercase_ )
												pipe.enable_attention_slicing(1 )
												pipe.enable_sequential_cpu_offload()

												UpperCAmelCase					=			'a cat sitting on a park bench'

												UpperCAmelCase					=			torch.manual_seed(0 )
												UpperCAmelCase					=			pipe(
												    prompt=lowercase_       ,  image=lowercase_       ,  generator=lowercase_       ,  num_inference_steps=5       ,  output_type='np'       ,  )

												UpperCAmelCase					=			torch.cuda.max_memory_allocated()
												# make sure that less than 2.9 GB is allocated
												assert mem_bytes < 2.9 * 10**9





