




'''simple docstring'''


import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, 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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class    __lowercase						(  __magic_name__       ,							__magic_name__       ,							unittest.TestCase ):
   _a							=      CycleDiffusionPipeline
   _a							=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
       """negative_prompt""",
       """height""",
       """width""",
       """negative_prompt_embeds""",
   }
   _a							=      PipelineTesterMixin.required_optional_params - {"""latents"""}
   _a							=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""} )
   _a							=      IMAGE_TO_IMAGE_IMAGE_PARAMS
   _a							=      IMAGE_TO_IMAGE_IMAGE_PARAMS







   def 		UpperCamelCase__			(	self      )				->    Dict:
     torch.manual_seed(0      )
     __a			=					UNetaDConditionModel(
         block_out_channels=(32, 64)      ,							layers_per_block=2      ,							sample_size=32      ,							in_channels=4      ,							out_channels=4      ,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,							cross_attention_dim=32      ,							)
     __a			=					DDIMScheduler(
         beta_start=0.00_085      ,							beta_end=0.012      ,							beta_schedule='scaled_linear'      ,							num_train_timesteps=1000      ,							clip_sample=UpperCamelCase      ,							set_alpha_to_one=UpperCamelCase      ,							)
     torch.manual_seed(0      )
     __a			=					AutoencoderKL(
         block_out_channels=[32, 64]      ,							in_channels=3      ,							out_channels=3      ,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,							latent_channels=4      ,							)
     torch.manual_seed(0      )
     __a			=					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=1000      ,							)
     __a			=					CLIPTextModel(UpperCamelCase      )
     __a			=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

     __a			=					{
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'safety_checker': None,
         'feature_extractor': None,
     }
     return components







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=0      )				->    Any:
     __a			=					floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(UpperCamelCase      )      ).to(UpperCamelCase      )
     __a			=					image / 2 + 0.5
     if str(UpperCamelCase      ).startswith('mps'      ):
       __a			=					torch.manual_seed(UpperCamelCase      )
     else:
       __a			=					torch.Generator(device=UpperCamelCase      ).manual_seed(UpperCamelCase      )
     __a			=					{
         'prompt': 'An astronaut riding an elephant',
         'source_prompt': 'An astronaut riding a horse',
         'image': image,
         'generator': generator,
         'num_inference_steps': 2,
         'eta': 0.1,
         'strength': 0.8,
         'guidance_scale': 3,
         'source_guidance_scale': 1,
         'output_type': 'numpy',
     }
     return inputs







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					'cpu'  # ensure determinism for the device-dependent torch.Generator

     __a			=					self.get_dummy_components()
     __a			=					CycleDiffusionPipeline(**UpperCamelCase      )
     __a			=					pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

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

     __a			=					images[0, -3:, -3:, -1]

     assert images.shape == (1, 32, 32, 3)
     __a			=					np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]      )

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







   @unittest.skipIf(torch_device != 'cuda'      ,							'This test requires a GPU'      )
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.get_dummy_components()
     for name, module in components.items():
       if hasattr(UpperCamelCase      ,							'half'      ):
         __a			=					module.half()
     __a			=					CycleDiffusionPipeline(**UpperCamelCase      )
     __a			=					pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )

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

     __a			=					images[0, -3:, -3:, -1]

     assert images.shape == (1, 32, 32, 3)
     __a			=					np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]      )

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







   @skip_mps
   def 		UpperCamelCase__			(	self      )				->    int:
     return super().test_save_load_local()







   @unittest.skip('non-deterministic pipeline'      )
   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     return super().test_inference_batch_single_identical()







   @skip_mps
   def 		UpperCamelCase__			(	self      )				->    Any:
     return super().test_dict_tuple_outputs_equivalent()







   @skip_mps
   def 		UpperCamelCase__			(	self      )				->    int:
     return super().test_save_load_optional_components()







   @skip_mps
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     return super().test_attention_slicing_forward_pass()





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







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







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/cycle-diffusion/black_colored_car.png'      )
     __a			=					load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'      )
     __a			=					init_image.resize((512, 512)      )

     __a			=					'CompVis/stable-diffusion-v1-4'
     __a			=					DDIMScheduler.from_pretrained(UpperCamelCase      ,							subfolder='scheduler'      )
     __a			=					CycleDiffusionPipeline.from_pretrained(
         UpperCamelCase      ,							scheduler=UpperCamelCase      ,							safety_checker=UpperCamelCase      ,							torch_dtype=torch.floataa      ,							revision='fp16'      )

     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )
     pipe.enable_attention_slicing()

     __a			=					'A black colored car'
     __a			=					'A blue colored car'

     __a			=					torch.manual_seed(0      )
     __a			=					pipe(
         prompt=UpperCamelCase      ,							source_prompt=UpperCamelCase      ,							image=UpperCamelCase      ,							num_inference_steps=100      ,							eta=0.1      ,							strength=0.85      ,							guidance_scale=3      ,							source_guidance_scale=1      ,							generator=UpperCamelCase      ,							output_type='np'      ,							)
     __a			=					output.images

     # the values aren't exactly equal, but the images look the same visually
     assert np.abs(image - expected_image      ).max() < 5e-1







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     __a			=					load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/cycle-diffusion/black_colored_car.png'      )
     __a			=					load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'      )
     __a			=					init_image.resize((512, 512)      )

     __a			=					'CompVis/stable-diffusion-v1-4'
     __a			=					DDIMScheduler.from_pretrained(UpperCamelCase      ,							subfolder='scheduler'      )
     __a			=					CycleDiffusionPipeline.from_pretrained(UpperCamelCase      ,							scheduler=UpperCamelCase      ,							safety_checker=UpperCamelCase      )

     pipe.to(UpperCamelCase      )
     pipe.set_progress_bar_config(disable=UpperCamelCase      )
     pipe.enable_attention_slicing()

     __a			=					'A black colored car'
     __a			=					'A blue colored car'

     __a			=					torch.manual_seed(0      )
     __a			=					pipe(
         prompt=UpperCamelCase      ,							source_prompt=UpperCamelCase      ,							image=UpperCamelCase      ,							num_inference_steps=100      ,							eta=0.1      ,							strength=0.85      ,							guidance_scale=3      ,							source_guidance_scale=1      ,							generator=UpperCamelCase      ,							output_type='np'      ,							)
     __a			=					output.images

     assert np.abs(image - expected_image      ).max() < 2e-2









'''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())



