
'''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   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				List[str]									=       CycleDiffusionPipeline
						_lowercase		:				List[str]									=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    '''negative_prompt''',
						    '''height''',
						    '''width''',
						    '''negative_prompt_embeds''',
						}
						_lowercase		:				int									=       PipelineTesterMixin.required_optional_params - {'''latents'''}
						_lowercase		:				str									=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}			)
						_lowercase		:				int									=       IMAGE_TO_IMAGE_IMAGE_PARAMS
						_lowercase		:				Optional[Any]									=       IMAGE_TO_IMAGE_IMAGE_PARAMS




						def  _lowercase     ( self				):

												"""simple docstring"""



												torch.manual_seed(0				)
												_lowerCAmelCase  						=			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   ,       )
												_lowerCAmelCase  						=			DDIMScheduler(
												    beta_start=0.0_0085   ,       beta_end=0.012   ,       beta_schedule="""scaled_linear"""   ,       num_train_timesteps=1_000   ,       clip_sample=_lowerCAmelCase   ,       set_alpha_to_one=_lowerCAmelCase   ,       )
												torch.manual_seed(0				)
												_lowerCAmelCase  						=			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				)
												_lowerCAmelCase  						=			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=1_000   ,       )
												_lowerCAmelCase  						=			CLIPTextModel(_lowerCAmelCase				)
												_lowerCAmelCase  						=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

												_lowerCAmelCase  						=			{
												    """unet""": unet,
												    """scheduler""": scheduler,
												    """vae""": vae,
												    """text_encoder""": text_encoder,
												    """tokenizer""": tokenizer,
												    """safety_checker""": None,
												    """feature_extractor""": None,
												}
												return components




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase=0				):

												"""simple docstring"""



												_lowerCAmelCase  						=			floats_tensor((1, 3, 32, 32)   ,       rng=random.Random(_lowerCAmelCase				)				).to(_lowerCAmelCase				)
												_lowerCAmelCase  						=			image / 2 + 0.5
												if str(_lowerCAmelCase				).startswith("""mps"""				):
																		_lowerCAmelCase  						=			torch.manual_seed(_lowerCAmelCase				)
												else:
																		_lowerCAmelCase  						=			torch.Generator(device=_lowerCAmelCase				).manual_seed(_lowerCAmelCase				)
												_lowerCAmelCase  						=			{
												    """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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator

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

												_lowerCAmelCase  						=			self.get_dummy_inputs(_lowerCAmelCase				)
												_lowerCAmelCase  						=			pipe(**_lowerCAmelCase				)
												_lowerCAmelCase  						=			output.images

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

												assert images.shape == (1, 32, 32, 3)
												_lowerCAmelCase  						=			np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]				)

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




						@unittest.skipIf(torch_device != """cuda"""   ,       """This test requires a GPU"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_dummy_components()
												for name, module in components.items():
																		if hasattr(_lowerCAmelCase   ,       """half"""				):
																								_lowerCAmelCase  						=			module.half()
												_lowerCAmelCase  						=			CycleDiffusionPipeline(**_lowerCAmelCase				)
												_lowerCAmelCase  						=			pipe.to(_lowerCAmelCase				)
												pipe.set_progress_bar_config(disable=_lowerCAmelCase				)

												_lowerCAmelCase  						=			self.get_dummy_inputs(_lowerCAmelCase				)
												_lowerCAmelCase  						=			pipe(**_lowerCAmelCase				)
												_lowerCAmelCase  						=			output.images

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

												assert images.shape == (1, 32, 32, 3)
												_lowerCAmelCase  						=			np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]				)

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




						@skip_mps
						def  _lowercase     ( self				):

												"""simple docstring"""



												return super().test_save_load_local()




						@unittest.skip("""non-deterministic pipeline"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												return super().test_inference_batch_single_identical()




						@skip_mps
						def  _lowercase     ( self				):

												"""simple docstring"""



												return super().test_dict_tuple_outputs_equivalent()




						@skip_mps
						def  _lowercase     ( self				):

												"""simple docstring"""



												return super().test_save_load_optional_components()




						@skip_mps
						def  _lowercase     ( self				):

												"""simple docstring"""



												return super().test_attention_slicing_forward_pass()






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






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/cycle-diffusion/black_colored_car.png"""				)
												_lowerCAmelCase  						=			load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"""				)
												_lowerCAmelCase  						=			init_image.resize((512, 512)				)

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

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

												_lowerCAmelCase  						=			"""A black colored car"""
												_lowerCAmelCase  						=			"""A blue colored car"""

												_lowerCAmelCase  						=			torch.manual_seed(0				)
												_lowerCAmelCase  						=			pipe(
												    prompt=_lowerCAmelCase   ,       source_prompt=_lowerCAmelCase   ,       image=_lowerCAmelCase   ,       num_inference_steps=100   ,       eta=0.1   ,       strength=0.85   ,       guidance_scale=3   ,       source_guidance_scale=1   ,       generator=_lowerCAmelCase   ,       output_type="""np"""   ,       )
												_lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/cycle-diffusion/black_colored_car.png"""				)
												_lowerCAmelCase  						=			load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"""				)
												_lowerCAmelCase  						=			init_image.resize((512, 512)				)

												_lowerCAmelCase  						=			"""CompVis/stable-diffusion-v1-4"""
												_lowerCAmelCase  						=			DDIMScheduler.from_pretrained(_lowerCAmelCase   ,       subfolder="""scheduler"""				)
												_lowerCAmelCase  						=			CycleDiffusionPipeline.from_pretrained(_lowerCAmelCase   ,       scheduler=_lowerCAmelCase   ,       safety_checker=_lowerCAmelCase				)

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

												_lowerCAmelCase  						=			"""A black colored car"""
												_lowerCAmelCase  						=			"""A blue colored car"""

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

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


'''simple docstring'''



import os


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

												_lowerCAmelCase  						=			0

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

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

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

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


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