




import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase  ,     unittest.TestCase      ):



	"""simple docstring"""


	__magic_name__      :					Union[str, Any]			  =		RePaintPipeline
	__magic_name__      :					Any			  =		IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
	__magic_name__      :					Dict			  =		PipelineTesterMixin.required_optional_params - {
	    'latents',
	    'num_images_per_prompt',
	    'callback',
	    'callback_steps',
	}
	__magic_name__      :					Any			  =		IMAGE_INPAINTING_BATCH_PARAMS
	__magic_name__      :					Union[str, Any]			  =		False






	def 				lowerCamelCase__    (				self		:	Optional[Any]  )  ->					List[str]:







						"""simple docstring"""







						torch.manual_seed(0  )
						torch.manual_seed(0  )
						__UpperCamelCase				:  Union[str, Any]								=			UNetaDModel(
						    block_out_channels=(32, 64)				,    layers_per_block=2				,    sample_size=32				,    in_channels=3				,    out_channels=3				,    down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")				,    up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")				,    )
						__UpperCamelCase				:  List[Any]								=			RePaintScheduler()
						__UpperCamelCase				:  Optional[int]								=			{"""unet""": unet, """scheduler""": scheduler}
						return components






	def 				lowerCamelCase__    (				self		:	Dict				,    lowerCAmelCase		:	Dict				,    lowerCAmelCase		:	Any=0  )  ->					Optional[int]:







						"""simple docstring"""







						if str(lowerCAmelCase  ).startswith("""mps"""  ):
											__UpperCamelCase				:  int								=			torch.manual_seed(lowerCAmelCase  )
						else:
											__UpperCamelCase				:  int								=			torch.Generator(device=lowerCAmelCase  ).manual_seed(lowerCAmelCase  )
						__UpperCamelCase				:  str								=			np.random.RandomState(lowerCAmelCase  ).standard_normal((1, 3, 32, 32)  )
						__UpperCamelCase				:  Optional[int]								=			torch.from_numpy(lowerCAmelCase  ).to(device=lowerCAmelCase				,    dtype=torch.floataa  )
						__UpperCamelCase				:  Any								=			(image > 0).to(device=lowerCAmelCase				,    dtype=torch.floataa  )
						__UpperCamelCase				:  str								=			{
						    """image""": image,
						    """mask_image""": mask,
						    """generator""": generator,
						    """num_inference_steps""": 5,
						    """eta""": 0.0,
						    """jump_length""": 2,
						    """jump_n_sample""": 2,
						    """output_type""": """numpy""",
						}
						return inputs






	def 				lowerCamelCase__    (				self		:	Any  )  ->					Dict:







						"""simple docstring"""







						__UpperCamelCase				:  Optional[Any]								=			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
						__UpperCamelCase				:  Tuple								=			self.get_dummy_components()
						__UpperCamelCase				:  str								=			RePaintPipeline(**lowerCAmelCase  )
						__UpperCamelCase				:  str								=			sd_pipe.to(lowerCAmelCase  )
						sd_pipe.set_progress_bar_config(disable=lowerCAmelCase  )

						__UpperCamelCase				:  Optional[int]								=			self.get_dummy_inputs(lowerCAmelCase  )
						__UpperCamelCase				:  Union[str, Any]								=			sd_pipe(**lowerCAmelCase  ).images
						__UpperCamelCase				:  Optional[int]								=			image[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						__UpperCamelCase				:  Optional[int]								=			np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]  )

						assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3






	@skip_mps
	def 				lowerCamelCase__    (				self		:	Dict  )  ->					Optional[int]:







						"""simple docstring"""







						return super().test_save_load_local()






	@unittest.skip("""non-deterministic pipeline"""  )
	def 				lowerCamelCase__    (				self		:	Any  )  ->					int:







						"""simple docstring"""







						return super().test_inference_batch_single_identical()






	@skip_mps
	def 				lowerCamelCase__    (				self		:	List[Any]  )  ->					str:







						"""simple docstring"""







						return super().test_dict_tuple_outputs_equivalent()






	@skip_mps
	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					str:







						"""simple docstring"""







						return super().test_save_load_optional_components()






	@skip_mps
	def 				lowerCamelCase__    (				self		:	Dict  )  ->					List[str]:







						"""simple docstring"""







						return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class       SCREAMING_SNAKE_CASE_							(					unittest.TestCase      ):



	"""simple docstring"""


	def 				lowerCamelCase__    (				self		:	str  )  ->					Optional[int]:







						"""simple docstring"""







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






	def 				lowerCamelCase__    (				self		:	Tuple  )  ->					Union[str, Any]:







						"""simple docstring"""







						__UpperCamelCase				:  int								=			load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
						    """repaint/celeba_hq_256.png"""  )
						__UpperCamelCase				:  Dict								=			load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""  )
						__UpperCamelCase				:  Tuple								=			load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
						    """repaint/celeba_hq_256_result.npy"""  )

						__UpperCamelCase				:  Tuple								=			"""google/ddpm-ema-celebahq-256"""
						__UpperCamelCase				:  Any								=			UNetaDModel.from_pretrained(lowerCAmelCase  )
						__UpperCamelCase				:  List[Any]								=			RePaintScheduler.from_pretrained(lowerCAmelCase  )

						__UpperCamelCase				:  List[Any]								=			RePaintPipeline(unet=lowerCAmelCase				,    scheduler=lowerCAmelCase  ).to(lowerCAmelCase  )
						repaint.set_progress_bar_config(disable=lowerCAmelCase  )
						repaint.enable_attention_slicing()

						__UpperCamelCase				:  str								=			torch.manual_seed(0  )
						__UpperCamelCase				:  Union[str, Any]								=			repaint(
						    lowerCAmelCase				,    lowerCAmelCase				,    num_inference_steps=250				,    eta=0.0				,    jump_length=10				,    jump_n_sample=10				,    generator=lowerCAmelCase				,    output_type="""np"""				,    )
						__UpperCamelCase				:  str								=			output.images[0]

						assert image.shape == (256, 256, 3)
						assert np.abs(expected_image - image  ).mean() < 1E-2












import os





def 							A__   ()    ->	int:
					__UpperCamelCase				:  int								=			os.path.dirname(os.path.realpath(snake_case							)							)
					__UpperCamelCase				:  List[str]								=			os.path.join(snake_case		,  """triangle.txt"""							)

					with open(snake_case							) as in_file:
										__UpperCamelCase				:  Optional[Any]								=			[[int(snake_case							) for i in line.split()] for line in in_file]

					while len(snake_case							) != 1:
										__UpperCamelCase				:  Union[str, Any]								=			triangle.pop()
										__UpperCamelCase				:  List[str]								=			triangle[-1]
										for j in range(len(snake_case							) - 1							):
															curr_row[j] += max(last_row[j]		,  last_row[j + 1]							)
					return triangle[0][0]


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






