
import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class        _UpperCamelCase				(					_UpperCamelCase     ,       _UpperCamelCase     ,       unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						str					      = IFInpaintingPipeline
			__UpperCamelCase      :						Tuple					      = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
			__UpperCamelCase      :						Dict					      = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			__UpperCamelCase      :						int					      = PipelineTesterMixin.required_optional_params - {"""latents"""}

			def       lowerCAmelCase__	(       self :       Tuple      ):
							return self._get_dummy_components()

			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       Dict						,	snake_case_ :       List[Any]=0      ):
							if str(snake_case_      ).startswith("""mps"""      ):
											UpperCamelCase_:							Tuple	=						torch.manual_seed(snake_case_      )
							else:
											UpperCamelCase_:							Dict	=						torch.Generator(device=snake_case_      ).manual_seed(snake_case_      )

							UpperCamelCase_:							Optional[int]	=						floats_tensor((1, 3, 32, 32)						,	rng=random.Random(snake_case_      )      ).to(snake_case_      )
							UpperCamelCase_:							List[str]	=						floats_tensor((1, 3, 32, 32)						,	rng=random.Random(snake_case_      )      ).to(snake_case_      )

							UpperCamelCase_:							Any	=						{
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": image,
							    "mask_image": mask_image,
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "numpy",
							}

							return inputs

			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()						,	reason="""XFormers attention is only available with CUDA and `xformers` installed"""						,	)
			def       lowerCAmelCase__	(       self :       Dict      ):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							self._test_save_load_optional_components()

			@unittest.skipIf(torch_device != """cuda"""						,	reason="""float16 requires CUDA"""      )
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
							super().test_save_load_floataa(expected_max_diff=1e-1      )

			def       lowerCAmelCase__	(       self :       Any      ):
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							self._test_save_load_local()




			def       lowerCAmelCase__	(       self :       List[Any]      ):
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2						,	)








def        A__					(						lowerCamelCase = 50						)  ->	int:
				UpperCamelCase_:							List[Any]	=						[[0] * 3 for _ in range(length + 1						)]

				for row_length in range(length + 1						):
								for tile_length in range(2     ,    5						):
												for tile_start in range(row_length - tile_length + 1						):
																different_colour_ways_number[row_length][tile_length - 2] += (
																    different_colour_ways_number[row_length - tile_start - tile_length][
																        tile_length - 2
																    ]
																    + 1
																)

				return sum(different_colour_ways_number[length]						)


if __name__ == "__main__":
							print(F"""{solution() = }""")






