"""simple docstring"""




from __future__ import annotations






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__ = None				):






							"""simple docstring"""





							A__	     =							word_bank or []
							# create a table
							A__	     =							len(UpperCamelCase__				) + 1

							A__	     =							[]
							for _ in range(UpperCamelCase__				):
														table.append([]				)
							# seed value
							A__	     =							[[]]  # because empty string has empty combination

							# iterate through the indices
							for i in range(UpperCamelCase__				):
														# condition
														if table[i] != []:
																					for word in word_bank:
																												# slice condition
																												if target[i : i + len(UpperCamelCase__				)] == word:
																																			A__	     =							[
																																			    [word, *way] for way in table[i]
																																			]
																																			# adds the word to every combination the current position holds
																																			# now,push that combination to the table[i+len(word)]
																																			table[i + len(UpperCamelCase__				)] += new_combinations

    # combinations are in reverse order so reverse for better output
							for combination in table[len(UpperCamelCase__				)]:
														combination.reverse()

							return table[len(UpperCamelCase__				)]


if __name__ == "__main__":
				print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
				print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
				print(
				    all_construct(
				        "hexagonosaurus",
				        ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
				    )
				)






"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
				import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class     UpperCamelCase__(			unittest.TestCase ):





						@property
						def 				snake_case__	(       self     )					->				int:
													return (
													    "CUDAExecutionProvider",
													    {
													        "gpu_mem_limit": "15000000000",  # 15GB
													        "arena_extend_strategy": "kSameAsRequested",
													    },
													)





						@property
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							ort.SessionOptions()
													A__	     =							False
													return options





						def 				snake_case__	(       self     )					->				str:
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo.png'     )
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'     )
													A__	     =							load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'     )

													# using the PNDM scheduler by default
													A__	     =							OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'		,revision='onnx'		,safety_checker=__UpperCAmelCase		,feature_extractor=__UpperCAmelCase		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
													pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							'A red cat sitting on a park bench'

													A__	     =							np.random.RandomState(0     )
													A__	     =							pipe(
													    prompt=__UpperCAmelCase		,image=__UpperCAmelCase		,mask_image=__UpperCAmelCase		,strength=0.7_5		,guidance_scale=7.5		,num_inference_steps=15		,generator=__UpperCAmelCase		,output_type='np'		,)

													A__	     =							output.images[0]

													assert image.shape == (5_12, 5_12, 3)
													assert np.abs(expected_image - image     ).max() < 1e-2





