"""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       _lowercase  (self       :   str)					->			Optional[int]:
					return (
					    "CUDAExecutionProvider",
					    {
					        "gpu_mem_limit": "15000000000",  # 15GB
					        "arena_extend_strategy": "kSameAsRequested",
					    },
					)



				@property
				def       _lowercase  (self       :   List[str])					->			str:
					__snake_case    :							Optional[int]   	=				ort.SessionOptions()
					__snake_case    :							List[Any]   	=				False
					return options






				def       _lowercase  (self       :   str)					->			int:
					__snake_case    :							List[str]   	=				load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/in_paint/overture-creations-5sI6fQgYIuo.png')
					__snake_case    :							Tuple   	=				load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
					    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png')
					__snake_case    :							Dict   	=				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
					__snake_case    :							Dict   	=				OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
					    'CompVis/stable-diffusion-v1-4'			,						revision='onnx'			,						safety_checker=_A			,						feature_extractor=_A			,						provider=self.gpu_provider			,						sess_options=self.gpu_options			,						)
					pipe.set_progress_bar_config(disable=_A)

					__snake_case    :							Optional[int]   	=				'A red cat sitting on a park bench'

					__snake_case    :							Tuple   	=				np.random.RandomState(0)
					__snake_case    :							List[Any]   	=				pipe(
					    prompt=_A			,						image=_A			,						mask_image=_A			,						strength=0.75			,						guidance_scale=7.5			,						num_inference_steps=15			,						generator=_A			,						output_type='np'			,						)

					__snake_case    :							Optional[int]   	=				output.images[0]

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






"""simple docstring"""







from __future__ import annotations

_a  :  str=				"#"

class 	UpperCamelCase    :



				def __init__(self       :   str)					->			None:
					__snake_case    :							dict   	=				{}



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			None:
					__snake_case    :							Optional[Any]   	=				self._trie
					for char in text:
						if char not in trie:
							__snake_case    :							Optional[int]   	=				{}
						__snake_case    :							Optional[int]   	=				trie[char]
					__snake_case    :							Optional[int]   	=				True



				def       _lowercase  (self       :   List[str]			,						_A       :   str)					->			tuple | list:
					__snake_case    :							str   	=				self._trie
					for char in prefix:
						if char in trie:
							__snake_case    :							Optional[int]   	=				trie[char]
						else:
							return []
					return self._elements(_A)






				def       _lowercase  (self       :   List[Any]			,						_A       :   dict)					->			tuple:
					__snake_case    :							str   	=				[]
					for c, v in d.items():
						__snake_case    :							Optional[Any]   	=				[' '] if c == END else [(c + s) for s in self._elements(_A)]
						result.extend(_A)
					return tuple(_A)







_a  :  str=				Trie()
_a  :  Any=				("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
	trie.insert_word(word)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    tuple:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				trie.find_word(UpperCAmelCase_						)
	return tuple(string + word for word in suffixes						)
def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	print(autocomplete_using_trie('de'						)						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





