


'''simple docstring'''

from __future__ import annotations

a_  :				int						= """#"""





class      snake_case    :





  """simple docstring"""




  def __init__(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  {}






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self._trie
   for char in text:
    if char not in trie:
     lowerCamelCase_      =  {}
    lowerCamelCase_      =  trie[char]
   lowerCamelCase_      =  True






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self._trie
   for char in prefix:
    if char in trie:
     lowerCamelCase_      =  trie[char]
    else:
     return []
   return self._elements(UpperCamelCase     )







  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  []
   for c, v in d.items():
    lowerCamelCase_      =  [" "] if c == END else [(c + s) for s in self._elements(UpperCamelCase     )]
    result.extend(UpperCamelCase     )
   return tuple(UpperCamelCase     )



a_  :				Any						= Trie()
a_  :				Dict						= ("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
     trie.insert_word(word)





def __snake_case					(      UpperCAmelCase_						:						str		):
 lowerCamelCase_      =  trie.find_word(UpperCAmelCase_		)
 return tuple(string + word for word in suffixes		)





def __snake_case					(      ):
 print(autocomplete_using_trie("de"		)		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()







'''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       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  ort.SessionOptions()
						__a			:       Dict					    =  False
						return options





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'							)
						__a			:       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
						__a			:       str					    =  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							)

						__a			:       Tuple					    =  'A red cat sitting on a park bench'

						__a			:       int					    =  np.random.RandomState(0							)
						__a			:       Tuple					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				strength=0.75						,				guidance_scale=7.5						,				num_inference_steps=15						,				generator=__a						,				output_type='np'						,				)

						__a			:       Tuple					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 1E-2



