



"""simple docstring"""





class 				lowercase__       :







       '''simple docstring'''


       def __init__(							self      :  str   )			->						None:


             '''simple docstring'''




             UpperCAmelCase_      			=							{}  # Mapping from char to TrieNode
             UpperCAmelCase_      			=							False



       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  list[str]   )			->						None:


             '''simple docstring'''




             for word in words:
                   self.insert(_UpperCAmelCase   )



       def 					lowercase__    (							self      :  List[Any]		,    _UpperCAmelCase      :  str   )			->						None:


             '''simple docstring'''




             UpperCAmelCase_      			=							self
             for char in word:
                   if char not in curr.nodes:
                         UpperCAmelCase_      			=							TrieNode()
                   UpperCAmelCase_      			=							curr.nodes[char]
             UpperCAmelCase_      			=							True



       def 					lowercase__    (							self      :  List[Any]		,    _UpperCAmelCase      :  str   )			->						bool:


             '''simple docstring'''




             UpperCAmelCase_      			=							self
             for char in word:
                   if char not in curr.nodes:
                         return False
                   UpperCAmelCase_      			=							curr.nodes[char]
             return curr.is_leaf







       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  str   )			->						None:


             '''simple docstring'''




             def _delete(_UpperCAmelCase      :  TrieNode		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  int   ) -> bool:
                   if index == len(_UpperCAmelCase   ):
                         # If word does not exist
                         if not curr.is_leaf:
                               return False
                         UpperCAmelCase_      			=							False
                         return len(curr.nodes   ) == 0
                   UpperCAmelCase_      			=							word[index]
                   UpperCAmelCase_      			=							curr.nodes.get(_UpperCAmelCase   )
                   # If char not in current trie node
                   if not char_node:
                         return False
                   # Flag to check if node can be deleted
                   UpperCAmelCase_      			=							_delete(_UpperCAmelCase		,    _UpperCAmelCase		,    index + 1   )
                   if delete_curr:
                         del curr.nodes[char]
                         return len(curr.nodes   ) == 0
                   return delete_curr

             _delete(self		,    _UpperCAmelCase		,    0   )



def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      if node.is_leaf:
            print(lowerCAmelCase__     ,      end=" "				)

      for key, value in node.nodes.items():
            print_words(lowerCAmelCase__     ,      word + key				)
def        a__     (				):
      UpperCAmelCase_      			=							"banana bananas bandana band apple all beast".split()
      UpperCAmelCase_      			=							TrieNode()
      root.insert_many(lowerCAmelCase__				)
      # print_words(root, "")
      assert all(root.find(lowerCAmelCase__				) for word in words				)
      assert root.find("banana"				)
      assert not root.find("bandanas"				)
      assert not root.find("apps"				)
      assert root.find("apple"				)
      assert root.find("all"				)
      root.delete("all"				)
      assert not root.find("all"				)
      root.delete("banana"				)
      assert not root.find("banana"				)
      assert root.find("bananas"				)
      return True
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__				):
      print(str(lowerCAmelCase__				)     ,      "works!" if passes else "doesn't work :("				)
def        a__     (				):
      assert test_trie()
def        a__     (				):
      print_results("Testing trie functionality"     ,      test_trie()				)


if __name__ == "__main__":
  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 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       @property
       def 					lowercase__    (							self      :  Tuple   )			->						Any:


             '''simple docstring'''




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



       @property
       def 					lowercase__    (							self      :  List[str]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							ort.SessionOptions()
             UpperCAmelCase_      			=							False
             return options







       def 					lowercase__    (							self      :  Dict   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo.png"   )
             UpperCAmelCase_      			=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"   )
             UpperCAmelCase_      			=							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
             UpperCAmelCase_      			=							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   )

             UpperCAmelCase_      			=							"A red cat sitting on a park bench"

             UpperCAmelCase_      			=							np.random.RandomState(0   )
             UpperCAmelCase_      			=							pipe(
                 prompt=_UpperCAmelCase		,    image=_UpperCAmelCase		,    mask_image=_UpperCAmelCase		,    strength=0.75		,    guidance_scale=7.5		,    num_inference_steps=15		,    generator=_UpperCAmelCase		,    output_type="np"		,    )

             UpperCAmelCase_      			=							output.images[0]

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




