






import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
		import onnxruntime as ort





class __lowerCamelCase (      snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	pass





@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self    ):
							"""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									=			OnnxStableDiffusionInpaintPipeline.from_pretrained(
							    'runwayml/stable-diffusion-inpainting'       ,    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       ,    guidance_scale=7.5       ,    num_inference_steps=10       ,    generator=UpperCAmelCase       ,    output_type='np'       ,    )
							_UpperCAmelCase									=			output.images
							_UpperCAmelCase									=			images[0, 255:258, 255:258, -1]

							assert images.shape == (1, 512, 512, 3)
							_UpperCAmelCase									=			np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3

	def        UpperCamelCase    (							self    ):
							"""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									=			LMSDiscreteScheduler.from_pretrained(
							    'runwayml/stable-diffusion-inpainting'       ,    subfolder='scheduler'       ,    revision='onnx'    )
							_UpperCAmelCase									=			OnnxStableDiffusionInpaintPipeline.from_pretrained(
							    'runwayml/stable-diffusion-inpainting'       ,    revision='onnx'       ,    scheduler=UpperCAmelCase       ,    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       ,    guidance_scale=7.5       ,    num_inference_steps=20       ,    generator=UpperCAmelCase       ,    output_type='np'       ,    )
							_UpperCAmelCase									=			output.images
							_UpperCAmelCase									=			images[0, 255:258, 255:258, -1]

							assert images.shape == (1, 512, 512, 3)
							_UpperCAmelCase									=			np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3








class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							for word in words:
													self.insert(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""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        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							def _delete(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ) -> 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	)->  None:





						"""simple docstring"""



						if node.is_leaf:
												print(__lowerCAmelCase ,		end=' '	)

						for key, value in node.nodes.items():
												print_words(__lowerCAmelCase ,		word + key	)


def __A    (      )->  bool:





						"""simple docstring"""



						_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	)->  None:





						"""simple docstring"""



						print(str(__lowerCAmelCase	) ,		'works!' if passes else 'doesn\'t work :('	)


def __A    (      )->  None:





						"""simple docstring"""



						assert test_trie()


def __A    (      )->  None:





						"""simple docstring"""



						print_results('Testing trie functionality' ,		test_trie()	)


if __name__ == "__main__":
		main()
