






class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self							):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				for word in words:
					self.insert(_lowercase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


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







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


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

				_delete(self ,				_lowercase ,				0							)







def a(lowercase__			, lowercase__	):





	'''simple docstring'''





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

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





def a():





	'''simple docstring'''
	snake_case_      		= 'banana bananas bandana band apple all beast'.split()
	snake_case_      		= TrieNode()
	root.insert_many(UpperCamelCase__	)
	# print_words(root, "")
	assert all(root.find(UpperCamelCase__	) 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(lowercase__			, lowercase__	):





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





def a():





	'''simple docstring'''
	assert test_trie()





def a():





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


if __name__ == "__main__":
		main()



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A											=     argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='Enable DPMSolver or not')
parser.add_argument('--steps', default=None, type=int, help='Num inference steps')
A											=     parser.parse_args()


A											=     'cpu'
A											=     'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

A											=     'path-to-your-trained-model'
A											=     StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
		A											=     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
A											=     pipe.to(device)

# to channels last
A											=     pipe.unet.to(memory_format=torch.channels_last)
A											=     pipe.vae.to(memory_format=torch.channels_last)
A											=     pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		A											=     pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
A											=     torch.randn(2, 4, 64, 64)
A											=     torch.rand(1) * 999
A											=     torch.randn(2, 77, 768)
A											=     (sample, timestep, encoder_hidden_status)
try:
		A											=     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
		A											=     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A											=     ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A											=     ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		A											=     ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
A											=     666
A											=     torch.Generator(device).manual_seed(seed)
A											=     {'generator': generator}
if args.steps is not None:
		A											=     args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
		A											=     pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('generated.png')
