

class SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			def __init__(    self							):

				"""simple docstring"""


				snake_case_      		= ''
				snake_case_      		= ''
				snake_case_      		= []


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				if m == -1:
					return n + 1
				elif n == -1:
					return m + 1
				elif self.dp[m][n] > -1:
					return self.dp[m][n]
				else:
					if self.worda[m] == self.worda[n]:
						snake_case_      		= self.__min_dist_top_down_dp(m - 1 ,				n - 1							)
					else:
						snake_case_      		= self.__min_dist_top_down_dp(__UpperCamelCase ,				n - 1							)
						snake_case_      		= self.__min_dist_top_down_dp(m - 1 ,				__UpperCamelCase							)
						snake_case_      		= self.__min_dist_top_down_dp(m - 1 ,				n - 1							)
						snake_case_      		= 1 + min(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)

					return self.dp[m][n]


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= worda
				snake_case_      		= worda
				snake_case_      		= [[-1 for _ in range(len(__UpperCamelCase							)							)] for _ in range(len(__UpperCamelCase							)							)]

				return self.__min_dist_top_down_dp(len(__UpperCamelCase							) - 1 ,				len(__UpperCamelCase							) - 1							)







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= worda
				snake_case_      		= worda
				snake_case_      		= len(__UpperCamelCase							)
				snake_case_      		= len(__UpperCamelCase							)
				snake_case_      		= [[0 for _ in range(n + 1							)] for _ in range(m + 1							)]

				for i in range(m + 1							):
					for j in range(n + 1							):
						if i == 0:  # first string is empty
							snake_case_      		= j
						elif j == 0:  # second string is empty
							snake_case_      		= i
						elif worda[i - 1] == worda[j - 1]:  # last characters are equal
							snake_case_      		= self.dp[i - 1][j - 1]
						else:
							snake_case_      		= self.dp[i][j - 1]
							snake_case_      		= self.dp[i - 1][j]
							snake_case_      		= self.dp[i - 1][j - 1]
							snake_case_      		= 1 + min(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)
				return self.dp[m][n]


if __name__ == "__main__":
		A											=     EditDistance()

		print('****************** Testing Edit Distance DP Algorithm ******************')
		print()

		A											=     input('Enter the first string: ').strip()
		A											=     input('Enter the second string: ').strip()

		print()
		print(f"""The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}""")
		print(f"""The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}""")
		print()
		print('*************** End of Testing Edit Distance DP Algorithm ***************')



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')
