

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowerCAmelCase_       	=	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''')
lowerCAmelCase_       	=	parser.parse_args()


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

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

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

# optimize with ipex
lowerCAmelCase_       	=	torch.randn(2, 4, 64, 64)
lowerCAmelCase_       	=	torch.rand(1) * 9_99
lowerCAmelCase_       	=	torch.randn(2, 77, 7_68)
lowerCAmelCase_       	=	(sample, timestep, encoder_hidden_status)
try:
					lowerCAmelCase_       	=	ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
					lowerCAmelCase_       	=	ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowerCAmelCase_       	=	ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowerCAmelCase_       	=	ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
					lowerCAmelCase_       	=	ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowerCAmelCase_       	=	6_66
lowerCAmelCase_       	=	torch.Generator(device).manual_seed(seed)
lowerCAmelCase_       	=	{'''generator''': generator}
if args.steps is not None:
					lowerCAmelCase_       	=	args.steps

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

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

"""simple docstring"""





from __future__ import annotations

__magic_name__       =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__magic_name__       =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)

		for i in range(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for j in range(i + 1  ,				UpperCamelCase_				):
						if arr[i] < arr[j]:
								__SCREAMING_SNAKE_CASE            = arr[j]
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		for i, outer in enumerate(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								__SCREAMING_SNAKE_CASE            = inner
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = [-1] * arr_size

		for index in reversed(range(UpperCamelCase_				)				):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						__SCREAMING_SNAKE_CASE            = stack[-1]
				stack.append(arr[index]				)
		return result


if __name__ == "__main__":
							from doctest import testmod
							from timeit import timeit

							testmod()
							print(next_greatest_element_slow(arr))
							print(next_greatest_element_fast(arr))
							print(next_greatest_element(arr))

							__magic_name__       =			(
							    "from __main__ import arr, next_greatest_element_slow, "
							    "next_greatest_element_fast, next_greatest_element"
							)
							print(
							    "next_greatest_element_slow():",
							    timeit("next_greatest_element_slow(arr)", setup=setup),
							)
							print(
							    "next_greatest_element_fast():",
							    timeit("next_greatest_element_fast(arr)", setup=setup),
							)
							print(
							    "     next_greatest_element():",
							    timeit("next_greatest_element(arr)", setup=setup),
							)




