
import argparse
from collections import defaultdict




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    List[Any]		,						_lowerCamelCase				:    Any		,						_lowerCamelCase				:    str		,						_lowerCamelCase				:    str)      ->					Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      F'{file}_{class_name}_{test_name}'
							done_test[_id] += 1

							with open(_lowerCamelCase		,						"r") as f:
														__UpperCamelCase			:     Union[str, Any]         =      f.readlines()

							__UpperCamelCase			:     List[Any]         =      F'class {class_name}('
							__UpperCamelCase			:     Union[str, Any]         =      F'{4 * " "}def {test_name}('
							__UpperCamelCase			:     Optional[int]         =      F'{8 * " "}{correct_line.split()[0]}'
							__UpperCamelCase			:     str         =      F'{16 * " "}{correct_line.split()[0]}'
							__UpperCamelCase			:     int         =      False
							__UpperCamelCase			:     str         =      False
							__UpperCamelCase			:     List[Any]         =      False
							__UpperCamelCase			:     List[str]         =      False
							__UpperCamelCase			:     Tuple         =      0
							__UpperCamelCase			:     str         =      0

							__UpperCamelCase			:     List[Any]         =      []
							for line in lines:
														if line.startswith(_lowerCamelCase):
																					__UpperCamelCase			:     Optional[Any]         =      True
														elif in_class and line.startswith(_lowerCamelCase):
																					__UpperCamelCase			:     List[Any]         =      True
														elif in_class and in_func and (line.startswith(_lowerCamelCase) or line.startswith(_lowerCamelCase)):
																					__UpperCamelCase			:     Any         =      len(line.split(correct_line.split()[0])[0])
																					count += 1

																					if count == done_test[_id]:
																												__UpperCamelCase			:     Any         =      True

														if in_class and in_func and in_line:
																					if ")" not in line:
																												continue
																					else:
																												__UpperCamelCase			:     Optional[int]         =      True

														if in_class and in_func and in_line and insert_line:
																					new_lines.append(F'{spaces * " "}{correct_line}')
																					__UpperCamelCase			:     int         =      False
														else:
																					new_lines.append(_lowerCamelCase)

							with open(_lowerCamelCase		,						"w") as f:
														for line in new_lines:
																					f.write(_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    Any=None)      ->					Tuple:






							'''simple docstring'''





							if fail is not None:
														with open(_lowerCamelCase		,						"r") as f:
																					__UpperCamelCase			:     Dict         =      {l.strip() for l in f.readlines()}
							else:
														__UpperCamelCase			:     List[Any]         =      None

							with open(_lowerCamelCase		,						"r") as f:
														__UpperCamelCase			:     int         =      f.readlines()

							__UpperCamelCase			:     List[str]         =      defaultdict(_lowerCamelCase)
							for line in correct_lines:
														__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     str         =      line.split(";")
														if test_failures is None or "::".join([file, class_name, test_name]) in test_failures:
																					overwrite_file(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)


if __name__ == "__main__":
		lowercase						:      Any									= argparse.ArgumentParser()
		parser.add_argument('--correct_filename', help='filename of tests with expected result')
		parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
		lowercase						:      Optional[Any]									= parser.parse_args()

		main(args.correct_filename, args.fail_filename)

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase						:      Any									= 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')
lowercase						:      Tuple									= parser.parse_args()


lowercase						:      str									= 'cpu'
lowercase						:      List[Any]									= 'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

lowercase						:      Any									= 'path-to-your-trained-model'
lowercase						:      List[Any]									= StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
		lowercase						:      Any									= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowercase						:      Dict									= pipe.to(device)

# to channels last
lowercase						:      int									= pipe.unet.to(memory_format=torch.channels_last)
lowercase						:      int									= pipe.vae.to(memory_format=torch.channels_last)
lowercase						:      Dict									= pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
		lowercase						:      List[str]									= pipe.safety_checker.to(memory_format=torch.channels_last)

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

# compute
lowercase						:      Any									= 666
lowercase						:      int									= torch.Generator(device).manual_seed(seed)
lowercase						:      List[str]									= {'generator': generator}
if args.steps is not None:
		lowercase						:      List[str]									= args.steps

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

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