
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowercase						:      Any									= ''
lowercase						:      List[Any]									= ''
lowercase						:      Dict									= ''
lowercase						:      Tuple									= 1  # (0 is vertical, 1 is horizontal)




def 			_SCREAMING_SNAKE_CASE      (	)      ->					None:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     Tuple         =      get_dataset(_lowerCamelCase		,						_lowerCamelCase)
							print("Processing...")
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      update_image_and_anno(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)

							for index, image in enumerate(_lowerCamelCase):
														# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
														__UpperCamelCase			:     Optional[Any]         =      random_chars(32)
														__UpperCamelCase			:     List[str]         =      paths[index].split(os.sep)[-1].rsplit("."		,						1)[0]
														__UpperCamelCase			:     Tuple         =      F'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
														cva.imwrite(F'/{file_root}.jpg'		,						_lowerCamelCase		,						[cva.IMWRITE_JPEG_QUALITY, 85])
														print(F'Success {index+1}/{len(_lowerCamelCase)} with {file_name}')
														__UpperCamelCase			:     List[Any]         =      []
														for anno in new_annos[index]:
																					__UpperCamelCase			:     Union[str, Any]         =      F'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
																					annos_list.append(_lowerCamelCase)
														with open(F'/{file_root}.txt'		,						"w") as outfile:
																					outfile.write("\n".join(line for line in annos_list))




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str		,						_lowerCamelCase				:    str)      ->					tuple[list, list]:






							'''simple docstring'''





							__UpperCamelCase			:     Any         =      []
							__UpperCamelCase			:     Optional[Any]         =      []
							for label_file in glob.glob(os.path.join(_lowerCamelCase		,						"*.txt")):
														__UpperCamelCase			:     List[str]         =      label_file.split(os.sep)[-1].rsplit("."		,						1)[0]
														with open(_lowerCamelCase) as in_file:
																					__UpperCamelCase			:     Optional[int]         =      in_file.readlines()
														__UpperCamelCase			:     Optional[Any]         =      os.path.join(_lowerCamelCase		,						F'{label_name}.jpg')

														__UpperCamelCase			:     Any         =      []
														for obj_list in obj_lists:
																					__UpperCamelCase			:     Optional[Any]         =      obj_list.rstrip("\n").split(" ")
																					boxes.append(
																					    [
																					        int(obj[0]),
																					        float(obj[1]),
																					        float(obj[2]),
																					        float(obj[3]),
																					        float(obj[4]),
																					    ])
														if not boxes:
																					continue
														img_paths.append(_lowerCamelCase)
														labels.append(_lowerCamelCase)
							return img_paths, labels




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    list		,						_lowerCamelCase				:    list		,						_lowerCamelCase				:    int = 1)      ->					tuple[list, list, list]:






							'''simple docstring'''





							__UpperCamelCase			:     List[Any]         =      []
							__UpperCamelCase			:     Any         =      []
							__UpperCamelCase			:     List[Any]         =      []
							for idx in range(len(_lowerCamelCase)):
														__UpperCamelCase			:     Union[str, Any]         =      []
														__UpperCamelCase			:     Optional[int]         =      img_list[idx]
														path_list.append(_lowerCamelCase)
														__UpperCamelCase			:     Tuple         =      anno_list[idx]
														__UpperCamelCase			:     Union[str, Any]         =      cva.imread(_lowerCamelCase)
														if flip_type == 1:
																					__UpperCamelCase			:     Union[str, Any]         =      cva.flip(_lowerCamelCase		,						_lowerCamelCase)
																					for bbox in img_annos:
																												__UpperCamelCase			:     List[Any]         =      1 - bbox[1]
																												new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]])
														elif flip_type == 0:
																					__UpperCamelCase			:     Optional[int]         =      cva.flip(_lowerCamelCase		,						_lowerCamelCase)
																					for bbox in img_annos:
																												__UpperCamelCase			:     Any         =      1 - bbox[2]
																												new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]])
														new_annos_lists.append(_lowerCamelCase)
														new_imgs_list.append(_lowerCamelCase)
							return new_imgs_list, new_annos_lists, path_list




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int = 32)      ->					str:






							'''simple docstring'''





							assert number_char > 1, "The number of character should greater than 1"
							__UpperCamelCase			:     Optional[Any]         =      ascii_lowercase + digits
							return "".join(random.choice(_lowerCamelCase) for _ in range(_lowerCamelCase))


if __name__ == "__main__":
		main()
		print('DONE ✅')

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')