

'''simple docstring'''

import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel




def 				UpperCamelCase(					):
	UpperCAmelCase	:     Optional[Any]						 =			argparse.ArgumentParser()
	parser.add_argument(
	    '-m'  ,	'--pretrained_model_name_or_path'  ,	type=_SCREAMING_SNAKE_CASE  ,	default=_SCREAMING_SNAKE_CASE  ,	required=_SCREAMING_SNAKE_CASE  ,	help='Path to pretrained model or model identifier from huggingface.co/models.'  ,	)
	parser.add_argument(
	    '-c'  ,	'--caption'  ,	type=_SCREAMING_SNAKE_CASE  ,	default='robotic cat with wings'  ,	help='Text used to generate images.'  ,	)
	parser.add_argument(
	    '-n'  ,	'--images_num'  ,	type=_SCREAMING_SNAKE_CASE  ,	default=4  ,	help='How much images to generate.'  ,	)
	parser.add_argument(
	    '-s'  ,	'--seed'  ,	type=_SCREAMING_SNAKE_CASE  ,	default=42  ,	help='Seed for random process.'  ,	)
	parser.add_argument(
	    '-ci'  ,	'--cuda_id'  ,	type=_SCREAMING_SNAKE_CASE  ,	default=0  ,	help='cuda_id.'  ,	)
	UpperCAmelCase	:     Optional[int]						 =			parser.parse_args()
	return args




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):





	if not len(_SCREAMING_SNAKE_CASE     ) == rows * cols:
		raise ValueError('The specified number of rows and columns are not correct.'     )

	UpperCAmelCase	:     Tuple						 =			imgs[0].size
	UpperCAmelCase	:     List[str]						 =			Image.new('RGB'  ,	size=(cols * w, rows * h)     )
	UpperCAmelCase	:     Any						 =			grid.size

	for i, img in enumerate(_SCREAMING_SNAKE_CASE     ):
		grid.paste(_SCREAMING_SNAKE_CASE  ,	box=(i % cols * w, i // cols * h)     )
	return grid




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_="robotic cat with wings"  ,	UpperCAmelCase_=7.5  ,	UpperCAmelCase_=50  ,	UpperCAmelCase_=1  ,	UpperCAmelCase_=42  ,	):
	UpperCAmelCase	:     List[str]						 =			torch.Generator(pipeline.device     ).manual_seed(_SCREAMING_SNAKE_CASE     )
	UpperCAmelCase	:     Tuple						 =			pipeline(
	    _SCREAMING_SNAKE_CASE  ,	guidance_scale=_SCREAMING_SNAKE_CASE  ,	num_inference_steps=_SCREAMING_SNAKE_CASE  ,	generator=_SCREAMING_SNAKE_CASE  ,	num_images_per_prompt=_SCREAMING_SNAKE_CASE  ,	).images
	UpperCAmelCase	:     Dict						 =			int(math.sqrt(_SCREAMING_SNAKE_CASE     )     )
	UpperCAmelCase	:     Dict						 =			image_grid(_SCREAMING_SNAKE_CASE  ,	rows=_rows  ,	cols=num_images_per_prompt // _rows     )
	return grid, images


lowercase__         =      parse_args()
# Load models and create wrapper for stable diffusion
lowercase__         =      CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
lowercase__         =      CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
lowercase__         =      AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
lowercase__         =      UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

lowercase__         =      StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowercase__         =      lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
	lowercase__         =      load(args.pretrained_model_name_or_path, model=unet)
	unet.eval()
	setattr(pipeline, "unet", unet)
else:
	lowercase__         =      unet.to(torch.device("cuda", args.cuda_id))
lowercase__         =      pipeline.to(unet.device)
lowercase__   ,  lowercase__         =      generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, "{}.png".format("_".join(args.caption.split()))))
lowercase__         =      os.path.join(args.pretrained_model_name_or_path, "_".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
	image.save(os.path.join(dirname, "{}.png".format(idx + 1)))







'''simple docstring'''

def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			len(UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			len(UpperCAmelCase_     )
 UpperCAmelCase	:     int						 =			(
     first_str_length if first_str_length > second_str_length else second_str_length
 )
 UpperCAmelCase	:     list						 =			[]
 for char_count in range(UpperCAmelCase_     ):
  if char_count < first_str_length:
   output_list.append(first_str[char_count]     )
  if char_count < second_str_length:
   output_list.append(second_str[char_count]     )
 return "".join(UpperCAmelCase_     )


if __name__ == "__main__":
 print(alternative_string_arrange("AB", "XYZ"), end=" ")




