


'''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 lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =argparse.ArgumentParser()
   parser.add_argument(
       '-m'	,   '--pretrained_model_name_or_path'	,   type=__a	,   default=__a	,   required=__a	,   help='Path to pretrained model or model identifier from huggingface.co/models.'	,   )
   parser.add_argument(
       '-c'	,   '--caption'	,   type=__a	,   default='robotic cat with wings'	,   help='Text used to generate images.'	,   )
   parser.add_argument(
       '-n'	,   '--images_num'	,   type=__a	,   default=4	,   help='How much images to generate.'	,   )
   parser.add_argument(
       '-s'	,   '--seed'	,   type=__a	,   default=4_2	,   help='Seed for random process.'	,   )
   parser.add_argument(
       '-ci'	,   '--cuda_id'	,   type=__a	,   default=0	,   help='cuda_id.'	,   )
   _UpperCAmelCase       :       Dict							   =parser.parse_args()
   return args





def lowerCamelCase__      (						__lowerCamelCase	:							str	,   __lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							List[str]							):







   '''simple docstring'''




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

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

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





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[Any]	,   __lowerCamelCase	:							Optional[int]="robotic cat with wings"	,   __lowerCamelCase	:							Any=7.5	,   __lowerCamelCase	:							Any=5_0	,   __lowerCamelCase	:							int=1	,   __lowerCamelCase	:							List[str]=4_2	,   ):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =torch.Generator(pipeline.device							).manual_seed(__a							)
   _UpperCAmelCase       :       Optional[int]							   =pipeline(
       __a	,   guidance_scale=__a	,   num_inference_steps=__a	,   generator=__a	,   num_images_per_prompt=__a	,   ).images
   _UpperCAmelCase       :       List[str]							   =int(math.sqrt(__a							)							)
   _UpperCAmelCase       :       Any							   =image_grid(__a	,   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'''






from __future__ import annotations





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]							):







   '''simple docstring'''




   if not nums:
      return 0
   _UpperCAmelCase       :       Tuple							   =nums[0]
   _UpperCAmelCase       :       int							   =0
   for num in nums[1:]:
      _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =(
          max_excluding + num,
          max(__lowerCamelCase	,   __lowerCamelCase							),
      )
   return max(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

