






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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[str]:
  if not len(UpperCAmelCase__  ) == rows * cols:
    raise ValueError('The specified number of rows and columns are not correct.'  )

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

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__="robotic cat with wings"     ,   UpperCAmelCase__=7.5     ,   UpperCAmelCase__=5_0     ,   UpperCAmelCase__=1     ,   UpperCAmelCase__=4_2     ,   )	->    Dict:
  UpperCamelCase_: Any   				=    torch.Generator(pipeline.device  ).manual_seed(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    pipeline(
      UpperCAmelCase__     ,   guidance_scale=UpperCAmelCase__     ,   num_inference_steps=UpperCAmelCase__     ,   generator=UpperCAmelCase__     ,   num_images_per_prompt=UpperCAmelCase__     ,   ).images
  UpperCamelCase_: Dict   				=    int(math.sqrt(UpperCAmelCase__  )  )
  UpperCamelCase_: Tuple   				=    image_grid(UpperCAmelCase__     ,   rows=_rows     ,   cols=num_images_per_prompt // _rows  )
  return grid, images


A_		:		str		     =			parse_args()
# Load models and create wrapper for stable diffusion
A_		:		Optional[Any]		     =			CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
A_		:		Dict		     =			CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
A_		:		List[str]		     =			AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
A_		:		Union[str, Any]		     =			UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

A_		:		int		     =			StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
A_		:		int		     =			lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
   A_		:		List[Any]		     =			load(args.pretrained_model_name_or_path, model=unet)
   unet.eval()
   setattr(pipeline, 'unet', unet)
else:
   A_		:		List[str]		     =			unet.to(torch.device('cuda', args.cuda_id))
A_		:		Any		     =			pipeline.to(unet.device)
A_     ,			A_		:		Optional[Any]		     =			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()))))
A_		:		Tuple		     =			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)))







import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A_		:		List[str]		     =			'.'


if __name__ == "__main__":
   A_		:		Dict		     =			os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
   A_		:		Dict		     =			[]
   A_		:		Optional[Any]		     =			[]
   with open(doctest_file_path) as fp:
      for line in fp:
         A_		:		Tuple		     =			line.strip()
         A_		:		Any		     =			os.path.join(REPO_PATH, line)
         if not (os.path.isfile(path) or os.path.isdir(path)):
            non_existent_paths.append(line)
         all_paths.append(path)
   if len(non_existent_paths) > 0:
      A_		:		str		     =			'\n'.join(non_existent_paths)
      raise ValueError(F'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
   if all_paths != sorted(all_paths):
      raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')