



"""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   (   )						->       List[Any]:

  """simple docstring"""




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

def     UpperCamelCase   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    Any   ,       lowerCAmelCase__				:    Tuple						)						->       List[Any]:

  """simple docstring"""




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

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

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

def     UpperCamelCase   (   lowerCAmelCase__				:    Any   ,       lowerCAmelCase__				:    Optional[int]="robotic cat with wings"   ,       lowerCAmelCase__				:    Tuple=7.5   ,       lowerCAmelCase__				:    List[Any]=50   ,       lowerCAmelCase__				:    Any=1   ,       lowerCAmelCase__				:    str=42   ,       )						->       str:

  """simple docstring"""




  lowerCAmelCase_       :    int								=			torch.Generator(pipeline.device						).manual_seed(lowerCAmelCase__						)
  lowerCAmelCase_       :    int								=			pipeline(
      lowerCAmelCase__   ,       guidance_scale=lowerCAmelCase__   ,       num_inference_steps=lowerCAmelCase__   ,       generator=lowerCAmelCase__   ,       num_images_per_prompt=lowerCAmelCase__   ,       ).images
  lowerCAmelCase_       :    Optional[Any]								=			int(math.sqrt(lowerCAmelCase__						)						)
  lowerCAmelCase_       :    Optional[Any]								=			image_grid(lowerCAmelCase__   ,       rows=_rows   ,       cols=num_images_per_prompt // _rows						)
  return grid, images


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

lowercase__		:    Optional[Any]				   =     StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowercase__		:    List[Any]				   =     lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
     lowercase__		:    Union[str, Any]				   =     load(args.pretrained_model_name_or_path, model=unet)
     unet.eval()
     setattr(pipeline, """unet""", unet)
else:
     lowercase__		:    Optional[Any]				   =     unet.to(torch.device("""cuda""", args.cuda_id))
lowercase__		:    List[str]				   =     pipeline.to(unet.device)
lowercase__		:    List[str]				   =     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__		:    Optional[int]				   =     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_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    str						)						->       int:

  """simple docstring"""




  if len(lowerCAmelCase__						) != len(lowerCAmelCase__						):
    raise ValueError('String lengths must match!'						)

  lowerCAmelCase_       :    List[Any]								=			0

  for chara, chara in zip(lowerCAmelCase__   ,       lowerCAmelCase__						):
    if chara != chara:
      count += 1

  return count


if __name__ == "__main__":
     import doctest

     doctest.testmod()





