


'''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[str]:
     __lowerCamelCase  :      int														=						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=42 ,       help='Seed for random process.' ,       )
     parser.add_argument(
         '-ci' ,       '--cuda_id' ,       type=UpperCAmelCase_ ,       default=0 ,       help='cuda_id.' ,       )
     __lowerCamelCase  :      Union[str, Any]														=						parser.parse_args()
     return args



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

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

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



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


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

A__						:   Any				=    StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
A__						:   Union[str, Any]				=    lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
      A__						:   Optional[Any]				=    load(args.pretrained_model_name_or_path, model=unet)
      unet.eval()
      setattr(pipeline, """unet""", unet)
else:
      A__						:   List[Any]				=    unet.to(torch.device("""cuda""", args.cuda_id))
A__						:   str				=    pipeline.to(unet.device)
A__		,   A__						:   int				=    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__						:   Union[str, Any]				=    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_       :							int = 10_00				)	->						int:
     __lowerCamelCase  :      Union[str, Any]														=						3
     __lowerCamelCase  :      Dict														=						0
     while a < n:
          if a % 3 == 0 or a % 5 == 0:
               result += a
          elif a % 15 == 0:
               result -= a
          a += 1
     return result


if __name__ == "__main__":
      print(f'''{solution() = }''')






