





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 		A_				(     )						-> List[Any]:
 UpperCamelCase	:					int       	=   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."							,   )
 UpperCamelCase	:					Optional[Any]       	=   parser.parse_args()
 return args



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase     )						-> Union[str, Any]:
 if not len(_lowerCAmelCase     ) == rows * cols:
  raise ValueError("The specified number of rows and columns are not correct."     )

 UpperCamelCase	,    UpperCamelCase	:					Dict       	=   imgs[0].size
 UpperCamelCase	:					int       	=   Image.new("RGB"							,   size=(cols * w, rows * h)     )
 UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   grid.size

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



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


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

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

def 			lowercase_				(			_lowerCamelCase							:       int):
    lowercase__ :   Dict    					=				n ** (1 / 3)
    return (val * val * val) == n


if __name__ == "__main__":
      print(perfect_cube(27))
      print(perfect_cube(4))



