



'''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"""



     _a           =    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=42    ,      help='''Seed for random process.'''    ,      )
     parser.add_argument(
         '''-ci'''    ,      '''--cuda_id'''    ,      type=__A    ,      default=0    ,      help='''cuda_id.'''    ,      )
     _a           =    parser.parse_args()
     return args




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



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

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

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




def lowerCAmelCase    (__A    ,      __A="robotic cat with wings"    ,      __A=7.5    ,      __A=50    ,      __A=1    ,      __A=42    ,      ):



     """simple docstring"""



     _a           =    torch.Generator(pipeline.device).manual_seed(__A)
     _a           =    pipeline(
         __A    ,      guidance_scale=__A    ,      num_inference_steps=__A    ,      generator=__A    ,      num_images_per_prompt=__A    ,      ).images
     _a           =    int(math.sqrt(__A))
     _a           =    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'''





def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     if digit_amount > 0:
          return round(number - int(__A)    ,      __A)
     return number - int(__A)


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))



