

"""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   _A					(	):



      """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=4_2     ,   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   _A					(	_a	:						Optional[int]     ,   _a	:						int     ,   _a	:						Union[str, Any]				):



      """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   _A					(	_a	:						Optional[int]     ,   _a	:						str="robotic cat with wings"     ,   _a	:						Union[str, Any]=7.5     ,   _a	:						Optional[Any]=5_0     ,   _a	:						int=1     ,   _a	:						List[str]=4_2     ,   ):



      """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


UpperCAmelCase													=parse_args()
# Load models and create wrapper for stable diffusion
UpperCAmelCase													=CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
UpperCAmelCase													=CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
UpperCAmelCase													=AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
UpperCAmelCase													=UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

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


import re

from filelock import FileLock


try:
  import nltk

  UpperCAmelCase													=True
except (ImportError, ModuleNotFoundError):
  UpperCAmelCase													=False

if NLTK_AVAILABLE:
  with FileLock(".lock") as lock:
    nltk.download("punkt", quiet=True)



def   _A					(	_a	:						str				):



      """simple docstring"""






      re.sub("""<n>"""     ,   """"""     ,   _a				)  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(_a				)				)




