
'''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"""




      _lowerCamelCase				:   Optional[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				:   Union[str, Any]								=   parser.parse_args()
      return args







def 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Optional[Any]							):



      """simple docstring"""




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

      _lowerCamelCase						,		_lowerCamelCase				:   Tuple								=   imgs[0].size
      _lowerCamelCase				:   List[str]								=   Image.new("RGB"				,	size=(cols * w, rows * h)							)
      _lowerCamelCase						,		_lowerCamelCase				:   List[Any]								=   grid.size

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







def 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    Union[str, Any]="robotic cat with wings"				,	_lowerCAmelCase   :    int=7.5				,	_lowerCAmelCase   :    Optional[Any]=50				,	_lowerCAmelCase   :    Union[str, Any]=1				,	_lowerCAmelCase   :    Dict=42				,	):



      """simple docstring"""




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


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

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



      """simple docstring"""




      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'float' object cannot be interpreted as an integer"							)
      if isinstance(_lowerCAmelCase				,	_lowerCAmelCase							):
            raise TypeError("'str' object cannot be interpreted as an integer"							)

      if num == 0:
            return "0b0"

      _lowerCamelCase				:   Tuple								=   False

      if num < 0:
            _lowerCamelCase				:   List[Any]								=   True
            _lowerCamelCase				:   int								=   -num

      _lowerCamelCase				:   list[int]								=   []
      while num > 0:
            binary.insert(0				,	num % 2							)
            num >>= 1

      if negative:
            return "-0b" + "".join(str(_lowerCAmelCase							) for e in binary							)

      return "0b" + "".join(str(_lowerCAmelCase							) for e in binary							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()