

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    SCREAMING_SNAKE_CASE__       (  )  ->     Optional[Any]:
	__lowerCamelCase				:				Tuple					       = 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=4_2	, help='Seed for random process.'	, )
	parser.add_argument(
	    '-ci'	, '--cuda_id'	, type=lowerCamelCase__	, default=0	, help='cuda_id.'	, )
	__lowerCamelCase				:				Any					       = parser.parse_args()
	return args






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)  ->     Any:
	if not len(lowerCamelCase__			) == rows * cols:
		raise ValueError('The specified number of rows and columns are not correct.'			)

	__lowerCamelCase       , __lowerCamelCase				:				Optional[int]					       = imgs[0].size
	__lowerCamelCase				:				List[Any]					       = Image.new('RGB'	, size=(cols * w, rows * h)			)
	__lowerCamelCase       , __lowerCamelCase				:				List[str]					       = grid.size

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






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__="robotic cat with wings"	, lowerCamelCase__=7.5	, lowerCamelCase__=5_0	, lowerCamelCase__=1	, lowerCamelCase__=4_2	, )  ->     int:
	__lowerCamelCase				:				Any					       = torch.Generator(pipeline.device			).manual_seed(lowerCamelCase__			)
	__lowerCamelCase				:				int					       = pipeline(
	    lowerCamelCase__	, guidance_scale=lowerCamelCase__	, num_inference_steps=lowerCamelCase__	, generator=lowerCamelCase__	, num_images_per_prompt=lowerCamelCase__	, ).images
	__lowerCamelCase				:				Union[str, Any]					       = int(math.sqrt(lowerCamelCase__			)			)
	__lowerCamelCase				:				str					       = image_grid(lowerCamelCase__	, rows=_rows	, cols=num_images_per_prompt // _rows			)
	return grid, images


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

a		=StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
a		=lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
					a		=load(args.pretrained_model_name_or_path, model=unet)
					unet.eval()
					setattr(pipeline, """unet""", unet)
else:
					a		=unet.to(torch.device("""cuda""", args.cuda_id))
a		=pipeline.to(unet.device)
a		,       a		=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		=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   (	_SCREAMING_SNAKE_CASE = 1000000   )	->int:
  a__:     int										=	limit + 1
  a__:     Optional[int]										=	[0] * limit
  for first_term in range(1		,     _SCREAMING_SNAKE_CASE   ):
    for n in range(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
      a__:     List[Any]										=	first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  a__:     Any										=	sum(1 for x in frequency[1:limit] if x == 10   )

  return count


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


