
'''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 	_snake_case						(      ):
  """simple docstring"""







  a_      :	List[str]      =							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_      :	Any      =							parser.parse_args()
  return args






def 	_snake_case						(      A_       :    Optional[int]     ,		A_       :    Dict     ,		A_       :    Any   ):
  """simple docstring"""







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

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

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






def 	_snake_case						(      A_       :    int     ,		A_       :    Dict="robotic cat with wings"     ,		A_       :    Any=7.5     ,		A_       :    List[Any]=50     ,		A_       :    Optional[Any]=1     ,		A_       :    Dict=42     ,		):
  """simple docstring"""







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


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

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







  if len(A_   ) <= 1:
    return [tuple(A_   )]

  a_      :	List[Any]      =							[]

  def generate(A_       :    int     ,		A_       :    list   ):
    a_      :	List[Any]      =							[0] * n
    res.append(tuple(A_   )   )

    a_      :	List[str]      =							0
    while i < n:
      if c[i] < i:
        if i % 2 == 0:
          a_      , a_      :	Union[str, Any]      =							arr[i], arr[0]
        else:
          a_      , a_      :	List[str]      =							arr[i], arr[c[i]]
        res.append(tuple(A_   )   )
        c[i] += 1
        a_      :	Optional[Any]      =							0
      else:
        a_      :	Union[str, Any]      =							0
        i += 1

  generate(len(A_   )     ,		A_   )
  return res


if __name__ == "__main__":
    __snake_case:  Dict								=			input("Enter numbers separated by a comma:\n").strip()
    __snake_case:  Optional[Any]								=			[int(item) for item in user_input.split(",")]
    print(heaps(arr))


