'''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  UpperCamelCase__					(					)							->	List[Any]:
  __UpperCAmelCase:					Union[str, Any]		= argparse.ArgumentParser()
  parser.add_argument(
      """-m"""   ,    """--pretrained_model_name_or_path"""   ,    type=_lowercase   ,    default=_lowercase   ,    required=_lowercase   ,    help="""Path to pretrained model or model identifier from huggingface.co/models."""   ,    )
  parser.add_argument(
      """-c"""   ,    """--caption"""   ,    type=_lowercase   ,    default="""robotic cat with wings"""   ,    help="""Text used to generate images."""   ,    )
  parser.add_argument(
      """-n"""   ,    """--images_num"""   ,    type=_lowercase   ,    default=4   ,    help="""How much images to generate."""   ,    )
  parser.add_argument(
      """-s"""   ,    """--seed"""   ,    type=_lowercase   ,    default=4_2   ,    help="""Seed for random process."""   ,    )
  parser.add_argument(
      """-ci"""   ,    """--cuda_id"""   ,    type=_lowercase   ,    default=0   ,    help="""cuda_id."""   ,    )
  __UpperCAmelCase:					Optional[Any]		= parser.parse_args()
  return args






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				Union[str, Any]   ,    _lowercase    :				List[str]     )							->	int:
  if not len(_lowercase     ) == rows * cols:
    raise ValueError("""The specified number of rows and columns are not correct."""     )

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

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






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


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

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






from __future__ import annotations






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				int   ,    _lowercase    :				int   ,    _lowercase    :				int     )							->	None:
  if (direction == 1 and array[indexa] > array[indexa]) or (
      direction == 0 and array[indexa] < array[indexa]
  ):
    __UpperCAmelCase, __UpperCAmelCase:					str		= array[indexa], array[indexa]






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				int   ,    _lowercase    :				int   ,    _lowercase    :				int     )							->	None:
  if length > 1:
    __UpperCAmelCase:					List[str]		= int(length / 2     )
    for i in range(_lowercase   ,    low + middle     ):
      comp_and_swap(_lowercase   ,    _lowercase   ,    i + middle   ,    _lowercase     )
    bitonic_merge(_lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase     )
    bitonic_merge(_lowercase   ,    low + middle   ,    _lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				list[int]   ,    _lowercase    :				int   ,    _lowercase    :				int   ,    _lowercase    :				int     )							->	None:
  if length > 1:
    __UpperCAmelCase:					Any		= int(length / 2     )
    bitonic_sort(_lowercase   ,    _lowercase   ,    _lowercase   ,    1     )
    bitonic_sort(_lowercase   ,    low + middle   ,    _lowercase   ,    0     )
    bitonic_merge(_lowercase   ,    _lowercase   ,    _lowercase   ,    _lowercase     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			input('Enter numbers separated by a comma:\n').strip()
    SCREAMING_SNAKE_CASE_	   =			[int(item.strip()) for item in user_input.split(',')]

    bitonic_sort(unsorted, 0, len(unsorted), 1)
    print('\nSorted array in ascending order is: ', end='')
    print(*unsorted, sep=', ')

    bitonic_merge(unsorted, 0, len(unsorted), 0)
    print('Sorted array in descending order is: ', end='')
    print(*unsorted, sep=', ')