
"""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 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      str   			=  argparse.ArgumentParser()
  parser.add_argument(
      '''-m'''			,					'''--pretrained_model_name_or_path'''			,					type=_UpperCamelCase			,					default=_UpperCamelCase			,					required=_UpperCamelCase			,					help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,					)
  parser.add_argument(
      '''-c'''			,					'''--caption'''			,					type=_UpperCamelCase			,					default='''robotic cat with wings'''			,					help='''Text used to generate images.'''			,					)
  parser.add_argument(
      '''-n'''			,					'''--images_num'''			,					type=_UpperCamelCase			,					default=4			,					help='''How much images to generate.'''			,					)
  parser.add_argument(
      '''-s'''			,					'''--seed'''			,					type=_UpperCamelCase			,					default=42			,					help='''Seed for random process.'''			,					)
  parser.add_argument(
      '''-ci'''			,					'''--cuda_id'''			,					type=_UpperCamelCase			,					default=0			,					help='''cuda_id.'''			,					)
  _lowercase:      Dict   			=  parser.parse_args()
  return args




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



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

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

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




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase="robotic cat with wings"			,					_UpperCamelCase=7.5			,					_UpperCamelCase=50			,					_UpperCamelCase=1			,					_UpperCamelCase=42			,					):






  """simple docstring"""



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


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

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




from cva import destroyAllWindows, imread, imshow, waitKey




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase			,					_lowercase:      Union[str, Any]   			=  img.shape[0], img.shape[1]

  # converting each pixel's color to its negative
  for i in range(_UpperCamelCase    ):
    for j in range(_UpperCamelCase    ):
      _lowercase:      Dict   			=  [255, 255, 255] - img[i][j]

  return img


if __name__ == "__main__":
  # read original image
  A__ :       Optional[Any]				= imread('image_data/lena.jpg', 1)

  # convert to its negative
  A__ :       Dict				= convert_to_negative(img)

  # show result image
  imshow('negative of original image', img)
  waitKey(0)
  destroyAllWindows()
