





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
      __lowerCamelCase						: List[str]		=   argparse.ArgumentParser()

      parser.add_argument(
          '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
      )
      # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
      parser.add_argument(
          '--original_config_file',
          default=None,
          type=str,
          help='The YAML config file corresponding to the original architecture.',
      )
      parser.add_argument(
          '--num_in_channels',
          default=None,
          type=int,
          help='The number of input channels. If `None` number of input channels will be automatically inferred.',
      )
      parser.add_argument(
          '--scheduler_type',
          default='pndm',
          type=str,
          help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
      )
      parser.add_argument(
          '--pipeline_type',
          default=None,
          type=str,
          help=(
              'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
              '. If `None` pipeline will be automatically inferred.'
          ),
      )
      parser.add_argument(
          '--image_size',
          default=None,
          type=int,
          help=(
              'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
              ' Base. Use 768 for Stable Diffusion v2.'
          ),
      )
      parser.add_argument(
          '--prediction_type',
          default=None,
          type=str,
          help=(
              'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
              ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
          ),
      )
      parser.add_argument(
          '--extract_ema',
          action='store_true',
          help=(
              'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
              ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
              ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
          ),
      )
      parser.add_argument(
          '--upcast_attention',
          action='store_true',
          help=(
              'Whether the attention computation should always be upcasted. This is necessary when running stable'
              ' diffusion 2.1.'
          ),
      )
      parser.add_argument(
          '--from_safetensors',
          action='store_true',
          help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
      )
      parser.add_argument(
          '--to_safetensors',
          action='store_true',
          help='Whether to store pipeline in safetensors format or not.',
      )
      parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
      parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
      parser.add_argument(
          '--stable_unclip',
          type=str,
          default=None,
          required=False,
          help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
      )
      parser.add_argument(
          '--stable_unclip_prior',
          type=str,
          default=None,
          required=False,
          help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
      )
      parser.add_argument(
          '--clip_stats_path',
          type=str,
          help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
          required=False,
      )
      parser.add_argument(
          '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
      )
      parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
      parser.add_argument(
          '--vae_path',
          type=str,
          default=None,
          required=False,
          help='Set to a path, hub id to an already converted vae to not convert it again.',
      )
      __lowerCamelCase						: List[Any]		=   parser.parse_args()

      __lowerCamelCase						: Tuple		=   download_from_original_stable_diffusion_ckpt(
          checkpoint_path=args.checkpoint_path,
          original_config_file=args.original_config_file,
          image_size=args.image_size,
          prediction_type=args.prediction_type,
          model_type=args.pipeline_type,
          extract_ema=args.extract_ema,
          scheduler_type=args.scheduler_type,
          num_in_channels=args.num_in_channels,
          upcast_attention=args.upcast_attention,
          from_safetensors=args.from_safetensors,
          device=args.device,
          stable_unclip=args.stable_unclip,
          stable_unclip_prior=args.stable_unclip_prior,
          clip_stats_path=args.clip_stats_path,
          controlnet=args.controlnet,
          vae_path=args.vae_path,
      )

      if args.half:
            pipe.to(torch_dtype=torch.floataa)

      if args.controlnet:
            # only save the controlnet model
            pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
      else:
            pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)













'''simple docstring'''






def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[[] for _ in range(__SCREAMING_SNAKE_CASE   )]
  _UpperCamelCase       			=key - 1

  if key <= 0:
    raise ValueError('''Height of grid can\'t be 0 or negative'''   )
  if key == 1 or len(__SCREAMING_SNAKE_CASE   ) <= key:
    return input_string

  for position, character in enumerate(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    temp_grid[num].append(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=[''''''.join(__SCREAMING_SNAKE_CASE   ) for row in temp_grid]
  _UpperCamelCase       			=''''''.join(__SCREAMING_SNAKE_CASE   )

  return output_string


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[]
  _UpperCamelCase       			=key - 1

  if key <= 0:
    raise ValueError('''Height of grid can\'t be 0 or negative'''   )
  if key == 1:
    return input_string

  _UpperCamelCase       			=[[] for _ in range(__SCREAMING_SNAKE_CASE   )]  # generates template
  for position in range(len(__SCREAMING_SNAKE_CASE   )   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    temp_grid[num].append('''*'''   )

  _UpperCamelCase       			=0
  for row in temp_grid:  # fills in the characters
    _UpperCamelCase       			=input_string[counter : counter + len(__SCREAMING_SNAKE_CASE   )]
    grid.append(list(__SCREAMING_SNAKE_CASE   )   )
    counter += len(__SCREAMING_SNAKE_CASE   )

  _UpperCamelCase       			=''''''  # reads as zigzag
  for position in range(len(__SCREAMING_SNAKE_CASE   )   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    output_string += grid[num][0]
    grid[num].pop(0   )
  return output_string


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			={}
  for key_guess in range(1  ,      len(__SCREAMING_SNAKE_CASE   )   ):  # tries every key
    _UpperCamelCase       			=decrypt(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  return results


if __name__ == "__main__":
      import doctest

      doctest.testmod()






