
"""simple docstring"""




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
  A__ :       Any				= 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.',
  )
  A__ :       Tuple				= parser.parse_args()

  A__ :       List[str]				= 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"""




import math
import sys




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  ''''''
  try:
    with open(_UpperCamelCase			,					'''rb'''    ) as binary_file:
      _lowercase:      Dict   			=  binary_file.read()
    for dat in data:
      _lowercase:      List[str]   			=  f'''{dat:08b}'''
      result += curr_byte
    return result
  except OSError:
    print('''File not accessible'''    )
    sys.exit()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  {'''0''': '''0''', '''1''': '''1'''}
  _lowercase			,					_lowercase:      Optional[int]   			=  '''''', ''''''
  _lowercase:      Dict   			=  len(_UpperCamelCase    )

  for i in range(len(_UpperCamelCase    )    ):
    curr_string += data_bits[i]
    if curr_string not in lexicon:
      continue

    _lowercase:      Any   			=  lexicon[curr_string]
    result += last_match_id
    _lowercase:      Optional[int]   			=  last_match_id + '''0'''

    if math.loga(_UpperCamelCase    ).is_integer():
      _lowercase:      str   			=  {}
      for curr_key in list(_UpperCamelCase    ):
        _lowercase:      List[Any]   			=  lexicon.pop(_UpperCamelCase    )
      _lowercase:      Tuple   			=  new_lex

    _lowercase:      List[Any]   			=  last_match_id + '''1'''
    index += 1
    _lowercase:      List[Any]   			=  ''''''
  return result




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Optional[Any]   			=  8
  try:
    with open(_UpperCamelCase			,					'''wb'''    ) as opened_file:
      _lowercase:      List[Any]   			=  [
          to_write[i : i + byte_length]
          for i in range(0			,					len(_UpperCamelCase    )			,					_UpperCamelCase    )
      ]

      if len(result_byte_array[-1]    ) % byte_length == 0:
        result_byte_array.append('''10000000'''    )
      else:
        result_byte_array[-1] += "1" + "0" * (
            byte_length - len(result_byte_array[-1]    ) - 1
        )

      for elem in result_byte_array[:-1]:
        opened_file.write(int(_UpperCamelCase			,					2    ).to_bytes(1			,					byteorder='''big'''    )    )
  except OSError:
    print('''File not accessible'''    )
    sys.exit()




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  0
  for letter in data_bits:
    if letter == "1":
      break
    counter += 1

  _lowercase:      List[str]   			=  data_bits[counter:]
  _lowercase:      Union[str, Any]   			=  data_bits[counter + 1 :]
  return data_bits




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Tuple   			=  read_file_binary(_UpperCamelCase    )
  _lowercase:      Optional[Any]   			=  remove_prefix(_UpperCamelCase    )
  _lowercase:      Dict   			=  decompress_data(_UpperCamelCase    )
  write_file_binary(_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  compress(sys.argv[1], sys.argv[2])
