

'''simple docstring'''





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
  UpperCAmelCase__ :     Dict				    =    argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
  )
  parser.add_argument(
      """--original_config_file""",
      type=str,
      required=True,
      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(
      """--image_size""",
      default=512,
      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(
      """--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.)""")






  def 					__lowercase      (     _A			)     ->						Dict:

        if string == "True":
              return True
        elif string == "False":
              return False
        else:
              raise ValueError(F"could not parse string as bool {string}"			)

  parser.add_argument(
      """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
  )

  parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

  UpperCAmelCase__ :     Optional[Any]				    =    parser.parse_args()

  UpperCAmelCase__ :     Optional[Any]				    =    download_controlnet_from_original_ckpt(
      checkpoint_path=args.checkpoint_path,
      original_config_file=args.original_config_file,
      image_size=args.image_size,
      extract_ema=args.extract_ema,
      num_in_channels=args.num_in_channels,
      upcast_attention=args.upcast_attention,
      from_safetensors=args.from_safetensors,
      device=args.device,
      use_linear_projection=args.use_linear_projection,
      cross_attention_dim=args.cross_attention_dim,
  )







  controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__ :     str				    =    {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Any				    =    ["""LayoutXLMTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     List[str]				    =    ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
  from .processing_layoutxlm import LayoutXLMProcessor

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm import LayoutXLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
  import sys

  UpperCAmelCase__ :     List[str]				    =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




