



from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
   _lowercase						  =		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 		lowerCAmelCase__    (					UpperCamelCase_				:       Any	)->  Union[str, Any]:
       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)

   _lowercase						  =		parser.parse_args()

   _lowercase						  =		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)
