



'''simple docstring'''






import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


lowerCAmelCase       		=      {
    """debug""": logging.DEBUG,
    """info""": logging.INFO,
    """warning""": logging.WARNING,
    """error""": logging.ERROR,
    """critical""": logging.CRITICAL,
}

lowerCAmelCase       		=      logging.WARNING






def      __A						(							):
		lowerCAmelCase     :    List[Any]						=   os.getenv("DATASETS_VERBOSITY"      ,a_	)
		if env_level_str:
				if env_level_str in log_levels:
						return log_levels[env_level_str]
				else:
						logging.getLogger().warning(
						    f'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
						    f'''has to be one of: { ", ".join(log_levels.keys()	) }'''	)
		return _default_log_level






def      __A						(							):
		return __name__.split("."	)[0]






def      __A						(							):
		return logging.getLogger(_get_library_name()	)






def      __A						(							):
		# Apply our default configuration to the library root logger.
		lowerCAmelCase     :    List[Any]						=   _get_library_root_logger()
		library_root_logger.setLevel(_get_default_logging_level()	)






def      __A						(							):
		lowerCAmelCase     :    int						=   _get_library_root_logger()
		library_root_logger.setLevel(logging.NOTSET	)






def      __A						(							a_						:  Optional[str] = None	):
		if name is None:
				lowerCAmelCase     :    Dict						=   _get_library_name()
		return logging.getLogger(a_	)






def      __A						(							):
		return _get_library_root_logger().getEffectiveLevel()






def      __A						(							a_						:  int	):
		_get_library_root_logger().setLevel(a_	)






def      __A						(							):
		return set_verbosity(a_	)






def      __A						(							):
		return set_verbosity(a_	)






def      __A						(							):
		return set_verbosity(a_	)






def      __A						(							):
		return set_verbosity(a_	)






def      __A						(							):
		lowerCAmelCase     :    Optional[int]						=   False






def      __A						(							):
		lowerCAmelCase     :    List[str]						=   True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class 		lowerCamelCase  :



	def __init__(   self		,      *a_		,      **a_	):  # pylint: disable=unused-argument
			lowerCAmelCase     :    Union[str, Any]						=   args[0] if args else None



	def __iter__(   self	):
			return iter(self._iterator	)



	def __getattr__(   self		,      a_	):

			def empty_fn(*a_		,      **a_	):  # pylint: disable=unused-argument
					return

			return empty_fn



	def __enter__(   self	):
			return self





	def __exit__(   self		,      a_		,      a_		,      a_	):
			return






lowerCAmelCase       		=      True
class 		lowerCamelCase  :



	def __call__(   self		,      *a_		,      a_=False		,      **a_	):
			if _tqdm_active and not disable:
					return tqdm_lib.tqdm(*a_		,      **a_	)
			else:
					return EmptyTqdm(*a_		,      **a_	)



	def   _lowerCamelCase    (   self		,      *a_		,      **a_	):
			lowerCAmelCase     :    str						=   None
			if _tqdm_active:
					return tqdm_lib.tqdm.set_lock(*a_		,      **a_	)





	def   _lowerCamelCase    (   self	):
			if _tqdm_active:
					return tqdm_lib.tqdm.get_lock()






lowerCAmelCase       		=      _tqdm_cls()






def      __A						(							):
		global _tqdm_active
		return bool(_tqdm_active	)






def      __A						(							):
		global _tqdm_active
		lowerCAmelCase     :    List[str]						=   True






def      __A						(							):
		global _tqdm_active
		lowerCAmelCase     :    str						=   False










'''simple docstring'''






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
					lowerCAmelCase       		=      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=5_12,
					    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      __A						(							a_						:  List[str]	):
							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)

					lowerCAmelCase       		=      parser.parse_args()

					lowerCAmelCase       		=      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)



