




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


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

UpperCamelCase__         =      logging.WARNING







def      _a				(						):
					__lowerCAmelCase           =	os.getenv("DATASETS_VERBOSITY"				,				SCREAMING_SNAKE_CASE_					)
					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           =	_get_library_root_logger()
					library_root_logger.setLevel(_get_default_logging_level()					)







def      _a				(						):
					__lowerCAmelCase           =	_get_library_root_logger()
					library_root_logger.setLevel(logging.NOTSET					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[str] = None					):
					if name is None:
										__lowerCAmelCase           =	_get_library_name()
					return logging.getLogger(SCREAMING_SNAKE_CASE_					)







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







def      _a				(						SCREAMING_SNAKE_CASE_							:    int					):
					_get_library_root_logger().setLevel(SCREAMING_SNAKE_CASE_					)







def      _a				(						):
					return set_verbosity(SCREAMING_SNAKE_CASE_					)







def      _a				(						):
					return set_verbosity(SCREAMING_SNAKE_CASE_					)







def      _a				(						):
					return set_verbosity(SCREAMING_SNAKE_CASE_					)







def      _a				(						):
					return set_verbosity(SCREAMING_SNAKE_CASE_					)







def      _a				(						):
					__lowerCAmelCase           =	False







def      _a				(						):
					__lowerCAmelCase           =	True


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






						def __init__(       self		,      *_A		,      **_A      ):  # pylint: disable=unused-argument




											"""simple docstring"""



											__lowerCAmelCase           =	args[0] if args else None






						def __iter__(       self      ):




											"""simple docstring"""



											return iter(self._iterator      )






						def __getattr__(       self		,      _A      ):




											"""simple docstring"""



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

											return empty_fn






						def __enter__(       self      ):




											"""simple docstring"""



											return self






						def __exit__(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											return






UpperCamelCase__         =      True
class       a__   :






						def __call__(       self		,      *_A		,      _A=False		,      **_A      ):




											"""simple docstring"""



											if _tqdm_active and not disable:
																return tqdm_lib.tqdm(*_A		,      **_A      )
											else:
																return EmptyTqdm(*_A		,      **_A      )






						def     __SCREAMING_SNAKE_CASE(       self		,      *_A		,      **_A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	None
											if _tqdm_active:
																return tqdm_lib.tqdm.set_lock(*_A		,      **_A      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											if _tqdm_active:
																return tqdm_lib.tqdm.get_lock()






UpperCamelCase__         =      _tqdm_cls()







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







def      _a				(						):
					global _tqdm_active
					__lowerCAmelCase           =	True







def      _a				(						):
					global _tqdm_active
					__lowerCAmelCase           =	False










import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
					UpperCamelCase__         =      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      _a				(						SCREAMING_SNAKE_CASE_							:    List[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)

					UpperCamelCase__         =      parser.parse_args()

					UpperCamelCase__         =      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)




