




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class       a__   ( __snake_case				):
						_a							:   int =   ["""image_processor""", """tokenizer"""]
						_a							:   List[str] =   """CLIPImageProcessor"""
						_a							:   Any =   ("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")






						def __init__(       self		,      _A=None		,      _A=None		,      **_A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	None
											if "feature_extractor" in kwargs:
																warnings.warn(
																    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																    " instead."		,      a_		,      )
																__lowerCAmelCase           =	kwargs.pop("feature_extractor"      )

											__lowerCAmelCase           =	image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																raise ValueError("You need to specify an `image_processor`."      )
											if tokenizer is None:
																raise ValueError("You need to specify a `tokenizer`."      )

											super().__init__(a_		,      a_      )






						def __call__(       self		,      _A=None		,      _A=None		,      _A=None		,      **_A      ):




											"""simple docstring"""



											if text is None and images is None:
																raise ValueError("You have to specify either text or images. Both cannot be none."      )

											if text is not None:
																__lowerCAmelCase           =	self.tokenizer(a_		,      return_tensors=a_		,      **a_      )

											if images is not None:
																__lowerCAmelCase           =	self.image_processor(a_		,      return_tensors=a_		,      **a_      )

											if text is not None and images is not None:
																__lowerCAmelCase           =	image_features.pixel_values
																return encoding
											elif text is not None:
																return encoding
											else:
																return BatchEncoding(data=dict(**a_      )		,      tensor_type=a_      )






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




											"""simple docstring"""



											return self.tokenizer.batch_decode(*a_		,      **a_      )






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




											"""simple docstring"""



											return self.tokenizer.decode(*a_		,      **a_      )






						@property
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	self.tokenizer.model_input_names
											__lowerCAmelCase           =	self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )










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)




