






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
       a						:			int     	=					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__ (       __lowerCamelCase     :	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)

       a						:			List[str]     	=					parser.parse_args()

       a						:			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)





'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "CLIPImageProcessor"
 lowerCAmelCase__       = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
 def __init__(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					Dict:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE      = 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__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					int:


      """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:
           __SCREAMING_SNAKE_CASE      = self.tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

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

      if text is not None and images is not None:
           __SCREAMING_SNAKE_CASE      = image_features.pixel_values
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE     )   ,				tensor_type=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]   ,				*__SCREAMING_SNAKE_CASE   :			Tuple   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]   ,				*__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Optional[Any]:


      """simple docstring"""




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


 @property
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					List[Any]:


      """simple docstring"""




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






