




'''simple docstring'''







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
      lowercase      :   Any      =       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__				(      __lowercase				):
           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      :   Optional[int]      =       parser.parse_args()

      lowercase      :   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 logging

from transformers.configuration_utils import PretrainedConfig


lowercase      :   int      =       logging.getLogger(__name__)







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					"""masked_bert"""







     def __init__(				self							,__a=30_522							,__a=768							,__a=12							,__a=12							,__a=3_072							,__a="gelu"							,__a=0.1							,__a=0.1							,__a=512							,__a=2							,__a=0.02							,__a=1E-12							,__a=0							,__a="topK"							,__a="constant"							,__a=0.0							,**__a							,)      ->				List[str]:
          super().__init__(pad_token_id=__a							,**__a							)

          snake_case			:      Dict        =			vocab_size
          snake_case			:      Optional[Any]        =			hidden_size
          snake_case			:      Dict        =			num_hidden_layers
          snake_case			:      List[Any]        =			num_attention_heads
          snake_case			:      Dict        =			hidden_act
          snake_case			:      Any        =			intermediate_size
          snake_case			:      Optional[int]        =			hidden_dropout_prob
          snake_case			:      Optional[Any]        =			attention_probs_dropout_prob
          snake_case			:      List[Any]        =			max_position_embeddings
          snake_case			:      int        =			type_vocab_size
          snake_case			:      int        =			initializer_range
          snake_case			:      int        =			layer_norm_eps
          snake_case			:      Any        =			pruning_method
          snake_case			:      Union[str, Any]        =			mask_init
          snake_case			:      int        =			mask_scale



