




import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
 lowerCamelCase				:		Union[str, 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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Any     ):


       '''simple docstring'''



       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				:		List[Any]     =   parser.parse_args()

 lowerCamelCase				:		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)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase				:		str     =   {
    "configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		List[str]     =   [
     "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NezhaForNextSentencePrediction",
     "NezhaForMaskedLM",
     "NezhaForPreTraining",
     "NezhaForMultipleChoice",
     "NezhaForQuestionAnswering",
     "NezhaForSequenceClassification",
     "NezhaForTokenClassification",
     "NezhaModel",
     "NezhaPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nezha import (
      NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
      NezhaForMaskedLM,
      NezhaForMultipleChoice,
      NezhaForNextSentencePrediction,
      NezhaForPreTraining,
      NezhaForQuestionAnswering,
      NezhaForSequenceClassification,
      NezhaForTokenClassification,
      NezhaModel,
      NezhaPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase				:		str     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



