import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
      lowercase_     :   Optional[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=5_1_2,
          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__      (	snake_case_ :		Tuple      ):

       '''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)

      lowercase_     :   Tuple 				=				parser.parse_args()

      lowercase_     :   Tuple 				=				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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_     :   Dict 				=				{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'AutoformerForPrediction',
          'AutoformerModel',
          'AutoformerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      lowercase_     :   Tuple 				=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

