



"""simple docstring"""
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=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      __magic_name__				(							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)

   _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)








"""simple docstring"""
from typing import TYPE_CHECKING

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


_UpperCAmelCase     =			{"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			["""DPTFeatureExtractor"""]
   _UpperCAmelCase     =			["""DPTImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DPTForDepthEstimation""",
       """DPTForSemanticSegmentation""",
       """DPTModel""",
       """DPTPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_dpt import DPTFeatureExtractor
      from .image_processing_dpt import DPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dpt import (
          DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPTForDepthEstimation,
          DPTForSemanticSegmentation,
          DPTModel,
          DPTPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



