import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
   __lowerCamelCase      :			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=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  ( __UpperCamelCase   :	List[str]     )							-> int:







     """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      :			List[str]            =		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_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCamelCase      :			Optional[Any]							       =		{
    '''configuration_efficientformer''': [
        '''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientFormerConfig''',
    ]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Dict							       =		['''EfficientFormerImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Optional[int]							       =		[
       '''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''EfficientFormerForImageClassification''',
       '''EfficientFormerForImageClassificationWithTeacher''',
       '''EfficientFormerModel''',
       '''EfficientFormerPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase      :			Any							       =		[
       '''TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFEfficientFormerForImageClassification''',
       '''TFEfficientFormerForImageClassificationWithTeacher''',
       '''TFEfficientFormerModel''',
       '''TFEfficientFormerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_efficientformer import EfficientFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_efficientformer import (
          EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          EfficientFormerForImageClassification,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerModel,
          EfficientFormerPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_efficientformer import (
          TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFEfficientFormerForImageClassification,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerModel,
          TFEfficientFormerPreTrainedModel,
      )

else:
   import sys

   __lowerCamelCase      :			List[Any]							       =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
