
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
					__magic_name__            =  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__    (      __lowerCAmelCase							):
									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)

					__magic_name__            =  parser.parse_args()

					__magic_name__            =  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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__magic_name__            =  {
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  ['''CLIPTokenizerFast''']

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  ['''CLIPFeatureExtractor''']
     __magic_name__            =  ['''CLIPImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''CLIPModel''',
         '''CLIPPreTrainedModel''',
         '''CLIPTextModel''',
         '''CLIPTextModelWithProjection''',
         '''CLIPVisionModel''',
         '''CLIPVisionModelWithProjection''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''TFCLIPModel''',
         '''TFCLIPPreTrainedModel''',
         '''TFCLIPTextModel''',
         '''TFCLIPVisionModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__            =  [
         '''FlaxCLIPModel''',
         '''FlaxCLIPPreTrainedModel''',
         '''FlaxCLIPTextModel''',
         '''FlaxCLIPTextPreTrainedModel''',
         '''FlaxCLIPVisionModel''',
         '''FlaxCLIPVisionPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_clip import (
         CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CLIPConfig,
         CLIPOnnxConfig,
         CLIPTextConfig,
         CLIPVisionConfig,
     )
     from .processing_clip import CLIPProcessor
     from .tokenization_clip import CLIPTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_clip_fast import CLIPTokenizerFast

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_clip import CLIPFeatureExtractor
          from .image_processing_clip import CLIPImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_clip import (
              CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              CLIPModel,
              CLIPPreTrainedModel,
              CLIPTextModel,
              CLIPTextModelWithProjection,
              CLIPVisionModel,
              CLIPVisionModelWithProjection,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_clip import (
              TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFCLIPModel,
              TFCLIPPreTrainedModel,
              TFCLIPTextModel,
              TFCLIPVisionModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_clip import (
              FlaxCLIPModel,
              FlaxCLIPPreTrainedModel,
              FlaxCLIPTextModel,
              FlaxCLIPTextPreTrainedModel,
              FlaxCLIPVisionModel,
              FlaxCLIPVisionPreTrainedModel,
          )


else:
     import sys

     __magic_name__            =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



