



import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
		_lowercase						: List[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       lowerCAmelCase_	(    _lowercase							:  Optional[Any])				->			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)

		_lowercase						: Dict     						=parser.parse_args()

		_lowercase						: Optional[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)





'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''camembert'''




  def __init__(		self      :       Union[str, Any]   ,				lowercase_      :       Union[str, Any]=30_522   ,				lowercase_      :       Optional[Any]=768   ,				lowercase_      :       Tuple=12   ,				lowercase_      :       Dict=12   ,				lowercase_      :       Tuple=3_072   ,				lowercase_      :       Optional[Any]="gelu"   ,				lowercase_      :       Optional[int]=0.1   ,				lowercase_      :       Optional[int]=0.1   ,				lowercase_      :       List[Any]=512   ,				lowercase_      :       Optional[int]=2   ,				lowercase_      :       str=0.0_2   ,				lowercase_      :       int=1e-12   ,				lowercase_      :       str=1   ,				lowercase_      :       List[str]=0   ,				lowercase_      :       int=2   ,				lowercase_      :       Union[str, Any]="absolute"   ,				lowercase_      :       Optional[Any]=True   ,				lowercase_      :       List[Any]=None   ,				**lowercase_      :       int   ,				):







    '''simple docstring'''


    super().__init__(pad_token_id=lowercase_   ,				bos_token_id=lowercase_   ,				eos_token_id=lowercase_   ,				**lowercase_      )

    lowercase_     =       vocab_size
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       hidden_act
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       max_position_embeddings
    lowercase_     =       type_vocab_size
    lowercase_     =       initializer_range
    lowercase_     =       layer_norm_eps
    lowercase_     =       position_embedding_type
    lowercase_     =       use_cache
    lowercase_     =       classifier_dropout






class       _a    (		__a      ):


  """simple docstring"""

  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    if self.task == "multiple-choice":
      lowercase_     =       {0: """batch""", 1: """choice""", 2: """sequence"""}
    else:
      lowercase_     =       {0: """batch""", 1: """sequence"""}
    return OrderedDict(
        [
            ("""input_ids""", dynamic_axis),
            ("""attention_mask""", dynamic_axis),
        ]      )

