'''simple docstring'''


import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
       __snake_case						  =		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   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
         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)

       __snake_case						  =		parser.parse_args()

       __snake_case						  =		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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''mra'''




  def __init__(		self      :       Any   ,				lowercase_      :       List[str]=50_265   ,				lowercase_      :       Tuple=768   ,				lowercase_      :       Optional[Any]=12   ,				lowercase_      :       str=12   ,				lowercase_      :       Optional[int]=3_072   ,				lowercase_      :       Union[str, Any]="gelu"   ,				lowercase_      :       str=0.1   ,				lowercase_      :       Tuple=0.1   ,				lowercase_      :       str=512   ,				lowercase_      :       Optional[int]=1   ,				lowercase_      :       Tuple=0.0_2   ,				lowercase_      :       List[Any]=1e-5   ,				lowercase_      :       Optional[Any]="absolute"   ,				lowercase_      :       int=4   ,				lowercase_      :       int="full"   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       str=0   ,				lowercase_      :       List[str]=1   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       Optional[Any]=2   ,				**lowercase_      :       List[str]   ,				):







    '''simple docstring'''


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

    lowercase_     =       vocab_size
    lowercase_     =       max_position_embeddings
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_act
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       initializer_range
    lowercase_     =       type_vocab_size
    lowercase_     =       layer_norm_eps
    lowercase_     =       position_embedding_type
    lowercase_     =       block_per_row
    lowercase_     =       approx_mode
    lowercase_     =       initial_prior_first_n_blocks
    lowercase_     =       initial_prior_diagonal_n_blocks

