




'''simple docstring'''

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
							a  :				List[str]									=  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 		__magic_name__    (	__UpperCAmelCase	)		->					Any:

												'''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)

							a  :				List[str]									=  parser.parse_args()

							a  :				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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
    "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
    # See all FNet models at https://huggingface.co/models?filter=fnet
}







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						"fnet"

     def __init__(  self		,     _a=3_2000		,     _a=768		,     _a=12		,     _a=3072		,     _a="gelu_new"		,     _a=0.1		,     _a=512		,     _a=4		,     _a=0.02		,     _a=1e-12		,     _a=False		,     _a=512		,     _a=3		,     _a=1		,     _a=2		,     **_a		,     )						->							int:
           super().__init__(pad_token_id=_a		,     bos_token_id=_a		,     eos_token_id=_a		,     **_a				)

           _A							:     Any			=						vocab_size
           _A							:     str			=						max_position_embeddings
           _A							:     Optional[Any]			=						hidden_size
           _A							:     List[str]			=						num_hidden_layers
           _A							:     List[str]			=						intermediate_size
           _A							:     List[Any]			=						hidden_act
           _A							:     List[str]			=						hidden_dropout_prob
           _A							:     List[str]			=						initializer_range
           _A							:     List[Any]			=						type_vocab_size
           _A							:     List[Any]			=						layer_norm_eps
           _A							:     List[str]			=						use_tpu_fourier_optimizations
           _A							:     str			=						tpu_short_seq_length

