






"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A		:       Union[str, Any]  				=		logging.get_logger(__name__)





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				str							    =   ["pixel_values"]




					def __init__(				self     :						str    ,						A     :						bool = True    ,						A     :						Optional[Dict[str, int]] = None    ,						A     :						PILImageResampling = PILImageResampling.BICUBIC    ,						A     :						bool = True    ,						A     :						bool = True    ,						A     :						Union[int, float] = 1 / 2_55    ,						A     :						Dict[str, int] = None    ,						A     :						bool = True    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						Optional[Union[float, List[float]]] = None    ,						**A     :						Dict    ,						)  ->  None:
							super().__init__(**A   )
							lowercase_ : Tuple							       =   size if size is not None else {'''height''': 2_24, '''width''': 2_24}
							lowercase_ : Dict							       =   get_size_dict(A   )
							lowercase_ : Union[str, Any]							       =   crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							lowercase_ : List[Any]							       =   get_size_dict(A    ,						default_to_square=A    ,						param_name='''crop_size'''   )

							lowercase_ : Optional[Any]							       =   do_resize
							lowercase_ : List[str]							       =   do_rescale
							lowercase_ : Optional[int]							       =   do_normalize
							lowercase_ : List[str]							       =   do_center_crop
							lowercase_ : Union[str, Any]							       =   crop_size
							lowercase_ : Optional[Any]							       =   size
							lowercase_ : Optional[Any]							       =   resample
							lowercase_ : Optional[Any]							       =   rescale_factor
							lowercase_ : Tuple							       =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							lowercase_ : Tuple							       =   image_std if image_std is not None else IMAGENET_DEFAULT_STD




					def   A  (				self     :						str    ,						A     :						np.ndarray    ,						A     :						Dict[str, int]    ,						A     :						PILImageResampling = PILImageResampling.BILINEAR    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						List[str]    ,						)  ->  np.ndarray:
							lowercase_ : str							       =   get_size_dict(A   )

							if "shortest_edge" in size:
									lowercase_ : Dict							       =   get_resize_output_image_size(A    ,						size=size['''shortest_edge''']    ,						default_to_square=A   )
									# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
							elif "height" in size and "width" in size:
									lowercase_ : List[Any]							       =   (size['''height'''], size['''width'''])
							else:
									raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''   )
							return resize(A    ,						size=A    ,						resample=A    ,						data_format=A    ,						**A   )




					def   A  (				self     :						str    ,						A     :						np.ndarray    ,						A     :						Dict[str, int]    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						Dict    ,						)  ->  np.ndarray:
							lowercase_ : List[Any]							       =   get_size_dict(A   )
							if "height" not in size or "width" not in size:
									raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''   )
							return center_crop(A    ,						size=(size['''height'''], size['''width'''])    ,						data_format=A    ,						**A   )




					def   A  (				self     :						Tuple    ,						A     :						np.ndarray    ,						A     :						float    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						Any   )  ->  np.ndarray:
							return rescale(A    ,						scale=A    ,						data_format=A    ,						**A   )




					def   A  (				self     :						Optional[int]    ,						A     :						np.ndarray    ,						A     :						Union[float, List[float]]    ,						A     :						Union[float, List[float]]    ,						A     :						Optional[Union[str, ChannelDimension]] = None    ,						**A     :						Tuple    ,						)  ->  np.ndarray:
							return normalize(A    ,						mean=A    ,						std=A    ,						data_format=A    ,						**A   )






					def   A  (				self     :						Dict    ,						A     :						ImageInput    ,						A     :						Optional[bool] = None    ,						A     :						Dict[str, int] = None    ,						A     :						PILImageResampling = None    ,						A     :						bool = None    ,						A     :						int = None    ,						A     :						Optional[bool] = None    ,						A     :						Optional[float] = None    ,						A     :						Optional[bool] = None    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						Optional[Union[float, List[float]]] = None    ,						A     :						Optional[Union[str, TensorType]] = None    ,						A     :						Union[str, ChannelDimension] = ChannelDimension.FIRST    ,						**A     :						List[str]    ,						)  ->  BatchFeature:
							lowercase_ : str							       =   do_resize if do_resize is not None else self.do_resize
							lowercase_ : int							       =   do_rescale if do_rescale is not None else self.do_rescale
							lowercase_ : str							       =   do_normalize if do_normalize is not None else self.do_normalize
							lowercase_ : Any							       =   do_center_crop if do_center_crop is not None else self.do_center_crop
							lowercase_ : str							       =   crop_size if crop_size is not None else self.crop_size
							lowercase_ : int							       =   get_size_dict(A    ,						param_name='''crop_size'''    ,						default_to_square=A   )
							lowercase_ : Union[str, Any]							       =   resample if resample is not None else self.resample
							lowercase_ : Any							       =   rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase_ : Tuple							       =   image_mean if image_mean is not None else self.image_mean
							lowercase_ : Tuple							       =   image_std if image_std is not None else self.image_std

							lowercase_ : Union[str, Any]							       =   size if size is not None else self.size
							lowercase_ : str							       =   get_size_dict(A   )

							if not is_batched(A   ):
									lowercase_ : List[Any]							       =   [images]

							if not valid_images(A   ):
									raise ValueError(
									    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
									    '''torch.Tensor, tf.Tensor or jax.ndarray.'''   )

							if do_resize and size is None:
									raise ValueError('''Size must be specified if do_resize is True.'''   )

							if do_center_crop and crop_size is None:
									raise ValueError('''Crop size must be specified if do_center_crop is True.'''   )

							if do_rescale and rescale_factor is None:
									raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

							# All transformations expect numpy arrays.
							lowercase_ : Tuple							       =   [to_numpy_array(A   ) for image in images]

							if do_resize:
									lowercase_ : List[str]							       =   [self.resize(image=A    ,						size=A    ,						resample=A   ) for image in images]

							if do_center_crop:
									lowercase_ : List[Any]							       =   [self.center_crop(image=A    ,						size=A   ) for image in images]

							if do_rescale:
									lowercase_ : List[Any]							       =   [self.rescale(image=A    ,						scale=A   ) for image in images]

							if do_normalize:
									lowercase_ : Dict							       =   [self.normalize(image=A    ,						mean=A    ,						std=A   ) for image in images]

							lowercase_ : List[str]							       =   [to_channel_dimension_format(A    ,						A   ) for image in images]

							lowercase_ : Optional[Any]							       =   {'''pixel_values''': images}
							return BatchFeature(data=A    ,						tensor_type=A   )





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=5_12,
				    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 							_lowercase							(     UpperCamelCase_		)		->		Dict:
									'''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)

				__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)





