



'''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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase       		=      logging.get_logger(__name__)


if is_vision_available():
					import PIL
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   ["pixel_values"]



	def __init__(   self		,      a_ = True		,      a_ = None		,      a_ = PILImageResampling.BICUBIC		,      a_ = True		,      a_ = None		,      a_ = True		,      a_ = 1 / 255		,      a_ = True		,      a_ = None		,      a_ = None		,      a_ = True		,      **a_		,      ):
			super().__init__(**a_	)
			lowerCAmelCase     :    Optional[int]						=   size if size is not None else {"shortest_edge": 224}
			lowerCAmelCase     :    int						=   get_size_dict(a_		,      default_to_square=a_	)
			lowerCAmelCase     :    List[Any]						=   crop_size if crop_size is not None else {"height": 224, "width": 224}
			lowerCAmelCase     :    Tuple						=   get_size_dict(a_		,      default_to_square=a_		,      param_name="crop_size"	)

			lowerCAmelCase     :    Optional[int]						=   do_resize
			lowerCAmelCase     :    Any						=   size
			lowerCAmelCase     :    int						=   resample
			lowerCAmelCase     :    Dict						=   do_center_crop
			lowerCAmelCase     :    int						=   crop_size
			lowerCAmelCase     :    int						=   do_rescale
			lowerCAmelCase     :    str						=   rescale_factor
			lowerCAmelCase     :    Dict						=   do_normalize
			lowerCAmelCase     :    Any						=   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
			lowerCAmelCase     :    Any						=   image_std if image_std is not None else OPENAI_CLIP_STD
			lowerCAmelCase     :    List[str]						=   do_convert_rgb



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_ = PILImageResampling.BICUBIC		,      a_ = None		,      **a_		,      ):
			lowerCAmelCase     :    Tuple						=   get_size_dict(a_		,      default_to_square=a_	)
			if "shortest_edge" not in size:
					raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''	)
			lowerCAmelCase     :    Tuple						=   get_resize_output_image_size(a_		,      size=size["shortest_edge"]		,      default_to_square=a_	)
			return resize(a_		,      size=a_		,      resample=a_		,      data_format=a_		,      **a_	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_ = None		,      **a_		,      ):
			lowerCAmelCase     :    List[str]						=   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   _lowerCamelCase    (   self		,      a_		,      a_		,      a_ = None		,      **a_		,      ):
			return rescale(a_		,      scale=a_		,      data_format=a_		,      **a_	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_ = None		,      **a_		,      ):
			return normalize(a_		,      mean=a_		,      std=a_		,      data_format=a_		,      **a_	)





	def   _lowerCamelCase    (   self		,      a_		,      a_ = None		,      a_ = None		,      a_ = None		,      a_ = None		,      a_ = None		,      a_ = None		,      a_ = None		,      a_ = None		,      a_ = None		,      a_ = None		,      a_ = None		,      a_ = None		,      a_ = ChannelDimension.FIRST		,      **a_		,      ):
			lowerCAmelCase     :    Any						=   do_resize if do_resize is not None else self.do_resize
			lowerCAmelCase     :    List[Any]						=   size if size is not None else self.size
			lowerCAmelCase     :    int						=   get_size_dict(a_		,      param_name="size"		,      default_to_square=a_	)
			lowerCAmelCase     :    int						=   resample if resample is not None else self.resample
			lowerCAmelCase     :    Optional[Any]						=   do_center_crop if do_center_crop is not None else self.do_center_crop
			lowerCAmelCase     :    List[str]						=   crop_size if crop_size is not None else self.crop_size
			lowerCAmelCase     :    Tuple						=   get_size_dict(a_		,      param_name="crop_size"		,      default_to_square=a_	)
			lowerCAmelCase     :    List[Any]						=   do_rescale if do_rescale is not None else self.do_rescale
			lowerCAmelCase     :    Optional[int]						=   rescale_factor if rescale_factor is not None else self.rescale_factor
			lowerCAmelCase     :    Optional[int]						=   do_normalize if do_normalize is not None else self.do_normalize
			lowerCAmelCase     :    Any						=   image_mean if image_mean is not None else self.image_mean
			lowerCAmelCase     :    List[str]						=   image_std if image_std is not None else self.image_std
			lowerCAmelCase     :    Union[str, Any]						=   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

			lowerCAmelCase     :    Optional[Any]						=   make_list_of_images(a_	)

			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."	)

			if do_normalize and (image_mean is None or image_std is None):
					raise ValueError("Image mean and std must be specified if do_normalize is True."	)

			# PIL RGBA images are converted to RGB
			if do_convert_rgb:
					lowerCAmelCase     :    int						=   [convert_to_rgb(a_	) for image in images]

			# All transformations expect numpy arrays.
			lowerCAmelCase     :    int						=   [to_numpy_array(a_	) for image in images]

			if do_resize:
					lowerCAmelCase     :    int						=   [self.resize(image=a_		,      size=a_		,      resample=a_	) for image in images]

			if do_center_crop:
					lowerCAmelCase     :    Dict						=   [self.center_crop(image=a_		,      size=a_	) for image in images]

			if do_rescale:
					lowerCAmelCase     :    str						=   [self.rescale(image=a_		,      scale=a_	) for image in images]

			if do_normalize:
					lowerCAmelCase     :    List[Any]						=   [self.normalize(image=a_		,      mean=a_		,      std=a_	) for image in images]

			lowerCAmelCase     :    int						=   [to_channel_dimension_format(a_		,      a_	) for image in images]

			lowerCAmelCase     :    Any						=   {"pixel_values": images}
			return BatchFeature(data=a_		,      tensor_type=a_	)










'''simple docstring'''






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
					lowerCAmelCase       		=      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      __A						(							a_						:  List[str]	):
							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)

					lowerCAmelCase       		=      parser.parse_args()

					lowerCAmelCase       		=      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)



