





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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


__A							:			Optional[Any]         =			logging.get_logger(__name__)







class      lowerCamelCase(						__lowercase	):
     '''simple docstring'''


     __magic_name__   =			["pixel_values"]




     def __init__(    self				,     snake_case_ = True				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = PILImageResampling.BILINEAR				,     snake_case_ = True				,     snake_case_ = 1 / 255				,     snake_case_ = True				,     snake_case_ = None				,     snake_case_ = None				,     **snake_case_				,     ):
         super().__init__(**_a							)
         _A     =							size if size is not None else {'''shortest_edge''': 384}
         _A     =							get_size_dict(_a				,     default_to_square=_a							)

         _A     =							do_resize
         _A     =							size
         # Default value set here for backwards compatibility where the value in config is None
         _A     =							crop_pct if crop_pct is not None else 224 / 256
         _A     =							resample
         _A     =							do_rescale
         _A     =							rescale_factor
         _A     =							do_normalize
         _A     =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         _A     =							image_std if image_std is not None else IMAGENET_STANDARD_STD




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_ = PILImageResampling.BICUBIC				,     snake_case_ = None				,     **snake_case_				,     ):
         _A     =							get_size_dict(_a				,     default_to_square=_a							)
         if "shortest_edge" not in size:
             raise ValueError(F"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"							)
         _A     =							size['''shortest_edge''']

         if shortest_edge < 384:
             # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
             _A     =							int(shortest_edge / crop_pct							)
             _A     =							get_resize_output_image_size(_a				,     size=_a				,     default_to_square=_a							)
             _A     =							resize(image=_a				,     size=_a				,     resample=_a				,     data_format=_a				,     **_a							)
             # then crop to (shortest_edge, shortest_edge)
             return center_crop(image=_a				,     size=(shortest_edge, shortest_edge)				,     data_format=_a				,     **_a							)
         else:
             # warping (no cropping) when evaluated at 384 or larger
             return resize(
                 _a				,     size=(shortest_edge, shortest_edge)				,     resample=_a				,     data_format=_a				,     **_a							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_ = None				,     **snake_case_				,     ):
         return rescale(_a				,     scale=_a				,     data_format=_a				,     **_a							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_ = None				,     **snake_case_				,     ):
         return normalize(_a				,     mean=_a				,     std=_a				,     data_format=_a				,     **_a							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = ChannelDimension.FIRST				,     **snake_case_				,     ):
         _A     =							do_resize if do_resize is not None else self.do_resize
         _A     =							crop_pct if crop_pct is not None else self.crop_pct
         _A     =							resample if resample is not None else self.resample
         _A     =							do_rescale if do_rescale is not None else self.do_rescale
         _A     =							rescale_factor if rescale_factor is not None else self.rescale_factor
         _A     =							do_normalize if do_normalize is not None else self.do_normalize
         _A     =							image_mean if image_mean is not None else self.image_mean
         _A     =							image_std if image_std is not None else self.image_std

         _A     =							size if size is not None else self.size
         _A     =							get_size_dict(_a				,     default_to_square=_a							)

         _A     =							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 or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'							)

         if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
             raise ValueError('crop_pct must be specified if size < 384.'							)

         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.'							)

         # All transformations expect numpy arrays.
         _A     =							[to_numpy_array(_a							) for image in images]

         if do_resize:
             _A     =							[self.resize(image=_a				,     size=_a				,     crop_pct=_a				,     resample=_a							) for image in images]

         if do_rescale:
             _A     =							[self.rescale(image=_a				,     scale=_a							) for image in images]

         if do_normalize:
             _A     =							[self.normalize(image=_a				,     mean=_a				,     std=_a							) for image in images]

         _A     =							[to_channel_dimension_format(_a				,     _a							) for image in images]

         _A     =							{'''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__":
			a__												=						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   __UpperCAmelCase					(      __a :     Any      )     ->	List[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__												=						parser.parse_args()

			a__												=						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)



