



from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

_lowercase						  =		logging.get_logger(__name__)



@add_end_docstrings(A__     )
class     _UpperCAmelCase			(       A__     ):




     def __init__(				self ,						*a__ ,						**a__):
         super().__init__(*a__ ,						**a__)
         requires_backends(self ,						'''vision''')
         self.check_model_type(a__)




     def __call__(				self ,						a__ ,						**a__):
         return super().__call__(a__ ,						**a__)




     def 		snake_case_		(				self ,						**a__):
         return {}, {}, {}




     def 		snake_case_		(				self ,						a__):
         A__		 =					load_image(a__)
         A__		 =					image.size
         A__		 =					self.image_processor(images=a__ ,						return_tensors=self.framework)
         return model_inputs




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.model(**a__)
         return model_outputs




     def 		snake_case_		(				self ,						a__):
         A__		 =					model_outputs.predicted_depth
         A__		 =					torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1) ,						size=self.image_size[::-1] ,						mode='''bicubic''' ,						align_corners=a__)
         A__		 =					prediction.squeeze().cpu().numpy()
         A__		 =					(output * 2_5_5 / np.max(a__)).astype('''uint8''')
         A__		 =					Image.fromarray(a__)
         A__		 =					{}
         A__		 =					predicted_depth
         A__		 =					depth
         return output_dict





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
   _lowercase						  =		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 		lowerCAmelCase__    (					UpperCamelCase_				:       Any	)->  Union[str, Any]:
       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)

   _lowercase						  =		parser.parse_args()

   _lowercase						  =		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)
