

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
						_lowerCAmelCase					:			Optional[int]  				=			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_(							_snake_case   :  int	):




									"""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)

						_lowerCAmelCase					:			Optional[int]  				=			parser.parse_args()

						_lowerCAmelCase					:			Union[str, Any]  				=			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)














"""simple docstring"""



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class        a     :
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     int
    UpperCAmelCase_ :     int
    UpperCAmelCase_ :     float
    UpperCAmelCase_ :     float
    UpperCAmelCase_ :     Tuple[int]







    def 			UpperCamelCase_      (			self       ):
      assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
      assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
      assert len(self.x.shape       ) == len(self.y.shape       ) == len(self.z.shape       ) == len(self.origin.shape       ) == 2







    def 			UpperCamelCase_      (			self       ):
      return torch.from_numpy(np.array([self.width, self.height]							,		dtype=np.floataa       )       )







    def 			UpperCamelCase_      (			self       ):
      return torch.from_numpy(np.array([self.x_fov, self.y_fov]							,		dtype=np.floataa       )       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       torch.arange(self.height * self.width       )
      lowercase							    =       torch.stack(
          [
              pixel_indices % self.width,
              torch.div(_lowerCamelCase							,		self.width							,		rounding_mode='trunc'       ),
          ]							,		axis=1							,		)
      return coords







    @property
    def 			UpperCamelCase_      (			self       ):
      lowercase    ,     *lowercase							    =       self.shape
      lowercase							    =       int(np.prod(_lowerCamelCase       )       )

      lowercase							    =       self.get_image_coords()
      lowercase							    =       torch.broadcast_to(coords.unsqueeze(0       )							,		[batch_size * inner_batch_size, *coords.shape]       )
      lowercase							    =       self.get_camera_rays(_lowerCamelCase       )

      lowercase							    =       rays.view(_lowerCamelCase							,		inner_batch_size * self.height * self.width							,		2							,		3       )

      return rays







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase    ,     *lowercase    ,     lowercase							    =       coords.shape
      assert n_coords == 2
      assert batch_size == self.origin.shape[0]

      lowercase							    =       coords.view(_lowerCamelCase							,		-1							,		2       )

      lowercase							    =       self.resolution()
      lowercase							    =       self.fov()

      lowercase							    =       (flat.float() / (res - 1)) * 2 - 1
      lowercase							    =       fracs * torch.tan(fov / 2       )

      lowercase							    =       fracs.view(_lowerCamelCase							,		-1							,		2       )
      lowercase							    =       (
          self.z.view(_lowerCamelCase							,		1							,		3       )
          + self.x.view(_lowerCamelCase							,		1							,		3       ) * fracs[:, :, :1]
          + self.y.view(_lowerCamelCase							,		1							,		3       ) * fracs[:, :, 1:]
      )
      lowercase							    =       directions / directions.norm(dim=-1							,		keepdim=_lowerCamelCase       )
      lowercase							    =       torch.stack(
          [
              torch.broadcast_to(self.origin.view(_lowerCamelCase							,		1							,		3       )							,		[batch_size, directions.shape[1], 3]       ),
              directions,
          ]							,		dim=2							,		)
      return rays.view(_lowerCamelCase							,		*_lowerCamelCase							,		2							,		3       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      assert width * self.height == height * self.width, "The aspect ratio should not change."
      return DifferentiableProjectiveCamera(
          origin=self.origin							,		x=self.x							,		y=self.y							,		z=self.z							,		width=_lowerCamelCase							,		height=_lowerCamelCase							,		x_fov=self.x_fov							,		y_fov=self.y_fov							,		)


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int    ):





  '''simple docstring'''





  lowercase							    =       []
  lowercase							    =       []
  lowercase							    =       []
  lowercase							    =       []
  for theta in np.linspace(0		,					2 * np.pi		,					num=20    ):
    lowercase							    =       np.array([np.sin(__snake_case    ), np.cos(__snake_case    ), -0.5]    )
    z /= np.sqrt(np.sum(z**2    )    )
    lowercase							    =       -z * 4
    lowercase							    =       np.array([np.cos(__snake_case    ), -np.sin(__snake_case    ), 0.0]    )
    lowercase							    =       np.cross(__snake_case		,					__snake_case    )
    origins.append(__snake_case    )
    xs.append(__snake_case    )
    ys.append(__snake_case    )
    zs.append(__snake_case    )
  return DifferentiableProjectiveCamera(
      origin=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					x=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					y=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					z=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					width=__snake_case		,					height=__snake_case		,					x_fov=0.7		,					y_fov=0.7		,					shape=(1, len(__snake_case    ))		,					)


