





'''simple docstring'''

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
			__lowerCamelCase			:			List[str]									=      argparse.ArgumentParser()

			parser.add_argument(
			    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
			)
			# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
			parser.add_argument(
			    "--original_config_file",
			    default=None,
			    type=str,
			    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(
			    "--scheduler_type",
			    default="pndm",
			    type=str,
			    help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
			)
			parser.add_argument(
			    "--pipeline_type",
			    default=None,
			    type=str,
			    help=(
			        "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"
			        ". If `None` pipeline will be automatically inferred."
			    ),
			)
			parser.add_argument(
			    "--image_size",
			    default=None,
			    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(
			    "--prediction_type",
			    default=None,
			    type=str,
			    help=(
			        "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"
			        " Diffusion v2 Base. Use 'v_prediction' 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.)")
			parser.add_argument(
			    "--stable_unclip",
			    type=str,
			    default=None,
			    required=False,
			    help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.",
			)
			parser.add_argument(
			    "--stable_unclip_prior",
			    type=str,
			    default=None,
			    required=False,
			    help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.",
			)
			parser.add_argument(
			    "--clip_stats_path",
			    type=str,
			    help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.",
			    required=False,
			)
			parser.add_argument(
			    "--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint."
			)
			parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
			parser.add_argument(
			    "--vae_path",
			    type=str,
			    default=None,
			    required=False,
			    help="Set to a path, hub id to an already converted vae to not convert it again.",
			)
			__lowerCamelCase			:			List[str]									=      parser.parse_args()

			__lowerCamelCase			:			Dict									=      download_from_original_stable_diffusion_ckpt(
			    checkpoint_path=args.checkpoint_path,
			    original_config_file=args.original_config_file,
			    image_size=args.image_size,
			    prediction_type=args.prediction_type,
			    model_type=args.pipeline_type,
			    extract_ema=args.extract_ema,
			    scheduler_type=args.scheduler_type,
			    num_in_channels=args.num_in_channels,
			    upcast_attention=args.upcast_attention,
			    from_safetensors=args.from_safetensors,
			    device=args.device,
			    stable_unclip=args.stable_unclip,
			    stable_unclip_prior=args.stable_unclip_prior,
			    clip_stats_path=args.clip_stats_path,
			    controlnet=args.controlnet,
			    vae_path=args.vae_path,
			)

			if args.half:
						pipe.to(torch_dtype=torch.floataa)

			if args.controlnet:
						# only save the controlnet model
						pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
			else:
						pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





"""simple docstring"""






from copy import deepcopy





class 					_A		:

      def __init__(		self						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None		):







            """simple docstring"""



            if arr is None and size is not None:
                  lowercase								=      size
                  lowercase								=      [0] * size
            elif arr is not None:
                  self.init(__lowerCAmelCase		)
            else:
                  raise ValueError("""Either arr or size must be specified"""		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      len(__lowerCAmelCase		)
            lowercase								=      deepcopy(__lowerCAmelCase		)
            for i in range(1						,			self.size		):
                  lowercase								=      self.next_(__lowerCAmelCase		)
                  if j < self.size:
                        self.tree[j] += self.tree[i]

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.tree[:]
            for i in range(self.size - 1						,			0						,			-1		):
                  lowercase								=      self.next_(__lowerCAmelCase		)
                  if j < self.size:
                        arr[j] -= arr[i]
            return arr

      @staticmethod
      def 							A__			(		__lowerCAmelCase		):







            """simple docstring"""



            return index + (index & (-index))

      @staticmethod
      def 							A__			(		__lowerCAmelCase		):







            """simple docstring"""



            return index - (index & (-index))

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            if index == 0:
                  self.tree[0] += value
                  return
            while index < self.size:
                  self.tree[index] += value
                  lowercase								=      self.next_(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            self.add(__lowerCAmelCase						,			value - self.get(__lowerCAmelCase		)		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if right == 0:
                  return 0
            lowercase								=      self.tree[0]
            right -= 1  # make right inclusive
            while right > 0:
                  result += self.tree[right]
                  lowercase								=      self.prev(__lowerCAmelCase		)
            return result

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.prefix(__lowerCAmelCase		) - self.prefix(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.query(__lowerCAmelCase						,			index + 1		)







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            value -= self.tree[0]
            if value < 0:
                  return -1

            lowercase								=      1  # Largest power of 2 <= size
            while j * 2 < self.size:
                  j *= 2

            lowercase								=      0

            while j > 0:
                  if i + j < self.size and self.tree[i + j] <= value:
                        value -= self.tree[i + j]
                        i += j
                  j //= 2
            return i


if __name__ == "__main__":
       import doctest

       doctest.testmod()



