





import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __init__(							self		:					str		,lowerCamelCase__		:					Union[List[ControlNetModel], Tuple[ControlNetModel]] )  ->	Dict:




           '''simple docstring'''


           super().__init__()
           SCREAMING_SNAKE_CASE	=		nn.ModuleList(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					torch.FloatTensor		,lowerCamelCase__		:					Union[torch.Tensor, float, int]		,lowerCamelCase__		:					torch.Tensor		,lowerCamelCase__		:					List[torch.tensor]		,lowerCamelCase__		:					List[float]		,lowerCamelCase__		:					Optional[torch.Tensor] = None		,lowerCamelCase__		:					Optional[torch.Tensor] = None		,lowerCamelCase__		:					Optional[torch.Tensor] = None		,lowerCamelCase__		:					Optional[Dict[str, Any]] = None		,lowerCamelCase__		:					bool = False		,lowerCamelCase__		:					bool = True		,)  ->	Union[ControlNetOutput, Tuple]:




           '''simple docstring'''


           for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase__		,lowerCamelCase__		,self.nets ) ):
                 SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		controlnet(
                     lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,)

                 # merge samples
                 if i == 0:
                       SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		down_samples, mid_sample
                 else:
                       SCREAMING_SNAKE_CASE	=		[
                           samples_prev + samples_curr
                           for samples_prev, samples_curr in zip(lowerCamelCase__		,lowerCamelCase__ )
                       ]
                       mid_block_res_sample += mid_sample

           return down_block_res_samples, mid_block_res_sample






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					Union[str, os.PathLike]		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					Callable = None		,lowerCamelCase__		:					bool = False		,lowerCamelCase__		:					Optional[str] = None		,)  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		0
           SCREAMING_SNAKE_CASE	=		save_directory
           for controlnet in self.nets:
                 controlnet.save_pretrained(
                     lowerCamelCase__		,is_main_process=lowerCamelCase__		,save_function=lowerCamelCase__		,safe_serialization=lowerCamelCase__		,variant=lowerCamelCase__		,)

                 idx += 1
                 SCREAMING_SNAKE_CASE	=		model_path_to_save + F"""_{idx}"""






     @classmethod
     def        SCREAMING_SNAKE_CASE__						(							cls		:					Optional[Any]		,lowerCamelCase__		:					Optional[Union[str, os.PathLike]]		,**lowerCamelCase__		:					Union[str, Any] )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		0
           SCREAMING_SNAKE_CASE	=		[]

           # load controlnet and append to list until no controlnet directory exists anymore
           # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
           # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
           SCREAMING_SNAKE_CASE	=		pretrained_model_path
           while os.path.isdir(lowerCamelCase__ ):
                 SCREAMING_SNAKE_CASE	=		ControlNetModel.from_pretrained(lowerCamelCase__		,**lowerCamelCase__ )
                 controlnets.append(lowerCamelCase__ )

                 idx += 1
                 SCREAMING_SNAKE_CASE	=		pretrained_model_path + F"""_{idx}"""

           logger.info(F"""{len(lowerCamelCase__ )} controlnets loaded from {pretrained_model_path}.""" )

           if len(lowerCamelCase__ ) == 0:
                 raise ValueError(
                     F"""No ControlNets found under {os.path.dirname(lowerCamelCase__ )}. Expected at least {pretrained_model_path + '_0'}.""" )

           return cls(lowerCamelCase__ )









import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		     = 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.""",
      )
      SCREAMING_SNAKE_CASE_		     = parser.parse_args()

      SCREAMING_SNAKE_CASE_		     = 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)


