


'''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							:   Tuple  						=	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							:   Dict  						=	parser.parse_args()

 lowerCamelCase							:   List[str]  						=	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'''

class   __lowerCAmelCase						:  # Public class to implement a graph





      '''simple docstring'''






      def __init__(self    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    list[list[bool]] ):

             '''simple docstring'''







             lowercase__      			= row
             lowercase__      			= col
             lowercase__      			= graph



      def       UpperCamelCase__		(self    :    Optional[int]   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    list[list[bool]] ):

             '''simple docstring'''







             return (
                 0 <= i < self.ROW
                 and 0 <= j < self.COL
                 and not visited[i][j]
                 and self.graph[i][j]
             )



      def       UpperCamelCase__		(self    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    list[list[bool]] ):

             '''simple docstring'''







             lowercase__      			= [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
             lowercase__      			= [-1, 0, 1, -1, 1, -1, 0, 1]
             lowercase__      			= True  # Make those cells visited
             for k in range(8 ):
                    if self.is_safe(i + row_nbr[k]   ,						j + col_nbr[k]   ,						UpperCamelCase ):
                           self.diffs(i + row_nbr[k]   ,						j + col_nbr[k]   ,						UpperCamelCase )






      def       UpperCamelCase__		(self    :    Dict ):  # And finally, count all islands.

             '''simple docstring'''







             lowercase__      			= [[False for j in range(self.COL )] for i in range(self.ROW )]
             lowercase__      			= 0
             for i in range(self.ROW ):
                    for j in range(self.COL ):
                           if visited[i][j] is False and self.graph[i][j] == 1:
                                  self.diffs(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )
                                  count += 1
             return count




