






'''simple docstring'''





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
 __A							:							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.""",
 )
 __A							:							Optional[int]  							=		parser.parse_args()

 __A							:							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 				lowercase       :


    '''simple docstring'''

    def __init__(					self     :     Union[str, Any]     )							->							None:

     '''simple docstring'''




     lowerCamelCase__  =     {}  # Mapping from char to TrieNode
     lowerCamelCase__  =     False

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     list[str]     )							->							None:

     '''simple docstring'''




     for word in words:
      self.insert(__lowerCamelCase     )

    def    a__				(					self     :     int				,     __lowerCamelCase     :     str     )							->							None:

     '''simple docstring'''




     lowerCamelCase__  =     self
     for char in word:
      if char not in curr.nodes:
       lowerCamelCase__  =     TrieNode()
      lowerCamelCase__  =     curr.nodes[char]
     lowerCamelCase__  =     True

    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     str     )							->							bool:

     '''simple docstring'''




     lowerCamelCase__  =     self
     for char in word:
      if char not in curr.nodes:
       return False
      lowerCamelCase__  =     curr.nodes[char]
     return curr.is_leaf




    def    a__				(					self     :     Optional[Any]				,     __lowerCamelCase     :     str     )							->							None:

     '''simple docstring'''




     def _delete(__lowerCamelCase     :     TrieNode				,     __lowerCamelCase     :     str				,     __lowerCamelCase     :     int     ) -> bool:
      if index == len(__lowerCamelCase     ):
       # If word does not exist
       if not curr.is_leaf:
        return False
       lowerCamelCase__  =     False
       return len(curr.nodes     ) == 0
      lowerCamelCase__  =     word[index]
      lowerCamelCase__  =     curr.nodes.get(__lowerCamelCase     )
      # If char not in current trie node
      if not char_node:
       return False
      # Flag to check if node can be deleted
      lowerCamelCase__  =     _delete(__lowerCamelCase				,     __lowerCamelCase				,     index + 1     )
      if delete_curr:
       del curr.nodes[char]
       return len(curr.nodes     ) == 0
      return delete_curr

     _delete(self				,     __lowerCamelCase				,     0     )






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 if node.is_leaf:
  print(lowercase__				,     end=" ")

 for key, value in node.nodes.items():
  print_words(lowercase__				,     word + key)






def    lowerCamelCase_     (    ):
 lowerCamelCase__  =     "banana bananas bandana band apple all beast".split()
 lowerCamelCase__  =     TrieNode()
 root.insert_many(lowercase__)
 # print_words(root, "")
 assert all(root.find(lowercase__) for word in words)
 assert root.find("banana")
 assert not root.find("bandanas")
 assert not root.find("apps")
 assert root.find("apple")
 assert root.find("all")
 root.delete("all")
 assert not root.find("all")
 root.delete("banana")
 assert not root.find("banana")
 assert root.find("bananas")
 return True






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 print(str(lowercase__)				,     "works!" if passes else "doesn't work :(")






def    lowerCamelCase_     (    ):
 assert test_trie()






def    lowerCamelCase_     (    ):
 print_results("Testing trie functionality"				,     test_trie())


if __name__ == "__main__":
 main()

