
'''simple docstring'''




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
      lowercase     :							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.',
      )
      lowercase     :							Optional[Any]       						=     parser.parse_args()

      lowercase     :							int       						=     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'''







import numpy as np

lowercase      :   str      =       [
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]







class 					_a :





     '''simple docstring'''



     def __init__(				self							)      ->				None:
          snake_case			:      Dict        =			np.array(__a							)







     def 			snake_case_      (				self							,__a							)      ->				np.ndarray:
          snake_case ,		snake_case			:      Optional[Any]        =			np.where(letter == self.SQUARE							)
          snake_case			:      List[Any]        =			np.concatenate([indexa + 1, indexa + 1]							)
          return indexes







     def 			snake_case_      (				self							,__a							,__a							)      ->				str:
          snake_case			:      List[Any]        =			self.SQUARE[indexa - 1, indexa - 1]
          return letter







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      List[Any]        =			message.lower()
          snake_case			:      Dict        =			message.replace(""" """							,""""""							)
          snake_case			:      List[str]        =			message.replace("""j"""							,"""i"""							)

          snake_case			:      Optional[int]        =			np.empty((2, len(__a							))							)
          for letter_index in range(len(__a							)							):
               snake_case			:      Tuple        =			self.letter_to_numbers(message[letter_index]							)

               snake_case			:      Optional[Any]        =			numbers[0]
               snake_case			:      List[str]        =			numbers[1]

          snake_case			:      List[str]        =			first_step.reshape(2 * len(__a							)							)
          snake_case			:      List[Any]        =			""""""
          for numbers_index in range(len(__a							)							):
               snake_case			:      List[str]        =			int(second_step[numbers_index * 2]							)
               snake_case			:      List[Any]        =			int(second_step[(numbers_index * 2) + 1]							)
               snake_case			:      str        =			self.numbers_to_letter(__a							,__a							)
               snake_case			:      Any        =			encoded_message + letter

          return encoded_message







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      Any        =			message.lower()
          message.replace(""" """							,""""""							)
          snake_case			:      Dict        =			np.empty(2 * len(__a							)							)
          for letter_index in range(len(__a							)							):
               snake_case			:      str        =			self.letter_to_numbers(message[letter_index]							)
               snake_case			:      Any        =			numbers[0]
               snake_case			:      int        =			numbers[1]

          snake_case			:      List[str]        =			first_step.reshape((2, len(__a							))							)
          snake_case			:      Dict        =			""""""
          for numbers_index in range(len(__a							)							):
               snake_case			:      Optional[Any]        =			int(second_step[0, numbers_index]							)
               snake_case			:      Optional[int]        =			int(second_step[1, numbers_index]							)
               snake_case			:      Tuple        =			self.numbers_to_letter(__a							,__a							)
               snake_case			:      int        =			decoded_message + letter

          return decoded_message



