





'''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__    :      Any						=    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__    :      str						=    parser.parse_args()

  lowercase__    :      Any						=    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 sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403






def        _lowerCAmelCase			(  __snake_case  : str ,							__snake_case  : complex ,							__snake_case  : str = "x" ,							__snake_case  : float = 10**-10 ,							__snake_case  : int = 1 ,							)     ->     complex:
     __A						: int      =       symbols(__snake_case			)
     __A						: Tuple      =       lambdify(__snake_case ,							__snake_case			)
     __A						: Any      =       lambdify(__snake_case ,							diff(__snake_case ,							__snake_case			)			)

     __A						: str      =       starting_point

     while True:
          if diff_function(__snake_case			) != 0:
               __A						: Optional[Any]      =       prev_guess - multiplicity * func(__snake_case			) / diff_function(
                   __snake_case			)
          else:
               raise ZeroDivisionError('Could not find root'			) from None

          # Precision is checked by comparing the difference of consecutive guesses
          if abs(next_guess - prev_guess			) < precision:
               return next_guess

          __A						: Dict      =       next_guess


# Let's Execute
if __name__ == "__main__":
  # Find root of trigonometric function
  # Find value of pi
  print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

  # Find root of polynomial
  # Find fourth Root of 5
  print(f"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}""")

  # Find value of e
  print(
      '''The root of log(y) - 1 = 0 is ''',
      f"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
  )

  # Exponential Roots
  print(
      '''The root of exp(x) - 1 = 0 is''',
      f"""{newton_raphson("exp(x) - 1", 10, precision=0.005)}""",
  )

  # Find root of cos(x)
  print(f"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")