



"""simple docstring"""
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
       UpperCAmelCase						:		List[str]			= argparse.ArgumentParser()

       parser.add_argument(
           "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
       )
       parser.add_argument(
           "--original_config_file",
           type=str,
           required=True,
           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(
           "--image_size",
           default=512,
           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(
           "--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.)")


       def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						Optional[int]:







         '''simple docstring'''


         if string == "True":
           return True
         elif string == "False":
           return False
         else:
           raise ValueError(F'''could not parse string as bool {string}'''							)

       parser.add_argument(
           "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
       )

       parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

       UpperCAmelCase						:		Optional[Any]			= parser.parse_args()

       UpperCAmelCase						:		Union[str, Any]			= download_controlnet_from_original_ckpt(
           checkpoint_path=args.checkpoint_path,
           original_config_file=args.original_config_file,
           image_size=args.image_size,
           extract_ema=args.extract_ema,
           num_in_channels=args.num_in_channels,
           upcast_attention=args.upcast_attention,
           from_safetensors=args.from_safetensors,
           device=args.device,
           use_linear_projection=args.use_linear_projection,
           cross_attention_dim=args.cross_attention_dim,
       )


       controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






"""simple docstring"""
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						None:







  '''simple docstring'''


  lowercase_					,    lowercase_								= analyze_text(__lowerCAmelCase							)
  lowercase_								= list(""" """ + ascii_lowercase							)
  # what is our total sum of probabilities.
  lowercase_								= sum(single_char_strings.values()							)

  # one length string
  lowercase_								= 0
  # for each alpha we go in our dict and if it is in it we calculate entropy
  for ch in my_alphas:
    if ch in single_char_strings:
      lowercase_								= single_char_strings[ch]
      lowercase_								= my_str / all_sum
      my_fir_sum += prob * math.loga(__lowerCAmelCase							)  # entropy formula.

    # print entropy
  print(F'''{round(-1 * my_fir_sum							):.1f}'''							)

  # two len string
  lowercase_								= sum(two_char_strings.values()							)
  lowercase_								= 0
  # for each alpha (two in size) calculate entropy.
  for cha in my_alphas:
    for cha in my_alphas:
      lowercase_								= cha + cha
      if sequence in two_char_strings:
        lowercase_								= two_char_strings[sequence]
        lowercase_								= int(__lowerCAmelCase							) / all_sum
        my_sec_sum += prob * math.loga(__lowerCAmelCase							)

    # print second entropy
  print(F'''{round(-1 * my_sec_sum							):.1f}'''							)

  # print the difference between them
  print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}'''							)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						tuple[dict, dict]:







  '''simple docstring'''


  lowercase_								= Counter()  # type: ignore
  lowercase_								= Counter()  # type: ignore
  single_char_strings[text[-1]] += 1

  # first case when we have space at start.
  two_char_strings[" " + text[0]] += 1
  for i in range(0 ,  len(__lowerCAmelCase							) - 1							):
    single_char_strings[text[i]] += 1
    two_char_strings[text[i : i + 2]] += 1
  return single_char_strings, two_char_strings


def 			_SCREAMING_SNAKE_CASE						() ->						str:







  '''simple docstring'''


  import doctest

  doctest.testmod()
  # text = (
  #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
  #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
  #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
  #     "beloved evident through no service elderly is. Blind there if every no so "
  #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
  #     "of message cottage windows do besides against uncivil.  Delightful "
  #     "unreserved impossible few estimating men favourable see entreaties. She "
  #     "propriety immediate was improving. He or entrance humoured likewise "
  #     "moderate. Much nor game son say feel. Fat make met can must form into "
  #     "gate. Me we offending prevailed discovery. "
  # )

  # calculate_prob(text)


if __name__ == "__main__":
       main()

