






"""simple docstring"""







import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
lowerCAmelCase_	:    List[str]            =      {
    '''E''': 12.70,
    '''T''': 9.06,
    '''A''': 8.17,
    '''O''': 7.51,
    '''I''': 6.97,
    '''N''': 6.75,
    '''S''': 6.33,
    '''H''': 6.09,
    '''R''': 5.99,
    '''D''': 4.25,
    '''L''': 4.03,
    '''C''': 2.78,
    '''U''': 2.76,
    '''M''': 2.41,
    '''W''': 2.36,
    '''F''': 2.23,
    '''G''': 2.02,
    '''Y''': 1.97,
    '''P''': 1.93,
    '''B''': 1.29,
    '''V''': 0.98,
    '''K''': 0.77,
    '''J''': 0.15,
    '''X''': 0.15,
    '''Q''': 0.10,
    '''Z''': 0.07,
}
lowerCAmelCase_	:    Union[str, Any]            =      '''ETAOINSHRDLCUMWFGYPBVKJXQZ'''
lowerCAmelCase_	:    Dict            =      '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			{letter: 0 for letter in string.ascii_uppercase}
       for letter in message.upper():
              if letter in LETTERS:
                     letter_count[letter] += 1

       return letter_count






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       return x[0]






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			get_letter_count(lowerCAmelCase		)
       UpperCAmelCase	    =			{
           freq: [] for letter, freq in letter_to_freq.items()
       }
       for letter in LETTERS:
              freq_to_letter[letter_to_freq[letter]].append(lowerCAmelCase		)

       UpperCAmelCase	    =			{}

       for freq in freq_to_letter:
              freq_to_letter[freq].sort(key=ETAOIN.find	,      reverse=lowerCAmelCase		)
              UpperCAmelCase	    =			"""""".join(freq_to_letter[freq]		)

       UpperCAmelCase	    =			list(freq_to_letter_str.items()		)
       freq_pairs.sort(key=lowerCAmelCase	,      reverse=lowerCAmelCase		)

       UpperCAmelCase	    =			[freq_pair[1] for freq_pair in freq_pairs]

       return "".join(lowerCAmelCase		)






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			get_frequency_order(lowerCAmelCase		)
       UpperCAmelCase	    =			0
       for common_letter in ETAOIN[:6]:
              if common_letter in freq_order[:6]:
                     match_score += 1

       for uncommon_letter in ETAOIN[-6:]:
              if uncommon_letter in freq_order[-6:]:
                     match_score += 1

       return match_score


if __name__ == "__main__":
 import doctest

 doctest.testmod()








"""simple docstring"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_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.'''
 )
 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=5_1_2,
     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       _lowerCAmelCase       (      lowerCAmelCase		):







        '''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)

 lowerCAmelCase_	:    int            =      parser.parse_args()

 lowerCAmelCase_	:    Optional[int]            =      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)
