






"""simple docstring"""







def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			word.split()

       def justify(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		) -> str:
              UpperCAmelCase	    =			max_width - width
              UpperCAmelCase	    =			len(__lowercase		)
              if len(__lowercase		) == 1:
                     # if there is only word in line
                     # just insert overall_spaces_count for the remainder of line
                     return line[0] + " " * overall_spaces_count
              else:
                     UpperCAmelCase	    =			words_count - 1
                     # num_spaces_between_words_list[i] : tells you to insert
                     # num_spaces_between_words_list[i] spaces
                     # after word on line[i]
                     UpperCAmelCase	    =			spaces_to_insert_between_words * [
                         overall_spaces_count // spaces_to_insert_between_words
                     ]
                     UpperCAmelCase	    =			(
                         overall_spaces_count % spaces_to_insert_between_words
                     )
                     # distribute spaces via round robin to the left words
                     for i in range(__lowercase		):
                            num_spaces_between_words_list[i] += 1
                     UpperCAmelCase	    =			[]
                     for i in range(__lowercase		):
                            # add the word
                            aligned_words_list.append(line[i]		)
                            # add the spaces to insert
                            aligned_words_list.append(num_spaces_between_words_list[i] * """ """		)
                     # just add the last word to the sentence
                     aligned_words_list.append(line[-1]		)
                     # join the aligned words list to form a justified line
                     return "".join(__lowercase		)

       UpperCAmelCase	    =			[]
       UpperCAmelCase	    =			[]
       UpperCAmelCase	    =			0
       for word in words:
              if width + len(__lowercase		) + len(__lowercase		) <= max_width:
                     # keep adding words until we can fill out max_width
                     # width = sum of length of all words (without overall_spaces_count)
                     # len(word) = length of current word
                     # len(line) = number of overall_spaces_count to insert between words
                     line.append(__lowercase		)
                     width += len(__lowercase		)
              else:
                     # justify the line and add it to result
                     answer.append(justify(__lowercase	,      __lowercase	,      __lowercase		)		)
                     # reset new line and new width
                     UpperCAmelCase	    =			[word], len(__lowercase		)
       UpperCAmelCase	    =			max_width - width - len(__lowercase		)
       answer.append(""" """.join(__lowercase		) + (remaining_spaces + 1) * """ """		)
       return answer


if __name__ == "__main__":
 from doctest import testmod

 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)
