

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     def get_matched_characters(SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   str      ) -> str:
          lowercase__   :       str				=	[]
          lowercase__   :       Tuple				=	min(len(_stra      )     ,					len(_stra      )      ) // 2
          for i, l in enumerate(_stra      ):
               lowercase__   :       List[Any]				=	int(max(0     ,					i - limit      )      )
               lowercase__   :       Any				=	int(min(i + limit + 1     ,					len(_stra      )      )      )
               if l in _stra[left:right]:
                    matched.append(SCREAMING_SNAKE_CASE_      )
                    lowercase__   :       Dict				=	f"""{_stra[0:_stra.index(SCREAMING_SNAKE_CASE_      )]} {_stra[_stra.index(SCREAMING_SNAKE_CASE_      ) + 1:]}"""

          return "".join(SCREAMING_SNAKE_CASE_      )

     # matching characters
     lowercase__   :       Optional[Any]				=	get_matched_characters(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     lowercase__   :       List[str]				=	get_matched_characters(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Tuple				=	len(SCREAMING_SNAKE_CASE_      )

     # transposition
     lowercase__   :       int				=	(
         len([(ca, ca) for ca, ca in zip(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ) if ca != ca]      ) // 2
     )

     if not match_count:
          lowercase__   :       Any				=	0.0
     else:
          lowercase__   :       Optional[Any]				=	(
              1
              / 3
              * (
                  match_count / len(SCREAMING_SNAKE_CASE_      )
                  + match_count / len(SCREAMING_SNAKE_CASE_      )
                  + (match_count - transpositions) / match_count
              )
          )

     # common prefix up to 4 characters
     lowercase__   :       Optional[Any]				=	0
     for ca, ca in zip(stra[:4]     ,					stra[:4]      ):
          if ca == ca:
               prefix_len += 1
          else:
               break

     return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(jaro_winkler('''hello''', '''world'''))




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


snake_case_        =			False
snake_case_        =			True
snake_case_        =			False


if __name__ == "__main__":
       snake_case_        =			argparse.ArgumentParser()

       parser.add_argument(
           '''--repo_path''',
           default=None,
           type=str,
           required=True,
           help='''The config json file corresponding to the architecture.''',
       )

       parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

       snake_case_        =			parser.parse_args()

       snake_case_        =			{
           '''image_size''': '''sample_size''',
           '''num_res_blocks''': '''layers_per_block''',
           '''block_channels''': '''block_out_channels''',
           '''down_blocks''': '''down_block_types''',
           '''up_blocks''': '''up_block_types''',
           '''downscale_freq_shift''': '''freq_shift''',
           '''resnet_num_groups''': '''norm_num_groups''',
           '''resnet_act_fn''': '''act_fn''',
           '''resnet_eps''': '''norm_eps''',
           '''num_head_channels''': '''attention_head_dim''',
       }

       snake_case_        =			{
           '''time_steps''': '''time_proj''',
           '''mid''': '''mid_block''',
           '''downsample_blocks''': '''down_blocks''',
           '''upsample_blocks''': '''up_blocks''',
       }

       snake_case_        =			'''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

       with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
              snake_case_        =			reader.read()
              snake_case_        =			json.loads(text)

       if do_only_config:
              for key in config_parameters_to_change.keys():
                     config.pop(key, None)

       if has_file(args.repo_path, '''config.json'''):
              snake_case_        =			UNetaDModel(**config)
       else:
              snake_case_        =			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
              snake_case_        =			class_name(**config)

       if do_only_config:
              model.save_config(os.path.join(args.repo_path, subfolder))

       snake_case_        =			dict(model.config)

       if do_only_renaming:
              for key, value in config_parameters_to_change.items():
                     if key in config:
                            snake_case_        =			config[key]
                            del config[key]

              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

       if do_only_weights:
              snake_case_        =			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

              snake_case_        =			{}
              for param_key, param_value in state_dict.items():
                     if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
                            continue
                     snake_case_        =			False
                     for key, new_key in key_parameters_to_change.items():
                            if not has_changed and param_key.split('''.''')[0] == key:
                                   snake_case_        =			param_value
                                   snake_case_        =			True
                     if not has_changed:
                            snake_case_        =			param_value

              model.load_state_dict(new_state_dict)
              model.save_pretrained(os.path.join(args.repo_path, subfolder))

