



"""simple docstring"""

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase__       :    Optional[Any]				       =      False
lowercase__       :    Optional[Any]				       =      True
lowercase__       :    List[Any]				       =      False


if __name__ == "__main__":
     lowercase__       :    int				       =      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.''')

     lowercase__       :    Tuple				       =      parser.parse_args()

     lowercase__       :    Any				       =      {
         '''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''',
     }

     lowercase__       :    Optional[int]				       =      {
         '''time_steps''': '''time_proj''',
         '''mid''': '''mid_block''',
         '''downsample_blocks''': '''down_blocks''',
         '''upsample_blocks''': '''up_blocks''',
     }

     lowercase__       :    int				       =      '''''' 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:
          lowercase__       :    Dict				       =      reader.read()
          lowercase__       :    str				       =      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'''):
          lowercase__       :    Optional[int]				       =      UNetaDModel(**config)
     else:
          lowercase__       :    Any				       =      UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
          lowercase__       :    Optional[int]				       =      class_name(**config)

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

     lowercase__       :    List[str]				       =      dict(model.config)

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

          lowercase__       :    Dict				       =      [k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
          lowercase__       :    List[Any]				       =      [k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

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

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

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








from collections import deque





def      A__					(			__A  :						Optional[Any]			)					->Tuple:
      __A    =len(__A			)
      __A    =deque()
      __A    =[False for _ in range(__A			)]
      __A    =[-1 for _ in range(__A			)]
      __A    =index_of[:]

      def strong_connect(__A  :						Union[str, Any]     ,				__A  :						int     ,				__A  :						Optional[int]			):
            __A    =index  # the number when this node is seen
            __A    =index  # lowest rank node reachable from here
            index += 1
            stack.append(__A			)
            __A    =True

            for w in g[v]:
                  if index_of[w] == -1:
                        __A    =strong_connect(__A     ,				__A     ,				__A			)
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )
                  elif on_stack[w]:
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )

            if lowlink_of[v] == index_of[v]:
                  __A    =[]
                  __A    =stack.pop()
                  __A    =False
                  component.append(__A			)
                  while w != v:
                        __A    =stack.pop()
                        __A    =False
                        component.append(__A			)
                  components.append(__A			)
            return index

      __A    =[]
      for v in range(__A			):
            if index_of[v] == -1:
                  strong_connect(__A     ,				0     ,				__A			)

      return components





def      A__					(			__A  :						List[Any]     ,				__A  :						Optional[int]			)					->Tuple:
      __A    =[[] for _ in range(__A			)]
      for u, v in edges:
            g[u].append(__A			)
      return g


if __name__ == "__main__":
   # Test
   _lowerCamelCase      :					int					    =      7
   _lowerCamelCase      :					Tuple					    =      [0, 0, 1, 2, 3, 3, 4, 4, 6]
   _lowerCamelCase      :					Dict					    =      [1, 3, 2, 0, 1, 4, 5, 6, 5]
   _lowerCamelCase      :					Optional[Any]					    =      [(u, v) for u, v in zip(source, target)]
   _lowerCamelCase      :					Dict					    =      create_graph(n_vertices, edges)

   assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


