


"""simple docstring"""





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


snake_case__						:   Optional[Any]			 = False
snake_case__						:   Union[str, Any]			 = True
snake_case__						:   Dict			 = False


if __name__ == "__main__":
 snake_case__						:   Tuple			 = 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__						:   Tuple			 = parser.parse_args()

 snake_case__						:   List[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''',
 }

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

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

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

 snake_case__						:   Any			 = dict(model.config)

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

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

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

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

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






"""simple docstring"""







from itertools import permutations


def      lowercase			(				A_       )->      bool:
  '''simple docstring'''







  if num[3] % 2 != 0:
    return False

  if (num[2] + num[3] + num[4]) % 3 != 0:
    return False

  if num[5] % 5 != 0:
    return False

  a						:		Optional[int]      							=			[7, 11, 13, 17]
  for i, test in enumerate(A_       ):
    if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
      return False
  return True


def      lowercase			(				A_ = 10       )->      int:
  '''simple docstring'''







  return sum(
      int("".join(map(A_  ,   A_       )       )       )
      for num in permutations(range(A_       )       )
      if is_substring_divisible(A_       )       )


if __name__ == "__main__":
 print(f'''{solution() = }''')






