

import os
from collections.abc import Iterator






def 					__lowercase      (     _A = "."			)     ->						Optional[int]:

      '''simple docstring'''





      for dir_path, dir_names, filenames in os.walk(lowerCamelCase__			):
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   [d for d in dir_names if d != "scripts" and d[0] not in "._"]
            for filename in filenames:
                  if filename == "__init__.py":
                        continue
                  if os.path.splitext(lowerCamelCase__			)[1] in (".py", ".ipynb"):
                        yield os.path.join(lowerCamelCase__						,    lowerCamelCase__			).lstrip("""./"""			)






def 					__lowercase      (     _A			)     ->						int:

      '''simple docstring'''





      return F"{i * '  '}*" if i else "\n##"






def 					__lowercase      (     _A						,    _A			)     ->						int:

      '''simple docstring'''





      SCREAMING_SNAKE_CASE :      List[Any]       =   old_path.split(os.sep			)
      for i, new_part in enumerate(new_path.split(os.sep			)			):
            if (i + 1 > len(lowerCamelCase__			) or old_parts[i] != new_part) and new_part:
                  print(F"{md_prefix(lowerCamelCase__			)} {new_part.replace('_'						,    ' '			).title()}"			)
      return new_path






def 					__lowercase      (     _A = "."			)     ->						List[Any]:

      '''simple docstring'''





      SCREAMING_SNAKE_CASE :      int       =   ""
      for filepath in sorted(good_file_paths(lowerCamelCase__			)			):
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   os.path.split(lowerCamelCase__			)
            if filepath != old_path:
                  SCREAMING_SNAKE_CASE :      List[Any]       =   print_path(lowerCamelCase__						,    lowerCamelCase__			)
            SCREAMING_SNAKE_CASE :      str       =   (filepath.count(os.sep			) + 1) if filepath else 0
            SCREAMING_SNAKE_CASE :      int       =   F"{filepath}/{filename}".replace(""" """						,    """%20"""			)
            SCREAMING_SNAKE_CASE :      str       =   os.path.splitext(filename.replace("""_"""						,    """ """			).title()			)[0]
            print(F"{md_prefix(lowerCamelCase__			)} [{filename}]({url})"			)


if __name__ == "__main__":
  print_directory_md(""".""")







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase__ :     Optional[Any]				    =    False
UpperCAmelCase__ :     Union[str, Any]				    =    True
UpperCAmelCase__ :     int				    =    False


if __name__ == "__main__":
  UpperCAmelCase__ :     List[Any]				    =    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.""")

  UpperCAmelCase__ :     List[str]				    =    parser.parse_args()

  UpperCAmelCase__ :     Dict				    =    {
      """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""",
  }

  UpperCAmelCase__ :     str				    =    {
      """time_steps""": """time_proj""",
      """mid""": """mid_block""",
      """downsample_blocks""": """down_blocks""",
      """upsample_blocks""": """up_blocks""",
  }

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

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

  UpperCAmelCase__ :     Optional[Any]				    =    dict(model.config)

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

    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

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

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

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




