



"""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__		:    Dict				   =     True
lowercase__		:    Optional[int]				   =     False


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

     lowercase__		:    int				   =     parser.parse_args()

     lowercase__		:    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""",
     }

     lowercase__		:    Union[str, Any]				   =     {
         """time_steps""": """time_proj""",
         """mid""": """mid_block""",
         """downsample_blocks""": """down_blocks""",
         """upsample_blocks""": """up_blocks""",
     }

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

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

     lowercase__		:    Any				   =     dict(model.config)

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

          lowercase__		:    str				   =     [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
          lowercase__		:    Dict				   =     [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

     if do_only_weights:
          lowercase__		:    Union[str, Any]				   =     torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

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

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










"""simple docstring"""







from collections.abc import Iterable
from typing import Generic, TypeVar

lowercase__		:    Any				   =     TypeVar("""_T""")







class 				UpperCamelCase__       (     Generic[_T]  ):




      """simple docstring"""




      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Iterable[_T] | None = None	):
        lowerCAmelCase_       :    list[_T]								=			list(iterable or []	)
        lowerCAmelCase_       :    list[_T]								=			[]



      def __len__(				self    :				List[str]	):
        return len(self._stacka	) + len(self._stacka	)



      def __repr__(				self    :				int	):
        return F"Queue({tuple(self._stacka[::-1] + self._stacka	)})"



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				_T	):
        self._stacka.append(SCREAMING_SNAKE_CASE_	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    List[str]								=			self._stacka.pop
        lowerCAmelCase_       :    int								=			self._stacka.append

        if not self._stacka:
          while self._stacka:
            stacka_append(stacka_pop()	)

        if not self._stacka:
          raise IndexError('Queue is empty'	)
        return self._stacka.pop()


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

     testmod()





