


'''simple docstring'''




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))









'''simple docstring'''




print((lambda quine: quine % quine)("""print((lambda quine: quine %% quine)(%r))"""))





