





'''simple docstring'''


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_SCREAMING_SNAKE_CASE		       =							False
_SCREAMING_SNAKE_CASE		       =							True
_SCREAMING_SNAKE_CASE		       =							False


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

       _SCREAMING_SNAKE_CASE		       =							parser.parse_args()

       _SCREAMING_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''',
       }

       _SCREAMING_SNAKE_CASE		       =							{
           '''time_steps''': '''time_proj''',
           '''mid''': '''mid_block''',
           '''downsample_blocks''': '''down_blocks''',
           '''upsample_blocks''': '''up_blocks''',
       }

       _SCREAMING_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:
              _SCREAMING_SNAKE_CASE		       =							reader.read()
              _SCREAMING_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"):
              _SCREAMING_SNAKE_CASE		       =							UNetaDModel(**config)
       else:
              _SCREAMING_SNAKE_CASE		       =							UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
              _SCREAMING_SNAKE_CASE		       =							class_name(**config)

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

       _SCREAMING_SNAKE_CASE		       =							dict(model.config)

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

              _SCREAMING_SNAKE_CASE		       =							[k.replace("UNetRes", "") for k in config['''down_block_types''']]
              _SCREAMING_SNAKE_CASE		       =							[k.replace("UNetRes", "") for k in config['''up_block_types''']]

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

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

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





"""simple docstring"""





from ..utils import DummyObject, requires_backends







class 				SCREAMING_SNAKE_CASE					(		metaclass=a_       ):






					"""simple docstring"""



					lowercase__				 =       ["speech"]







					def __init__(       self			:				Tuple   ,*lowercase_			:				Tuple   ,**lowercase_			:				List[str]		):
						requires_backends(self   ,['''speech''']		)







class 				SCREAMING_SNAKE_CASE					(		metaclass=a_       ):






					"""simple docstring"""



					lowercase__				 =       ["speech"]







					def __init__(       self			:				Union[str, Any]   ,*lowercase_			:				List[str]   ,**lowercase_			:				Any		):
						requires_backends(self   ,['''speech''']		)




