





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase    : int    	=  False
lowercase    : Optional[int]    	=  True
lowercase    : str    	=  False


if __name__ == "__main__":
							lowercase    : Union[str, 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    : Optional[int]    	=  parser.parse_args()

							lowercase    : 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""",
							}

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

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

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

							lowercase    : Optional[int]    	=  dict(model.config)

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

														lowercase    : Tuple    	=  [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    : Optional[int]    	=  torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

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

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













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						[]
       if len(lowerCAmelCase					) == 1:
              return [nums.copy()]
       for _ in range(len(lowerCAmelCase					)					):
              _UpperCamelCase              =						nums.pop(0					)
              _UpperCamelCase              =						permute(lowerCAmelCase					)
              for perm in permutations:
                     perm.append(lowerCAmelCase					)
              result.extend(lowerCAmelCase					)
              nums.append(lowerCAmelCase					)
       return result


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):

       def backtrack(lowerCAmelCase					):
              if start == len(lowerCAmelCase					) - 1:
                     output.append(nums[:]					)
              else:
                     for i in range(lowerCAmelCase    ,       len(lowerCAmelCase					)					):
                            _UpperCamelCase       ,		_UpperCamelCase              =						nums[i], nums[start]
                            backtrack(start + 1					)
                            _UpperCamelCase       ,		_UpperCamelCase              =						nums[i], nums[start]  # backtrack

       _UpperCamelCase              =						[]
       backtrack(0					)
       return output


if __name__ == "__main__":
       import doctest

       # use res to print the data in permute2 function
       lowercase    : Optional[Any]    	=  permutea([1, 2, 3])
       print(res)
       doctest.testmod()






