


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


_UpperCAmelCase			:  List[Any]     =False
_UpperCAmelCase			:  Union[str, Any]     =True
_UpperCAmelCase			:  List[Any]     =False


if __name__ == "__main__":
						_UpperCAmelCase			:  List[str]     =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			:  Dict     =parser.parse_args()

						_UpperCAmelCase			:  List[str]     ={
						    """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			:  Dict     ={
						    """time_steps""": """time_proj""",
						    """mid""": """mid_block""",
						    """downsample_blocks""": """down_blocks""",
						    """upsample_blocks""": """up_blocks""",
						}

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

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

						_UpperCAmelCase			:  Tuple     =dict(model.config)

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

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

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

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

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



_UpperCAmelCase			:  Dict     =[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]



def 			lowerCAmelCase (		lowerCAmelCase_ )-> int:
				lowerCAmelCase_ :      Any    				=				{'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1_000}
				lowerCAmelCase_ :      Optional[int]    				=				0
				lowerCAmelCase_ :      List[str]    				=				0
				while place < len(lowerCAmelCase_ ):
								if (place + 1 < len(lowerCAmelCase_ )) and (vals[roman[place]] < vals[roman[place + 1]]):
												total += vals[roman[place + 1]] - vals[roman[place]]
												place += 2
								else:
												total += vals[roman[place]]
												place += 1
				return total



def 			lowerCAmelCase (		lowerCAmelCase_ )-> str:
				lowerCAmelCase_ :      List[Any]    				=				[]
				for arabic, roman in ROMAN:
								((lowerCAmelCase_)				,  (lowerCAmelCase_)) :      Optional[int]    				=				divmod(lowerCAmelCase_ ,   lowerCAmelCase_ )
								result.append(roman * factor )
								if number == 0:
												break
				return "".join(lowerCAmelCase_ )


if __name__ == "__main__":
						import doctest

						doctest.testmod()