





"""simple docstring"""


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__magic_name__       :					Dict						=			False
__magic_name__       :					str						=			True
__magic_name__       :					Union[str, Any]						=			False


if __name__ == "__main__":
		__magic_name__       :					int						=			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.""")

		__magic_name__       :					Union[str, Any]						=			parser.parse_args()

		__magic_name__       :					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""",
		}

		__magic_name__       :					Tuple						=			{
		    """time_steps""": """time_proj""",
		    """mid""": """mid_block""",
		    """downsample_blocks""": """down_blocks""",
		    """upsample_blocks""": """up_blocks""",
		}

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

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

		__magic_name__       :					Dict						=			dict(model.config)

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

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

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

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

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










"""simple docstring"""


import math

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						return math.pow(SCREAMING_SNAKE_CASE							,     2     ) - a

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return 2 * x

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Union[str, Any]				   =			2.0

						while start <= a:
												UpperCamelCase				:		Optional[Any]				   =			math.pow(SCREAMING_SNAKE_CASE							,     2     )

						return start

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE = 9999							,     SCREAMING_SNAKE_CASE = 0.00_00_00_00_00_00_01     ):

						if a < 0:
												raise ValueError("""math domain error"""     )

						UpperCamelCase				:		Optional[Any]				   =			get_initial_point(SCREAMING_SNAKE_CASE     )

						for _ in range(SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		List[str]				   =			value
												UpperCamelCase				:		Tuple				   =			value - fx(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ) / fx_derivative(SCREAMING_SNAKE_CASE     )
												if abs(prev_value - value     ) < tolerance:
																		return value

						return value


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

		testmod()



