

"""simple docstring"""




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


A_				:      Tuple           =			False
A_				:      int           =			True
A_				:      Any           =			False


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

						A_				:      Dict           =			parser.parse_args()

						A_				:      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",
						}

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

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

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

						A_				:      int           =			dict(model.config)

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

												A_				:      Optional[Any]           =			[k.replace("UNetRes", "") for k in config["down_block_types"]]
												A_				:      Tuple           =			[k.replace("UNetRes", "") for k in config["up_block_types"]]

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

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

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





"""simple docstring"""




def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__										=   2
			while i * i <= n:
						SCREAMING_SNAKE_CASE__										=   0
						while n % i == 0:
									n //= i
									multiplicity += 1
						n_divisors *= multiplicity + 1
						i += 1
			if n > 1:
						n_divisors *= 2
			return n_divisors

def 						A							(  ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__										=   1

			while True:
						i += 1
						t_num += i

						if count_divisors(snake_case__     ) > 5_00:
									break

			return t_num


if __name__ == "__main__":
						print(solution())


