





'''simple docstring'''

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__lowerCamelCase : Optional[int]											=			False
__lowerCamelCase : Optional[Any]											=			True
__lowerCamelCase : int											=			False


if __name__ == "__main__":
			__lowerCamelCase : Tuple											=			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.")

			__lowerCamelCase : List[str]											=			parser.parse_args()

			__lowerCamelCase : Union[str, 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",
			}

			__lowerCamelCase : int											=			{
			    "time_steps": "time_proj",
			    "mid": "mid_block",
			    "downsample_blocks": "down_blocks",
			    "upsample_blocks": "up_blocks",
			}

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

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

			__lowerCamelCase : List[Any]											=			dict(model.config)

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

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

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

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

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











'''simple docstring'''

from functools import lru_cache







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = 2
				lowercase         = set()
				while i * i <= n:
								if n % i:
												i += 1
								else:
												n //= i
												factors.add(lowerCAmelCase_							)
				if n > 1:
								factors.add(lowerCAmelCase_							)
				return factors







@lru_cache
def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return len(unique_prime_factors(lowerCAmelCase_							)							)







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return len(set(lowerCAmelCase_							)							) in (0, 1)







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = 2

				while True:
								# Increment each value of a generated range
								lowercase         = [base + i for i in range(lowerCAmelCase_							)]

								# Run elements through out unique_prime_factors function
								# Append our target number to the end.
								lowercase         = [upf_len(lowerCAmelCase_							) for x in group]
								checker.append(lowerCAmelCase_							)

								# If all numbers in the list are equal, return the group variable.
								if equality(lowerCAmelCase_							):
												return group

								# Increment our base variable by 1
								base += 1







def 							UpperCAmelCase_    (	lowerCAmelCase_ = 4							):

				"""simple docstring"""






				lowercase         = run(lowerCAmelCase_							)
				return results[0] if len(lowerCAmelCase_							) else None


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




