

'''simple docstring'''





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


A_	= False
A_	= True
A_	= False


if __name__ == "__main__":
				A_	= 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_	= parser.parse_args()

				A_	= {
				    "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_	= {
				    "time_steps": "time_proj",
				    "mid": "mid_block",
				    "downsample_blocks": "down_blocks",
				    "upsample_blocks": "up_blocks",
				}

				A_	= "" 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_	= reader.read()
								A_	= 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_	= UNetaDModel(**config)
				else:
								A_	= UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
								A_	= class_name(**config)

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

				A_	= dict(model.config)

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

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

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

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

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










from ..utils import DummyObject, requires_backends




class 			__UpperCamelCase    (							metaclass=A__    ):
     __A	:  str							  =   ["""torch""", """scipy"""]





     def __init__(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        requires_backends(self				,							['''torch''', '''scipy''']					)





     @classmethod
     def 		UpperCamelCase(     cls				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        requires_backends(cls				,							['''torch''', '''scipy''']					)





     @classmethod
     def 		UpperCamelCase(     cls				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        requires_backends(cls				,							['''torch''', '''scipy''']					)