






'''simple docstring'''




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=False						)->    Dict:



			"""simple docstring"""



			snake_case_				:					Any   =       OmegaConf.load(__magic_name__						)
			if display:
						print(yaml.dump(OmegaConf.to_container(__magic_name__						)						)						)
			return config

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=None     ,__magic_name__=None						)->    List[str]:



			"""simple docstring"""



			if conf_path is None:
						snake_case_				:					str   =       "./model_checkpoints/vqgan_only.yaml"
			snake_case_				:					List[Any]   =       load_config(__magic_name__     ,display=__magic_name__						)
			snake_case_				:					Dict   =       VQModel(**config.model.params						)
			if ckpt_path is None:
						snake_case_				:					Union[str, Any]   =       "./model_checkpoints/vqgan_only.pt"
			snake_case_				:					Optional[Any]   =       torch.load(__magic_name__     ,map_location=__magic_name__						)
			if ".ckpt" in ckpt_path:
						snake_case_				:					Optional[int]   =       sd["state_dict"]
			model.load_state_dict(__magic_name__     ,strict=__magic_name__						)
			model.to(__magic_name__						)
			del sd
			return model

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_,	snake_case_,	snake_case_				:					Tuple   =       model.encode(__magic_name__						)
			print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''						)
			snake_case_				:					List[Any]   =       model.decode(__magic_name__						)
			return xrec

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__=False						)->    List[Any]:



			"""simple docstring"""



			snake_case_,	snake_case_				:					List[str]   =       string.rsplit("."     ,1						)
			if reload:
						snake_case_				:					Dict   =       importlib.import_module(__magic_name__						)
						importlib.reload(__magic_name__						)
			return getattr(importlib.import_module(__magic_name__     ,package=__magic_name__						)     ,cls						)

def 	__UpperCAmelCase       ( __magic_name__						)->    Tuple:



			"""simple docstring"""



			if "target" not in config:
						raise KeyError("Expected key `target` to instantiate."						)
			return get_obj_from_str(config["target"]						)(**config.get("params"     ,{}						)						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__=True     ,__magic_name__=True						)->    Optional[int]:



			"""simple docstring"""



			snake_case_				:					Any   =       instantiate_from_config(__magic_name__						)
			if sd is not None:
						model.load_state_dict(__magic_name__						)
			if gpu:
						model.cuda()
			if eval_mode:
						model.eval()
			return {"model": model}






def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)->    str:



			"""simple docstring"""



			if ckpt:
						snake_case_				:					Any   =       torch.load(__magic_name__     ,map_location="cpu"						)
						snake_case_				:					Any   =       pl_sd["global_step"]
						print(F'''loaded model from global step {global_step}.'''						)
			else:
						snake_case_				:					List[str]   =       {"state_dict": None}
						snake_case_				:					List[Any]   =       None
			snake_case_				:					str   =       load_model_from_config(config.model     ,pl_sd["state_dict"]     ,gpu=__magic_name__     ,eval_mode=__magic_name__						)["model"]
			return model, global_step












'''simple docstring'''




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def 	__UpperCAmelCase       ( __magic_name__=None						)->    List[str]:



			"""simple docstring"""



			if subparsers is not None:
						snake_case_				:					List[str]   =       subparsers.add_parser("test"						)
			else:
						snake_case_				:					List[Any]   =       argparse.ArgumentParser("Accelerate test command"						)

			parser.add_argument(
			    "--config_file"     ,default=__magic_name__     ,help=(
			        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
			        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
			        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
			        "with 'huggingface'."
			    )     ,)

			if subparsers is not None:
						parser.set_defaults(func=__magic_name__						)
			return parser

def 	__UpperCAmelCase       ( __magic_name__						)->    Tuple:



			"""simple docstring"""



			snake_case_				:					Optional[Any]   =       os.path.sep.join(__file__.split(os.path.sep						)[:-2] + ["test_utils", "scripts", "test_script.py"]						)

			if args.config_file is None:
						snake_case_				:					str   =       script_name
			else:
						snake_case_				:					Any   =       F'''--config_file={args.config_file} {script_name}'''

			snake_case_				:					Union[str, Any]   =       ["accelerate-launch"] + test_args.split()
			snake_case_				:					Optional[int]   =       execute_subprocess_async(__magic_name__     ,env=os.environ.copy()						)
			if result.returncode == 0:
						print("Test is a success! You are ready for your distributed training!"						)

def 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Dict   =       test_command_parser()
			snake_case_				:					Dict   =       parser.parse_args()
			test_command(__magic_name__						)


if __name__ == "__main__":
		main()




