



"""simple docstring"""

# Copyright 2022 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
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available





def       __magic_name__    (   _lowerCamelCase				: Union[str, Any]=None					):
							if subparsers is not None:
														__a		:		Union[str, Any]									=       subparsers.add_parser("""env"""					)
							else:
														__a		:		int									=       argparse.ArgumentParser("""Accelerate env command"""					)

							parser.add_argument(
							    """--config_file"""  ,       default=_lowerCamelCase  ,       help="""The config file to use for the default values in the launching script."""					)

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





def       __magic_name__    (   _lowerCamelCase				: List[str]					):
							__a		:		List[Any]									=       torch.__version__
							__a		:		Any									=       torch.cuda.is_available()
							__a		:		Dict									=       is_xpu_available()
							__a		:		Tuple									=       is_npu_available()

							__a		:		List[str]									=       """Not found"""
							# Get the default from the config file.
							if args.config_file is not None or os.path.isfile(_lowerCamelCase					):
														__a		:		Optional[Any]									=       load_config_from_file(args.config_file					).to_dict()

							__a		:		List[Any]									=       {
							    """`Accelerate` version""": version,
							    """Platform""": platform.platform(),
							    """Python version""": platform.python_version(),
							    """Numpy version""": np.__version__,
							    """PyTorch version (GPU?)""": F'''{pt_version} ({pt_cuda_available})''',
							    """PyTorch XPU available""": str(_lowerCamelCase					),
							    """PyTorch NPU available""": str(_lowerCamelCase					),
							    """System RAM""": F'''{psutil.virtual_memory().total / 1_0_2_4 ** 3:.2f} GB''',
							}
							if pt_cuda_available:
														__a		:		List[str]									=       torch.cuda.get_device_name()

							print("""\nCopy-and-paste the text below in your GitHub issue\n"""					)
							print("""\n""".join([F'''- {prop}: {val}''' for prop, val in info.items()]					)					)

							print("""- `Accelerate` default config:""" if args.config_file is None else """- `Accelerate` config passed:"""					)
							__a		:		List[str]									=       (
							    """\n""".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]					)
							    if isinstance(_lowerCamelCase  ,       _lowerCamelCase					)
							    else F'''\t{accelerate_config}'''
							)
							print(_lowerCamelCase					)

							__a		:		str									=       accelerate_config

							return info





def       __magic_name__    (   ):
							__a		:		Optional[Any]									=       env_command_parser()
							__a		:		Optional[Any]									=       parser.parse_args()
							env_command(_lowerCamelCase					)
							return 0


if __name__ == "__main__":
	raise SystemExit(main())






"""simple docstring"""

import importlib

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





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=False					):
							__a		:		Dict									=       OmegaConf.load(_lowerCamelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
							return config





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





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]					):
							__a							,     __a							,     __a		:		Tuple									=       model.encode(_lowerCamelCase					)
							print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
							__a		:		Union[str, Any]									=       model.decode(_lowerCamelCase					)
							return xrec





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]=False					):
							__a							,     __a		:		Optional[Any]									=       string.rsplit("""."""  ,       1					)
							if reload:
														__a		:		Optional[Any]									=       importlib.import_module(_lowerCamelCase					)
														importlib.reload(_lowerCamelCase					)
							return getattr(importlib.import_module(_lowerCamelCase  ,       package=_lowerCamelCase					)  ,       cls					)





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





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int=True  ,       _lowerCamelCase				: int=True					):
							__a		:		Union[str, Any]									=       instantiate_from_config(_lowerCamelCase					)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase					)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}







def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							# load the specified checkpoint
							if ckpt:
														__a		:		List[str]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
														__a		:		Any									=       pl_sd["""global_step"""]
														print(F'''loaded model from global step {global_step}.'''					)
							else:
														__a		:		List[Any]									=       {"""state_dict""": None}
														__a		:		Any									=       None
							__a		:		Union[str, Any]									=       load_model_from_config(config.model  ,       pl_sd["""state_dict"""]  ,       gpu=_lowerCamelCase  ,       eval_mode=_lowerCamelCase					)["""model"""]
							return model, global_step

