



"""simple docstring"""

import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict=()  ,       _lowerCamelCase				: Union[str, Any]=None  ,       _lowerCamelCase				: List[str]="no"  ,       _lowerCamelCase				: List[Any]="29500"					):
							__a		:		Tuple									=       False
							__a		:		Union[str, Any]									=       False
							if any(key.startswith("""KAGGLE"""					) for key in os.environ.keys()					):
														__a		:		List[str]									=       True
							elif "IPython" in sys.modules:
														__a		:		str									=       """google.colab""" in str(sys.modules["""IPython"""].get_ipython()					)

							try:
														__a		:		int									=       PrecisionType(mixed_precision.lower()					)
							except ValueError:
														raise ValueError(
														    F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''					)

							if (in_colab or in_kaggle) and (os.environ.get("""TPU_NAME"""  ,       _lowerCamelCase					) is not None):
														# TPU launch
														import torch_xla.distributed.xla_multiprocessing as xmp

														if len(AcceleratorState._shared_state					) > 0:
																					raise ValueError(
																					    """To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside """
																					    """your training function. Restart your notebook and make sure no cells initializes an """
																					    """`Accelerator`."""					)
														if num_processes is None:
																					__a		:		int									=       8

														__a		:		Any									=       PrepareForLaunch(_lowerCamelCase  ,       distributed_type="""TPU"""					)
														print(F'''Launching a training on {num_processes} TPU cores.'''					)
														xmp.spawn(_lowerCamelCase  ,       args=_lowerCamelCase  ,       nprocs=_lowerCamelCase  ,       start_method="""fork"""					)
							elif in_colab:
														# No need for a distributed launch otherwise as it's either CPU or one GPU.
														if torch.cuda.is_available():
																					print("""Launching training on one GPU."""					)
														else:
																					print("""Launching training on one CPU."""					)
														function(*_lowerCamelCase					)
							else:
														if num_processes is None:
																					raise ValueError(
																					    """You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."""					)

														if num_processes > 1:
																					# Multi-GPU launch
																					from torch.multiprocessing import start_processes
																					from torch.multiprocessing.spawn import ProcessRaisedException

																					if len(AcceleratorState._shared_state					) > 0:
																												raise ValueError(
																												    """To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized """
																												    """inside your training function. Restart your notebook and make sure no cells initializes an """
																												    """`Accelerator`."""					)

																					if torch.cuda.is_initialized():
																												raise ValueError(
																												    """To launch a multi-GPU training from your notebook, you need to avoid running any instruction """
																												    """using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA """
																												    """function."""					)

																					# torch.distributed will expect a few environment variable to be here. We set the ones common to each
																					# process here (the other ones will be set be the launcher).
																					with patch_environment(
																					    world_size=_lowerCamelCase  ,       master_addr="""127.0.01"""  ,       master_port=_lowerCamelCase  ,       mixed_precision=_lowerCamelCase					):
																												__a		:		str									=       PrepareForLaunch(_lowerCamelCase  ,       distributed_type="""MULTI_GPU"""					)
																												print(F'''Launching training on {num_processes} GPUs.'''					)
																												try:
																																			start_processes(_lowerCamelCase  ,       args=_lowerCamelCase  ,       nprocs=_lowerCamelCase  ,       start_method="""fork"""					)
																												except ProcessRaisedException as e:
																																			if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																																										raise RuntimeError(
																																										    """CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. """
																																										    """This likely stems from an outside import causing issues once the `notebook_launcher()` is called. """
																																										    """Please review your imports and test them when running the `notebook_launcher()` to identify """
																																										    """which one is problematic."""					) from e

														else:
																					# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
																					if is_mps_available():
																												__a		:		List[str]									=       """1"""
																												print("""Launching training on MPS."""					)
																					elif torch.cuda.is_available():
																												print("""Launching training on one GPU."""					)
																					else:
																												print("""Launching training on CPU."""					)
																					function(*_lowerCamelCase					)







def       __magic_name__    (   _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Optional[int]=()  ,       _lowerCamelCase				: Optional[int]=2					):
							from torch.multiprocessing import start_processes

							with tempfile.NamedTemporaryFile() as tmp_file:
														# torch.distributed will expect a few environment variable to be here. We set the ones common to each
														# process here (the other ones will be set be the launcher).
														with patch_environment(
														    world_size=_lowerCamelCase  ,       master_addr="""127.0.01"""  ,       master_port="""29500"""  ,       accelerate_mixed_precision="""no"""  ,       accelerate_debug_rdv_file=tmp_file.name  ,       accelerate_use_cpu="""yes"""  ,       ):
																					__a		:		Union[str, Any]									=       PrepareForLaunch(_lowerCamelCase  ,       debug=_lowerCamelCase					)
																					start_processes(_lowerCamelCase  ,       args=_lowerCamelCase  ,       nprocs=_lowerCamelCase  ,       start_method="""fork"""					)






"""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

