


import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
				from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Dict:




						'''simple docstring'''







						if is_torch_version('''<'''      ,       '''2.0.0'''      ) or not hasattr(lowercase_      ,       '''_dynamo'''      ):
												return False
						return isinstance(lowercase_      ,       torch._dynamo.eval_frame.OptimizedModule      )




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_ = True      )		->     Union[str, Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Union[str, Any]	    =	(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

						__UpperCAmelCase			:							List[Any]	    =	is_compiled_module(lowercase_      )
						if is_compiled:
												__UpperCAmelCase			:							Tuple	    =	model
												__UpperCAmelCase			:							List[Any]	    =	model._orig_mod

						if is_deepspeed_available():
												options += (DeepSpeedEngine,)

						while isinstance(lowercase_      ,       lowercase_      ):
												__UpperCAmelCase			:							Tuple	    =	model.module

						if not keep_fpaa_wrapper:
												__UpperCAmelCase			:							Any	    =	getattr(lowercase_      ,       '''forward'''      )
												__UpperCAmelCase			:							Optional[int]	    =	model.__dict__.pop('''_original_forward'''      ,       lowercase_      )
												if original_forward is not None:
																		while hasattr(lowercase_      ,       '''__wrapped__'''      ):
																								__UpperCAmelCase			:							Optional[int]	    =	forward.__wrapped__
																								if forward == original_forward:
																														break
																		__UpperCAmelCase			:							Tuple	    =	forward
												if getattr(lowercase_      ,       '''_converted_to_transformer_engine'''      ,       lowercase_      ):
																		convert_model(lowercase_      ,       to_transformer_engine=lowercase_      )

						if is_compiled:
												__UpperCAmelCase			:							List[str]	    =	model
												__UpperCAmelCase			:							Any	    =	compiled_model

						return model




def     __SCREAMING_SNAKE_CASE							(       )		->     List[Any]:




						'''simple docstring'''







						PartialState().wait_for_everyone()




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     List[Any]:




						'''simple docstring'''







						if PartialState().distributed_type == DistributedType.TPU:
												xm.save(lowercase_      ,       lowercase_      )
						elif PartialState().local_process_index == 0:
												torch.save(lowercase_      ,       lowercase_      )




@contextmanager
def     __SCREAMING_SNAKE_CASE							(       **lowercase_      )		->     Optional[Any]:




						'''simple docstring'''







						for key, value in kwargs.items():
												__UpperCAmelCase			:							Optional[Any]	    =	str(lowercase_      )

						yield

						for key in kwargs:
												if key.upper() in os.environ:
																		del os.environ[key.upper()]




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Optional[int]:




						'''simple docstring'''







						if not hasattr(lowercase_      ,       '''__qualname__'''      ) and not hasattr(lowercase_      ,       '''__name__'''      ):
												__UpperCAmelCase			:							Optional[int]	    =	getattr(lowercase_      ,       '''__class__'''      ,       lowercase_      )
						if hasattr(lowercase_      ,       '''__qualname__'''      ):
												return obj.__qualname__
						if hasattr(lowercase_      ,       '''__name__'''      ):
												return obj.__name__
						return str(lowercase_      )




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     int:




						'''simple docstring'''







						for key, value in source.items():
												if isinstance(lowercase_      ,       lowercase_      ):
																		__UpperCAmelCase			:							Tuple	    =	destination.setdefault(lowercase_      ,       {}      )
																		merge_dicts(lowercase_      ,       lowercase_      )
												else:
																		__UpperCAmelCase			:							str	    =	value

						return destination





def     __SCREAMING_SNAKE_CASE							(       lowercase_ = None      )		->     bool:




						'''simple docstring'''







						if port is None:
												__UpperCAmelCase			:							int	    =	29500
						with socket.socket(socket.AF_INET      ,       socket.SOCK_STREAM      ) as s:
												return s.connect_ex(('''localhost''', port)      ) == 0





import importlib

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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=False      )		->     Optional[Any]:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[Any]	    =	OmegaConf.load(lowercase_      )
						if display:
												print(yaml.dump(OmegaConf.to_container(lowercase_      )      )      )
						return config




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=None      ,       lowercase_=None      )		->     Union[str, Any]:




						'''simple docstring'''







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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      )		->     List[Any]:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[Any]	    =	model.encode(lowercase_      )
						print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"      )
						__UpperCAmelCase			:							List[str]	    =	model.decode(lowercase_      )
						return xrec




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=False      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	string.rsplit('''.'''      ,       1      )
						if reload:
												__UpperCAmelCase			:							Optional[int]	    =	importlib.import_module(lowercase_      )
												importlib.reload(lowercase_      )
						return getattr(importlib.import_module(lowercase_      ,       package=lowercase_      )      ,       cls      )




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Union[str, Any]:




						'''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     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_=True      ,       lowercase_=True      )		->     int:




						'''simple docstring'''







						__UpperCAmelCase			:							Dict	    =	instantiate_from_config(lowercase_      )
						if sd is not None:
												model.load_state_dict(lowercase_      )
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}





def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       lowercase_      )		->     Optional[Any]:




						'''simple docstring'''







						if ckpt:
												__UpperCAmelCase			:							Optional[int]	    =	torch.load(lowercase_      ,       map_location='''cpu'''      )
												__UpperCAmelCase			:							Union[str, Any]	    =	pl_sd['''global_step''']
												print(f"loaded model from global step {global_step}."      )
						else:
												__UpperCAmelCase			:							Union[str, Any]	    =	{'''state_dict''': None}
												__UpperCAmelCase			:							Optional[int]	    =	None
						__UpperCAmelCase			:							str	    =	load_model_from_config(config.model      ,       pl_sd['''state_dict''']      ,       gpu=lowercase_      ,       eval_mode=lowercase_      )['''model''']
						return model, global_step

