


"""simple docstring"""






import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
							import transformers
def      lowerCamelCase       (     _UpperCamelCase		:      int							)		->       Tuple:

							'''simple docstring'''



							random.seed(SCREAMING_SNAKE_CASE_							)
							np.random.seed(SCREAMING_SNAKE_CASE_							)
							torch.manual_seed(SCREAMING_SNAKE_CASE_							)
							torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE_							)
							# ^^ safe to call this function even if cuda is not available





class      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : List[Any] ,       UpperCamelCase    : List[str] ,       UpperCamelCase    : List[Any] = 0.9999 ,       UpperCamelCase    : List[str] = 0.0 ,       UpperCamelCase    : List[Any] = 0 ,       UpperCamelCase    : List[Any] = False ,       UpperCamelCase    : str = 1.0 ,       UpperCamelCase    : List[str] = 2 / 3 ,       UpperCamelCase    : Any = None ,       UpperCamelCase    : Any = None ,       **UpperCamelCase    : Tuple ,       ):
												'''simple docstring'''






												if isinstance(lowerCAmelCase__ ,       torch.nn.Module  ):
																			__UpperCAmelCase   :						int      							=					(
																			    """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
																			    """Please pass the parameters of the module instead."""
																			)
																			deprecate(
																			    """passing a `torch.nn.Module` to `ExponentialMovingAverage`""" ,       """1.0.0""" ,       lowerCAmelCase__ ,       standard_warn=lowerCAmelCase__ ,       )
																			__UpperCAmelCase   :						str      							=					parameters.parameters()

																			# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
																			__UpperCAmelCase   :						Dict      							=					True

												if kwargs.get("""max_value""" ,       lowerCAmelCase__  ) is not None:
																			__UpperCAmelCase   :						Any      							=					"""The `max_value` argument is deprecated. Please use `decay` instead."""
																			deprecate("""max_value""" ,       """1.0.0""" ,       lowerCAmelCase__ ,       standard_warn=lowerCAmelCase__  )
																			__UpperCAmelCase   :						Tuple      							=					kwargs["""max_value"""]

												if kwargs.get("""min_value""" ,       lowerCAmelCase__  ) is not None:
																			__UpperCAmelCase   :						List[str]      							=					"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
																			deprecate("""min_value""" ,       """1.0.0""" ,       lowerCAmelCase__ ,       standard_warn=lowerCAmelCase__  )
																			__UpperCAmelCase   :						Optional[int]      							=					kwargs["""min_value"""]

												__UpperCAmelCase   :						List[str]      							=					list(lowerCAmelCase__  )
												__UpperCAmelCase   :						Dict      							=					[p.clone().detach() for p in parameters]

												if kwargs.get("""device""" ,       lowerCAmelCase__  ) is not None:
																			__UpperCAmelCase   :						Optional[int]      							=					"""The `device` argument is deprecated. Please use `to` instead."""
																			deprecate("""device""" ,       """1.0.0""" ,       lowerCAmelCase__ ,       standard_warn=lowerCAmelCase__  )
																			self.to(device=kwargs["""device"""]  )

												__UpperCAmelCase   :						Optional[int]      							=					None

												__UpperCAmelCase   :						Optional[Any]      							=					decay
												__UpperCAmelCase   :						Optional[Any]      							=					min_decay
												__UpperCAmelCase   :						List[str]      							=					update_after_step
												__UpperCAmelCase   :						Dict      							=					use_ema_warmup
												__UpperCAmelCase   :						Any      							=					inv_gamma
												__UpperCAmelCase   :						List[str]      							=					power
												__UpperCAmelCase   :						Dict      							=					0
												__UpperCAmelCase   :						Dict      							=					None  # set in `step()`

												__UpperCAmelCase   :						List[Any]      							=					model_cls
												__UpperCAmelCase   :						Optional[Any]      							=					model_config






					@classmethod
					def lowerCamelCase__							(       cls    : Optional[int] ,       UpperCamelCase    : int ,       UpperCamelCase    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase							,__UpperCAmelCase   :						Dict      							=					model_cls.load_config(lowerCAmelCase__ ,       return_unused_kwargs=lowerCAmelCase__  )
												__UpperCAmelCase   :						Dict      							=					model_cls.from_pretrained(lowerCAmelCase__  )

												__UpperCAmelCase   :						Optional[Any]      							=					cls(model.parameters() ,       model_cls=lowerCAmelCase__ ,       model_config=model.config  )

												ema_model.load_state_dict(lowerCAmelCase__  )
												return ema_model






					def lowerCamelCase__							(       self    : Optional[Any] ,       UpperCamelCase    : Optional[Any]  ):
												'''simple docstring'''






												if self.model_cls is None:
																			raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""  )

												if self.model_config is None:
																			raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""  )

												__UpperCAmelCase   :						List[Any]      							=					self.model_cls.from_config(self.model_config  )
												__UpperCAmelCase   :						Dict      							=					self.state_dict()
												state_dict.pop("""shadow_params""" ,       lowerCAmelCase__  )

												model.register_to_config(**lowerCAmelCase__  )
												self.copy_to(model.parameters()  )
												model.save_pretrained(lowerCAmelCase__  )






					def lowerCamelCase__							(       self    : Dict ,       UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					max(0 ,       optimization_step - self.update_after_step - 1  )

												if step <= 0:
																			return 0.0

												if self.use_ema_warmup:
																			__UpperCAmelCase   :						Any      							=					1 - (1 + step / self.inv_gamma) ** -self.power
												else:
																			__UpperCAmelCase   :						Union[str, Any]      							=					(1 + step) / (10 + step)

												__UpperCAmelCase   :						List[Any]      							=					min(lowerCAmelCase__ ,       self.decay  )
												# make sure decay is not smaller than min_decay
												__UpperCAmelCase   :						List[Any]      							=					max(lowerCAmelCase__ ,       self.min_decay  )
												return cur_decay_value






					@torch.no_grad()
					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : Dict  ):
												'''simple docstring'''






												if isinstance(lowerCAmelCase__ ,       torch.nn.Module  ):
																			__UpperCAmelCase   :						Optional[int]      							=					(
																			    """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
																			    """Please pass the parameters of the module instead."""
																			)
																			deprecate(
																			    """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`""" ,       """1.0.0""" ,       lowerCAmelCase__ ,       standard_warn=lowerCAmelCase__ ,       )
																			__UpperCAmelCase   :						str      							=					parameters.parameters()

												__UpperCAmelCase   :						Any      							=					list(lowerCAmelCase__  )

												self.optimization_step += 1

												# Compute the decay factor for the exponential moving average.
												__UpperCAmelCase   :						Dict      							=					self.get_decay(self.optimization_step  )
												__UpperCAmelCase   :						Dict      							=					decay
												__UpperCAmelCase   :						Union[str, Any]      							=					1 - decay

												__UpperCAmelCase   :						int      							=					contextlib.nullcontext
												if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																			import deepspeed

												for s_param, param in zip(self.shadow_params ,       lowerCAmelCase__  ):
																			if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																										__UpperCAmelCase   :						List[str]      							=					deepspeed.zero.GatheredParameters(lowerCAmelCase__ ,       modifier_rank=lowerCAmelCase__  )

																			with context_manager():
																										if param.requires_grad:
																																	s_param.sub_(one_minus_decay * (s_param - param)  )
																										else:
																																	s_param.copy_(lowerCAmelCase__  )






					def lowerCamelCase__							(       self    : Optional[Any] ,       UpperCamelCase    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					list(lowerCAmelCase__  )
												for s_param, param in zip(self.shadow_params ,       lowerCAmelCase__  ):
																			param.data.copy_(s_param.to(param.device  ).data  )






					def lowerCamelCase__							(       self    : Optional[Any] ,       UpperCamelCase    : str=None ,       UpperCamelCase    : Union[str, Any]=None  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					[
												    p.to(device=lowerCAmelCase__ ,       dtype=lowerCAmelCase__  ) if p.is_floating_point() else p.to(device=lowerCAmelCase__  )
												    for p in self.shadow_params
												]






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												return {
												    "decay": self.decay,
												    "min_decay": self.min_decay,
												    "optimization_step": self.optimization_step,
												    "update_after_step": self.update_after_step,
												    "use_ema_warmup": self.use_ema_warmup,
												    "inv_gamma": self.inv_gamma,
												    "power": self.power,
												    "shadow_params": self.shadow_params,
												}






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					[param.detach().cpu().clone() for param in parameters]






					def lowerCamelCase__							(       self    : Union[str, Any] ,       UpperCamelCase    : Any  ):
												'''simple docstring'''






												if self.temp_stored_params is None:
																			raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""  )
												for c_param, param in zip(self.temp_stored_params ,       lowerCAmelCase__  ):
																			param.data.copy_(c_param.data  )

												# Better memory-wise.
												__UpperCAmelCase   :						Any      							=					None






					def lowerCamelCase__							(       self    : Tuple ,       UpperCamelCase    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					copy.deepcopy(lowerCAmelCase__  )

												__UpperCAmelCase   :						Dict      							=					state_dict.get("""decay""" ,       self.decay  )
												if self.decay < 0.0 or self.decay > 1.0:
																			raise ValueError("""Decay must be between 0 and 1"""  )

												__UpperCAmelCase   :						Tuple      							=					state_dict.get("""min_decay""" ,       self.min_decay  )
												if not isinstance(self.min_decay ,       lowerCAmelCase__  ):
																			raise ValueError("""Invalid min_decay"""  )

												__UpperCAmelCase   :						str      							=					state_dict.get("""optimization_step""" ,       self.optimization_step  )
												if not isinstance(self.optimization_step ,       lowerCAmelCase__  ):
																			raise ValueError("""Invalid optimization_step"""  )

												__UpperCAmelCase   :						Dict      							=					state_dict.get("""update_after_step""" ,       self.update_after_step  )
												if not isinstance(self.update_after_step ,       lowerCAmelCase__  ):
																			raise ValueError("""Invalid update_after_step"""  )

												__UpperCAmelCase   :						Optional[Any]      							=					state_dict.get("""use_ema_warmup""" ,       self.use_ema_warmup  )
												if not isinstance(self.use_ema_warmup ,       lowerCAmelCase__  ):
																			raise ValueError("""Invalid use_ema_warmup"""  )

												__UpperCAmelCase   :						str      							=					state_dict.get("""inv_gamma""" ,       self.inv_gamma  )
												if not isinstance(self.inv_gamma ,       (float, int)  ):
																			raise ValueError("""Invalid inv_gamma"""  )

												__UpperCAmelCase   :						List[str]      							=					state_dict.get("""power""" ,       self.power  )
												if not isinstance(self.power ,       (float, int)  ):
																			raise ValueError("""Invalid power"""  )

												__UpperCAmelCase   :						Optional[Any]      							=					state_dict.get("""shadow_params""" ,       lowerCAmelCase__  )
												if shadow_params is not None:
																			__UpperCAmelCase   :						int      							=					shadow_params
																			if not isinstance(self.shadow_params ,       lowerCAmelCase__  ):
																										raise ValueError("""shadow_params must be a list"""  )
																			if not all(isinstance(lowerCAmelCase__ ,       torch.Tensor  ) for p in self.shadow_params  ):
																										raise ValueError("""shadow_params must all be Tensors"""  )




"""simple docstring"""




import importlib

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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						str:
						SCREAMING_SNAKE_CASE													=					OmegaConf.load(SCREAMING_SNAKE_CASE_  )
						if display:
												print(yaml.dump(OmegaConf.to_container(SCREAMING_SNAKE_CASE_  )  )  )
						return config


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Tuple=None ,			SCREAMING_SNAKE_CASE_		:				Tuple=None  )   ->						Any:
						if conf_path is None:
												SCREAMING_SNAKE_CASE													=					'./model_checkpoints/vqgan_only.yaml'
						SCREAMING_SNAKE_CASE													=					load_config(SCREAMING_SNAKE_CASE_ ,			display=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					VQModel(**config.model.params  )
						if ckpt_path is None:
												SCREAMING_SNAKE_CASE													=					'./model_checkpoints/vqgan_only.pt'
						SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			map_location=SCREAMING_SNAKE_CASE_  )
						if ".ckpt" in ckpt_path:
												SCREAMING_SNAKE_CASE													=					sd['state_dict']
						model.load_state_dict(SCREAMING_SNAKE_CASE_ ,			strict=SCREAMING_SNAKE_CASE_  )
						model.to(SCREAMING_SNAKE_CASE_  )
						del sd
						return model


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					model.encode(SCREAMING_SNAKE_CASE_  )
						print(F'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'  )
						SCREAMING_SNAKE_CASE													=					model.decode(SCREAMING_SNAKE_CASE_  )
						return xrec


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Any=False  )   ->						Dict:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					string.rsplit('.' ,			1  )
						if reload:
												SCREAMING_SNAKE_CASE													=					importlib.import_module(SCREAMING_SNAKE_CASE_  )
												importlib.reload(SCREAMING_SNAKE_CASE_  )
						return getattr(importlib.import_module(SCREAMING_SNAKE_CASE_ ,			package=SCREAMING_SNAKE_CASE_  ) ,			cls  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Any  )   ->						Dict:
						if "target" not in config:
												raise KeyError('Expected key `target` to instantiate.'  )
						return get_obj_from_str(config['target']  )(**config.get('params' ,			{}  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				List[Any]=True ,			SCREAMING_SNAKE_CASE_		:				int=True  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					instantiate_from_config(SCREAMING_SNAKE_CASE_  )
						if sd is not None:
												model.load_state_dict(SCREAMING_SNAKE_CASE_  )
						if gpu:
												model.cuda()
						if eval_mode:
												model.eval()
						return {"model": model}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Optional[Any] ,			SCREAMING_SNAKE_CASE_		:				List[str]  )   ->						Any:
						# load the specified checkpoint
						if ckpt:
												SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			map_location='cpu'  )
												SCREAMING_SNAKE_CASE													=					pl_sd['global_step']
												print(F'loaded model from global step {global_step}.'  )
						else:
												SCREAMING_SNAKE_CASE													=					{'state_dict': None}
												SCREAMING_SNAKE_CASE													=					None
						SCREAMING_SNAKE_CASE													=					load_model_from_config(config.model ,			pl_sd['state_dict'] ,			gpu=SCREAMING_SNAKE_CASE_ ,			eval_mode=SCREAMING_SNAKE_CASE_  )['model']
						return model, global_step


