'''simple docstring'''

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class     __UpperCAmelCase       :
  # setable values
  __A :				Optional[int]											= None
  __A :				Optional[jnp.ndarray]											= None
  __A :				Optional[jnp.ndarray]											= None  # sigma(t_i)





  @classmethod
  def 						UpperCAmelCase_	(	cls ):
     return cls()






@dataclass
class     __UpperCAmelCase       ( __a		):
  __A :				jnp.ndarray
  __A :				jnp.ndarray
  __A :				KarrasVeSchedulerState






class     __UpperCAmelCase       ( __a						,       __a		):





  @property
  def 						UpperCAmelCase_	(	self ):
     return True





  @register_to_config
  def __init__(	self    ,     _lowerCamelCase = 0.02    ,     _lowerCamelCase = 100    ,     _lowerCamelCase = 1.0_07    ,     _lowerCamelCase = 80    ,     _lowerCamelCase = 0.05    ,     _lowerCamelCase = 50    ,     ):
     pass





  def 						UpperCAmelCase_	(	self ):
     return KarrasVeSchedulerState.create()





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = () ):
     lowerCAmelCase_			=      jnp.arange(0    ,     _lowerCamelCase )[::-1].copy()
     lowerCAmelCase_			=      [
         (
             self.config.sigma_max**2
             * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
         )
         for i in timesteps
     ]

     return state.replace(
         num_inference_steps=_lowerCamelCase    ,     schedule=jnp.array(_lowerCamelCase    ,     dtype=jnp.floataa )    ,     timesteps=_lowerCamelCase    ,     )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     ):
     if self.config.s_min <= sigma <= self.config.s_max:
        lowerCAmelCase_			=      min(self.config.s_churn / state.num_inference_steps    ,     2**0.5 - 1 )
     else:
        lowerCAmelCase_			=      0

     # sample eps ~ N(0, S_noise^2 * I)
     lowerCAmelCase_			=      random.split(_lowerCamelCase    ,     num=1 )
     lowerCAmelCase_			=      self.config.s_noise * random.normal(key=_lowerCamelCase    ,     shape=sample.shape )
     lowerCAmelCase_			=      sigma + gamma * sigma
     lowerCAmelCase_			=      sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

     return sample_hat, sigma_hat





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = True    ,     ):
     lowerCAmelCase_			=      sample_hat + sigma_hat * model_output
     lowerCAmelCase_			=      (sample_hat - pred_original_sample) / sigma_hat
     lowerCAmelCase_			=      sample_hat + (sigma_prev - sigma_hat) * derivative

     if not return_dict:
        return (sample_prev, derivative, state)

     return FlaxKarrasVeOutput(prev_sample=_lowerCamelCase    ,     derivative=_lowerCamelCase    ,     state=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = True    ,     ):
     lowerCAmelCase_			=      sample_prev + sigma_prev * model_output
     lowerCAmelCase_			=      (sample_prev - pred_original_sample) / sigma_prev
     lowerCAmelCase_			=      sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

     if not return_dict:
        return (sample_prev, derivative, state)

     return FlaxKarrasVeOutput(prev_sample=_lowerCamelCase    ,     derivative=_lowerCamelCase    ,     state=_lowerCamelCase )







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     raise NotImplementedError()






'''simple docstring'''

import importlib

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



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							Union[str, Any]=False)			->					Optional[int]:
   lowerCAmelCase_			=      OmegaConf.load(__snake_case)
   if display:
      print(yaml.dump(OmegaConf.to_container(__snake_case)))
   return config



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



def snake_case_      (					__snake_case  :							Union[str, Any]	,   __snake_case  :							str)			->					Optional[int]:
   lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      model.encode(__snake_case)
   print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''')
   lowerCAmelCase_			=      model.decode(__snake_case)
   return xrec



def snake_case_      (					__snake_case  :							int	,   __snake_case  :							List[Any]=False)			->					Union[str, Any]:
   lowerCAmelCase_	,lowerCAmelCase_			=      string.rsplit('''.'''	,   1)
   if reload:
      lowerCAmelCase_			=      importlib.import_module(__snake_case)
      importlib.reload(__snake_case)
   return getattr(importlib.import_module(__snake_case	,   package=__snake_case)	,   cls)



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



def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Any	,   __snake_case  :							Optional[int]=True	,   __snake_case  :							Any=True)			->					List[str]:
   lowerCAmelCase_			=      instantiate_from_config(__snake_case)
   if sd is not None:
      model.load_state_dict(__snake_case)
   if gpu:
      model.cuda()
   if eval_mode:
      model.eval()
   return {"model": model}






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





