'''simple docstring'''

import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_     :						Optional[Any]    							=logging.get_logger(__name__)

A_     :						Union[str, Any]    							={
    '''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
    '''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}


class     __UpperCAmelCase       ( __a		):
  __A :				int											= 'encodec'





  def __init__(	self    ,     _lowerCamelCase=[1.5, 3.0, 6.0, 12.0, 24.0]    ,     _lowerCamelCase=2_4000    ,     _lowerCamelCase=1    ,     _lowerCamelCase=False    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     _lowerCamelCase=128    ,     _lowerCamelCase=32    ,     _lowerCamelCase=1    ,     _lowerCamelCase=[8, 5, 4, 2]    ,     _lowerCamelCase="weight_norm"    ,     _lowerCamelCase=7    ,     _lowerCamelCase=7    ,     _lowerCamelCase=3    ,     _lowerCamelCase=2    ,     _lowerCamelCase=True    ,     _lowerCamelCase="reflect"    ,     _lowerCamelCase=2    ,     _lowerCamelCase=2    ,     _lowerCamelCase=1.0    ,     _lowerCamelCase=1024    ,     _lowerCamelCase=None    ,     _lowerCamelCase=True    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      target_bandwidths
     lowerCAmelCase_			=      sampling_rate
     lowerCAmelCase_			=      audio_channels
     lowerCAmelCase_			=      normalize
     lowerCAmelCase_			=      chunk_length_s
     lowerCAmelCase_			=      overlap
     lowerCAmelCase_			=      hidden_size
     lowerCAmelCase_			=      num_filters
     lowerCAmelCase_			=      num_residual_layers
     lowerCAmelCase_			=      upsampling_ratios
     lowerCAmelCase_			=      norm_type
     lowerCAmelCase_			=      kernel_size
     lowerCAmelCase_			=      last_kernel_size
     lowerCAmelCase_			=      residual_kernel_size
     lowerCAmelCase_			=      dilation_growth_rate
     lowerCAmelCase_			=      use_causal_conv
     lowerCAmelCase_			=      pad_mode
     lowerCAmelCase_			=      compress
     lowerCAmelCase_			=      num_lstm_layers
     lowerCAmelCase_			=      trim_right_ratio
     lowerCAmelCase_			=      codebook_size
     lowerCAmelCase_			=      codebook_dim if codebook_dim is not None else hidden_size
     lowerCAmelCase_			=      use_conv_shortcut

     if self.norm_type not in ["weight_norm", "time_group_norm"]:
        raise ValueError(
            F'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}''' )

     super().__init__(**_lowerCamelCase )





  @property
  def 						UpperCAmelCase_	(	self ):
     if self.chunk_length_s is None:
        return None
     else:
        return int(self.chunk_length_s * self.sampling_rate )





  @property
  def 						UpperCAmelCase_	(	self ):
     if self.chunk_length_s is None or self.overlap is None:
        return None
     else:
        return max(1    ,     int((1.0 - self.overlap) * self.chunk_length ) )





  @property
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      np.prod(self.upsampling_ratios )
     return math.ceil(self.sampling_rate / hop_length )







  @property
  def 						UpperCAmelCase_	(	self ):
     return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )






'''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





