
import math
from typing import Optional

import numpy as np

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


lowercase__		:Any   =    logging.get_logger(__name__)

lowercase__		: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 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Optional[Any]       					='''encodec'''




							def __init__(				self   ,A__=[1.5, 3.0, 6.0, 12.0, 24.0]   ,A__=2_4_0_0_0   ,A__=1   ,A__=False   ,A__=None   ,A__=None   ,A__=1_2_8   ,A__=3_2   ,A__=1   ,A__=[8, 5, 4, 2]   ,A__="weight_norm"   ,A__=7   ,A__=7   ,A__=3   ,A__=2   ,A__=True   ,A__="reflect"   ,A__=2   ,A__=2   ,A__=1.0   ,A__=1_0_2_4   ,A__=None   ,A__=True   ,**A__   ,):
												lowercase							=						target_bandwidths
												lowercase							=						sampling_rate
												lowercase							=						audio_channels
												lowercase							=						normalize
												lowercase							=						chunk_length_s
												lowercase							=						overlap
												lowercase							=						hidden_size
												lowercase							=						num_filters
												lowercase							=						num_residual_layers
												lowercase							=						upsampling_ratios
												lowercase							=						norm_type
												lowercase							=						kernel_size
												lowercase							=						last_kernel_size
												lowercase							=						residual_kernel_size
												lowercase							=						dilation_growth_rate
												lowercase							=						use_causal_conv
												lowercase							=						pad_mode
												lowercase							=						compress
												lowercase							=						num_lstm_layers
												lowercase							=						trim_right_ratio
												lowercase							=						codebook_size
												lowercase							=						codebook_dim if codebook_dim is not None else hidden_size
												lowercase							=						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__(**A__)




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




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





							@property
							def  A__      (				self):
												return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0))








"""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


