
'''simple docstring'''


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


snake_case_ :  Tuple     =					'scheduler_config.json'


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							1
   _snake_case		   =							2
   _snake_case		   =							3
   _snake_case		   =							4
   _snake_case		   =							5




@dataclass
class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							42




class 						lowercase__	:






   '''simple docstring'''







   _snake_case		   =							SCHEDULER_CONFIG_NAME
   _snake_case		   =							['''dtype''']
   _snake_case		   =							[]
   _snake_case		   =							True







   @classmethod
   def       UpperCAmelCase				(			cls ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__=False ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase     , UpperCamelCase       =  cls.load_config(
          pretrained_model_name_or_path=lowerCamelCase__ ,   subfolder=lowerCamelCase__ ,   return_unused_kwargs=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
      UpperCamelCase     , UpperCamelCase       =  cls.from_config(lowerCamelCase__ ,   return_unused_kwargs=lowerCamelCase__ ,   **lowerCamelCase__   )

      if hasattr(lowerCamelCase__ ,   '''create_state'''   ) and getattr(lowerCamelCase__ ,   '''has_state''' ,   lowerCamelCase__   ):
         UpperCamelCase       =  scheduler.create_state()

      if return_unused_kwargs:
         return scheduler, state, unused_kwargs

      return scheduler, state







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ = False ,   **lowerCamelCase__   ):

      '''simple docstring'''







      self.save_config(save_directory=lowerCamelCase__ ,   push_to_hub=lowerCamelCase__ ,   **lowerCamelCase__   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self._get_compatibles()







   @classmethod
   def       UpperCAmelCase				(			cls   ):

      '''simple docstring'''







      UpperCamelCase       =  list(set([cls.__name__] + cls._compatibles   )   )
      UpperCamelCase       =  importlib.import_module(__name__.split('''.'''   )[0]   )
      UpperCamelCase       =  [
          getattr(lowerCamelCase__ ,   lowerCamelCase__   ) for c in compatible_classes_str if hasattr(lowerCamelCase__ ,   lowerCamelCase__   )
      ]
      return compatible_classes




def  __snake_case     (    _UpperCAmelCase					:       jnp.ndarray,      _UpperCAmelCase					:       Tuple[int]):
   assert len(_UpperCAmelCase) >= x.ndim
   return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_UpperCAmelCase) - x.ndim)),      _UpperCAmelCase)



def  __snake_case     (    _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Optional[int]=0.9_9_9,      _UpperCAmelCase					:       Tuple=jnp.floataa):

   def alpha_bar(_UpperCAmelCase					:       Optional[Any]):
      return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2) ** 2

   UpperCamelCase       =  []
   for i in range(_UpperCAmelCase):
      UpperCamelCase       =  i / num_diffusion_timesteps
      UpperCamelCase       =  (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar(_UpperCAmelCase) / alpha_bar(_UpperCAmelCase),      _UpperCAmelCase))
   return jnp.array(_UpperCAmelCase,      dtype=_UpperCAmelCase)


@flax.struct.dataclass
class 						lowercase__	:






   '''simple docstring'''







   _snake_case		   =							42
   _snake_case		   =							42
   _snake_case		   =							42







   @classmethod
   def       UpperCAmelCase				(			cls ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  scheduler.config

      if config.trained_betas is not None:
         UpperCamelCase       =  jnp.asarray(config.trained_betas ,   dtype=scheduler.dtype   )
      elif config.beta_schedule == "linear":
         UpperCamelCase       =  jnp.linspace(config.beta_start ,   config.beta_end ,   config.num_train_timesteps ,   dtype=scheduler.dtype   )
      elif config.beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         UpperCamelCase       =  (
             jnp.linspace(
                 config.beta_start**0.5 ,   config.beta_end**0.5 ,   config.num_train_timesteps ,   dtype=scheduler.dtype   )
             ** 2
         )
      elif config.beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         UpperCamelCase       =  betas_for_alpha_bar(config.num_train_timesteps ,   dtype=scheduler.dtype   )
      else:
         raise NotImplementedError(
             f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'   )

      UpperCamelCase       =  1.0 - betas

      UpperCamelCase       =  jnp.cumprod(lowerCamelCase__ ,   axis=0   )

      return cls(
          alphas=lowerCamelCase__ ,   betas=lowerCamelCase__ ,   alphas_cumprod=lowerCamelCase__ ,   )




def  __snake_case     (    _UpperCAmelCase					:       CommonSchedulerState,      _UpperCAmelCase					:       jnp.ndarray,      _UpperCAmelCase					:       jnp.ndarray,      _UpperCAmelCase					:       jnp.ndarray):
   UpperCamelCase       =  state.alphas_cumprod

   UpperCamelCase       =  alphas_cumprod[timesteps] ** 0.5
   UpperCamelCase       =  sqrt_alpha_prod.flatten()
   UpperCamelCase       =  broadcast_to_shape_from_left(_UpperCAmelCase,      original_samples.shape)

   UpperCamelCase       =  (1 - alphas_cumprod[timesteps]) ** 0.5
   UpperCamelCase       =  sqrt_one_minus_alpha_prod.flatten()
   UpperCamelCase       =  broadcast_to_shape_from_left(_UpperCAmelCase,      original_samples.shape)

   return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def  __snake_case     (    _UpperCAmelCase					:       CommonSchedulerState,      _UpperCAmelCase					:       jnp.ndarray,      _UpperCAmelCase					:       jnp.ndarray,      _UpperCAmelCase					:       jnp.ndarray):
   UpperCamelCase     , UpperCamelCase       =  get_sqrt_alpha_prod(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase       =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
   return noisy_samples



def  __snake_case     (    _UpperCAmelCase					:       CommonSchedulerState,      _UpperCAmelCase					:       jnp.ndarray,      _UpperCAmelCase					:       jnp.ndarray,      _UpperCAmelCase					:       jnp.ndarray):
   UpperCamelCase     , UpperCamelCase       =  get_sqrt_alpha_prod(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase       =  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
   return velocity







'''simple docstring'''


import math
from typing import Optional

import numpy as np

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


snake_case_ :  Dict     =					logging.get_logger(__name__)

snake_case_ :  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__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							'''encodec'''







   def __init__(			self ,   lowerCamelCase__=[1.5, 3.0, 6.0, 12.0, 24.0] ,   lowerCamelCase__=2_4_0_0_0 ,   lowerCamelCase__=1 ,   lowerCamelCase__=False ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__=1_2_8 ,   lowerCamelCase__=3_2 ,   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__=1_0_2_4 ,   lowerCamelCase__=None ,   lowerCamelCase__=True ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  target_bandwidths
      UpperCamelCase       =  sampling_rate
      UpperCamelCase       =  audio_channels
      UpperCamelCase       =  normalize
      UpperCamelCase       =  chunk_length_s
      UpperCamelCase       =  overlap
      UpperCamelCase       =  hidden_size
      UpperCamelCase       =  num_filters
      UpperCamelCase       =  num_residual_layers
      UpperCamelCase       =  upsampling_ratios
      UpperCamelCase       =  norm_type
      UpperCamelCase       =  kernel_size
      UpperCamelCase       =  last_kernel_size
      UpperCamelCase       =  residual_kernel_size
      UpperCamelCase       =  dilation_growth_rate
      UpperCamelCase       =  use_causal_conv
      UpperCamelCase       =  pad_mode
      UpperCamelCase       =  compress
      UpperCamelCase       =  num_lstm_layers
      UpperCamelCase       =  trim_right_ratio
      UpperCamelCase       =  codebook_size
      UpperCamelCase       =  codebook_dim if codebook_dim is not None else hidden_size
      UpperCamelCase       =  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   ):

      '''simple docstring'''







      if self.chunk_length_s is None:
         return None
      else:
         return int(self.chunk_length_s * self.sampling_rate   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      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   ):

      '''simple docstring'''







      UpperCamelCase       =  np.prod(self.upsampling_ratios   )
      return math.ceil(self.sampling_rate / hop_length   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)   )





