






import math
from typing import Optional

import numpy as np

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


UpperCamelCase    							=      logging.get_logger(__name__)

UpperCamelCase    							=      {
    '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 		_A						( UpperCAmelCase_ ):
  lowercase_						:			Union[str, Any]						=					'''encodec'''

  def __init__(    self						:	int   ,				lowerCamelCase__						:	Dict=[1.5, 3.0, 6.0, 12.0, 24.0]   ,				lowerCamelCase__						:	Optional[Any]=2_40_00   ,				lowerCamelCase__						:	List[Any]=1   ,				lowerCamelCase__						:	str=False   ,				lowerCamelCase__						:	str=None   ,				lowerCamelCase__						:	Optional[int]=None   ,				lowerCamelCase__						:	Optional[int]=1_28   ,				lowerCamelCase__						:	List[Any]=32   ,				lowerCamelCase__						:	int=1   ,				lowerCamelCase__						:	List[Any]=[8, 5, 4, 2]   ,				lowerCamelCase__						:	List[str]="weight_norm"   ,				lowerCamelCase__						:	Optional[Any]=7   ,				lowerCamelCase__						:	Dict=7   ,				lowerCamelCase__						:	Optional[Any]=3   ,				lowerCamelCase__						:	Optional[Any]=2   ,				lowerCamelCase__						:	List[str]=True   ,				lowerCamelCase__						:	Union[str, Any]="reflect"   ,				lowerCamelCase__						:	str=2   ,				lowerCamelCase__						:	Union[str, Any]=2   ,				lowerCamelCase__						:	str=1.0   ,				lowerCamelCase__						:	Dict=10_24   ,				lowerCamelCase__						:	Any=None   ,				lowerCamelCase__						:	int=True   ,				**lowerCamelCase__						:	str   ,				):
     """simple docstring"""

     __UpperCamelCase	:			Optional[int]					=					target_bandwidths
     __UpperCamelCase	:			str					=					sampling_rate
     __UpperCamelCase	:			Any					=					audio_channels
     __UpperCamelCase	:			Optional[Any]					=					normalize
     __UpperCamelCase	:			List[str]					=					chunk_length_s
     __UpperCamelCase	:			Optional[int]					=					overlap
     __UpperCamelCase	:			List[Any]					=					hidden_size
     __UpperCamelCase	:			Dict					=					num_filters
     __UpperCamelCase	:			List[str]					=					num_residual_layers
     __UpperCamelCase	:			Tuple					=					upsampling_ratios
     __UpperCamelCase	:			Optional[int]					=					norm_type
     __UpperCamelCase	:			List[str]					=					kernel_size
     __UpperCamelCase	:			Dict					=					last_kernel_size
     __UpperCamelCase	:			Tuple					=					residual_kernel_size
     __UpperCamelCase	:			Dict					=					dilation_growth_rate
     __UpperCamelCase	:			int					=					use_causal_conv
     __UpperCamelCase	:			Optional[int]					=					pad_mode
     __UpperCamelCase	:			Dict					=					compress
     __UpperCamelCase	:			int					=					num_lstm_layers
     __UpperCamelCase	:			Dict					=					trim_right_ratio
     __UpperCamelCase	:			Dict					=					codebook_size
     __UpperCamelCase	:			Union[str, Any]					=					codebook_dim if codebook_dim is not None else hidden_size
     __UpperCamelCase	:			Dict					=					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    a					(    self						:	List[Any]			):
     """simple docstring"""

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

  @property
  def    a					(    self						:	Dict			):
     """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    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			List[str]					=					np.prod(self.upsampling_ratios			)
     return math.ceil(self.sampling_rate / hop_length			)

  @property
  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)			)











import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase    							=      logging.get_logger(__name__)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Tuple							)     ->  List[str]:
   __UpperCamelCase	:			List[str]					=					R"""\w+[.]\d+"""
   __UpperCamelCase	:			Optional[int]					=					re.findall(__lowerCAmelCase					,							__lowerCAmelCase							)
   for pat in pats:
      __UpperCamelCase	:			str					=					key.replace(__lowerCAmelCase					,							"""_""".join(pat.split("""."""							)							)							)
   return key


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					Union[str, Any]							)     ->  str:
   __UpperCamelCase	:			Dict					=					pt_tuple_key[:-1] + ("""scale""",)
   if (
       any("""norm""" in str_ for str_ in pt_tuple_key							)
       and (pt_tuple_key[-1] == "bias")
       and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
       and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
   ):
      __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""scale""",)
      return renamed_pt_tuple_key, pt_tensor
   elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
      __UpperCamelCase	:			Dict					=					pt_tuple_key[:-1] + ("""scale""",)
      return renamed_pt_tuple_key, pt_tensor

   # embedding
   if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
      __UpperCamelCase	:			Any					=					pt_tuple_key[:-1] + ("""embedding""",)
      return renamed_pt_tuple_key, pt_tensor

   # conv layer
   __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""kernel""",)
   if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
      __UpperCamelCase	:			Dict					=					pt_tensor.transpose(2					,							3					,							1					,							0							)
      return renamed_pt_tuple_key, pt_tensor

   # linear layer
   __UpperCamelCase	:			Union[str, Any]					=					pt_tuple_key[:-1] + ("""kernel""",)
   if pt_tuple_key[-1] == "weight":
      __UpperCamelCase	:			int					=					pt_tensor.T
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm weight
   __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""weight""",)
   if pt_tuple_key[-1] == "gamma":
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm bias
   __UpperCamelCase	:			Tuple					=					pt_tuple_key[:-1] + ("""bias""",)
   if pt_tuple_key[-1] == "beta":
      return renamed_pt_tuple_key, pt_tensor

   return pt_tuple_key, pt_tensor



def 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict					,							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Optional[int]=42							)     ->  int:
   # Step 1: Convert pytorch tensor to numpy
   __UpperCamelCase	:			Dict					=					{k: v.numpy() for k, v in pt_state_dict.items()}

   # Step 2: Since the model is stateless, get random Flax params
   __UpperCamelCase	:			str					=					flax_model.init_weights(PRNGKey(__lowerCAmelCase							)							)

   __UpperCamelCase	:			Union[str, Any]					=					flatten_dict(__lowerCAmelCase							)
   __UpperCamelCase	:			List[str]					=					{}

   # Need to change some parameters name to match Flax names
   for pt_key, pt_tensor in pt_state_dict.items():
      __UpperCamelCase	:			str					=					rename_key(__lowerCAmelCase							)
      __UpperCamelCase	:			Any					=					tuple(renamed_pt_key.split("""."""							)							)

      # Correctly rename weight parameters
      __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					rename_key_and_reshape_tensor(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							)

      if flax_key in random_flax_state_dict:
         if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
            raise ValueError(
                f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'							)

        # also add unexpected weight so that warning is thrown
      __UpperCamelCase	:			Any					=					jnp.asarray(__lowerCAmelCase							)

   return unflatten_dict(__lowerCAmelCase							)



