

import math
from typing import Optional

import numpy as np

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


__lowercase     : List[str]       =						logging.get_logger(__name__)

__lowercase     : List[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 						_A     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      Dict					=   '''encodec'''





  def __init__(  self   ,SCREAMING_SNAKE_CASE_=[1.5, 3.0, 6.0, 12.0, 24.0]   ,SCREAMING_SNAKE_CASE_=24000   ,SCREAMING_SNAKE_CASE_=1   ,SCREAMING_SNAKE_CASE_=False   ,SCREAMING_SNAKE_CASE_=None   ,SCREAMING_SNAKE_CASE_=None   ,SCREAMING_SNAKE_CASE_=128   ,SCREAMING_SNAKE_CASE_=32   ,SCREAMING_SNAKE_CASE_=1   ,SCREAMING_SNAKE_CASE_=[8, 5, 4, 2]   ,SCREAMING_SNAKE_CASE_="weight_norm"   ,SCREAMING_SNAKE_CASE_=7   ,SCREAMING_SNAKE_CASE_=7   ,SCREAMING_SNAKE_CASE_=3   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=True   ,SCREAMING_SNAKE_CASE_="reflect"   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=2   ,SCREAMING_SNAKE_CASE_=1.0   ,SCREAMING_SNAKE_CASE_=1024   ,SCREAMING_SNAKE_CASE_=None   ,SCREAMING_SNAKE_CASE_=True   ,**SCREAMING_SNAKE_CASE_   ,):

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





  @property
  def    snake_case_       (  self    ):

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





  @property
  def    snake_case_       (  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    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Optional[Any]        =		np.prod(self.upsampling_ratios    )
      return math.ceil(self.sampling_rate / hop_length    )





  @property
  def    snake_case_       (  self    ):

      '''simple docstring'''
      return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)    )





"""simple docstring"""






_lowercase							=				'''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

_lowercase							=				[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_lowercase							=				{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





