




import math
from typing import Optional

import numpy as np

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


lowerCamelCase       :       Optional[int]        =    logging.get_logger(__name__)

lowerCamelCase       :       str        =    {
    '''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		(lowerCAmelCase_							):




      """simple docstring"""





      _snake_case							     =						"""encodec"""

      def __init__(      self				,      A=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]				,      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				,      )		->				List[Any]:
         snake_case      :				Optional[Any]				=       target_bandwidths
         snake_case      :				Union[str, Any]				=       sampling_rate
         snake_case      :				Dict				=       audio_channels
         snake_case      :				str				=       normalize
         snake_case      :				List[Any]				=       chunk_length_s
         snake_case      :				Any				=       overlap
         snake_case      :				Optional[int]				=       hidden_size
         snake_case      :				Union[str, Any]				=       num_filters
         snake_case      :				Dict				=       num_residual_layers
         snake_case      :				str				=       upsampling_ratios
         snake_case      :				str				=       norm_type
         snake_case      :				int				=       kernel_size
         snake_case      :				Optional[Any]				=       last_kernel_size
         snake_case      :				str				=       residual_kernel_size
         snake_case      :				str				=       dilation_growth_rate
         snake_case      :				Optional[int]				=       use_causal_conv
         snake_case      :				Optional[Any]				=       pad_mode
         snake_case      :				Optional[int]				=       compress
         snake_case      :				Any				=       num_lstm_layers
         snake_case      :				List[str]				=       trim_right_ratio
         snake_case      :				int				=       codebook_size
         snake_case      :				Optional[Any]				=       codebook_dim if codebook_dim is not None else hidden_size
         snake_case      :				List[Any]				=       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__(**snake_case_	)

      @property
      def 	UpperCAmelCase      (      self	)		->				str:
         if self.chunk_length_s is None:
            return None
         else:
            return int(self.chunk_length_s * self.sampling_rate	)

      @property
      def 	UpperCAmelCase      (      self	)		->				int:
         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	)		->				Optional[int]:
         snake_case      :				List[str]				=       np.prod(self.upsampling_ratios	)
         return math.ceil(self.sampling_rate / hop_length	)

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












'''simple docstring'''




import re

from filelock import FileLock


try:
  import nltk

  __SCREAMING_SNAKE_CASE     :Optional[int]       =				True
except (ImportError, ModuleNotFoundError):
  __SCREAMING_SNAKE_CASE     :str       =				False

if NLTK_AVAILABLE:
  with FileLock('''.lock''') as lock:
    nltk.download('''punkt''', quiet=True)
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       str:


     '''simple docstring'''



     re.sub("<n>"			,     ""			,     __lowercase       )  # remove pegasus newline char
     assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
     return "\n".join(nltk.sent_tokenize(__lowercase       )       )




