






"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_fnet import FNetTokenizer
else:
 lowerCAmelCase_	:    List[Any]            =      None

lowerCAmelCase_	:    Optional[Any]            =      logging.get_logger(__name__)
lowerCAmelCase_	:    Tuple            =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_	:    str            =      {
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase_	:    List[str]            =      {
    '''google/fnet-base''': 5_1_2,
    '''google/fnet-large''': 5_1_2,
}

lowerCAmelCase_	:    Tuple            =      '''▁'''





class   UpperCamelCase_ (       a_  ):
  _A       :					Tuple				=	VOCAB_FILES_NAMES
  _A       :					Dict				=	PRETRAINED_VOCAB_FILES_MAP
  _A       :					int				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _A       :					List[str]				=	['input_ids', 'token_type_ids']
  _A       :					Dict				=	FNetTokenizer



  def __init__(      self ,		snake_case__=None ,		snake_case__=None ,		snake_case__=False ,		snake_case__=True ,		snake_case__=True ,		snake_case__="<unk>" ,		snake_case__="[SEP]" ,		snake_case__="<pad>" ,		snake_case__="[CLS]" ,		snake_case__="[MASK]" ,		**snake_case__ ,		)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			(
             AddedToken(snake_case__ ,		lstrip=snake_case__ ,		rstrip=snake_case__ ,		normalized=snake_case__				)
             if isinstance(snake_case__ ,		snake_case__				)
             else mask_token
         )

         super().__init__(
             snake_case__ ,		tokenizer_file=snake_case__ ,		do_lower_case=snake_case__ ,		remove_space=snake_case__ ,		keep_accents=snake_case__ ,		unk_token=snake_case__ ,		sep_token=snake_case__ ,		pad_token=snake_case__ ,		cls_token=snake_case__ ,		mask_token=snake_case__ ,		**snake_case__ ,		)

         UpperCAmelCase	    =			do_lower_case
         UpperCAmelCase	    =			remove_space
         UpperCAmelCase	    =			keep_accents
         UpperCAmelCase	    =			vocab_file
         UpperCAmelCase	    =			False if not self.vocab_file else True



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None				)			->			List[int]:



         """simple docstring"""





         UpperCAmelCase	    =			[self.sep_token_id]
         UpperCAmelCase	    =			[self.cls_token_id]
         if token_ids_a is None:
                return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None				)			->			List[int]:



         """simple docstring"""





         UpperCAmelCase	    =			[self.sep_token_id]
         UpperCAmelCase	    =			[self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep				) * [0]
         return len(cls + token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1]



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None				)			->			Tuple[str]:



         """simple docstring"""





         if not os.path.isdir(snake_case__				):
                logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
                return
         UpperCAmelCase	    =			os.path.join(
             snake_case__ ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

         if os.path.abspath(self.vocab_file				) != os.path.abspath(snake_case__				):
                copyfile(self.vocab_file ,		snake_case__				)

         return (out_vocab_file,)








"""simple docstring"""







import math
from typing import Optional

import numpy as np

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


lowerCAmelCase_	:    List[Any]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Optional[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   UpperCamelCase_ (       a_  ):
  _A       :					Dict				=	'encodec'



  def __init__(      self ,		snake_case__=[1.5, 3.0, 6.0, 12.0, 24.0] ,		snake_case__=2_40_00 ,		snake_case__=1 ,		snake_case__=False ,		snake_case__=None ,		snake_case__=None ,		snake_case__=1_28 ,		snake_case__=32 ,		snake_case__=1 ,		snake_case__=[8, 5, 4, 2] ,		snake_case__="weight_norm" ,		snake_case__=7 ,		snake_case__=7 ,		snake_case__=3 ,		snake_case__=2 ,		snake_case__=True ,		snake_case__="reflect" ,		snake_case__=2 ,		snake_case__=2 ,		snake_case__=1.0 ,		snake_case__=10_24 ,		snake_case__=None ,		snake_case__=True ,		**snake_case__ ,		)			->			List[Any]:



         """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__(**snake_case__				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """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				)			->			Optional[int]:



         """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				)			->			int:



         """simple docstring"""





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



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





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