




'''simple docstring'''







import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A									=logging.get_logger(__name__)

A									={'vocab_file': 'spiece.model'}

A									={
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

A									={
    'google/bigbird-roberta-base': 40_96,
    'google/bigbird-roberta-large': 40_96,
    'google/bigbird-base-trivia-itc': 40_96,
}


class     _a						(			__a				):
       __a       :  Any            =      VOCAB_FILES_NAMES
       __a       :  str            =      PRETRAINED_VOCAB_FILES_MAP
       __a       :  List[Any]            =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __a       :  int            =      ["""input_ids""", """attention_mask"""]
       __a       :  List[int]            =      []
       def __init__(			self		:			int     ,       lowercase		:			Tuple     ,       lowercase		:			Any="<unk>"     ,       lowercase		:			Tuple="<s>"     ,       lowercase		:			Union[str, Any]="</s>"     ,       lowercase		:			str="<pad>"     ,       lowercase		:			Any="[SEP]"     ,       lowercase		:			Optional[int]="[MASK]"     ,       lowercase		:			Union[str, Any]="[CLS]"     ,       lowercase		:			Optional[Dict[str, Any]] = None     ,       **lowercase		:			str     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	AddedToken(lowercase     ,       lstrip=lowercase     ,       rstrip=lowercase       ) if isinstance(lowercase     ,       lowercase       ) else bos_token
           UpperCAmelCase     =	AddedToken(lowercase     ,       lstrip=lowercase     ,       rstrip=lowercase       ) if isinstance(lowercase     ,       lowercase       ) else eos_token
           UpperCAmelCase     =	AddedToken(lowercase     ,       lstrip=lowercase     ,       rstrip=lowercase       ) if isinstance(lowercase     ,       lowercase       ) else unk_token
           UpperCAmelCase     =	AddedToken(lowercase     ,       lstrip=lowercase     ,       rstrip=lowercase       ) if isinstance(lowercase     ,       lowercase       ) else pad_token
           UpperCAmelCase     =	AddedToken(lowercase     ,       lstrip=lowercase     ,       rstrip=lowercase       ) if isinstance(lowercase     ,       lowercase       ) else cls_token
           UpperCAmelCase     =	AddedToken(lowercase     ,       lstrip=lowercase     ,       rstrip=lowercase       ) if isinstance(lowercase     ,       lowercase       ) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           UpperCAmelCase     =	AddedToken(lowercase     ,       lstrip=lowercase     ,       rstrip=lowercase       ) if isinstance(lowercase     ,       lowercase       ) else mask_token

           UpperCAmelCase     =	{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               bos_token=lowercase     ,       eos_token=lowercase     ,       unk_token=lowercase     ,       pad_token=lowercase     ,       sep_token=lowercase     ,       mask_token=lowercase     ,       cls_token=lowercase     ,       sp_model_kwargs=self.sp_model_kwargs     ,       **lowercase     ,       )

           UpperCAmelCase     =	vocab_file

           UpperCAmelCase     =	spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(lowercase       )
       @property
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           return self.sp_model.get_piece_size()
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	{self.convert_ids_to_tokens(lowercase       ): i for i in range(self.vocab_size       )}
           vocab.update(self.added_tokens_encoder       )
           return vocab
       def __getstate__(			self		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.__dict__.copy()
           UpperCAmelCase     =	None
           return state
       def __setstate__(			self		:			str     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	d

           # for backward compatibility
           if not hasattr(self     ,       '''sp_model_kwargs'''       ):
               UpperCAmelCase     =	{}

           UpperCAmelCase     =	spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(self.vocab_file       )
       def 					A   (			self		:			Dict     ,       lowercase		:			str       ):



           '''simple docstring'''

           return self.sp_model.encode(lowercase     ,       out_type=lowercase       )
       def 					A   (			self		:			Tuple     ,       lowercase		:			Any       ):



           '''simple docstring'''

           return self.sp_model.piece_to_id(lowercase       )
       def 					A   (			self		:			str     ,       lowercase		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.sp_model.IdToPiece(lowercase       )
           return token
       def 					A   (			self		:			List[str]     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	[]
           UpperCAmelCase     =	''''''
           UpperCAmelCase     =	False
           for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                   if not prev_is_special:
                       out_string += " "
                   out_string += self.sp_model.decode(lowercase       ) + token
                   UpperCAmelCase     =	True
                   UpperCAmelCase     =	[]
               else:
                   current_sub_tokens.append(lowercase       )
                   UpperCAmelCase     =	False
           out_string += self.sp_model.decode(lowercase       )
           return out_string.strip()
       def 					A   (			self		:			str     ,       lowercase		:			List[int]     ,       lowercase		:			bool = False     ,       lowercase		:			bool = None     ,       lowercase		:			bool = True     ,       **lowercase		:			List[str]     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	kwargs.pop('''use_source_tokenizer'''     ,       lowercase       )

           UpperCAmelCase     =	self.convert_ids_to_tokens(lowercase     ,       skip_special_tokens=lowercase       )

           # To avoid mixing byte-level and unicode for byte-level BPT
           # we need to build string separately for added tokens and byte-level tokens
           # cf. https://github.com/huggingface/transformers/issues/1133
           UpperCAmelCase     =	[]
           UpperCAmelCase     =	[]
           for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                   continue
               if token in self.added_tokens_encoder:
                   if current_sub_text:
                       sub_texts.append(self.convert_tokens_to_string(lowercase       )       )
                       UpperCAmelCase     =	[]
                   sub_texts.append(lowercase       )
               else:
                   current_sub_text.append(lowercase       )
           if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(lowercase       )       )

           # Mimic the behavior of the Rust tokenizer:
           # No space before [MASK] and [SEP]
           if spaces_between_special_tokens:
               UpperCAmelCase     =	re.sub(R''' (\[(MASK|SEP)\])'''     ,       R'''\1'''     ,       ''' '''.join(lowercase       )       )
           else:
               UpperCAmelCase     =	''''''.join(lowercase       )

           UpperCAmelCase     =	(
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
               UpperCAmelCase     =	self.clean_up_tokenization(lowercase       )
               return clean_text
           else:
               return text
       def 					A   (			self		:			int     ,       lowercase		:			str     ,       lowercase		:			Optional[str] = None       ):



           '''simple docstring'''

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

           if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowercase       ) and os.path.isfile(self.vocab_file       ):
               copyfile(self.vocab_file     ,       lowercase       )
           elif not os.path.isfile(self.vocab_file       ):
               with open(lowercase     ,       '''wb'''       ) as fi:
                   UpperCAmelCase     =	self.sp_model.serialized_model_proto()
                   fi.write(lowercase       )

           return (out_vocab_file,)
       def 					A   (			self		:			List[str]     ,       lowercase		:			List[int]     ,       lowercase		:			Optional[List[int]] = None       ):



           '''simple docstring'''

           if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           UpperCAmelCase     =	[self.cls_token_id]
           UpperCAmelCase     =	[self.sep_token_id]
           return cls + token_ids_a + sep + token_ids_a + sep
       def 					A   (			self		:			int     ,       lowercase		:			List[int]     ,       lowercase		:			Optional[List[int]] = None     ,       lowercase		:			bool = False       ):



           '''simple docstring'''

           if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowercase     ,       token_ids_a=lowercase     ,       already_has_special_tokens=lowercase       )

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







       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			List[int]     ,       lowercase		:			Optional[List[int]] = None       ):



           '''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]










'''simple docstring'''







import math
from typing import Optional

import numpy as np

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


A									=logging.get_logger(__name__)

A									={
    '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						(			__a				):
       __a       :  Union[str, Any]            =      """encodec"""
       def __init__(			self		:			Optional[int]     ,       lowercase		:			Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]     ,       lowercase		:			str=24_000     ,       lowercase		:			int=1     ,       lowercase		:			int=False     ,       lowercase		:			List[str]=None     ,       lowercase		:			Tuple=None     ,       lowercase		:			int=128     ,       lowercase		:			List[Any]=32     ,       lowercase		:			Optional[int]=1     ,       lowercase		:			Optional[int]=[8, 5, 4, 2]     ,       lowercase		:			Union[str, Any]="weight_norm"     ,       lowercase		:			Optional[int]=7     ,       lowercase		:			Tuple=7     ,       lowercase		:			Tuple=3     ,       lowercase		:			Union[str, Any]=2     ,       lowercase		:			Optional[Any]=True     ,       lowercase		:			Tuple="reflect"     ,       lowercase		:			Any=2     ,       lowercase		:			List[str]=2     ,       lowercase		:			str=1.0     ,       lowercase		:			List[str]=1_024     ,       lowercase		:			Optional[Any]=None     ,       lowercase		:			Any=True     ,       **lowercase		:			Optional[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__(**lowercase       )
       @property
       def 					A   (			self		:			List[str]       ):



           '''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		:			Optional[Any]       ):



           '''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		:			Union[str, Any]       ):



           '''simple docstring'''

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







       @property
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)       )




