


"""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


__magic_name__						: Dict    =				logging.get_logger(__name__)

__magic_name__						: str    =				{'vocab_file': 'spiece.model'}

__magic_name__						: Any    =				{
    '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'
        ),
    }
}

__magic_name__						: Optional[Any]    =				{
    'google/bigbird-roberta-base': 4_0_9_6,
    'google/bigbird-roberta-large': 4_0_9_6,
    'google/bigbird-base-trivia-itc': 4_0_9_6,
}




class 			__snake_case   (lowerCamelCase  ):
							__a					 =       VOCAB_FILES_NAMES
							__a					 =       PRETRAINED_VOCAB_FILES_MAP
							__a					 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__a					 =       ['''input_ids''', '''attention_mask''']
							__a					 =       []




							def __init__(     self:    Union[str, Any]     ,							A_:    Any     ,							A_:    int="<unk>"     ,							A_:    List[Any]="<s>"     ,							A_:    Tuple="</s>"     ,							A_:    Any="<pad>"     ,							A_:    List[Any]="[SEP]"     ,							A_:    Optional[Any]="[MASK]"     ,							A_:    int="[CLS]"     ,							A_:    Optional[Dict[str, Any]] = None     ,							**A_:    Optional[Any]     ,							):
										__lowerCamelCase          =      AddedToken(A_     ,							lstrip=A_     ,							rstrip=A_   ) if isinstance(A_     ,							A_   ) else bos_token
										__lowerCamelCase          =      AddedToken(A_     ,							lstrip=A_     ,							rstrip=A_   ) if isinstance(A_     ,							A_   ) else eos_token
										__lowerCamelCase          =      AddedToken(A_     ,							lstrip=A_     ,							rstrip=A_   ) if isinstance(A_     ,							A_   ) else unk_token
										__lowerCamelCase          =      AddedToken(A_     ,							lstrip=A_     ,							rstrip=A_   ) if isinstance(A_     ,							A_   ) else pad_token
										__lowerCamelCase          =      AddedToken(A_     ,							lstrip=A_     ,							rstrip=A_   ) if isinstance(A_     ,							A_   ) else cls_token
										__lowerCamelCase          =      AddedToken(A_     ,							lstrip=A_     ,							rstrip=A_   ) if isinstance(A_     ,							A_   ) else sep_token

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

										__lowerCamelCase          =      {} if sp_model_kwargs is None else sp_model_kwargs

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

										__lowerCamelCase          =      vocab_file

										__lowerCamelCase          =      spm.SentencePieceProcessor(**self.sp_model_kwargs   )
										self.sp_model.Load(A_   )




							@property
							def   __a   (     self:    Optional[int]   ):
										return self.sp_model.get_piece_size()




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      {self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
										vocab.update(self.added_tokens_encoder   )
										return vocab




							def __getstate__(     self:    int   ):
										__lowerCamelCase          =      self.__dict__.copy()
										__lowerCamelCase          =      None
										return state




							def __setstate__(     self:    Dict     ,							A_:    int   ):
										__lowerCamelCase          =      d

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

										__lowerCamelCase          =      spm.SentencePieceProcessor(**self.sp_model_kwargs   )
										self.sp_model.Load(self.vocab_file   )




							def   __a   (     self:    Tuple     ,							A_:    str   ):
										return self.sp_model.encode(A_     ,							out_type=A_   )




							def   __a   (     self:    Tuple     ,							A_:    int   ):
										return self.sp_model.piece_to_id(A_   )




							def   __a   (     self:    Union[str, Any]     ,							A_:    str   ):
										__lowerCamelCase          =      self.sp_model.IdToPiece(A_   )
										return token




							def   __a   (     self:    Any     ,							A_:    str   ):
										__lowerCamelCase          =      []
										__lowerCamelCase          =      """"""
										__lowerCamelCase          =      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(A_   ) + token
																__lowerCamelCase          =      True
																__lowerCamelCase          =      []
													else:
																current_sub_tokens.append(A_   )
																__lowerCamelCase          =      False
										out_string += self.sp_model.decode(A_   )
										return out_string.strip()




							def   __a   (     self:    Dict     ,							A_:    List[int]     ,							A_:    bool = False     ,							A_:    bool = None     ,							A_:    bool = True     ,							**A_:    List[str]     ,							):
										__lowerCamelCase          =      kwargs.pop("""use_source_tokenizer"""     ,							A_   )

										__lowerCamelCase          =      self.convert_ids_to_tokens(A_     ,							skip_special_tokens=A_   )

										# 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
										__lowerCamelCase          =      []
										__lowerCamelCase          =      []
										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(A_   )   )
																			__lowerCamelCase          =      []
																sub_texts.append(A_   )
													else:
																current_sub_text.append(A_   )
										if current_sub_text:
													sub_texts.append(self.convert_tokens_to_string(A_   )   )

										# Mimic the behavior of the Rust tokenizer:
										# No space before [MASK] and [SEP]
										if spaces_between_special_tokens:
													__lowerCamelCase          =      re.sub(r""" (\[(MASK|SEP)\])"""     ,							r"""\1"""     ,							""" """.join(A_   )   )
										else:
													__lowerCamelCase          =      """""".join(A_   )

										__lowerCamelCase          =      (
										    clean_up_tokenization_spaces
										    if clean_up_tokenization_spaces is not None
										    else self.clean_up_tokenization_spaces
										)
										if clean_up_tokenization_spaces:
													__lowerCamelCase          =      self.clean_up_tokenization(A_   )
													return clean_text
										else:
													return text




							def   __a   (     self:    Tuple     ,							A_:    str     ,							A_:    Optional[str] = None   ):
										if not os.path.isdir(A_   ):
													logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
													return
										__lowerCamelCase          =      os.path.join(
										    A_     ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

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

										return (out_vocab_file,)




							def   __a   (     self:    int     ,							A_:    List[int]     ,							A_:    Optional[List[int]] = None   ):
										if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
										__lowerCamelCase          =      [self.cls_token_id]
										__lowerCamelCase          =      [self.sep_token_id]
										return cls + token_ids_a + sep + token_ids_a + sep




							def   __a   (     self:    Tuple     ,							A_:    List[int]     ,							A_:    Optional[List[int]] = None     ,							A_:    bool = False   ):
										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=A_     ,							token_ids_a=A_     ,							already_has_special_tokens=A_   )

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




							def   __a   (     self:    Any     ,							A_:    List[int]     ,							A_:    Optional[List[int]] = None   ):
										__lowerCamelCase          =      [self.sep_token_id]
										__lowerCamelCase          =      [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


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

__magic_name__						: Tuple    =				{
    '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 			__snake_case   (lowerCamelCase  ):
							__a					 =       '''encodec'''




							def __init__(     self:    Union[str, Any]     ,							A_:    Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]     ,							A_:    Any=2_40_00     ,							A_:    List[Any]=1     ,							A_:    Optional[Any]=False     ,							A_:    Optional[int]=None     ,							A_:    int=None     ,							A_:    List[str]=1_28     ,							A_:    Union[str, Any]=32     ,							A_:    List[str]=1     ,							A_:    Dict=[8, 5, 4, 2]     ,							A_:    List[Any]="weight_norm"     ,							A_:    Any=7     ,							A_:    List[Any]=7     ,							A_:    Tuple=3     ,							A_:    List[str]=2     ,							A_:    Optional[Any]=True     ,							A_:    Optional[int]="reflect"     ,							A_:    Dict=2     ,							A_:    Union[str, Any]=2     ,							A_:    Union[str, Any]=1.0     ,							A_:    List[str]=10_24     ,							A_:    str=None     ,							A_:    List[str]=True     ,							**A_:    int     ,							):
										__lowerCamelCase          =      target_bandwidths
										__lowerCamelCase          =      sampling_rate
										__lowerCamelCase          =      audio_channels
										__lowerCamelCase          =      normalize
										__lowerCamelCase          =      chunk_length_s
										__lowerCamelCase          =      overlap
										__lowerCamelCase          =      hidden_size
										__lowerCamelCase          =      num_filters
										__lowerCamelCase          =      num_residual_layers
										__lowerCamelCase          =      upsampling_ratios
										__lowerCamelCase          =      norm_type
										__lowerCamelCase          =      kernel_size
										__lowerCamelCase          =      last_kernel_size
										__lowerCamelCase          =      residual_kernel_size
										__lowerCamelCase          =      dilation_growth_rate
										__lowerCamelCase          =      use_causal_conv
										__lowerCamelCase          =      pad_mode
										__lowerCamelCase          =      compress
										__lowerCamelCase          =      num_lstm_layers
										__lowerCamelCase          =      trim_right_ratio
										__lowerCamelCase          =      codebook_size
										__lowerCamelCase          =      codebook_dim if codebook_dim is not None else hidden_size
										__lowerCamelCase          =      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__(**A_   )




							@property
							def   __a   (     self:    Union[str, Any]   ):
										if self.chunk_length_s is None:
													return None
										else:
													return int(self.chunk_length_s * self.sampling_rate   )




							@property
							def   __a   (     self:    List[Any]   ):
										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:    List[Any]   ):
										__lowerCamelCase          =      np.prod(self.upsampling_ratios   )
										return math.ceil(self.sampling_rate / hop_length   )




							@property
							def   __a   (     self:    List[Any]   ):
										return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )




