




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__A						:							int							=  logging.get_logger(__name__)

__A						:							int							=  {"""vocab_file""": """sentencepiece.model"""}

__A						:							Optional[Any]							=  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

__A						:							str							=  {
    """google/rembert""": 2_5_6,
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





							def __init__(					self       :	Optional[int]       ,     a       :	Any       ,     a       :	Union[str, Any]=False       ,     a       :	Union[str, Any]=True       ,     a       :	Tuple=True       ,     a       :	Optional[int]="[CLS]"       ,     a       :	Any="[SEP]"       ,     a       :	List[Any]="[UNK]"       ,     a       :	str="[SEP]"       ,     a       :	List[str]="[PAD]"       ,     a       :	Dict="[CLS]"       ,     a       :	str="[MASK]"       ,     **a       :	int       ,     )			->					Optional[int]:
										super().__init__(
										    do_lower_case=a       ,     remove_space=a       ,     keep_accents=a       ,     bos_token=a       ,     eos_token=a       ,     unk_token=a       ,     sep_token=a       ,     pad_token=a       ,     cls_token=a       ,     mask_token=a       ,     **a       ,     )

										SCREAMING_SNAKE_CASE           =					do_lower_case
										SCREAMING_SNAKE_CASE           =					remove_space
										SCREAMING_SNAKE_CASE           =					keep_accents
										SCREAMING_SNAKE_CASE           =					vocab_file

										SCREAMING_SNAKE_CASE           =					spm.SentencePieceProcessor()
										self.sp_model.Load(a			)





							@property
							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					str:
										return len(self.sp_model			)





							def    _UpperCAmelCase     (					self       :	Tuple			)			->					Any:
										SCREAMING_SNAKE_CASE           =					{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       :	Optional[Any]			)			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					self.__dict__.copy()
										SCREAMING_SNAKE_CASE           =					None
										return state





							def __setstate__(					self       :	Any       ,     a       :	str			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					d
										SCREAMING_SNAKE_CASE           =					spm.SentencePieceProcessor()
										self.sp_model.Load(self.vocab_file			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	int       ,     a       :	Optional[Any]=False			)			->					int:
										SCREAMING_SNAKE_CASE           =					self.sp_model.EncodeAsPieces(a			)
										return pieces





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	Optional[Any]			)			->					Dict:
										return self.sp_model.PieceToId(a			)





							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	str			)			->					int:
										return self.sp_model.IdToPiece(a			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	Optional[Any]			)			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					self.sp_model.decode_pieces(a			)
										return out_string





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[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       :	List[str]       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None       ,     a       :	bool = False			)			->					List[int]:

										if already_has_special_tokens:
													if token_ids_a is not None:
																raise ValueError(
																    """You should not supply a second sequence if the provided sequence of """
																    """ids is already formatted with special tokens for the model."""			)
													return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

										if token_ids_a is not None:
													return [1] + ([0] * len(a			)) + [1] + ([0] * len(a			)) + [1]
										return [1] + ([0] * len(a			)) + [1]





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[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       :	int       ,     a       :	str       ,     a       :	Optional[str] = None			)			->					Tuple[str]:
										if not os.path.isdir(a			):
													logger.error("""Vocabulary path ({}) should be a directory""".format(a			)			)
													return
										SCREAMING_SNAKE_CASE           =					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			):
													copyfile(self.vocab_file       ,     a			)

										return (out_vocab_file,)












import math
from typing import Optional

import numpy as np

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


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

__A						:							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 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''encodec'''





							def __init__(					self       :	Dict       ,     a       :	Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]       ,     a       :	Union[str, Any]=24_000       ,     a       :	List[Any]=1       ,     a       :	str=False       ,     a       :	List[Any]=None       ,     a       :	List[Any]=None       ,     a       :	Optional[int]=128       ,     a       :	int=32       ,     a       :	int=1       ,     a       :	Dict=[8, 5, 4, 2]       ,     a       :	List[str]="weight_norm"       ,     a       :	str=7       ,     a       :	int=7       ,     a       :	Optional[int]=3       ,     a       :	Optional[int]=2       ,     a       :	Optional[int]=True       ,     a       :	Union[str, Any]="reflect"       ,     a       :	Dict=2       ,     a       :	Union[str, Any]=2       ,     a       :	Optional[Any]=1.0       ,     a       :	List[Any]=1_024       ,     a       :	int=None       ,     a       :	Dict=True       ,     **a       :	Tuple       ,     )			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					target_bandwidths
										SCREAMING_SNAKE_CASE           =					sampling_rate
										SCREAMING_SNAKE_CASE           =					audio_channels
										SCREAMING_SNAKE_CASE           =					normalize
										SCREAMING_SNAKE_CASE           =					chunk_length_s
										SCREAMING_SNAKE_CASE           =					overlap
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					num_filters
										SCREAMING_SNAKE_CASE           =					num_residual_layers
										SCREAMING_SNAKE_CASE           =					upsampling_ratios
										SCREAMING_SNAKE_CASE           =					norm_type
										SCREAMING_SNAKE_CASE           =					kernel_size
										SCREAMING_SNAKE_CASE           =					last_kernel_size
										SCREAMING_SNAKE_CASE           =					residual_kernel_size
										SCREAMING_SNAKE_CASE           =					dilation_growth_rate
										SCREAMING_SNAKE_CASE           =					use_causal_conv
										SCREAMING_SNAKE_CASE           =					pad_mode
										SCREAMING_SNAKE_CASE           =					compress
										SCREAMING_SNAKE_CASE           =					num_lstm_layers
										SCREAMING_SNAKE_CASE           =					trim_right_ratio
										SCREAMING_SNAKE_CASE           =					codebook_size
										SCREAMING_SNAKE_CASE           =					codebook_dim if codebook_dim is not None else hidden_size
										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[int]:
										if self.chunk_length_s is None:
													return None
										else:
													return int(self.chunk_length_s * self.sampling_rate			)





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







							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)			)






