






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


__magic_name__							=     logging.get_logger(__name__)

__magic_name__							=     {"vocab_file": "sentencepiece.model"}

__magic_name__							=     {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

__magic_name__							=     {
    "google/rembert": 256,
}





class 	snake_case__ (							__lowercase				):
	lowercase__						:					Optional[Any]											=   VOCAB_FILES_NAMES
	lowercase__						:					int											=   PRETRAINED_VOCAB_FILES_MAP
	lowercase__						:					Any											=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=False      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__="[CLS]"      ,       lowerCAmelCase__="[SEP]"      ,       lowerCAmelCase__="[UNK]"      ,       lowerCAmelCase__="[SEP]"      ,       lowerCAmelCase__="[PAD]"      ,       lowerCAmelCase__="[CLS]"      ,       lowerCAmelCase__="[MASK]"      ,       **lowerCAmelCase__      ,       )							->				List[Any]:
							super().__init__(
							    do_lower_case=UpperCAmelCase__      ,       remove_space=UpperCAmelCase__      ,       keep_accents=UpperCAmelCase__      ,       bos_token=UpperCAmelCase__      ,       eos_token=UpperCAmelCase__      ,       unk_token=UpperCAmelCase__      ,       sep_token=UpperCAmelCase__      ,       pad_token=UpperCAmelCase__      ,       cls_token=UpperCAmelCase__      ,       mask_token=UpperCAmelCase__      ,       **UpperCAmelCase__      ,       )

							__magic_name__      :       Union[str, Any]       						= do_lower_case
							__magic_name__      :       str       						= remove_space
							__magic_name__      :       Union[str, Any]       						= keep_accents
							__magic_name__      :       Any       						= vocab_file

							__magic_name__      :       int       						= spm.SentencePieceProcessor()
							self.sp_model.Load(UpperCAmelCase__			)
	@property
	def       __magic_name__    (    self			)							->				List[Any]:
							return len(self.sp_model			)
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       List[Any]       						= {self.convert_ids_to_tokens(UpperCAmelCase__			): i for i in range(self.vocab_size			)}
							vocab.update(self.added_tokens_encoder			)
							return vocab
	def __getstate__(    self			)							->				Union[str, Any]:
							__magic_name__      :       List[Any]       						= self.__dict__.copy()
							__magic_name__      :       Tuple       						= None
							return state
	def __setstate__(    self      ,       lowerCAmelCase__			)							->				Dict:
							__magic_name__      :       List[str]       						= d
							__magic_name__      :       Any       						= spm.SentencePieceProcessor()
							self.sp_model.Load(self.vocab_file			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=False			)							->				Any:
							__magic_name__      :       str       						= self.sp_model.EncodeAsPieces(UpperCAmelCase__			)
							return pieces
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Any:
							return self.sp_model.PieceToId(UpperCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				Optional[int]:
							return self.sp_model.IdToPiece(UpperCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__			)							->				int:
							__magic_name__      :       Optional[Any]       						= self.sp_model.decode_pieces(UpperCAmelCase__			)
							return out_string
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				List[int]:
							__magic_name__      :       Optional[Any]       						= [self.sep_token_id]
							__magic_name__      :       Optional[Any]       						= [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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = 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(UpperCAmelCase__			)) + [1] + ([0] * len(UpperCAmelCase__			)) + [1]
							return [1] + ([0] * len(UpperCAmelCase__			)) + [1]
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				List[int]:
							__magic_name__      :       Any       						= [self.sep_token_id]
							__magic_name__      :       Optional[Any]       						= [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       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				Tuple[str]:
							if not os.path.isdir(UpperCAmelCase__			):
													logger.error("""Vocabulary path ({}) should be a directory""".format(UpperCAmelCase__			)			)
													return
							__magic_name__      :       Tuple       						= os.path.join(
							    UpperCAmelCase__      ,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

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

							return (out_vocab_file,)








import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class 	snake_case__ (							tf.keras.layers.Layer				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None			)							->				int:
							super().__init__()
							__magic_name__      :       Any       						= pad_token_id
							__magic_name__      :       Any       						= max_length
							__magic_name__      :       List[str]       						= vocab
							__magic_name__      :       List[Any]       						= merges
							__magic_name__      :       int       						= BytePairTokenizer(lowerCAmelCase__      ,       lowerCAmelCase__      ,       sequence_length=lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Union[str, Any]       						= [""" """.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
							__magic_name__      :       Union[str, Any]       						= tokenizer.get_vocab()
							return cls(lowerCAmelCase__      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Optional[Any]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
							return cls.from_tokenizer(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__			)							->				List[Any]:
							return cls(**lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				int:
							__magic_name__      :       Dict       						= self.tf_tokenizer(lowerCAmelCase__			)
							__magic_name__      :       Dict       						= tf.ones_like(lowerCAmelCase__			)

							if self.pad_token_id is not None:
													# pad the tokens up to max length
													__magic_name__      :       List[Any]       						= max_length if max_length is not None else self.max_length

													if max_length is not None:
																			__magic_name__						,__magic_name__      :       List[Any]       						= pad_model_inputs(
																			    lowerCAmelCase__      ,       max_seq_length=lowerCAmelCase__      ,       pad_value=self.pad_token_id			)

							return {"attention_mask": attention_mask, "input_ids": input_ids}
