





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_												=     logging.get_logger(__name__)

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

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

A_												=     {
    "google/rembert": 2_5_6,
}

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    VOCAB_FILES_NAMES
					A__				 =    PRETRAINED_VOCAB_FILES_MAP
					A__				 =    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	,  ):
										super().__init__(
										    do_lower_case=_lowerCAmelCase	,  remove_space=_lowerCAmelCase	,  keep_accents=_lowerCAmelCase	,  bos_token=_lowerCAmelCase	,  eos_token=_lowerCAmelCase	,  unk_token=_lowerCAmelCase	,  sep_token=_lowerCAmelCase	,  pad_token=_lowerCAmelCase	,  cls_token=_lowerCAmelCase	,  mask_token=_lowerCAmelCase	,  **_lowerCAmelCase	,  )

										lowerCamelCase__       =       do_lower_case
										lowerCamelCase__       =       remove_space
										lowerCamelCase__       =       keep_accents
										lowerCamelCase__       =       vocab_file

										lowerCamelCase__       =       spm.SentencePieceProcessor()
										self.sp_model.Load(_lowerCAmelCase	)





					@property
					def 					__magic_name__ ( self	):
										return len(self.sp_model	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       {self.convert_ids_to_tokens(_lowerCAmelCase	): i for i in range(self.vocab_size	)}
										vocab.update(self.added_tokens_encoder	)
										return vocab





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





					def __setstate__( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       d
										lowerCamelCase__       =       spm.SentencePieceProcessor()
										self.sp_model.Load(self.vocab_file	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase=False	):
										lowerCamelCase__       =       self.sp_model.EncodeAsPieces(_lowerCAmelCase	)
										return pieces





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										return self.sp_model.PieceToId(_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										return self.sp_model.IdToPiece(_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       self.sp_model.decode_pieces(_lowerCAmelCase	)
										return out_string





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	):
										lowerCamelCase__       =       [self.sep_token_id]
										lowerCamelCase__       =       [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	):

										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(_lowerCAmelCase	)) + [1] + ([0] * len(_lowerCAmelCase	)) + [1]
										return [1] + ([0] * len(_lowerCAmelCase	)) + [1]





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = 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]






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	):
										if not os.path.isdir(_lowerCAmelCase	):
															logger.error("Vocabulary path ({}) should be a directory".format(_lowerCAmelCase	)	)
															return
										lowerCamelCase__       =       os.path.join(
										    _lowerCAmelCase	,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

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

										return (out_vocab_file,)








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


A_												=     {
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    "albert"





					def __init__( self	,  _lowerCAmelCase=3_0000	,  _lowerCAmelCase=128	,  _lowerCAmelCase=4096	,  _lowerCAmelCase=12	,  _lowerCAmelCase=1	,  _lowerCAmelCase=64	,  _lowerCAmelCase=1_6384	,  _lowerCAmelCase=1	,  _lowerCAmelCase="gelu_new"	,  _lowerCAmelCase=0	,  _lowerCAmelCase=0	,  _lowerCAmelCase=512	,  _lowerCAmelCase=2	,  _lowerCAmelCase=0.02	,  _lowerCAmelCase=1E-12	,  _lowerCAmelCase=0.1	,  _lowerCAmelCase="absolute"	,  _lowerCAmelCase=0	,  _lowerCAmelCase=2	,  _lowerCAmelCase=3	,  **_lowerCAmelCase	,  ):
										super().__init__(pad_token_id=_lowerCAmelCase	,  bos_token_id=_lowerCAmelCase	,  eos_token_id=_lowerCAmelCase	,  **_lowerCAmelCase	)

										lowerCamelCase__       =       vocab_size
										lowerCamelCase__       =       embedding_size
										lowerCamelCase__       =       hidden_size
										lowerCamelCase__       =       num_hidden_layers
										lowerCamelCase__       =       num_hidden_groups
										lowerCamelCase__       =       num_attention_heads
										lowerCamelCase__       =       inner_group_num
										lowerCamelCase__       =       hidden_act
										lowerCamelCase__       =       intermediate_size
										lowerCamelCase__       =       hidden_dropout_prob
										lowerCamelCase__       =       attention_probs_dropout_prob
										lowerCamelCase__       =       max_position_embeddings
										lowerCamelCase__       =       type_vocab_size
										lowerCamelCase__       =       initializer_range
										lowerCamelCase__       =       layer_norm_eps
										lowerCamelCase__       =       classifier_dropout_prob
										lowerCamelCase__       =       position_embedding_type

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					@property
					def 					__magic_name__ ( self	):
										if self.task == "multiple-choice":
															lowerCamelCase__       =       {0: "batch", 1: "choice", 2: "sequence"}
										else:
															lowerCamelCase__       =       {0: "batch", 1: "sequence"}
										return OrderedDict(
										    [
										        ("input_ids", dynamic_axis),
										        ("attention_mask", dynamic_axis),
										        ("token_type_ids", dynamic_axis),
										    ]	)

