





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

import sentencepiece as spm

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


_a 						=      logging.get_logger(__name__)

_a 						=      {"vocab_file": "spiece.model"}

_a 						=      {
    "vocab_file": {
        "bert_for_seq_generation": (
            "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
        ),
    }
}

_a 						=      {"bert_for_seq_generation": 512}






class 					__A    (    _UpperCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						VOCAB_FILES_NAMES
			lowerCAmelCase_							     =						PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_							     =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_							     =						[]
			lowerCAmelCase_							     =						["""input_ids""", """attention_mask"""]





			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase="<s>"       ,  __lowerCAmelCase="</s>"       ,  __lowerCAmelCase="<unk>"       ,  __lowerCAmelCase="<pad>"       ,  __lowerCAmelCase="<::::>"       ,  __lowerCAmelCase = None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		{} if sp_model_kwargs is None else sp_model_kwargs

				# Add extra_ids to the special token list
				super().__init__(
				    bos_token=lowercase_       ,  eos_token=lowercase_       ,  unk_token=lowercase_       ,  pad_token=lowercase_       ,  sep_token=lowercase_       ,  sp_model_kwargs=self.sp_model_kwargs       ,  **lowercase_       ,  )

				lowerCamelCase__								=		vocab_file

				lowerCamelCase__								=		spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(lowercase_							)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self.sp_model.get_piece_size()





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		{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							):



				'''simple docstring'''







				lowerCamelCase__								=		self.__dict__.copy()
				lowerCamelCase__								=		None
				return state





			def __setstate__(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				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 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.sp_model.encode(lowercase_       ,  out_type=lowercase_							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.sp_model.piece_to_id(lowercase_							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.sp_model.IdToPiece(lowercase_							)
				return token





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		[]
				lowerCamelCase__								=		""""""
				for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
						out_string += self.sp_model.decode(lowercase_							) + token
						lowerCamelCase__								=		[]
					else:
						current_sub_tokens.append(lowercase_							)
				out_string += self.sp_model.decode(lowercase_							)
				return out_string.strip()





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				if not os.path.isdir(lowercase_							):
					logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
					return
				lowerCamelCase__								=		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:
						lowerCamelCase__								=		self.sp_model.serialized_model_proto()
						fi.write(lowercase_							)

				return (out_vocab_file,)




'''simple docstring'''




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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_albert import AlbertTokenizer
else:
						_lowercase						: int						=						None

_lowercase						: Union[str, Any]						=						logging.get_logger(__name__)
_lowercase						: Tuple						=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase						: Dict						=						{
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

_lowercase						: int						=						{
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

_lowercase						: Tuple						=						"▁"





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       VOCAB_FILES_NAMES
			UpperCamelCase__				=       PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__				=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__				=       AlbertTokenizer







			def __init__(	self				:  Optional[Any]				,			lowercase_				:  Union[str, Any]=None				,			lowercase_				:  str=None				,			lowercase_				:  Any=True				,			lowercase_				:  Optional[int]=True				,			lowercase_				:  List[str]=False				,			lowercase_				:  Optional[int]="[CLS]"				,			lowercase_				:  Any="[SEP]"				,			lowercase_				:  int="<unk>"				,			lowercase_				:  Any="[SEP]"				,			lowercase_				:  int="<pad>"				,			lowercase_				:  Tuple="[CLS]"				,			lowercase_				:  Dict="[MASK]"				,			**lowercase_				:  Optional[int]				,			):
										# Mask token behave like a normal word, i.e. include the space before it and
										# is included in the raw text, there should be a match in a non-normalized sentence.
										lowercase_					:			Tuple										=       (
										    AddedToken(lowercase_				,			lstrip=lowercase_				,			rstrip=lowercase_				,			normalized=lowercase_    )
										    if isinstance(lowercase_				,			lowercase_    )
										    else mask_token
										)

										super().__init__(
										    lowercase_				,			tokenizer_file=lowercase_				,			do_lower_case=lowercase_				,			remove_space=lowercase_				,			keep_accents=lowercase_				,			bos_token=lowercase_				,			eos_token=lowercase_				,			unk_token=lowercase_				,			sep_token=lowercase_				,			pad_token=lowercase_				,			cls_token=lowercase_				,			mask_token=lowercase_				,			**lowercase_				,			)

										lowercase_					:			Optional[int]										=       do_lower_case
										lowercase_					:			Any										=       remove_space
										lowercase_					:			Dict										=       keep_accents
										lowercase_					:			Optional[int]										=       vocab_file
										lowercase_					:			Any										=       False if not self.vocab_file else True







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  List[int]				,			lowercase_				:  Optional[List[int]] = None    ):
										lowercase_					:			Tuple										=       [self.sep_token_id]
										lowercase_					:			str										=       [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 	SCREAMING_SNAKE_CASE_     (	self				:  Any				,			lowercase_				:  List[int]				,			lowercase_				:  Optional[List[int]] = None    ):
										lowercase_					:			Union[str, Any]										=       [self.sep_token_id]
										lowercase_					:			int										=       [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 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]				,			lowercase_				:  str				,			lowercase_				:  Optional[str] = None    ):
										if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
																	    """tokenizer."""    )

										if not os.path.isdir(lowercase_    ):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
																	return
										lowercase_					:			Optional[Any]										=       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_    ):
																	copyfile(self.vocab_file				,			lowercase_    )

										return (out_vocab_file,)





