import os
import unicodedata
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


__lowercase							 =					logging.get_logger(__name__)
__lowercase							 =					{'''vocab_file''': '''spiece.model'''}

__lowercase							 =					{
    '''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''',
    }
}

__lowercase							 =					{
    '''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							 =					'''▁'''


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Tuple						=      VOCAB_FILES_NAMES
    a__	:						Union[str, Any]						=      PRETRAINED_VOCAB_FILES_MAP
    a__	:						Union[str, Any]						=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







    def __init__(	self , __lowercase , __lowercase=True , __lowercase=True , __lowercase=False , __lowercase="[CLS]" , __lowercase="[SEP]" , __lowercase="<unk>" , __lowercase="[SEP]" , __lowercase="<pad>" , __lowercase="[CLS]" , __lowercase="[MASK]" , __lowercase = None , **__lowercase , )       ->	None:
        # 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.
        __UpperCamelCase     :Union[str, Any]			  =		(
            AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase , normalized=__lowercase)
            if isinstance(__lowercase , __lowercase)
            else mask_token
        )

        __UpperCamelCase     :List[str]			  =		{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            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 , sp_model_kwargs=self.sp_model_kwargs , **__lowercase , )

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

        __UpperCamelCase     :str			  =		spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(__lowercase)







    @property
    def 			UpperCamelCase__   (	self)       ->	Any:
        return len(self.sp_model)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Optional[int]			  =		{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)       ->	Union[str, Any]:
        __UpperCamelCase     :List[str]			  =		self.__dict__.copy()
        __UpperCamelCase     :Union[str, Any]			  =		None
        return state







    def __setstate__(	self , __lowercase)       ->	Any:
        __UpperCamelCase     :int			  =		d

        # for backward compatibility
        if not hasattr(self , '''sp_model_kwargs'''):
            __UpperCamelCase     :Dict			  =		{}

        __UpperCamelCase     :Dict			  =		spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(self.vocab_file)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	List[str]:
        if self.remove_space:
            __UpperCamelCase     :List[Any]			  =		''' '''.join(inputs.strip().split())
        else:
            __UpperCamelCase     :List[Any]			  =		inputs
        __UpperCamelCase     :str			  =		outputs.replace('''``''' , '''"''').replace('''\'\'''' , '''"''')

        if not self.keep_accents:
            __UpperCamelCase     :Optional[Any]			  =		unicodedata.normalize('''NFKD''' , __lowercase)
            __UpperCamelCase     :List[str]			  =		''''''.join([c for c in outputs if not unicodedata.combining(__lowercase)])
        if self.do_lower_case:
            __UpperCamelCase     :List[Any]			  =		outputs.lower()

        return outputs







    def 			UpperCamelCase__   (	self , __lowercase)       ->	List[str]:
        __UpperCamelCase     :Optional[Any]			  =		self.preprocess_text(__lowercase)
        __UpperCamelCase     :List[str]			  =		self.sp_model.encode(__lowercase , out_type=__lowercase)
        __UpperCamelCase     :Any			  =		[]
        for piece in pieces:
            if len(__lowercase) > 1 and piece[-1] == str(''',''') and piece[-2].isdigit():
                __UpperCamelCase     :Union[str, Any]			  =		self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowercase , ''''''))
                if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                    if len(cur_pieces[0]) == 1:
                        __UpperCamelCase     :int			  =		cur_pieces[1:]
                    else:
                        __UpperCamelCase     :List[Any]			  =		cur_pieces[0][1:]
                cur_pieces.append(piece[-1])
                new_pieces.extend(__lowercase)
            else:
                new_pieces.append(__lowercase)

        return new_pieces







    def 			UpperCamelCase__   (	self , __lowercase)       ->	str:
        return self.sp_model.PieceToId(__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	int:
        return self.sp_model.IdToPiece(__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	List[Any]:
        __UpperCamelCase     :int			  =		[]
        __UpperCamelCase     :List[Any]			  =		''''''
        __UpperCamelCase     :int			  =		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(__lowercase) + token
                __UpperCamelCase     :List[str]			  =		True
                __UpperCamelCase     :Optional[Any]			  =		[]
            else:
                current_sub_tokens.append(__lowercase)
                __UpperCamelCase     :Tuple			  =		False
        out_string += self.sp_model.decode(__lowercase)
        return out_string.strip()







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	List[int]:
        __UpperCamelCase     :str			  =		[self.sep_token_id]
        __UpperCamelCase     :Tuple			  =		[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 , __lowercase , __lowercase = None , __lowercase = False)       ->	List[int]:

        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__lowercase , token_ids_a=__lowercase , already_has_special_tokens=__lowercase)

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







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	List[int]:
        __UpperCamelCase     :List[Any]			  =		[self.sep_token_id]
        __UpperCamelCase     :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 			UpperCamelCase__   (	self , __lowercase , __lowercase = None)       ->	Tuple[str]:
        if not os.path.isdir(__lowercase):
            logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
            return
        __UpperCamelCase     :List[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) 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:
                __UpperCamelCase     :Optional[int]			  =		self.sp_model.serialized_model_proto()
                fi.write(__lowercase)

        return (out_vocab_file,)







'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase			:				Union[str, Any]    		=			logging.get_logger(__name__)

lowercase			:				str    		=			{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}





class  A				(							__snake_case						):
     __magic_name__						=		'''bert'''





     def __init__(							self						,			SCREAMING_SNAKE_CASE=30522						,			SCREAMING_SNAKE_CASE=768						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=3072						,			SCREAMING_SNAKE_CASE="gelu"						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=512						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=1e-12						,			SCREAMING_SNAKE_CASE=0						,			SCREAMING_SNAKE_CASE="absolute"						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE						,			) ->				Optional[int]:






         """simple docstring"""






         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)

         A       :   Optional[int]					=						vocab_size
         A       :   Optional[Any]					=						hidden_size
         A       :   List[Any]					=						num_hidden_layers
         A       :   List[str]					=						num_attention_heads
         A       :   Dict					=						hidden_act
         A       :   Optional[Any]					=						intermediate_size
         A       :   List[Any]					=						hidden_dropout_prob
         A       :   List[Any]					=						attention_probs_dropout_prob
         A       :   Optional[Any]					=						max_position_embeddings
         A       :   List[str]					=						type_vocab_size
         A       :   Dict					=						initializer_range
         A       :   str					=						layer_norm_eps
         A       :   int					=						position_embedding_type
         A       :   Dict					=						use_cache
         A       :   str					=						classifier_dropout





class  A				(							__snake_case						):





     @property
     def 				__lowerCAmelCase							(							self	) ->				Mapping[str, Mapping[int, str]]:






         """simple docstring"""






         if self.task == "multiple-choice":
             A       :   Optional[Any]					=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A       :   Optional[int]					=						{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''token_type_ids''', dynamic_axis),
             ]	)




