



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 SPIECE_UNDERLINE, logging


a_   :Tuple        =      logging.get_logger(__name__)

a_   :Tuple        =      {"vocab_file": "spiece.model"}

a_   :Dict        =      {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    }
}

a_   :Dict        =      {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

# Segments (not really needed)
a_   :Tuple        =      0
a_   :Optional[Any]        =      1
a_   :int        =      2
a_   :Dict        =      3
a_   :Dict        =      4




class 					snake_case__						(       snake_case_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE						=	PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE						=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE						=	'''left'''






     def __init__(       self  :  Dict, _snake_case  :  List[Any], _snake_case  :  Dict=False, _snake_case  :  Tuple=True, _snake_case  :  int=False, _snake_case  :  Tuple="<s>", _snake_case  :  Optional[Any]="</s>", _snake_case  :  Tuple="<unk>", _snake_case  :  List[str]="<sep>", _snake_case  :  str="<pad>", _snake_case  :  Dict="<cls>", _snake_case  :  Dict="<mask>", _snake_case  :  Tuple=["<eop>", "<eod>"], _snake_case  :  Any = None, **_snake_case  :  Union[str, Any], )   ->None:
            snake_case__							:	Tuple   	=	AddedToken(UpperCamelCase__, lstrip=UpperCamelCase__, rstrip=UpperCamelCase__					) if isinstance(UpperCamelCase__, UpperCamelCase__					) else mask_token

            snake_case__							:	str   	=	{} if sp_model_kwargs is None else sp_model_kwargs

            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__, additional_special_tokens=UpperCamelCase__, sp_model_kwargs=self.sp_model_kwargs, **UpperCamelCase__, )

            snake_case__							:	Optional[int]   	=	3

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

            snake_case__							:	Optional[int]   	=	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(UpperCamelCase__					)






     @property
     def    lowercase_   (       self  :  Dict					)   ->Tuple:
            return len(self.sp_model					)






     def    lowercase_   (       self  :  Optional[Any]					)   ->str:
            snake_case__							:	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  :  Optional[int]					)   ->Any:
            snake_case__							:	Tuple   	=	self.__dict__.copy()
            snake_case__							:	Dict   	=	None
            return state






     def __setstate__(       self  :  Optional[Any], _snake_case  :  Tuple					)   ->str:
            snake_case__							:	Dict   	=	d

            # for backward compatibility
            if not hasattr(self, 'sp_model_kwargs'					):
                   snake_case__							:	Optional[Any]   	=	{}

            snake_case__							:	List[Any]   	=	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
            self.sp_model.Load(self.vocab_file					)






     def    lowercase_   (       self  :  Dict, _snake_case  :  Tuple					)   ->Dict:




            if self.remove_space:
                   snake_case__							:	int   	=	" ".join(inputs.strip().split()					)
            else:
                   snake_case__							:	Any   	=	inputs
            snake_case__							:	List[Any]   	=	outputs.replace('``', '\"'					).replace('\'\'', '\"'					)

            if not self.keep_accents:
                   snake_case__							:	str   	=	unicodedata.normalize('NFKD', UpperCamelCase__					)
                   snake_case__							:	Any   	=	"".join([c for c in outputs if not unicodedata.combining(UpperCamelCase__					)]					)
            if self.do_lower_case:
                   snake_case__							:	int   	=	outputs.lower()

            return outputs






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

            return new_pieces






     def    lowercase_   (       self  :  List[str], _snake_case  :  Optional[int]					)   ->List[str]:
            return self.sp_model.PieceToId(UpperCamelCase__					)






     def    lowercase_   (       self  :  Tuple, _snake_case  :  str					)   ->Any:
            return self.sp_model.IdToPiece(UpperCamelCase__					)






     def    lowercase_   (       self  :  Any, _snake_case  :  Optional[Any]					)   ->Optional[int]:
            snake_case__							:	Any   	=	"".join(UpperCamelCase__					).replace(UpperCamelCase__, ' '					).strip()
            return out_string






     def    lowercase_   (       self  :  List[Any], _snake_case  :  Union[str, Any], _snake_case  :  Dict = False, _snake_case  :  Any = None, _snake_case  :  List[str] = True, **_snake_case  :  Dict, )   ->str:
            snake_case__							:	str   	=	kwargs.pop('use_source_tokenizer', UpperCamelCase__					)

            snake_case__							:	Union[str, Any]   	=	self.convert_ids_to_tokens(UpperCamelCase__, skip_special_tokens=UpperCamelCase__					)

            # To avoid mixing byte-level and unicode for byte-level BPT
            # we need to build string separately for added tokens and byte-level tokens
            # cf. https://github.com/huggingface/transformers/issues/1133
            snake_case__							:	List[str]   	=	[]
            snake_case__							:	Dict   	=	[]
            for token in filtered_tokens:
                   if skip_special_tokens and token in self.all_special_ids:
                          continue
                   if token in self.added_tokens_encoder:
                          if current_sub_text:
                                 sub_texts.append(self.convert_tokens_to_string(UpperCamelCase__					)					)
                                 snake_case__							:	str   	=	[]
                          sub_texts.append(UpperCamelCase__					)
                   else:
                          current_sub_text.append(UpperCamelCase__					)
            if current_sub_text:
                   sub_texts.append(self.convert_tokens_to_string(UpperCamelCase__					)					)

            # Mimic the behavior of the Rust tokenizer:
            # By default, there are no spaces between special tokens
            snake_case__							:	List[Any]   	=	"".join(UpperCamelCase__					)

            snake_case__							:	Tuple   	=	(
                clean_up_tokenization_spaces
                if clean_up_tokenization_spaces is not None
                else self.clean_up_tokenization_spaces
            )
            if clean_up_tokenization_spaces:
                   snake_case__							:	int   	=	self.clean_up_tokenization(UpperCamelCase__					)
                   return clean_text
            else:
                   return text






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  Dict, _snake_case  :  Dict = None					)   ->List[int]:
            snake_case__							:	List[Any]   	=	[self.sep_token_id]
            snake_case__							:	Tuple   	=	[self.cls_token_id]
            if token_ids_a is None:
                   return token_ids_a + sep + cls
            return token_ids_a + sep + token_ids_a + sep + cls






     def    lowercase_   (       self  :  List[Any], _snake_case  :  int, _snake_case  :  Dict = None, _snake_case  :  str = False					)   ->List[int]:




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

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






     def    lowercase_   (       self  :  int, _snake_case  :  Dict, _snake_case  :  Union[str, Any] = None					)   ->List[int]:
            snake_case__							:	str   	=	[self.sep_token_id]
            snake_case__							:	str   	=	[2]

            if token_ids_a is None:
                   return len(token_ids_a + sep					) * [0] + cls_segment_id
            return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1] + cls_segment_id






     def    lowercase_   (       self  :  Any, _snake_case  :  int, _snake_case  :  List[str] = None					)   ->Tuple[str]:




            if not os.path.isdir(UpperCamelCase__					):
                   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
                   return
            snake_case__							:	Dict   	=	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__					) and os.path.isfile(self.vocab_file					):
                   copyfile(self.vocab_file, UpperCamelCase__					)
            elif not os.path.isfile(self.vocab_file					):
                   with open(UpperCamelCase__, 'wb'					) as fi:
                          snake_case__							:	Dict   	=	self.sp_model.serialized_model_proto()
                          fi.write(UpperCamelCase__					)

            return (out_vocab_file,)










"""simple docstring"""



import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class _lowerCAmelCase				(      snake_case_    ,							unittest.TestCase	):
   __UpperCAmelCase :       str							  =  DebertaTokenizer
   __UpperCAmelCase :       Optional[int]							  =  True
   __UpperCAmelCase :       int							  =  DebertaTokenizerFast







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       snake_case    :      Union[str, Any]      					=    [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "[UNK]",
       ]
       snake_case    :      str      					=    dict(zip(UpperCamelCase__						,	range(len(UpperCamelCase__						)						)						)						)
       snake_case    :      int      					=    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       snake_case    :      Optional[Any]      					=    {"unk_token": "[UNK]"}

       snake_case    :      Union[str, Any]      					=    os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["vocab_file"]						)
       snake_case    :      Dict      					=    os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["merges_file"]						)
       with open(self.vocab_file						,	"w"						,	encoding="utf-8"						) as fp:
           fp.write(json.dumps(UpperCamelCase__						) + "\n"						)
       with open(self.merges_file						,	"w"						,	encoding="utf-8"						) as fp:
           fp.write("\n".join(UpperCamelCase__						)						)







   def  lowerCamelCase		(	self						,	**UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       kwargs.update(self.special_tokens_map						)
       return self.tokenizer_class.from_pretrained(self.tmpdirname						,	**UpperCamelCase__						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		List[str]:

       '''simple docstring'''




       snake_case    :      Tuple      					=    "lower newer"
       snake_case    :      Any      					=    "lower newer"
       return input_text, output_text







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      Optional[int]      					=    self.get_tokenizer()
       snake_case    :      str      					=    "lower newer"
       snake_case    :      Dict      					=    ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
       snake_case    :      Optional[Any]      					=    tokenizer.tokenize(UpperCamelCase__						)
       self.assertListEqual(UpperCamelCase__						,	UpperCamelCase__						)

       snake_case    :      List[Any]      					=    tokens + [tokenizer.unk_token]
       snake_case    :      Dict      					=    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__						)						,	UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.get_tokenizer()
       snake_case    :      Dict      					=    tokenizer("Hello"						,	"World"						)
       snake_case    :      str      					=    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd["token_type_ids"]						,	UpperCamelCase__						)







   @slow
   def  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.tokenizer_class.from_pretrained("microsoft/deberta-base"						)

       snake_case    :      Dict      					=    tokenizer.encode("sequence builders"						,	add_special_tokens=UpperCamelCase__						)
       snake_case    :      Tuple      					=    tokenizer.encode("multi-sequence build"						,	add_special_tokens=UpperCamelCase__						)

       snake_case    :      List[str]      					=    tokenizer.encode(
           "sequence builders"						,	add_special_tokens=UpperCamelCase__						,	add_prefix_space=UpperCamelCase__						)
       snake_case    :      Optional[int]      					=    tokenizer.encode(
           "sequence builders"						,	"multi-sequence build"						,	add_special_tokens=UpperCamelCase__						,	add_prefix_space=UpperCamelCase__						)

       snake_case    :      int      					=    tokenizer.build_inputs_with_special_tokens(UpperCamelCase__						)
       snake_case    :      int      					=    tokenizer.build_inputs_with_special_tokens(UpperCamelCase__						,	UpperCamelCase__						)

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode







   @slow
   def  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    [self.tokenizer_class]
       if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class						)

       for tokenizer_class in tokenizer_classes:
           snake_case    :      str      					=    tokenizer_class.from_pretrained("microsoft/deberta-base"						)

           snake_case    :      Union[str, Any]      					=    [
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           snake_case    :      Optional[int]      					=    tokenizer(UpperCamelCase__						,	padding=UpperCamelCase__						)
           snake_case    :      Optional[int]      					=    [tokenizer.decode(UpperCamelCase__						,	skip_special_tokens=UpperCamelCase__						) for seq in encoding["input_ids"]]

           # fmt: off
           snake_case    :      Dict      					=    {
               "input_ids": [
                   [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
               ],
               "token_type_ids": [
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
               ],
               "attention_mask": [
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
               ]
           }
           # fmt: on

           snake_case    :      int      					=    [
               "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
               "ALBERT incorporates two parameter reduction techniques",
               "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
               " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
               " vocabulary embedding.",
           ]

           self.assertDictEqual(encoding.data						,	UpperCamelCase__						)

           for expected, decoded in zip(UpperCamelCase__						,	UpperCamelCase__						):
               self.assertEqual(UpperCamelCase__						,	UpperCamelCase__						)





