

import os
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


__a    :List[Any]      =			logging.get_logger(__name__)

__a    :Tuple      =			'▁'

__a    :Union[str, Any]      =			{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

__a    :Any      =			{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

__a    :Any      =			{'vinai/bartpho-syllable': 1024}

class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			int      		=       VOCAB_FILES_NAMES
      _lowerCamelCase       :			List[str]      		=       PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase       :			List[Any]      		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase       :			int      		=       ['input_ids', 'attention_mask']





      def __init__(   self							:				Optional[int]						,			UpperCAmelCase							:				List[Any]						,			UpperCAmelCase							:				str						,			UpperCAmelCase							:				List[Any]="<s>"						,			UpperCAmelCase							:				Any="</s>"						,			UpperCAmelCase							:				int="</s>"						,			UpperCAmelCase							:				Tuple="<s>"						,			UpperCAmelCase							:				Any="<unk>"						,			UpperCAmelCase							:				List[str]="<pad>"						,			UpperCAmelCase							:				Union[str, Any]="<mask>"						,			UpperCAmelCase							:				Optional[int] = None						,			**UpperCAmelCase							:				int						,			):
            A_						  =				AddedToken(UpperCAmelCase						,			lstrip=UpperCAmelCase						,			rstrip=UpperCAmelCase	) if isinstance(UpperCAmelCase						,			UpperCAmelCase	) else mask_token

            A_						  =				{} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                bos_token=UpperCAmelCase						,			eos_token=UpperCAmelCase						,			unk_token=UpperCAmelCase						,			sep_token=UpperCAmelCase						,			cls_token=UpperCAmelCase						,			pad_token=UpperCAmelCase						,			mask_token=UpperCAmelCase						,			sp_model_kwargs=self.sp_model_kwargs						,			**UpperCAmelCase						,			)

            A_						  =				vocab_file
            A_						  =				monolingual_vocab_file
            A_						  =				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
            self.sp_model.Load(str(UpperCAmelCase	)	)

            # Load the reduced vocab

            # Keep order of special tokens for backward compatibility
            A_						  =				{}
            A_						  =				0
            for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
                  if str(UpperCAmelCase	) not in self.fairseq_tokens_to_ids:
                        A_						  =				cnt
                        cnt += 1
            with open(UpperCAmelCase						,			"r"						,			encoding="utf-8"	) as f:
                  for line in f.readlines():
                        A_						  =				line.strip().split()[0]
                        A_						  =				len(self.fairseq_tokens_to_ids	)
            if str(UpperCAmelCase	) not in self.fairseq_tokens_to_ids:
                  A_						  =				len(self.fairseq_tokens_to_ids	)

            A_						  =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}





      def __getstate__(   self							:				Optional[int]	):
            A_						  =				self.__dict__.copy()
            A_						  =				None
            A_						  =				self.sp_model.serialized_model_proto()
            return state





      def __setstate__(   self							:				Any						,			UpperCAmelCase							:				Union[str, Any]	):
            A_						  =				d

            # for backward compatibility
            if not hasattr(self						,			"sp_model_kwargs"	):
                  A_						  =				{}

            A_						  =				spm.SentencePieceProcessor(**self.sp_model_kwargs	)
            self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)





      def 		__A	(   self							:				Dict						,			UpperCAmelCase							:				Tuple						,			UpperCAmelCase							:				Dict = None	):

            if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            A_						  =				[self.cls_token_id]
            A_						  =				[self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep





      def 		__A	(   self							:				List[str]						,			UpperCAmelCase							:				Optional[Any]						,			UpperCAmelCase							:				List[str] = None						,			UpperCAmelCase							:				Dict = False	):

            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 None:
                  return [1] + ([0] * len(UpperCAmelCase	)) + [1]
            return [1] + ([0] * len(UpperCAmelCase	)) + [1, 1] + ([0] * len(UpperCAmelCase	)) + [1]





      def 		__A	(   self							:				Optional[Any]						,			UpperCAmelCase							:				str						,			UpperCAmelCase							:				int = None	):
            A_						  =				[self.sep_token_id]
            A_						  =				[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 + sep + token_ids_a + sep	) * [0]





      @property
      def 		__A	(   self							:				Dict	):
            return len(self.fairseq_ids_to_tokens	)





      def 		__A	(   self							:				List[Any]	):
            A_						  =				{self.convert_ids_to_tokens(UpperCAmelCase	): i for i in range(self.vocab_size	)}
            vocab.update(self.added_tokens_encoder	)
            return vocab





      def 		__A	(   self							:				Union[str, Any]						,			UpperCAmelCase							:				List[str]	):
            return self.sp_model.encode(UpperCAmelCase						,			out_type=UpperCAmelCase	)





      def 		__A	(   self							:				Any						,			UpperCAmelCase							:				Any	):

            if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
            else:
                  return self.unk_token_id





      def 		__A	(   self							:				Optional[Any]						,			UpperCAmelCase							:				Dict	):
            return self.fairseq_ids_to_tokens[index]





      def 		__A	(   self							:				Optional[Any]						,			UpperCAmelCase							:				Optional[Any]	):
            A_						  =				"".join(UpperCAmelCase	).replace(UpperCAmelCase						,			" "	).strip()
            return out_string







      def 		__A	(   self							:				Tuple						,			UpperCAmelCase							:				Dict						,			UpperCAmelCase							:				Optional[Any] = None	):

            if not os.path.isdir(UpperCAmelCase	):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
                  return
            A_						  =				os.path.join(
                UpperCAmelCase						,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
            A_						  =				os.path.join(
                UpperCAmelCase						,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_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:
                        A_						  =				self.sp_model.serialized_model_proto()
                        fi.write(UpperCAmelCase	)

            if os.path.abspath(self.monolingual_vocab_file	) != os.path.abspath(
                UpperCAmelCase	) and os.path.isfile(self.monolingual_vocab_file	):
                  copyfile(self.monolingual_vocab_file						,			UpperCAmelCase	)
            elif not os.path.isfile(self.monolingual_vocab_file	):
                  with open(UpperCAmelCase						,			"w"						,			encoding="utf-8"	) as fp:
                        for token in self.fairseq_tokens_to_ids:
                              if token not in self.all_special_tokens:
                                    fp.write(f'''{str(UpperCAmelCase	)} \n'''	)

            return out_vocab_file, out_monolingual_vocab_file


"""simple docstring"""

import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__a					(						__magic_name__       ,	unittest.TestCase       ):





       """simple docstring"""


       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizer
       __UpperCamelCase					:      Optional[Any]    =						LxmertTokenizerFast
       __UpperCamelCase					:      str    =						True
       __UpperCamelCase					:      Any    =						True






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        super().setUp()

        lowerCAmelCase__   :				Tuple 			=				[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        lowerCAmelCase__   :				Dict 			=				os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES["vocab_file"]   )
        with open(self.vocab_file			,					"w"			,					encoding="utf-8"   ) as vocab_writer:
         vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				"UNwant\u00E9d,running"
        lowerCAmelCase__   :				Union[str, Any] 			=				"unwanted, running"
        return input_text, output_text






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				self.tokenizer_class(self.vocab_file   )

        lowerCAmelCase__   :				List[Any] 			=				tokenizer.tokenize("UNwant\u00E9d,running"   )
        self.assertListEqual(snake_case			,					["un", "##want", "##ed", ",", "runn", "##ing"]   )
        self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case   )			,					[7, 4, 5, 10, 8, 9]   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        if not self.test_rust_tokenizer:
         return

        lowerCAmelCase__   :				int 			=				self.get_tokenizer()
        lowerCAmelCase__   :				List[str] 			=				self.get_rust_tokenizer()

        lowerCAmelCase__   :				Optional[Any] 			=				"I was born in 92000, and this is falsé."

        lowerCAmelCase__   :				int 			=				tokenizer.tokenize(snake_case   )
        lowerCAmelCase__   :				int 			=				rust_tokenizer.tokenize(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				str 			=				tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        lowerCAmelCase__   :				List[str] 			=				rust_tokenizer.encode(snake_case			,					add_special_tokens=snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )

        lowerCAmelCase__   :				Tuple 			=				self.get_rust_tokenizer()
        lowerCAmelCase__   :				Tuple 			=				tokenizer.encode(snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				rust_tokenizer.encode(snake_case   )
        self.assertListEqual(snake_case			,					snake_case   )






