






"""simple docstring"""







import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase					      =  """▁"""

_UpperCamelCase					      =  get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
class  lowerCamelCase__ (	snake_case   ,					unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      BertGenerationTokenizer
    SCREAMING_SNAKE_CASE		=      False
    SCREAMING_SNAKE_CASE		=      True






    def     _UpperCamelCase							(			self ):
          super().setUp()

          UpperCAmelCase          =	BertGenerationTokenizer(A     ,keep_accents=A )
          tokenizer.save_pretrained(self.tmpdirname )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""<s>"""
          UpperCAmelCase          =	1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(A )     ,A )
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(A )     ,A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	list(self.get_tokenizer().get_vocab().keys() )

          self.assertEqual(vocab_keys[0]     ,"""<unk>""" )
          self.assertEqual(vocab_keys[1]     ,"""<s>""" )
          self.assertEqual(vocab_keys[-1]     ,"""<pad>""" )
          self.assertEqual(len(A )     ,1_002 )






    def     _UpperCamelCase							(			self ):
          self.assertEqual(self.get_tokenizer().vocab_size     ,1_000 )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	BertGenerationTokenizer(A     ,keep_accents=A )

          UpperCAmelCase          =	tokenizer.tokenize("""This is a test""" )
          self.assertListEqual(A     ,["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(A )     ,[285, 46, 10, 170, 382]     ,)

          UpperCAmelCase          =	tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
          self.assertListEqual(
              A     ,[
                  SPIECE_UNDERLINE + """I""",
                  SPIECE_UNDERLINE + """was""",
                  SPIECE_UNDERLINE + """b""",
                  """or""",
                  """n""",
                  SPIECE_UNDERLINE + """in""",
                  SPIECE_UNDERLINE + """""",
                  """9""",
                  """2""",
                  """0""",
                  """0""",
                  """0""",
                  """,""",
                  SPIECE_UNDERLINE + """and""",
                  SPIECE_UNDERLINE + """this""",
                  SPIECE_UNDERLINE + """is""",
                  SPIECE_UNDERLINE + """f""",
                  """al""",
                  """s""",
                  """é""",
                  """.""",
              ]     ,)
          UpperCAmelCase          =	tokenizer.convert_tokens_to_ids(A )
          self.assertListEqual(
              A     ,[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]     ,)

          UpperCAmelCase          =	tokenizer.convert_ids_to_tokens(A )
          self.assertListEqual(
              A     ,[
                  SPIECE_UNDERLINE + """I""",
                  SPIECE_UNDERLINE + """was""",
                  SPIECE_UNDERLINE + """b""",
                  """or""",
                  """n""",
                  SPIECE_UNDERLINE + """in""",
                  SPIECE_UNDERLINE + """""",
                  """<unk>""",
                  """2""",
                  """0""",
                  """0""",
                  """0""",
                  """,""",
                  SPIECE_UNDERLINE + """and""",
                  SPIECE_UNDERLINE + """this""",
                  SPIECE_UNDERLINE + """is""",
                  SPIECE_UNDERLINE + """f""",
                  """al""",
                  """s""",
                  """<unk>""",
                  """.""",
              ]     ,)






    @cached_property
    def     _UpperCamelCase							(			self ):
          return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )






    @slow
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	"""Hello World!"""
          UpperCAmelCase          =	[18_536, 2_260, 101]

          self.assertListEqual(A     ,self.big_tokenizer.encode(A ) )






    @slow
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	(
              """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
              """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
          )
          UpperCAmelCase          =	[
              871,
              419,
              358,
              946,
              991,
              2_521,
              452,
              358,
              1_357,
              387,
              7_751,
              3_536,
              112,
              985,
              456,
              126,
              865,
              938,
              5_400,
              5_734,
              458,
              1_368,
              467,
              786,
              2_462,
              5_246,
              1_159,
              633,
              865,
              4_519,
              457,
              582,
              852,
              2_557,
              427,
              916,
              508,
              405,
              34_324,
              497,
              391,
              408,
              11_342,
              1_244,
              385,
              100,
              938,
              985,
              456,
              574,
              362,
              12_597,
              3_200,
              3_129,
              1_172,
          ]

          self.assertListEqual(A     ,self.big_tokenizer.encode(A ) )






    @require_torch
    @slow
    def     _UpperCamelCase							(			self ):
          import torch

          from transformers import BertGenerationConfig, BertGenerationEncoder

          # Build sequence
          UpperCAmelCase          =	list(self.big_tokenizer.get_vocab().keys() )[:10]
          UpperCAmelCase          =	""" """.join(A )
          UpperCAmelCase          =	self.big_tokenizer.encode_plus(A     ,return_tensors="""pt"""     ,return_token_type_ids=A )
          UpperCAmelCase          =	self.big_tokenizer.batch_encode_plus(
              [sequence + """ """ + sequence]     ,return_tensors="""pt"""     ,return_token_type_ids=A )

          UpperCAmelCase          =	BertGenerationConfig()
          UpperCAmelCase          =	BertGenerationEncoder(A )

          assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

          with torch.no_grad():
                model(**A )
                model(**A )






    @slow
    def     _UpperCamelCase							(			self ):
          # fmt: off
          UpperCAmelCase          =	{"""input_ids""": [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 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], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 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, 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, 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, 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, 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], [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, 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]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=A     ,model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""     ,revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""     ,)











"""simple docstring"""







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


_UpperCamelCase					      =  logging.get_logger(__name__)

_UpperCamelCase					      =  """▁"""

_UpperCamelCase					      =  {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

_UpperCamelCase					      =  {
    """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""",
    },
}

_UpperCamelCase					      =  {"""vinai/bartpho-syllable""": 1024}







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=      ['''input_ids''', '''attention_mask''']






    def __init__(			self     ,A     ,A     ,A="<s>"     ,A="</s>"     ,A="</s>"     ,A="<s>"     ,A="<unk>"     ,A="<pad>"     ,A="<mask>"     ,A = None     ,**A     ,):
          # Mask token behave like a normal word, i.e. include the space before it
          UpperCAmelCase          =	AddedToken(A     ,lstrip=A     ,rstrip=A ) if isinstance(A     ,A ) else mask_token

          UpperCAmelCase          =	{} if sp_model_kwargs is None else sp_model_kwargs

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

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

          # Load the reduced vocab

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

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






    def __getstate__(			self ):
          UpperCAmelCase          =	self.__dict__.copy()
          UpperCAmelCase          =	None
          UpperCAmelCase          =	self.sp_model.serialized_model_proto()
          return state






    def __setstate__(			self     ,A ):
          UpperCAmelCase          =	d

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

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






    def     _UpperCamelCase							(			self     ,A     ,A = None ):

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






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = False ):

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

          if token_ids_a is None:
                return [1] + ([0] * len(A )) + [1]
          return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          UpperCAmelCase          =	[self.sep_token_id]
          UpperCAmelCase          =	[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     _UpperCamelCase							(			self ):
          return len(self.fairseq_ids_to_tokens )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	{self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab






    def     _UpperCamelCase							(			self     ,A ):
          return self.sp_model.encode(A     ,out_type=A )






    def     _UpperCamelCase							(			self     ,A ):
          if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
          else:
                return self.unk_token_id






    def     _UpperCamelCase							(			self     ,A ):
          return self.fairseq_ids_to_tokens[index]






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	"""""".join(A ).replace(A     ,""" """ ).strip()
          return out_string






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          if not os.path.isdir(A ):
                logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
                return
          UpperCAmelCase          =	os.path.join(
              A     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
          UpperCAmelCase          =	os.path.join(
              A     ,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""]     ,)

          if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
                copyfile(self.vocab_file     ,A )
          elif not os.path.isfile(self.vocab_file ):
                with open(A     ,"""wb""" ) as fi:
                      UpperCAmelCase          =	self.sp_model.serialized_model_proto()
                      fi.write(A )

          if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
              A ) and os.path.isfile(self.monolingual_vocab_file ):
                copyfile(self.monolingual_vocab_file     ,A )
          elif not os.path.isfile(self.monolingual_vocab_file ):
                with open(A     ,"""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(A )} \n''' )

          return out_vocab_file, out_monolingual_vocab_file



