






import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class 				__A     (			a__      ,     unittest.TestCase ):





				"""simple docstring"""







				UpperCamelCase__ :      Any			 =PhobertTokenizer
				UpperCamelCase__ :      Union[str, Any]			 =False


				def       __lowercase							( self    ):




									"""simple docstring"""

									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									__UpperCamelCase      :					List[Any]       		=['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
									__UpperCamelCase      :					List[Any]       		=dict(zip(_UpperCAmelCase							,  range(len(_UpperCAmelCase    )    )    )    )
									__UpperCamelCase      :					Union[str, Any]       		=['#version: 0.2', 'l à</w>']
									__UpperCamelCase      :					str       		={'unk_token': '<unk>'}

									__UpperCamelCase      :					Optional[Any]       		=os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES['vocab_file']    )
									__UpperCamelCase      :					List[Any]       		=os.path.join(self.tmpdirname							,  VOCAB_FILES_NAMES['merges_file']    )

									with open(self.vocab_file							,  'w'							,  encoding='utf-8'    ) as fp:
														for token in vocab_tokens:
																			fp.write(f'{token} {vocab_tokens[token]}\n'    )
									with open(self.merges_file							,  'w'							,  encoding='utf-8'    ) as fp:
														fp.write('\n'.join(_UpperCAmelCase    )    )


				def       __lowercase							( self							,  **lowerCamelCase__    ):




									"""simple docstring"""

									kwargs.update(self.special_tokens_map    )
									return PhobertTokenizer.from_pretrained(self.tmpdirname							,  **_UpperCAmelCase    )


				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		='Tôi là VinAI Research'
									__UpperCamelCase      :					Dict       		='T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
									return input_text, output_text







				def       __lowercase							( self    ):




									"""simple docstring"""

									__UpperCamelCase      :					Tuple       		=PhobertTokenizer(self.vocab_file							,  self.merges_file							,  **self.special_tokens_map    )
									__UpperCamelCase      :					Union[str, Any]       		='Tôi là VinAI Research'
									__UpperCamelCase      :					List[str]       		='T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
									__UpperCamelCase      :					Tuple       		=tokenizer.tokenize(_UpperCAmelCase    )
									print(_UpperCAmelCase    )
									self.assertListEqual(_UpperCAmelCase							,  _UpperCAmelCase    )

									__UpperCamelCase      :					Union[str, Any]       		=tokens + [tokenizer.unk_token]

									__UpperCamelCase      :					int       		=[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase    )							,  _UpperCAmelCase    )








'''simple docstring'''




import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing


class  SCREAMING_SNAKE_CASE    (a__							):


     def __init__(						self						,	_UpperCAmelCase = "▁"						,	_UpperCAmelCase = True						,	_UpperCAmelCase = "<unk>"						,	_UpperCAmelCase = "</s>"						,	_UpperCAmelCase = "<pad>"						,	):

          '''simple docstring'''






          __A						: Dict      =       {
              'pad': {'id': 0, 'token': pad_token},
              'eos': {'id': 1, 'token': eos_token},
              'unk': {'id': 2, 'token': unk_token},
          }

          __A						: List[Any]      =       [None] * len(self.special_tokens)
          for token_dict in self.special_tokens.values():
               __A						: List[str]      =       token_dict['token']

          __A						: str      =       Tokenizer(Unigram())

          __A						: Dict      =       normalizers.Sequence(
              [
                  normalizers.Nmt(),
                  normalizers.NFKC(),
                  normalizers.Replace(Regex(' {2,}')						,	' '),
                  normalizers.Lowercase(),
              ])
          __A						: Union[str, Any]      =       pre_tokenizers.Sequence(
              [
                  pre_tokenizers.Metaspace(replacement=_UpperCAmelCase						,	add_prefix_space=_UpperCAmelCase),
                  pre_tokenizers.Digits(individual_digits=_UpperCAmelCase),
                  pre_tokenizers.Punctuation(),
              ])
          __A						: Any      =       decoders.Metaspace(replacement=_UpperCAmelCase						,	add_prefix_space=_UpperCAmelCase)

          __A						: Dict      =       TemplateProcessing(
              single=F'$A {self.special_tokens["eos"]["token"]}'						,	special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])]						,	)

          __A						: Any      =       {
              'model': 'SentencePieceUnigram',
              'replacement': replacement,
              'add_prefix_space': add_prefix_space,
          }

          super().__init__(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = 8000						,	_UpperCAmelCase = True						,	):

          '''simple docstring'''






          __A						: str      =       trainers.UnigramTrainer(
              vocab_size=_UpperCAmelCase						,	special_tokens=self.special_tokens_list						,	show_progress=_UpperCAmelCase						,	)

          if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
               __A						: Union[str, Any]      =       [files]
          self._tokenizer.train(_UpperCAmelCase						,	trainer=_UpperCAmelCase)

          self.add_unk_id()


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = 8000						,	_UpperCAmelCase = True						,	):

          '''simple docstring'''






          __A						: Dict      =       trainers.UnigramTrainer(
              vocab_size=_UpperCAmelCase						,	special_tokens=self.special_tokens_list						,	show_progress=_UpperCAmelCase						,	)

          self._tokenizer.train_from_iterator(_UpperCAmelCase						,	trainer=_UpperCAmelCase)

          self.add_unk_id()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       json.loads(self._tokenizer.to_str())

          __A						: Union[str, Any]      =       self.special_tokens['unk']['id']

          __A						: str      =       Tokenizer.from_str(json.dumps(_UpperCAmelCase))