'''simple docstring'''

import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin







class 				_lowercase     (				UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					PhobertTokenizer
 _SCREAMING_SNAKE_CASE     :       Dict      	=					False
 def a					(      self   :	Dict							)  ->    Optional[int]:
  super().setUp()

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

  __lowerCAmelCase											=						os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["""vocab_file"""]							)
  __lowerCAmelCase											=						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(snake_case_							)							)
 def a					(      self   :	Optional[Any]      ,       **SCREAMING_SNAKE_CASE__   :	Union[str, Any]							)  ->    Dict:
  kwargs.update(self.special_tokens_map							)
  return PhobertTokenizer.from_pretrained(self.tmpdirname      ,       **snake_case_							)
 def a					(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]							)  ->    Optional[int]:
  __lowerCAmelCase											=						'Tôi là VinAI Research'
  __lowerCAmelCase											=						'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
  return input_text, output_text





 def a					(      self   :	str							)  ->    Any:
  __lowerCAmelCase											=						PhobertTokenizer(self.vocab_file      ,       self.merges_file      ,       **self.special_tokens_map							)
  __lowerCAmelCase											=						'Tôi là VinAI Research'
  __lowerCAmelCase											=						'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
  __lowerCAmelCase											=						tokenizer.tokenize(snake_case_							)
  print(snake_case_							)
  self.assertListEqual(snake_case_      ,       snake_case_							)

  __lowerCAmelCase											=						tokens + [tokenizer.unk_token]

  __lowerCAmelCase											=						[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(snake_case_							)      ,       snake_case_							)













"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_     :						List[str]						=					{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						str						=					['XLNetTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						List[str]						=					['XLNetTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						int						=					[
       'XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'XLNetForMultipleChoice',
       'XLNetForQuestionAnswering',
       'XLNetForQuestionAnsweringSimple',
       'XLNetForSequenceClassification',
       'XLNetForTokenClassification',
       'XLNetLMHeadModel',
       'XLNetModel',
       'XLNetPreTrainedModel',
       'load_tf_weights_in_xlnet',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						Union[str, Any]						=					[
       'TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFXLNetForMultipleChoice',
       'TFXLNetForQuestionAnsweringSimple',
       'TFXLNetForSequenceClassification',
       'TFXLNetForTokenClassification',
       'TFXLNetLMHeadModel',
       'TFXLNetMainLayer',
       'TFXLNetModel',
       'TFXLNetPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet import XLNetTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xlnet_fast import XLNetTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlnet import (
          XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLNetForMultipleChoice,
          XLNetForQuestionAnswering,
          XLNetForQuestionAnsweringSimple,
          XLNetForSequenceClassification,
          XLNetForTokenClassification,
          XLNetLMHeadModel,
          XLNetModel,
          XLNetPreTrainedModel,
          load_tf_weights_in_xlnet,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlnet import (
          TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLNetForMultipleChoice,
          TFXLNetForQuestionAnsweringSimple,
          TFXLNetForSequenceClassification,
          TFXLNetForTokenClassification,
          TFXLNetLMHeadModel,
          TFXLNetMainLayer,
          TFXLNetModel,
          TFXLNetPreTrainedModel,
      )

else:
   import sys

   lowerCamelCase_     :						List[Any]						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)