

import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase    				=     get_tests_dir('fixtures/test_sentencepiece_bpe.model')





class A__    ( _snake_case  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						BartphoTokenizer
 _lowercase     :					List[Any]      =						False
 _lowercase     :					Union[str, Any]      =						True

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      super().setUp()

      _lowerCAmelCase   :					Any        =    ["▁This", "▁is", "▁a", "▁t", "est"]
      _lowerCAmelCase   :					str        =    dict(zip(A_     ,						range(len(A_       )       )       )       )
      _lowerCAmelCase   :					str        =    {"unk_token": "<unk>"}

      _lowerCAmelCase   :					int        =    os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES["monolingual_vocab_file"]       )
      with open(self.monolingual_vocab_file     ,						"w"     ,						encoding="utf-8"       ) as fp:
           for token in vocab_tokens:
                fp.write(F'''{token} {vocab_tokens[token]}\n'''       )

      _lowerCAmelCase   :					int        =    BartphoTokenizer(A_     ,						self.monolingual_vocab_file     ,						**self.special_tokens_map       )
      tokenizer.save_pretrained(self.tmpdirname       )

 def    __magic_name__       (		self :	str     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




      kwargs.update(self.special_tokens_map       )
      return BartphoTokenizer.from_pretrained(self.tmpdirname     ,						**A_       )

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    "This is a là test"
      _lowerCAmelCase   :					Tuple        =    "This is a<unk><unk> test"
      return input_text, output_text


 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    BartphoTokenizer(A_     ,						self.monolingual_vocab_file     ,						**self.special_tokens_map       )
      _lowerCAmelCase   :					Union[str, Any]        =    "This is a là test"
      _lowerCAmelCase   :					Optional[Any]        =    "▁This ▁is ▁a ▁l à ▁t est".split()
      _lowerCAmelCase   :					Dict        =    tokenizer.tokenize(A_       )
      self.assertListEqual(A_     ,						A_       )

      _lowerCAmelCase   :					Tuple        =    tokens + [tokenizer.unk_token]
      _lowerCAmelCase   :					Dict        =    [4, 5, 6, 3, 3, 7, 8, 3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A_       )     ,						A_       )









from typing import TYPE_CHECKING

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


__UpperCAmelCase    				=     {
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     ['LxmertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

  __UpperCAmelCase    				=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






