






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 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  BartphoTokenizer
      __snake_case       =  False
      __snake_case       =  True







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->Union[str, Any]:




       """simple docstring"""


       super().setUp()

       a		  =      ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
       a		  =      dict(zip(__UpperCAmelCase ,					range(len(__UpperCAmelCase  )  )  )  )
       a		  =      {'''unk_token''': '''<unk>'''}

       a		  =      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"""  )

       a		  =      BartphoTokenizer(__UpperCAmelCase ,					self.monolingual_vocab_file ,					**self.special_tokens_map  )
       tokenizer.save_pretrained(self.tmpdirname  )







      def       __lowerCAmelCase					(      self      :						str ,					**__UpperCAmelCase      :						Any  )    ->List[str]:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						str ,					__UpperCAmelCase      :						Union[str, Any]  )    ->List[Any]:




       """simple docstring"""


       a		  =      '''This is a là test'''
       a		  =      '''This is a<unk><unk> test'''
       return input_text, output_text







      def       __lowerCAmelCase					(      self      :						List[str]  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      BartphoTokenizer(__UpperCAmelCase ,					self.monolingual_vocab_file ,					**self.special_tokens_map  )
       a		  =      '''This is a là test'''
       a		  =      '''▁This ▁is ▁a ▁l à ▁t est'''.split()
       a		  =      tokenizer.tokenize(__UpperCAmelCase  )
       self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase  )

       a		  =      tokens + [tokenizer.unk_token]
       a		  =      [4, 5, 6, 3, 3, 7, 8, 3]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase  ) ,					__UpperCAmelCase  )








from typing import TYPE_CHECKING

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


_UpperCAmelCase		:  List[str]	=   {
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Optional[int]	=   ["BlenderbotTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  int	=   [
         "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BlenderbotForCausalLM",
         "BlenderbotForConditionalGeneration",
         "BlenderbotModel",
         "BlenderbotPreTrainedModel",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Any	=   [
         "TFBlenderbotForConditionalGeneration",
         "TFBlenderbotModel",
         "TFBlenderbotPreTrainedModel",
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Optional[int]	=   [
         "FlaxBlenderbotForConditionalGeneration",
         "FlaxBlenderbotModel",
         "FlaxBlenderbotPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_blenderbot import (
         BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BlenderbotConfig,
         BlenderbotOnnxConfig,
     )
     from .tokenization_blenderbot import BlenderbotTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_blenderbot import (
              BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BlenderbotForCausalLM,
              BlenderbotForConditionalGeneration,
              BlenderbotModel,
              BlenderbotPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_blenderbot import (
              TFBlenderbotForConditionalGeneration,
              TFBlenderbotModel,
              TFBlenderbotPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_blenderbot import (
              FlaxBlenderbotForConditionalGeneration,
              FlaxBlenderbotModel,
              FlaxBlenderbotPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase		:  Union[str, Any]	=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



