




import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class   UpperCAmelCase    (      A ,       unittest.TestCase ):
   __SCREAMING_SNAKE_CASE		:				Optional[int]      					=       BertweetTokenizer
   __SCREAMING_SNAKE_CASE		:				int      					=       False






   def 			_a    (						self :							Tuple  )->      List[Any]:
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     _lowercase:			Tuple      =     ['I', 'm', 'V@@', 'R@@', 'r', 'e@@']
     _lowercase:			Optional[int]      =     dict(zip(UpperCamelCase_    ,       range(len(UpperCamelCase_  )  )  )  )
     _lowercase:			Any      =     ['#version: 0.2', 'a m</w>']
     _lowercase:			Any      =     {'unk_token': '<unk>'}

     _lowercase:			int      =     os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES['vocab_file']  )
     _lowercase:			Union[str, 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 			_a    (						self :							Optional[int]    ,       **UpperCamelCase_ :							Dict  )->      Dict:
     kwargs.update(self.special_tokens_map  )
     return BertweetTokenizer.from_pretrained(self.tmpdirname    ,       **UpperCamelCase_  )






   def 			_a    (						self :							Union[str, Any]    ,       UpperCamelCase_ :							Any  )->      List[Any]:
     _lowercase:			List[Any]      =     'I am VinAI Research'
     _lowercase:			str      =     'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
     return input_text, output_text






   def 			_a    (						self :							Union[str, Any]  )->      str:
     _lowercase:			Tuple      =     BertweetTokenizer(self.vocab_file    ,       self.merges_file    ,       **self.special_tokens_map  )
     _lowercase:			List[str]      =     'I am VinAI Research'
     _lowercase:			Tuple      =     'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
     _lowercase:			Dict      =     tokenizer.tokenize(UpperCamelCase_  )
     self.assertListEqual(UpperCamelCase_    ,       UpperCamelCase_  )

     _lowercase:			Optional[Any]      =     tokens + [tokenizer.unk_token]

     _lowercase:			Any      =     [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_  )    ,       UpperCamelCase_  )






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


UpperCAmelCase__    :							List[str]       		=     {
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Any       		=     ['ErnieMTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__    :							Dict       		=     [
       'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ErnieMForMultipleChoice',
       'ErnieMForQuestionAnswering',
       'ErnieMForSequenceClassification',
       'ErnieMForTokenClassification',
       'ErnieMModel',
       'ErnieMPreTrainedModel',
       'ErnieMForInformationExtraction',
   ]


if TYPE_CHECKING:
   from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_ernie_m import ErnieMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie_m import (
          ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieMForInformationExtraction,
          ErnieMForMultipleChoice,
          ErnieMForQuestionAnswering,
          ErnieMForSequenceClassification,
          ErnieMForTokenClassification,
          ErnieMModel,
          ErnieMPreTrainedModel,
      )


else:
   import sys

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