






import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin
class 					a_(						lowercase__	,    unittest.TestCase					):



     """simple docstring"""



     __snake_case						:							Dict       			=BertweetTokenizer
     __snake_case						:							Union[str, Any]       			=False





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

      """simple docstring"""
      super().setUp()

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

      SCREAMING_SNAKE_CASE							  =						os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['vocab_file'])
      SCREAMING_SNAKE_CASE							  =						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(lowerCAmelCase__))





     def   __UpperCamelCase		(						self			:					Tuple       ,       **lowerCAmelCase__			:					Optional[Any])    ->					List[str]:

      """simple docstring"""
      kwargs.update(self.special_tokens_map)
      return BertweetTokenizer.from_pretrained(self.tmpdirname       ,       **lowerCAmelCase__)





     def   __UpperCamelCase		(						self			:					Tuple       ,       lowerCAmelCase__			:					Optional[Any])    ->					Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						'I am VinAI Research'
      SCREAMING_SNAKE_CASE							  =						'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
      return input_text, output_text





     def   __UpperCamelCase		(						self			:					int)    ->					Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						BertweetTokenizer(self.vocab_file       ,       self.merges_file       ,       **self.special_tokens_map)
      SCREAMING_SNAKE_CASE							  =						'I am VinAI Research'
      SCREAMING_SNAKE_CASE							  =						'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
      SCREAMING_SNAKE_CASE							  =						tokenizer.tokenize(lowerCAmelCase__)
      self.assertListEqual(lowerCAmelCase__       ,       lowerCAmelCase__)

      SCREAMING_SNAKE_CASE							  =						tokens + [tokenizer.unk_token]

      SCREAMING_SNAKE_CASE							  =						[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__)       ,       lowerCAmelCase__)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase      				=							{"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      				=							[
          "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "VisualBertForMultipleChoice",
          "VisualBertForPreTraining",
          "VisualBertForQuestionAnswering",
          "VisualBertForRegionToPhraseAlignment",
          "VisualBertForVisualReasoning",
          "VisualBertLayer",
          "VisualBertModel",
          "VisualBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_visual_bert import (
                VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                VisualBertForMultipleChoice,
                VisualBertForPreTraining,
                VisualBertForQuestionAnswering,
                VisualBertForRegionToPhraseAlignment,
                VisualBertForVisualReasoning,
                VisualBertLayer,
                VisualBertModel,
                VisualBertPreTrainedModel,
            )


else:
      import sys

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




