

import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin







class 							a__							( UpperCAmelCase						,      unittest.TestCase							):







  """simple docstring"""




  UpperCAmelCase__   :			List[str]      							=BertweetTokenizer
  UpperCAmelCase__   :			List[str]      							=False


  def 			_lowercase   (      self     :			Optional[Any]		)       ->Optional[Any]:
        """simple docstring"""







        super().setUp()

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

        SCREAMING_SNAKE_CASE			:   Tuple	  = os.path.join(self.tmpdirname					,					VOCAB_FILES_NAMES["""vocab_file"""]		)
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = 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 			_lowercase   (      self     :			str					,					**UpperCAmelCase__     :			Dict		)       ->Tuple:
        """simple docstring"""







        kwargs.update(self.special_tokens_map		)
        return BertweetTokenizer.from_pretrained(self.tmpdirname					,					**UpperCAmelCase__		)


  def 			_lowercase   (      self     :			List[Any]					,					UpperCAmelCase__     :			Union[str, Any]		)       ->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Tuple	  = """I am VinAI Research"""
        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = """I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
        return input_text, output_text




  def 			_lowercase   (      self     :			Tuple		)       ->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = BertweetTokenizer(self.vocab_file					,					self.merges_file					,					**self.special_tokens_map		)
        SCREAMING_SNAKE_CASE			:   str	  = """I am VinAI Research"""
        SCREAMING_SNAKE_CASE			:   Any	  = """I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = tokenizer.tokenize(UpperCAmelCase__		)
        self.assertListEqual(UpperCAmelCase__					,					UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE			:   Optional[int]	  = tokens + [tokenizer.unk_token]

        SCREAMING_SNAKE_CASE			:   Dict	  = [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

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__			:  Tuple					     =     {
    """configuration_speech_to_text_2""": ["""SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2Text2Config"""],
    """processing_speech_to_text_2""": ["""Speech2Text2Processor"""],
    """tokenization_speech_to_text_2""": ["""Speech2Text2Tokenizer"""],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__			:  Tuple					     =     [
      """SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """Speech2Text2ForCausalLM""",
      """Speech2Text2PreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
  from .processing_speech_to_text_a import SpeechaTextaProcessor
  from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_speech_to_text_a import (
        SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
        SpeechaTextaForCausalLM,
        SpeechaTextaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__			:  Optional[int]					     =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




