





import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin




class    _a  (  lowerCamelCase_		,	unittest.TestCase):

  """simple docstring"""


  UpperCamelCase__       						=   SpeechaTextaTokenizer
  UpperCamelCase__       						=   False






  def 	UpperCAmelCase_   (						self:	Optional[int]			):
      '''simple docstring'''


      super().setUp()

      UpperCamelCase__:					Optional[int]												=      "<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab".split(" "			)
      UpperCamelCase__:					Tuple												=      ["he re</w> 123", "here a 1456"]
      UpperCamelCase__:					List[str]												=      dict(zip(__lowerCamelCase					,  range(len(__lowerCamelCase			)			)			)			)

      UpperCamelCase__:					int												=      {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

      UpperCamelCase__:					Any												=      tempfile.mkdtemp()
      UpperCamelCase__:					Dict												=      os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES["vocab_file"]			)
      UpperCamelCase__:					List[Any]												=      os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES["merges_file"]			)

      with open(self.vocab_file					,  "w"					,  encoding="utf-8"			) as fp:
          fp.write(json.dumps(__lowerCamelCase			) + "\n"			)

      with open(self.merges_file					,  "w"			) as fp:
          fp.write("\n".join(__lowerCamelCase			)			)






  def 	UpperCAmelCase_   (						self:	Union[str, Any]			):
      '''simple docstring'''


      UpperCamelCase__:					Any												=      list(self.get_tokenizer().get_vocab().keys()			)

      self.assertEqual(vocab_keys[0]					,  "<s>"			)
      self.assertEqual(vocab_keys[1]					,  "<pad>"			)
      self.assertEqual(vocab_keys[-1]					,  "vocab"			)
      self.assertEqual(len(__lowerCamelCase			)					,  14			)






  def 	UpperCAmelCase_   (						self:	List[Any]			):
      '''simple docstring'''


      self.assertEqual(self.get_tokenizer().vocab_size					,  14			)






  def 	UpperCAmelCase_   (						self:	Any			):
      '''simple docstring'''


      UpperCamelCase__:					Any												=      SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

      # make sure @@ is correctly concatenated
      UpperCamelCase__:					Optional[int]												=      [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
      UpperCamelCase__:					List[str]												=      tokenizer.decode(__lowerCamelCase			)

      self.assertTrue(output_string == "herecouple words ofthe"			)






  def 	UpperCAmelCase_   (						self:	Union[str, Any]			):
      '''simple docstring'''


      UpperCamelCase__:					Optional[int]												=      SpeechaTextaTokenizer.from_pretrained(self.tmpdirname			)

      with tempfile.TemporaryDirectory() as tmp_dirname:
          tokenizer.save_pretrained(__lowerCamelCase			)
          os.remove(os.path.join(__lowerCamelCase					,  "merges.txt"			)			)

          # load tokenizer without merges file should not throw an error
          UpperCamelCase__:					Optional[int]												=      SpeechaTextaTokenizer.from_pretrained(__lowerCamelCase			)

      with tempfile.TemporaryDirectory() as tmp_dirname:
          # save tokenizer and load again
          tokenizer.save_pretrained(__lowerCamelCase			)
          UpperCamelCase__:					Optional[Any]												=      SpeechaTextaTokenizer.from_pretrained(__lowerCamelCase			)

      self.assertIsNotNone(__lowerCamelCase			)






  def 	UpperCAmelCase_   (						self:	str			):
      '''simple docstring'''


      if not self.test_slow_tokenizer:
          return

      UpperCamelCase__:					List[str]												=      inspect.signature(self.tokenizer_class.__init__			)
      UpperCamelCase__:					Dict												=      self.get_tokenizer()

      for parameter_name, parameter in signature.parameters.items():
          if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
              self.assertIn(__lowerCamelCase					,  tokenizer.init_kwargs			)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__:  List[str]       					=					{
    '''configuration_wav2vec2_conformer''': [
        '''WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Wav2Vec2ConformerConfig''',
    ],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:  int       					=					[
       '''WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Wav2Vec2ConformerForAudioFrameClassification''',
       '''Wav2Vec2ConformerForCTC''',
       '''Wav2Vec2ConformerForPreTraining''',
       '''Wav2Vec2ConformerForSequenceClassification''',
       '''Wav2Vec2ConformerForXVector''',
       '''Wav2Vec2ConformerModel''',
       '''Wav2Vec2ConformerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_wavaveca_conformer import (
       WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       WavaVecaConformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca_conformer import (
          WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaConformerForAudioFrameClassification,
          WavaVecaConformerForCTC,
          WavaVecaConformerForPreTraining,
          WavaVecaConformerForSequenceClassification,
          WavaVecaConformerForXVector,
          WavaVecaConformerModel,
          WavaVecaConformerPreTrainedModel,
      )

else:
   import sys

   A__:  Dict       					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

