


import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir


@dataclass
class     __lowerCAmelCase     :






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		    =       42






class     __lowerCAmelCase     ( unittest.TestCase					):






  """simple docstring"""





  def 					lowerCAmelCase__  (     self       :       List[str]  )   -> List[str]:




     """simple docstring"""
     snake_case_					    =					get_tests_dir("fixtures/test_sentencepiece.model"  )
     snake_case_					    =					get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model"  )

     snake_case_					    =					FakeOriginalTokenizer(vocab_file=_lowerCAmelCase  )

     with warnings.catch_warnings(record=_lowerCAmelCase  ) as w:
        snake_case_					    =					SpmConverter(_lowerCAmelCase  )
     self.assertEqual(len(_lowerCAmelCase  )    ,				0  )

     snake_case_					    =					FakeOriginalTokenizer(vocab_file=_lowerCAmelCase  )

     with warnings.catch_warnings(record=_lowerCAmelCase  ) as w:
        snake_case_					    =					SpmConverter(_lowerCAmelCase  )
     self.assertEqual(len(_lowerCAmelCase  )    ,				1  )

     self.assertIn(
         "The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"
         " which is not implemented in the fast tokenizers."    ,				str(w[0].message  )    ,				)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE	:List[str]  		=     {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE	:List[str]  		=     [
       '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ProphetNetDecoder''',
       '''ProphetNetEncoder''',
       '''ProphetNetForCausalLM''',
       '''ProphetNetForConditionalGeneration''',
       '''ProphetNetModel''',
       '''ProphetNetPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
   from .tokenization_prophetnet import ProphetNetTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_prophetnet import (
          PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
          ProphetNetDecoder,
          ProphetNetEncoder,
          ProphetNetForCausalLM,
          ProphetNetForConditionalGeneration,
          ProphetNetModel,
          ProphetNetPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE	:int  		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




