



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A__       =			{"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__       =			[
	    """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """SEWDForCTC""",
	    """SEWDForSequenceClassification""",
	    """SEWDModel""",
	    """SEWDPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sew_d import (
		    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SEWDForCTC,
		    SEWDForSequenceClassification,
		    SEWDModel,
		    SEWDPreTrainedModel,
		)


else:
	import sys

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







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    __magic_name__						:
     A:		str



class    __magic_name__						(  unittest.TestCase):


     def   UpperCAmelCase__						(   self				:  Any      )	->   List[Any]:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[Any] 							= get_tests_dir('''fixtures/test_sentencepiece.model'''      )
      UpperCamelCase__       :     Any 							= get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''      )

      UpperCamelCase__       :     List[str] 							= FakeOriginalTokenizer(vocab_file=lowerCamelCase__      )

      with warnings.catch_warnings(record=lowerCamelCase__      ) as w:
       UpperCamelCase__       :     Optional[int] 							= SpmConverter(lowerCamelCase__      )
      self.assertEqual(len(lowerCamelCase__      )				,						0      )

      UpperCamelCase__       :     Tuple 							= FakeOriginalTokenizer(vocab_file=lowerCamelCase__      )

      with warnings.catch_warnings(record=lowerCamelCase__      ) as w:
       UpperCamelCase__       :     str 							= 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      )				,						)


