


"""simple docstring"""



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       __a	:
				lowerCamelCase    :					str







class       __a	(   unittest.TestCase       ):







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = get_tests_dir('''fixtures/test_sentencepiece.model'''      )
					lowerCAmelCase_			       = get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''      )

					lowerCAmelCase_			       = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

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

					lowerCAmelCase_			       = FakeOriginalTokenizer(vocab_file=UpperCAmelCase      )

					with warnings.catch_warnings(record=UpperCAmelCase      ) as w:
						lowerCAmelCase_			       = SpmConverter(UpperCAmelCase      )
					self.assertEqual(len(UpperCAmelCase      )			,  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      )			,  )



"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_							 =       {
    'configuration_prophetnet': ['PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ProphetNetConfig'],
    'tokenization_prophetnet': ['ProphetNetTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_							 =       [
		    '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

		lowercase_							 =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)