
'''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   snake_case__					:







  """simple docstring"""

  lowerCamelCase           =	42







class   snake_case__					(			unittest.TestCase				):







  """simple docstring"""

  def 			lowerCAmelCase							(			self							: int			)	-> Optional[Any]:






    """simple docstring"""







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

    snake_case     :					Optional[int]     =	FakeOriginalTokenizer(vocab_file=__UpperCamelCase			)

    with warnings.catch_warnings(record=__UpperCamelCase			) as w:
      snake_case     :					Optional[Any]     =	SpmConverter(__UpperCamelCase			)
    self.assertEqual(len(__UpperCamelCase			)     ,  0			)

    snake_case     :					List[Any]     =	FakeOriginalTokenizer(vocab_file=__UpperCamelCase			)

    with warnings.catch_warnings(record=__UpperCamelCase			) as w:
      snake_case     :					Optional[Any]     =	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, is_vision_available


lowercase__						=			{
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						=			["FlavaFeatureExtractor"]
						lowercase__						=			["FlavaImageProcessor"]
						lowercase__						=			["FlavaProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						=			[
						    "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "FlavaForPreTraining",
						    "FlavaImageCodebook",
						    "FlavaImageModel",
						    "FlavaModel",
						    "FlavaMultimodalModel",
						    "FlavaPreTrainedModel",
						    "FlavaTextModel",
						]

if TYPE_CHECKING:
						from .configuration_flava import (
						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    FlavaConfig,
						    FlavaImageCodebookConfig,
						    FlavaImageConfig,
						    FlavaMultimodalConfig,
						    FlavaTextConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_flava import FlavaFeatureExtractor
												from .image_processing_flava import FlavaImageProcessor
												from .processing_flava import FlavaProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flava import (
												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlavaForPreTraining,
												    FlavaImageCodebook,
												    FlavaImageModel,
												    FlavaModel,
												    FlavaMultimodalModel,
												    FlavaPreTrainedModel,
												    FlavaTextModel,
												)

else:
						import sys

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


