



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__       =			{
    """configuration_mobilenet_v1""": [
        """MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV1Config""",
        """MobileNetV1OnnxConfig""",
    ],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__       =			["""MobileNetV1FeatureExtractor"""]
	A__       =			["""MobileNetV1ImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__       =			[
	    """MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MobileNetV1ForImageClassification""",
	    """MobileNetV1Model""",
	    """MobileNetV1PreTrainedModel""",
	    """load_tf_weights_in_mobilenet_v1""",
	]


if TYPE_CHECKING:
	from .configuration_mobilenet_va import (
	    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    MobileNetVaConfig,
	    MobileNetVaOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
		from .image_processing_mobilenet_va import MobileNetVaImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mobilenet_va import (
		    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MobileNetVaForImageClassification,
		    MobileNetVaModel,
		    MobileNetVaPreTrainedModel,
		    load_tf_weights_in_mobilenet_va,
		)


else:
	import sys

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







'''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							:


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					str



class       a							(						unittest.TestCase			):


     """simple docstring"""






     def 					lowerCamelCase__					(							self	:						str     )		->				Dict:
          __UpperCAmelCase      :					str    				=     get_tests_dir('''fixtures/test_sentencepiece.model'''     )
          __UpperCAmelCase      :					List[Any]    				=     get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''     )

          __UpperCAmelCase      :					Tuple    				=     FakeOriginalTokenizer(vocab_file=snake_case     )

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

          __UpperCAmelCase      :					str    				=     FakeOriginalTokenizer(vocab_file=snake_case     )

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