

import functools
from typing import Any





def       _lowercase     (   __UpperCamelCase  :		str ,							__UpperCamelCase  :		List[Any]			):

      # Validation
      if not isinstance(__UpperCamelCase ,							__UpperCamelCase			) or len(__UpperCamelCase			) == 0:
            raise ValueError("""the string should be not empty string"""			)

      if not isinstance(__UpperCamelCase ,							__UpperCamelCase			) or not all(
          isinstance(__UpperCamelCase ,							__UpperCamelCase			) and len(__UpperCamelCase			) > 0 for item in words			):
            raise ValueError("""the words should be a list of non-empty strings"""			)

      # Build trie
      snake_case__											= {}
      snake_case__											= "WORD_KEEPER"

      for word in words:
            snake_case__											= trie
            for c in word:
                  if c not in trie_node:
                        snake_case__											= {}

                  snake_case__											= trie_node[c]

            snake_case__											= True

      snake_case__											= len(__UpperCamelCase			)

      # Dynamic programming method
      @functools.cache
      def is_breakable(__UpperCamelCase  :		Tuple			) -> bool:
            if index == len_string:
                  return True

            snake_case__											= trie
            for i in range(__UpperCamelCase ,							__UpperCamelCase			):
                  snake_case__											= trie_node.get(string[i] ,							__UpperCamelCase			)

                  if trie_node is None:
                        return False

                  if trie_node.get(__UpperCamelCase ,							__UpperCamelCase			) and is_breakable(i + 1			):
                        return True

            return False

      return is_breakable(0			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()









from typing import TYPE_CHECKING

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


_lowerCAmelCase  :      Tuple			 ={
    """configuration_mobilenet_v2""": [
        """MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """MobileNetV2Config""",
        """MobileNetV2OnnxConfig""",
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Tuple			 =["""MobileNetV2FeatureExtractor"""]
					_lowerCAmelCase  :      Optional[int]			 =["""MobileNetV2ImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      List[str]			 =[
					    """MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MobileNetV2ForImageClassification""",
					    """MobileNetV2ForSemanticSegmentation""",
					    """MobileNetV2Model""",
					    """MobileNetV2PreTrainedModel""",
					    """load_tf_weights_in_mobilenet_v2""",
					]


if TYPE_CHECKING:
					from .configuration_mobilenet_va import (
					    MOBILENET_V2_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_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MobileNetVaForImageClassification,
										    MobileNetVaForSemanticSegmentation,
										    MobileNetVaModel,
										    MobileNetVaPreTrainedModel,
										    load_tf_weights_in_mobilenet_va,
										)


else:
					import sys

					_lowerCAmelCase  :      Any			 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)