






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE__		:							Any 				=				{
    """configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:							List[str] 				=				["""MobileViTFeatureExtractor"""]
      SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				["""MobileViTImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:							Dict 				=				[
          """MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MobileViTForImageClassification""",
          """MobileViTForSemanticSegmentation""",
          """MobileViTModel""",
          """MobileViTPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE__		:							Any 				=				[
          """TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFMobileViTForImageClassification""",
          """TFMobileViTForSemanticSegmentation""",
          """TFMobileViTModel""",
          """TFMobileViTPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_mobilevit import MobileViTFeatureExtractor
            from .image_processing_mobilevit import MobileViTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevit import (
                MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTForImageClassification,
                MobileViTForSemanticSegmentation,
                MobileViTModel,
                MobileViTPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mobilevit import (
                TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFMobileViTForImageClassification,
                TFMobileViTForSemanticSegmentation,
                TFMobileViTModel,
                TFMobileViTPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE__		:							str 				=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)














"""simple docstring"""



import os


def 	SCREAMING_SNAKE_CASE__       (  )->		Optional[Any]:






     '''simple docstring'''



     with open(os.path.dirname(snake_case				) + "/p022_names.txt"				) as file:
          UpperCAmelCase__						:    Tuple							=							str(file.readlines()[0]				)
          UpperCAmelCase__						:    str							=							names.replace("\""			,					""				).split(","				)

     names.sort()

     UpperCAmelCase__						:    Optional[int]							=							0
     UpperCAmelCase__						:    List[str]							=							0

     for i, name in enumerate(snake_case				):
          for letter in name:
               name_score += ord(snake_case				) - 64

          total_score += (i + 1) * name_score
          UpperCAmelCase__						:    Optional[int]							=							0
     return total_score


if __name__ == "__main__":
 print(solution())


