


from __future__ import annotations




def  a   (lowerCAmelCase__   ):

   # preprocessing the first row
   for i in range(1		,				len(matrix[0]   )   ):
      matrix[0][i] += matrix[0][i - 1]

   # preprocessing the first column
   for i in range(1		,				len(lowerCAmelCase__   )   ):
      matrix[i][0] += matrix[i - 1][0]

   # updating the path cost for current position
   for i in range(1		,				len(lowerCAmelCase__   )   ):
      for j in range(1		,				len(matrix[0]   )   ):
         matrix[i][j] += min(matrix[i - 1][j]		,				matrix[i][j - 1]   )

   return matrix[-1][-1]


if __name__ == "__main__":
  import doctest

  doctest.testmod()







from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE							    =							{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'AutoformerForPrediction',
      'AutoformerModel',
      'AutoformerPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_autoformer import (
      AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      AutoformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_autoformer import (
        AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        AutoformerForPrediction,
        AutoformerModel,
        AutoformerPreTrainedModel,
    )

else:
  import sys

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



