



"""simple docstring"""
def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						int:







  '''simple docstring'''


  if not grid or not grid[0]:
    raise TypeError("""The grid does not contain the appropriate information"""							)

  for cell_n in range(1 ,  len(grid[0]							)							):
    grid[0][cell_n] += grid[0][cell_n - 1]
  lowercase_								= grid[0]

  for row_n in range(1 ,  len(__lowerCAmelCase							)							):
    lowercase_								= grid[row_n]
    lowercase_								= fill_row(__lowerCAmelCase ,  __lowerCAmelCase							)
    lowercase_								= grid[row_n]

  return grid[-1][-1]


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase							) ->						list:







  '''simple docstring'''


  current_row[0] += row_above[0]
  for cell_n in range(1 ,  len(__lowerCAmelCase							)							):
    current_row[cell_n] += min(current_row[cell_n - 1] ,  row_above[cell_n]							)

  return current_row


if __name__ == "__main__":
       import doctest

       doctest.testmod()






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase						:		Optional[Any]			= {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		str			= [
           "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "WavLMForAudioFrameClassification",
           "WavLMForCTC",
           "WavLMForSequenceClassification",
           "WavLMForXVector",
           "WavLMModel",
           "WavLMPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavlm import (
                  WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavLMForAudioFrameClassification,
                  WavLMForCTC,
                  WavLMForSequenceClassification,
                  WavLMForXVector,
                  WavLMModel,
                  WavLMPreTrainedModel,
              )

else:
       import sys

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

