from typing import TYPE_CHECKING

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


lowercase_     :   List[str] 				=				{
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   Optional[Any] 				=				['MaskFormerFeatureExtractor']
      lowercase_     :   Dict 				=				['MaskFormerImageProcessor']


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MaskFormerForInstanceSegmentation',
          'MaskFormerModel',
          'MaskFormerPreTrainedModel',
      ]
      lowercase_     :   Optional[Any] 				=				[
          'MaskFormerSwinBackbone',
          'MaskFormerSwinModel',
          'MaskFormerSwinPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
      from .configuration_maskformer_swin import MaskFormerSwinConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_maskformer import MaskFormerFeatureExtractor
            from .image_processing_maskformer import MaskFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_maskformer import (
                MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaskFormerForInstanceSegmentation,
                MaskFormerModel,
                MaskFormerPreTrainedModel,
            )
            from .modeling_maskformer_swin import (
                MaskFormerSwinBackbone,
                MaskFormerSwinModel,
                MaskFormerSwinPreTrainedModel,
            )


else:
      import sys

      lowercase_     :   Any 				=				_LazyModule(__name__, globals()['__file__'], _import_structure)


import os







def 			A__      (	snake_case_ :		str = "matrix.txt"      ):
 with open(os.path.join(os.path.dirname(snake_case_      )       ,				snake_case_      )      ) as in_file:
  SCREAMING_SNAKE_CASE__:   Dict=      in_file.read()

 SCREAMING_SNAKE_CASE__:   Optional[int]=      [[int(snake_case_      ) for cell in row.split(''','''      )] for row in data.strip().splitlines()]
 SCREAMING_SNAKE_CASE__:   int=      [[0 for cell in row] for row in grid]
 SCREAMING_SNAKE_CASE__:   Dict=      len(grid[0]      )

 SCREAMING_SNAKE_CASE__:   Dict=      [[0 for i in range(snake_case_      )] for j in range(snake_case_      )]
 SCREAMING_SNAKE_CASE__:   Optional[int]=      grid[0][0]
 for i in range(1       ,				snake_case_      ):
  SCREAMING_SNAKE_CASE__:   Optional[int]=      grid[0][i] + dp[0][i - 1]
 for i in range(1       ,				snake_case_      ):
  SCREAMING_SNAKE_CASE__:   List[Any]=      grid[i][0] + dp[i - 1][0]

 for i in range(1       ,				snake_case_      ):
  for j in range(1       ,				snake_case_      ):
   SCREAMING_SNAKE_CASE__:   Dict=      grid[i][j] + min(dp[i - 1][j]       ,				dp[i][j - 1]      )

 return dp[-1][-1]


if __name__ == "__main__":
      print(f'''{solution() = }''')

