





'''simple docstring'''


import os


def 		__a(SCREAMING_SNAKE_CASE_     :    Any			):



    '''simple docstring'''





    _lowerCAmelCase			=					len(grid[0]			)
    _lowerCAmelCase			=					len(_UpperCAmelCase			)

    _lowerCAmelCase			=					0
    _lowerCAmelCase			=					0
    _lowerCAmelCase			=					0

    # Check vertically, horizontally, diagonally at the same time (only works
    # for nxn grid)
    for i in range(_UpperCAmelCase			):
        for j in range(n_rows - 3			):
            _lowerCAmelCase			=					grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
            _lowerCAmelCase			=					grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]

            # Left-to-right diagonal (\) product
            if i < n_columns - 3:
                _lowerCAmelCase			=					(
                    grid[i][j]
                    * grid[i + 1][j + 1]
                    * grid[i + 2][j + 2]
                    * grid[i + 3][j + 3]
                )

            # Right-to-left diagonal(/) product
            if i > 2:
                _lowerCAmelCase			=					(
                    grid[i][j]
                    * grid[i - 1][j + 1]
                    * grid[i - 2][j + 2]
                    * grid[i - 3][j + 3]
                )

            _lowerCAmelCase			=					max(
                _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase			)
            if max_product > largest:
                _lowerCAmelCase			=					max_product

    return largest


def 		__a():



    '''simple docstring'''





    _lowerCAmelCase			=					[]
    with open(os.path.dirname(_UpperCAmelCase			) + "/grid.txt"			) as file:
        for line in file:
            grid.append(line.strip("\n"			).split(" "			)			)

    _lowerCAmelCase			=					[[int(_UpperCAmelCase			) for i in grid[j]] for j in range(len(_UpperCAmelCase			)			)]

    return largest_product(_UpperCAmelCase			)


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









"""simple docstring"""




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Dict	    =   ["""image_processor""", """tokenizer"""]
      lowercase_      : Union[str, Any]	    =   """ViltImageProcessor"""
      lowercase_      : Any	    =   ("""BertTokenizer""", """BertTokenizerFast""")

      def __init__(     self					,      snake_case_=None					,      snake_case_=None					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  None
        if "feature_extractor" in kwargs:
          warnings.warn(
              'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
              ' instead.'					,      snake_case_					,      )
          A_  :       Dict										=  kwargs.pop('feature_extractor'  )

        A_  :       Dict										=  image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
          raise ValueError('You need to specify an `image_processor`.'  )
        if tokenizer is None:
          raise ValueError('You need to specify a `tokenizer`.'  )

        super().__init__(snake_case_					,      snake_case_  )
        A_  :       List[str]										=  self.image_processor

      def __call__(     self					,      snake_case_					,      snake_case_ = None					,      snake_case_ = True					,      snake_case_ = False					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = 0					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = True					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       str										=  self.tokenizer(
            text=snake_case_					,      add_special_tokens=snake_case_					,      padding=snake_case_					,      truncation=snake_case_					,      max_length=snake_case_					,      stride=snake_case_					,      pad_to_multiple_of=snake_case_					,      return_token_type_ids=snake_case_					,      return_attention_mask=snake_case_					,      return_overflowing_tokens=snake_case_					,      return_special_tokens_mask=snake_case_					,      return_offsets_mapping=snake_case_					,      return_length=snake_case_					,      verbose=snake_case_					,      return_tensors=snake_case_					,      **snake_case_					,      )
        # add pixel_values + pixel_mask
        A_  :       Optional[int]										=  self.image_processor(snake_case_					,      return_tensors=snake_case_  )
        encoding.update(snake_case_  )

        return encoding

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        return self.tokenizer.batch_decode(*snake_case_					,      **snake_case_  )

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        return self.tokenizer.decode(*snake_case_					,      **snake_case_  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  self.tokenizer.model_input_names
        A_  :       Any										=  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,      snake_case_					,      )
        return self.image_processor_class

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,      snake_case_					,      )
        return self.image_processor