






"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE					:				List[Any]					    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:				int					    =						{
    '''facebook/xmod-base''': '''https://huggingface.co/facebook/xmod-base/resolve/main/config.json''',
    '''facebook/xmod-large-prenorm''': '''https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json''',
    '''facebook/xmod-base-13-125k''': '''https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-125k''': '''https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json''',
    '''facebook/xmod-base-30-195k''': '''https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json''',
    '''facebook/xmod-base-60-125k''': '''https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json''',
    '''facebook/xmod-base-60-265k''': '''https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json''',
    '''facebook/xmod-base-75-125k''': '''https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json''',
    '''facebook/xmod-base-75-269k''': '''https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json''',
}






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     List[str]						= """xmod"""






    def __init__(       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=30522       ,						__SCREAMING_SNAKE_CASE     :							Tuple=768       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=12       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=12       ,						__SCREAMING_SNAKE_CASE     :							Any=3072       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]="gelu"       ,						__SCREAMING_SNAKE_CASE     :							str=0.1       ,						__SCREAMING_SNAKE_CASE     :							int=0.1       ,						__SCREAMING_SNAKE_CASE     :							Dict=512       ,						__SCREAMING_SNAKE_CASE     :							List[str]=2       ,						__SCREAMING_SNAKE_CASE     :							Any=0.02       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=1e-1_2       ,						__SCREAMING_SNAKE_CASE     :							str=1       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=0       ,						__SCREAMING_SNAKE_CASE     :							List[str]=2       ,						__SCREAMING_SNAKE_CASE     :							str="absolute"       ,						__SCREAMING_SNAKE_CASE     :							Any=True       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=None       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=False       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=2       ,						__SCREAMING_SNAKE_CASE     :							Any=False       ,						__SCREAMING_SNAKE_CASE     :							Dict=True       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=True       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=("en_XX",)       ,						__SCREAMING_SNAKE_CASE     :							str=None       ,						**__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						)     ->		List[str]:
      super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE       ,						bos_token_id=__SCREAMING_SNAKE_CASE       ,						eos_token_id=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						vocab_size
      lowerCamelCase_         =						hidden_size
      lowerCamelCase_         =						num_hidden_layers
      lowerCamelCase_         =						num_attention_heads
      lowerCamelCase_         =						hidden_act
      lowerCamelCase_         =						intermediate_size
      lowerCamelCase_         =						hidden_dropout_prob
      lowerCamelCase_         =						attention_probs_dropout_prob
      lowerCamelCase_         =						max_position_embeddings
      lowerCamelCase_         =						type_vocab_size
      lowerCamelCase_         =						initializer_range
      lowerCamelCase_         =						layer_norm_eps
      lowerCamelCase_         =						position_embedding_type
      lowerCamelCase_         =						use_cache
      lowerCamelCase_         =						classifier_dropout
      lowerCamelCase_         =						pre_norm
      lowerCamelCase_         =						adapter_reduction_factor
      lowerCamelCase_         =						adapter_layer_norm
      lowerCamelCase_         =						adapter_reuse_layer_norm
      lowerCamelCase_         =						ln_before_adapter
      lowerCamelCase_         =						list(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						default_language






class      a						(					__snake_case		):






    @property
    def    UpperCamelCase       (       self     :							List[str]					)     ->		Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
        lowerCamelCase_         =						{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
        lowerCamelCase_         =						{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]					)












"""simple docstring"""





import os







def      lowerCamelCase__       (							)      ->				List[Any]:
  with open(os.path.dirname(_lowerCamelCase ) + '/grid.txt' ) as f:
    lowerCamelCase_         =						[]  # noqa: E741
    for _ in range(20 ):
      l.append([int(_lowerCamelCase ) for x in f.readline().split()] )

    lowerCamelCase_         =						0

    # right
    for i in range(20 ):
      for j in range(17 ):
        lowerCamelCase_         =						l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
        if temp > maximum:
          lowerCamelCase_         =						temp

        # down
    for i in range(17 ):
      for j in range(20 ):
        lowerCamelCase_         =						l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
        if temp > maximum:
          lowerCamelCase_         =						temp

        # diagonal 1
    for i in range(17 ):
      for j in range(17 ):
        lowerCamelCase_         =						l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
        if temp > maximum:
          lowerCamelCase_         =						temp

        # diagonal 2
    for i in range(17 ):
      for j in range(3		,				20 ):
        lowerCamelCase_         =						l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
        if temp > maximum:
          lowerCamelCase_         =						temp
    return maximum


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




