






"""simple docstring"""





import os







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





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__A			   = {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = ["SpeechT5Tokenizer"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
           "SpeechT5ForSpeechToText",
           "SpeechT5ForSpeechToSpeech",
           "SpeechT5ForTextToSpeech",
           "SpeechT5Model",
           "SpeechT5PreTrainedModel",
           "SpeechT5HifiGan",
       ]

if TYPE_CHECKING:
       from .configuration_speechta import (
           SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
           SpeechTaConfig,
           SpeechTaHifiGanConfig,
       )
       from .feature_extraction_speechta import SpeechTaFeatureExtractor
       from .processing_speechta import SpeechTaProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_speechta import SpeechTaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_speechta import (
                  SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SpeechTaForSpeechToSpeech,
                  SpeechTaForSpeechToText,
                  SpeechTaForTextToSpeech,
                  SpeechTaHifiGan,
                  SpeechTaModel,
                  SpeechTaPreTrainedModel,
              )

else:
       import sys

       __A			   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
