






"""simple docstring"""




import os






def        lowerCAmelCase (      UpperCamelCase_:							Tuple					)       ->							Tuple:







  '''simple docstring'''







  _a							=	len(grid[0]					)
  _a							=	len(UpperCamelCase_					)

  _a							=	0
  _a							=	0
  _a							=	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					):
      _a							=	grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
      _a							=	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:
        _a							=	(
            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:
        _a							=	(
            grid[i][j]
            * grid[i - 1][j + 1]
            * grid[i - 2][j + 2]
            * grid[i - 3][j + 3]
        )

      _a							=	max(
          UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)
      if max_product > largest:
        _a							=	max_product

  return largest






def        lowerCAmelCase (      )       ->							List[Any]:







  '''simple docstring'''







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

  _a							=	[[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 importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


UpperCamelCase							       =    logging.get_logger(__name__)

UpperCamelCase							       =    OrderedDict(
    [
        ("""align""", """EfficientNetImageProcessor"""),
        ("""beit""", """BeitImageProcessor"""),
        ("""bit""", """BitImageProcessor"""),
        ("""blip""", """BlipImageProcessor"""),
        ("""blip-2""", """BlipImageProcessor"""),
        ("""bridgetower""", """BridgeTowerImageProcessor"""),
        ("""chinese_clip""", """ChineseCLIPImageProcessor"""),
        ("""clip""", """CLIPImageProcessor"""),
        ("""clipseg""", """ViTImageProcessor"""),
        ("""conditional_detr""", """ConditionalDetrImageProcessor"""),
        ("""convnext""", """ConvNextImageProcessor"""),
        ("""convnextv2""", """ConvNextImageProcessor"""),
        ("""cvt""", """ConvNextImageProcessor"""),
        ("""data2vec-vision""", """BeitImageProcessor"""),
        ("""deformable_detr""", """DeformableDetrImageProcessor"""),
        ("""deit""", """DeiTImageProcessor"""),
        ("""deta""", """DetaImageProcessor"""),
        ("""detr""", """DetrImageProcessor"""),
        ("""dinat""", """ViTImageProcessor"""),
        ("""donut-swin""", """DonutImageProcessor"""),
        ("""dpt""", """DPTImageProcessor"""),
        ("""efficientformer""", """EfficientFormerImageProcessor"""),
        ("""efficientnet""", """EfficientNetImageProcessor"""),
        ("""flava""", """FlavaImageProcessor"""),
        ("""focalnet""", """BitImageProcessor"""),
        ("""git""", """CLIPImageProcessor"""),
        ("""glpn""", """GLPNImageProcessor"""),
        ("""groupvit""", """CLIPImageProcessor"""),
        ("""imagegpt""", """ImageGPTImageProcessor"""),
        ("""instructblip""", """BlipImageProcessor"""),
        ("""layoutlmv2""", """LayoutLMv2ImageProcessor"""),
        ("""layoutlmv3""", """LayoutLMv3ImageProcessor"""),
        ("""levit""", """LevitImageProcessor"""),
        ("""mask2former""", """Mask2FormerImageProcessor"""),
        ("""maskformer""", """MaskFormerImageProcessor"""),
        ("""mgp-str""", """ViTImageProcessor"""),
        ("""mobilenet_v1""", """MobileNetV1ImageProcessor"""),
        ("""mobilenet_v2""", """MobileNetV2ImageProcessor"""),
        ("""mobilevit""", """MobileViTImageProcessor"""),
        ("""mobilevit""", """MobileViTImageProcessor"""),
        ("""mobilevitv2""", """MobileViTImageProcessor"""),
        ("""nat""", """ViTImageProcessor"""),
        ("""oneformer""", """OneFormerImageProcessor"""),
        ("""owlvit""", """OwlViTImageProcessor"""),
        ("""perceiver""", """PerceiverImageProcessor"""),
        ("""pix2struct""", """Pix2StructImageProcessor"""),
        ("""poolformer""", """PoolFormerImageProcessor"""),
        ("""regnet""", """ConvNextImageProcessor"""),
        ("""resnet""", """ConvNextImageProcessor"""),
        ("""sam""", """SamImageProcessor"""),
        ("""segformer""", """SegformerImageProcessor"""),
        ("""swiftformer""", """ViTImageProcessor"""),
        ("""swin""", """ViTImageProcessor"""),
        ("""swin2sr""", """Swin2SRImageProcessor"""),
        ("""swinv2""", """ViTImageProcessor"""),
        ("""table-transformer""", """DetrImageProcessor"""),
        ("""timesformer""", """VideoMAEImageProcessor"""),
        ("""tvlt""", """TvltImageProcessor"""),
        ("""upernet""", """SegformerImageProcessor"""),
        ("""van""", """ConvNextImageProcessor"""),
        ("""videomae""", """VideoMAEImageProcessor"""),
        ("""vilt""", """ViltImageProcessor"""),
        ("""vit""", """ViTImageProcessor"""),
        ("""vit_hybrid""", """ViTHybridImageProcessor"""),
        ("""vit_mae""", """ViTImageProcessor"""),
        ("""vit_msn""", """ViTImageProcessor"""),
        ("""xclip""", """CLIPImageProcessor"""),
        ("""yolos""", """YolosImageProcessor"""),
    ]
)

UpperCamelCase							       =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)






def        lowerCAmelCase (      UpperCamelCase_:							str					)       ->							Union[str, Any]:







  '''simple docstring'''







  for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
    if class_name in extractors:
      _a							=	model_type_to_module_name(UpperCamelCase_					)

      _a							=	importlib.import_module(f'''.{module_name}'''     , "transformers.models"					)
      try:
        return getattr(UpperCamelCase_     , UpperCamelCase_					)
      except AttributeError:
        continue

  for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
    if getattr(UpperCamelCase_     , "__name__"     , UpperCamelCase_					) == class_name:
      return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  _a							=	importlib.import_module("transformers"					)
  if hasattr(UpperCamelCase_     , UpperCamelCase_					):
    return getattr(UpperCamelCase_     , UpperCamelCase_					)

  return None






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, os.PathLike]     , UpperCamelCase_:							Optional[Union[str, os.PathLike]] = None     , UpperCamelCase_:							bool = False     , UpperCamelCase_:							bool = False     , UpperCamelCase_:							Optional[Dict[str, str]] = None     , UpperCamelCase_:							Optional[Union[bool, str]] = None     , UpperCamelCase_:							Optional[str] = None     , UpperCamelCase_:							bool = False     , **UpperCamelCase_:							Dict     , )       ->							Optional[int]:







  '''simple docstring'''







  _a							=	get_file_from_repo(
      UpperCamelCase_     , UpperCamelCase_     , cache_dir=UpperCamelCase_     , force_download=UpperCamelCase_     , resume_download=UpperCamelCase_     , proxies=UpperCamelCase_     , use_auth_token=UpperCamelCase_     , revision=UpperCamelCase_     , local_files_only=UpperCamelCase_     , )
  if resolved_config_file is None:
    logger.info(
        "Could not locate the image processor configuration file, will try to use the model config instead."					)
    return {}

  with open(UpperCamelCase_     , encoding="utf-8"					) as reader:
    return json.load(UpperCamelCase_					)




class    lowercase_    :






      def __init__(    self	)	->List[Any]:







        '''simple docstring'''


        raise EnvironmentError(
            "AutoImageProcessor is designed to be instantiated "
            "using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method."	)






      @classmethod
      @replace_list_option_in_docstrings(a_	)
      def 			lowerCamelCase__    (    cls     ,     a_     ,     **a_	)	->Dict:







        '''simple docstring'''


        _a							=	kwargs.pop("config"     ,     a_	)
        _a							=	kwargs.pop("trust_remote_code"     ,     a_	)
        _a							=	True

        _a			,	_a							=	ImageProcessingMixin.get_image_processor_dict(a_     ,     **a_	)
        _a							=	config_dict.get("image_processor_type"     ,     a_	)
        _a							=	None
        if "AutoImageProcessor" in config_dict.get("auto_map"     ,     {}	):
          _a							=	config_dict["auto_map"]["AutoImageProcessor"]

        # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
        # and if so, infer the image processor class from there.
        if image_processor_class is None and image_processor_auto_map is None:
          _a							=	config_dict.pop("feature_extractor_type"     ,     a_	)
          if feature_extractor_class is not None:
            logger.warning(
                "Could not find image processor class in the image processor config or the model config. Loading"
                " based on pattern matching with the model's feature extractor configuration."	)
            _a							=	feature_extractor_class.replace("FeatureExtractor"     ,     "ImageProcessor"	)
          if "AutoFeatureExtractor" in config_dict.get("auto_map"     ,     {}	):
            _a							=	config_dict["auto_map"]["AutoFeatureExtractor"]
            _a							=	feature_extractor_auto_map.replace("FeatureExtractor"     ,     "ImageProcessor"	)
            logger.warning(
                "Could not find image processor auto map in the image processor config or the model config."
                " Loading based on pattern matching with the model's feature extractor configuration."	)

        # If we don't find the image processor class in the image processor config, let's try the model config.
        if image_processor_class is None and image_processor_auto_map is None:
          if not isinstance(a_     ,     a_	):
            _a							=	AutoConfig.from_pretrained(a_     ,     **a_	)
          # It could be in `config.image_processor_type``
          _a							=	getattr(a_     ,     "image_processor_type"     ,     a_	)
          if hasattr(a_     ,     "auto_map"	) and "AutoImageProcessor" in config.auto_map:
            _a							=	config.auto_map["AutoImageProcessor"]

        if image_processor_class is not None:
          _a							=	image_processor_class_from_name(a_	)

        _a							=	image_processor_auto_map is not None
        _a							=	image_processor_class is not None or type(a_	) in IMAGE_PROCESSOR_MAPPING
        _a							=	resolve_trust_remote_code(
            a_     ,     a_     ,     a_     ,     a_	)

        if has_remote_code and trust_remote_code:
          _a							=	get_class_from_dynamic_module(
              a_     ,     a_     ,     **a_	)
          _a							=	kwargs.pop("code_revision"     ,     a_	)
          if os.path.isdir(a_	):
            image_processor_class.register_for_auto_class()
          return image_processor_class.from_dict(a_     ,     **a_	)
        elif image_processor_class is not None:
          return image_processor_class.from_dict(a_     ,     **a_	)
        # Last try: we use the IMAGE_PROCESSOR_MAPPING.
        elif type(a_	) in IMAGE_PROCESSOR_MAPPING:
          _a							=	IMAGE_PROCESSOR_MAPPING[type(a_	)]
          return image_processor_class.from_dict(a_     ,     **a_	)

        raise ValueError(
            f'''Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '''
            f'''`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '''
            f'''`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()	)}'''	)






      @staticmethod
      def 			lowerCamelCase__    (    a_     ,     a_	)	->Optional[int]:







        '''simple docstring'''


        IMAGE_PROCESSOR_MAPPING.register(a_     ,     a_	)


