'''simple docstring'''




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


_A								=   R'\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `" / "`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `" // "`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `"train"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `"compressed"`)\n            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and\n            `"compressed"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a "dummy" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n'


@add_start_docstrings(_snake_case			)
class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       str     =    '''rag'''
   A							:       int     =    True
   def __init__(self       ,				_a=None       ,				_a=True       ,				_a=None       ,				_a=None       ,				_a=None       ,				_a=None       ,				_a=None       ,				_a=" / "       ,				_a=" // "       ,				_a=5       ,				_a=300       ,				_a=768       ,				_a=8       ,				_a="wiki_dpr"       ,				_a="train"       ,				_a="compressed"       ,				_a=None       ,				_a=None       ,				_a=False       ,				_a=False       ,				_a=0.0       ,				_a=True       ,				_a=False       ,				_a=False       ,				_a=False       ,				_a=True       ,				_a=None       ,				**_a       ,				)    ->     Union[str, Any]:
        super().__init__(
            bos_token_id=_a       ,				pad_token_id=_a       ,				eos_token_id=_a       ,				decoder_start_token_id=_a       ,				forced_eos_token_id=_a       ,				is_encoder_decoder=_a       ,				prefix=_a       ,				vocab_size=_a       ,				**_a       ,				)
        assert (
            "question_encoder" in kwargs and "generator" in kwargs
        ), "Config has to be initialized with question_encoder and generator config"
        lowercase_					:       List[Any]     				=					kwargs.pop('question_encoder'				)
        lowercase_					:       Any     				=					question_encoder_config.pop('model_type'				)
        lowercase_					:       Tuple     				=					kwargs.pop('generator'				)
        lowercase_					:       Union[str, Any]     				=					decoder_config.pop('model_type'				)

        from ..auto.configuration_auto import AutoConfig

        lowercase_					:       Union[str, Any]     				=					AutoConfig.for_model(_a       ,				**_a				)
        lowercase_					:       Optional[int]     				=					AutoConfig.for_model(_a       ,				**_a				)

        lowercase_					:       List[str]     				=					reduce_loss
        lowercase_					:       Optional[int]     				=					label_smoothing
        lowercase_					:       List[str]     				=					exclude_bos_score
        lowercase_					:       Optional[int]     				=					do_marginalize

        lowercase_					:       Optional[int]     				=					title_sep
        lowercase_					:       Union[str, Any]     				=					doc_sep
        lowercase_					:       List[Any]     				=					n_docs
        lowercase_					:       Tuple     				=					max_combined_length

        lowercase_					:       int     				=					dataset
        lowercase_					:       List[str]     				=					dataset_split
        lowercase_					:       Any     				=					index_name

        lowercase_					:       Tuple     				=					retrieval_vector_size
        lowercase_					:       int     				=					retrieval_batch_size
        lowercase_					:       List[Any]     				=					passages_path
        lowercase_					:       Any     				=					index_path
        lowercase_					:       Dict     				=					use_dummy_dataset

        lowercase_					:       Any     				=					output_retrieved

        lowercase_					:       int     				=					do_deduplication

        lowercase_					:       Tuple     				=					use_cache

        if self.forced_eos_token_id is None:
             lowercase_					:       Union[str, Any]     				=					getattr(self.generator       ,				'forced_eos_token_id'       ,				_a				)
   @classmethod
   def 		_lowerCamelCase (cls       ,				_a       ,				_a       ,				**_a				)    ->     PretrainedConfig:
        return cls(question_encoder=question_encoder_config.to_dict()       ,				generator=generator_config.to_dict()       ,				**_a				)





   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       Optional[Any]     				=					copy.deepcopy(self.__dict__				)
        lowercase_					:       Any     				=					self.question_encoder.to_dict()
        lowercase_					:       Optional[int]     				=					self.generator.to_dict()
        lowercase_					:       str     				=					self.__class__.model_type
        return output





'''simple docstring'''




import os

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):
     lowercase_					:       List[Any]     				=					len(grid[0]							)
     lowercase_					:       Union[str, Any]     				=					len(SCREAMING_SNAKE_CASE_							)

     lowercase_					:       Union[str, Any]     				=					0
     lowercase_					:       int     				=					0
     lowercase_					:       Union[str, Any]     				=					0

     # Check vertically, horizontally, diagonally at the same time (only works
     # for nxn grid)
     for i in range(SCREAMING_SNAKE_CASE_							):
          for j in range(n_rows - 3							):
               lowercase_					:       Dict     				=					grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
               lowercase_					:       Optional[Any]     				=					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:
                    lowercase_					:       Tuple     				=					(
                        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:
                    lowercase_					:       int     				=					(
                        grid[i][j]
                        * grid[i - 1][j + 1]
                        * grid[i - 2][j + 2]
                        * grid[i - 3][j + 3]
                    )

               lowercase_					:       Optional[int]     				=					max(
                   SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
               if max_product > largest:
                    lowercase_					:       List[Any]     				=					max_product

     return largest

def _UpperCamelCase   (       ):
     lowercase_					:       int     				=					[]
     with open(os.path.dirname(SCREAMING_SNAKE_CASE_							) + '/grid.txt'							) as file:
          for line in file:
               grid.append(line.strip('\n'							).split(' '							)							)

     lowercase_					:       Dict     				=					[[int(SCREAMING_SNAKE_CASE_							) for i in grid[j]] for j in range(len(SCREAMING_SNAKE_CASE_							)							)]

     return largest_product(SCREAMING_SNAKE_CASE_							)


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




