




"""simple docstring"""

import os






def 						_snake_case					( _snake_case							: List[Any]							)   -> Optional[Any]:






      '''simple docstring'''


      _A        =       len(grid[0]							)
      _A        =       len(_snake_case							)

      _A        =       0
      _A        =       0
      _A        =       0

      # Check vertically, horizontally, diagonally at the same time (only works
      # for nxn grid)
      for i in range(_snake_case							):
            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(
                      _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                  if max_product > largest:
                        _A        =       max_product

      return largest






def 						_snake_case					( )   -> Tuple:






      '''simple docstring'''


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

      _A        =       [[int(_snake_case							) for i in grid[j]] for j in range(len(_snake_case							)							)]

      return largest_product(_snake_case							)


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







"""simple docstring"""

from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
       from fairscale.optim import OSS


a         =      logging.get_logger(__name__)

a         =      {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''

 def __init__(			self	:	Any   ,							_UpperCAmelCase	:	Optional[int]=None   ,							_UpperCAmelCase	:	Tuple=None   ,							*_UpperCAmelCase	:	int   ,							**_UpperCAmelCase	:	Tuple		):
       super().__init__(*_UpperCAmelCase   ,							**_UpperCAmelCase		)

       if config is None:
             assert isinstance(self.model   ,							_UpperCAmelCase		), (
                 "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                 F''' {self.model.__class__}'''
             )
             _A        =       self.model.config
       else:
             _A        =       config

       _A        =       data_args
       _A        =       self.config.tgt_vocab_size if isinstance(self.config   ,							_UpperCAmelCase		) else self.config.vocab_size

       if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
             assert self.config.pad_token_id is not None, (
                 "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                 " calculation or doing label smoothing."
             )

       if self.config.pad_token_id is None and self.config.eos_token_id is not None:
             logger.warning(
                 F'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
                 ' padding..'		)

       if self.args.label_smoothing == 0:
             _A        =       torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id		)
       else:
             # dynamically import label_smoothed_nll_loss
             from utils import label_smoothed_nll_loss

             _A        =       label_smoothed_nll_loss

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	int		):
       if self.optimizer is None:
             _A        =       ['bias', 'LayerNorm.weight']
             _A        =       [
                 {
                     'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay		)],
                     'weight_decay': self.args.weight_decay,
                 },
                 {
                     'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay		)],
                     'weight_decay': 0.0,
                 },
             ]
             _A        =       Adafactor if self.args.adafactor else AdamW
             if self.args.adafactor:
                   _A        =       Adafactor
                   _A        =       {'scale_parameter': False, 'relative_step': False}
             else:
                   _A        =       AdamW
                   _A        =       {
                       'betas': (self.args.adam_betaa, self.args.adam_betaa),
                       'eps': self.args.adam_epsilon,
                   }
             _A        =       self.args.learning_rate
             if self.sharded_ddp:
                   _A        =       OSS(
                       params=_UpperCAmelCase   ,							optim=_UpperCAmelCase   ,							**_UpperCAmelCase   ,							)
             else:
                   _A        =       optimizer_cls(_UpperCAmelCase   ,							**_UpperCAmelCase		)

       if self.lr_scheduler is None:
             _A        =       self._get_lr_scheduler(_UpperCAmelCase		)
       else:  # ignoring --lr_scheduler
             logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'		)

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Any		):
       _A        =       arg_to_scheduler[self.args.lr_scheduler]
       if self.args.lr_scheduler == "constant":
             _A        =       schedule_func(self.optimizer		)
       elif self.args.lr_scheduler == "constant_w_warmup":
             _A        =       schedule_func(self.optimizer   ,							num_warmup_steps=self.args.warmup_steps		)
       else:
             _A        =       schedule_func(
                 self.optimizer   ,							num_warmup_steps=self.args.warmup_steps   ,							num_training_steps=_UpperCAmelCase		)
       return scheduler

 def       lowerCAmelCase_							(			self	:	Any		):
       if isinstance(self.train_dataset   ,							torch.utils.data.IterableDataset		):
             return None
       elif is_torch_tpu_available():
             return get_tpu_sampler(self.train_dataset		)
       else:
             if self.args.sortish_sampler:
                   self.train_dataset.make_sortish_sampler(
                       self.args.per_device_train_batch_size   ,							distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)   ,							)

             return (
                 RandomSampler(self.train_dataset		)
                 if self.args.local_rank == -1
                 else DistributedSampler(self.train_dataset		)
             )

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int		):
       if self.args.label_smoothing == 0:
             if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                   # force training to ignore pad token
                   _A        =       model(**_UpperCAmelCase   ,							use_cache=_UpperCAmelCase		)[0]
                   _A        =       self.loss_fn(logits.view(-1   ,							logits.shape[-1]		)   ,							labels.view(-1		)		)
             else:
                   # compute usual loss via models
                   _A       ,   _A        =       model(**_UpperCAmelCase   ,							labels=_UpperCAmelCase   ,							use_cache=_UpperCAmelCase		)[:2]
       else:
             # compute label smoothed loss
             _A        =       model(**_UpperCAmelCase   ,							use_cache=_UpperCAmelCase		)[0]
             _A        =       torch.nn.functional.log_softmax(_UpperCAmelCase   ,							dim=-1		)
             _A       ,   _A        =       self.loss_fn(_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.args.label_smoothing   ,							ignore_index=self.config.pad_token_id		)
       return loss, logits

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	Optional[int]		):
       _A        =       inputs.pop('labels'		)
       _A       ,   _A        =       self._compute_loss(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       return loss

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	nn.Module   ,							_UpperCAmelCase	:	Dict[str, Union[torch.Tensor, Any]]   ,							_UpperCAmelCase	:	bool   ,							_UpperCAmelCase	:	Optional[List[str]] = None   ,							):
       _A        =       self._prepare_inputs(_UpperCAmelCase		)

       _A        =       {
           'max_length': self.data_args.val_max_target_length
           if self.data_args is not None
           else self.config.max_length,
           'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
       }

       if self.args.predict_with_generate and not self.args.prediction_loss_only:
             _A        =       self.model.generate(
                 inputs['input_ids']   ,							attention_mask=inputs['attention_mask']   ,							**_UpperCAmelCase   ,							)
             # in case the batch is shorter than max length, the output should be padded
             if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                   _A        =       self._pad_tensors_to_max_len(_UpperCAmelCase   ,							gen_kwargs['max_length']		)

       _A        =       inputs.pop('labels'		)
       with torch.no_grad():
             # compute loss on predict data
             _A       ,   _A        =       self._compute_loss(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

       _A        =       loss.mean().detach()
       if self.args.prediction_loss_only:
             return (loss, None, None)

       _A        =       generated_tokens if self.args.predict_with_generate else logits

       if labels.shape[-1] < gen_kwargs["max_length"]:
             _A        =       self._pad_tensors_to_max_len(_UpperCAmelCase   ,							gen_kwargs['max_length']		)

       return (loss, logits, labels)



 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	Tuple		):
       # If PAD token is not defined at least EOS token has to be defined
       _A        =       self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

       if pad_token_id is None:
             raise ValueError(
                 'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
                 F''' padded to `max_length`={max_length}'''		)

       _A        =       pad_token_id * torch.ones(
           (tensor.shape[0], max_length)   ,							dtype=tensor.dtype   ,							device=tensor.device		)
       _A        =       tensor
       return padded_tensor

