


"""simple docstring"""


import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


__lowerCamelCase      :str	    =		logging.getLogger(__name__)


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Any													='''summarization'''
     snake_case__	:					int													=['''loss''']
     snake_case__	:					Optional[int]													=ROUGE_KEYS
     snake_case__	:					str													='''rouge2'''

     def __init__(	self:						int     ,		__a:						List[Any]     ,		**__a:						List[Any]	)->    Optional[Any]:
           if hparams.sortish_sampler and hparams.gpus > 1:
                 lowerCamelCase					:					List[Any]        =	False
           elif hparams.max_tokens_per_batch is not None:
                 if hparams.gpus > 1:
                       raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training"""	)
                 if hparams.sortish_sampler:
                       raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously"""	)

           super().__init__(__a     ,		num_labels=__a     ,		mode=self.mode     ,		**__a	)
           use_task_specific_params(self.model     ,		"""summarization"""	)
           save_git_info(self.hparams.output_dir	)
           lowerCamelCase					:					str        =	Path(self.output_dir	) / """metrics.json"""
           lowerCamelCase					:					Optional[Any]        =	Path(self.output_dir	) / """hparams.pkl"""
           pickle_save(self.hparams     ,		self.hparams_save_path	)
           lowerCamelCase					:					Optional[int]        =	0
           lowerCamelCase					:					int        =	defaultdict(__a	)
           lowerCamelCase					:					Union[str, Any]        =	self.config.model_type
           lowerCamelCase					:					Optional[Any]        =	self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

           lowerCamelCase					:					dict        =	{
               "data_dir": self.hparams.data_dir,
               "max_source_length": self.hparams.max_source_length,
               "prefix": self.model.config.prefix or "",
           }
           lowerCamelCase					:					str        =	{
               """train""": self.hparams.n_train,
               """val""": self.hparams.n_val,
               """test""": self.hparams.n_test,
           }
           lowerCamelCase					:					str        =	{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

           lowerCamelCase					:					Optional[Any]        =	{
               """train""": self.hparams.max_target_length,
               """val""": self.hparams.val_max_target_length,
               """test""": self.hparams.test_max_target_length,
           }
           assert self.target_lens["train"] <= self.target_lens["val"], f'target_lens: {self.target_lens}'
           assert self.target_lens["train"] <= self.target_lens["test"], f'target_lens: {self.target_lens}'
           if self.hparams.freeze_embeds:
                 freeze_embeds(self.model	)
           if self.hparams.freeze_encoder:
                 freeze_params(self.model.get_encoder()	)
                 assert_all_frozen(self.model.get_encoder()	)

           lowerCamelCase					:					Optional[int]        =	get_git_info()["""repo_sha"""]
           lowerCamelCase					:					str        =	hparams.num_workers
           lowerCamelCase					:					str        =	None  # default to config
           if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer     ,		__a	):
                 lowerCamelCase					:					Any        =	self.tokenizer.lang_code_to_id[hparams.tgt_lang]
                 lowerCamelCase					:					List[str]        =	self.decoder_start_token_id
           lowerCamelCase					:					str        =	(
               SeqaSeqDataset if hasattr(self.tokenizer     ,		"""prepare_seq2seq_batch"""	) else LegacySeqaSeqDataset
           )
           lowerCamelCase					:					Tuple        =	False
           lowerCamelCase					:					Tuple        =	self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
           if self.hparams.eval_max_gen_length is not None:
                 lowerCamelCase					:					Optional[Any]        =	self.hparams.eval_max_gen_length
           else:
                 lowerCamelCase					:					Optional[Any]        =	self.model.config.max_length
           lowerCamelCase					:					Optional[int]        =	self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric

     def 		a__						(	self:						Any     ,		__a:						Dict[str, torch.Tensor]	)->    Dict[str, List[str]]:
           lowerCamelCase					:					List[Any]        =	{
               k: self.tokenizer.batch_decode(v.tolist()	) if """mask""" not in k else v.shape for k, v in batch.items()
           }
           save_json(__a     ,		Path(self.output_dir	) / """text_batch.json"""	)
           save_json({k: v.tolist() for k, v in batch.items()}     ,		Path(self.output_dir	) / """tok_batch.json"""	)

           lowerCamelCase					:					Dict        =	True
           return readable_batch

     def 		a__						(	self:						List[str]     ,		__a:						Any     ,		**__a:						Any	)->    List[Any]:
           return self.model(__a     ,		**__a	)

     def 		a__						(	self:						int     ,		__a:						List[int]	)->    str:
           lowerCamelCase					:					Union[str, Any]        =	self.tokenizer.batch_decode(
               __a     ,		skip_special_tokens=__a     ,		clean_up_tokenization_spaces=__a	)
           return lmap(str.strip     ,		__a	)

     def 		a__						(	self:						Union[str, Any]     ,		__a:						dict	)->    Tuple:
           lowerCamelCase					:					Any        =	self.tokenizer.pad_token_id
           lowerCamelCase     ,			lowerCamelCase					:					Optional[int]        =	batch["""input_ids"""], batch["""attention_mask"""]
           lowerCamelCase					:					Optional[Any]        =	batch["""labels"""]
           if isinstance(self.model     ,		__a	):
                 lowerCamelCase					:					Optional[int]        =	self.model._shift_right(__a	)
           else:
                 lowerCamelCase					:					List[str]        =	shift_tokens_right(__a     ,		__a	)
           if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
                 lowerCamelCase					:					Tuple        =	decoder_input_ids
                 self.save_readable_batch(__a	)

           lowerCamelCase					:					Optional[Any]        =	self(__a     ,		attention_mask=__a     ,		decoder_input_ids=__a     ,		use_cache=__a	)
           lowerCamelCase					:					Union[str, Any]        =	outputs["""logits"""]
           if self.hparams.label_smoothing == 0:
                 # Same behavior as modeling_bart.py, besides ignoring pad_token_id
                 lowerCamelCase					:					int        =	nn.CrossEntropyLoss(ignore_index=__a	)

                 assert lm_logits.shape[-1] == self.vocab_size
                 lowerCamelCase					:					Optional[int]        =	ce_loss_fct(lm_logits.view(-1     ,		lm_logits.shape[-1]	)     ,		tgt_ids.view(-1	)	)
           else:
                 lowerCamelCase					:					Union[str, Any]        =	nn.functional.log_softmax(__a     ,		dim=-1	)
                 lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	label_smoothed_nll_loss(
                     __a     ,		__a     ,		self.hparams.label_smoothing     ,		ignore_index=__a	)
           return (loss,)

     @property
     def 		a__						(	self:						List[str]	)->    int:
           return self.tokenizer.pad_token_id

     def 		a__						(	self:						str     ,		__a:						Optional[Any]     ,		__a:						Dict	)->    Dict:
           lowerCamelCase					:					Union[str, Any]        =	self._step(__a	)

           lowerCamelCase					:					Optional[int]        =	dict(zip(self.loss_names     ,		__a	)	)
           # tokens per batch
           lowerCamelCase					:					int        =	batch["""input_ids"""].ne(self.pad	).sum() + batch["""labels"""].ne(self.pad	).sum()
           lowerCamelCase					:					int        =	batch["""input_ids"""].shape[0]
           lowerCamelCase					:					int        =	batch["""input_ids"""].eq(self.pad	).sum()
           lowerCamelCase					:					List[str]        =	batch["""input_ids"""].eq(self.pad	).float().mean()
           # TODO(SS): make a wandb summary metric for this
           return {"loss": loss_tensors[0], "log": logs}

     def 		a__						(	self:						int     ,		__a:						List[str]     ,		__a:						Any	)->    Dict:
           return self._generative_step(__a	)

     def 		a__						(	self:						Any     ,		__a:						Any     ,		__a:						Any="val"	)->    Dict:
           self.step_count += 1
           lowerCamelCase					:					Dict        =	{k: torch.stack([x[k] for x in outputs]	).mean() for k in self.loss_names}
           lowerCamelCase					:					str        =	losses["""loss"""]
           lowerCamelCase					:					int        =	{
               k: np.array([x[k] for x in outputs]	).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
           }
           lowerCamelCase					:					Optional[int]        =	(
               generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
           )
           lowerCamelCase					:					torch.FloatTensor        =	torch.tensor(__a	).type_as(__a	)
           generative_metrics.update({k: v.item() for k, v in losses.items()}	)
           losses.update(__a	)
           lowerCamelCase					:					str        =	{f'{prefix}_avg_{k}': x for k, x in losses.items()}
           lowerCamelCase					:					int        =	self.step_count
           self.metrics[prefix].append(__a	)  # callback writes this to self.metrics_save_path
           lowerCamelCase					:					Union[str, Any]        =	flatten_list([x["""preds"""] for x in outputs]	)
           return {
               "log": all_metrics,
               "preds": preds,
               f'{prefix}_loss': loss,
               f'{prefix}_{self.val_metric}': metric_tensor,
           }

     def 		a__						(	self:						List[str]     ,		__a:						List[Any]     ,		__a:						Union[str, Any]	)->    Dict:
           return calculate_rouge(__a     ,		__a	)

     def 		a__						(	self:						Union[str, Any]     ,		__a:						dict	)->    dict:
           lowerCamelCase					:					Tuple        =	time.time()

           # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
           lowerCamelCase					:					Any        =	self.model.generate(
               batch["""input_ids"""]     ,		attention_mask=batch["""attention_mask"""]     ,		use_cache=__a     ,		decoder_start_token_id=self.decoder_start_token_id     ,		num_beams=self.eval_beams     ,		max_length=self.eval_max_length     ,		)
           lowerCamelCase					:					str        =	(time.time() - ta) / batch["""input_ids"""].shape[0]
           lowerCamelCase					:					List[str]        =	self.ids_to_clean_text(__a	)
           lowerCamelCase					:					List[str]        =	self.ids_to_clean_text(batch["""labels"""]	)
           lowerCamelCase					:					str        =	self._step(__a	)
           lowerCamelCase					:					Union[str, Any]        =	dict(zip(self.loss_names     ,		__a	)	)
           lowerCamelCase					:					Dict        =	self.calc_generative_metrics(__a     ,		__a	)
           lowerCamelCase					:					List[str]        =	np.mean(lmap(__a     ,		__a	)	)
           base_metrics.update(gen_time=__a     ,		gen_len=__a     ,		preds=__a     ,		target=__a     ,		**__a	)
           return base_metrics

     def 		a__						(	self:						List[str]     ,		__a:						Union[str, Any]     ,		__a:						Dict	)->    Any:
           return self._generative_step(__a	)

     def 		a__						(	self:						Any     ,		__a:						Optional[int]	)->    List[str]:
           return self.validation_epoch_end(__a     ,		prefix="""test"""	)

     def 		a__						(	self:						str     ,		__a:						int	)->    SeqaSeqDataset:
           lowerCamelCase					:					Union[str, Any]        =	self.n_obs[type_path]
           lowerCamelCase					:					int        =	self.target_lens[type_path]
           lowerCamelCase					:					Union[str, Any]        =	self.dataset_class(
               self.tokenizer     ,		type_path=__a     ,		n_obs=__a     ,		max_target_length=__a     ,		**self.dataset_kwargs     ,		)
           return dataset

     def 		a__						(	self:						Optional[int]     ,		__a:						str     ,		__a:						int     ,		__a:						bool = False	)->    DataLoader:
           lowerCamelCase					:					List[str]        =	self.get_dataset(__a	)

           if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
                 lowerCamelCase					:					Union[str, Any]        =	dataset.make_sortish_sampler(__a     ,		distributed=self.hparams.gpus > 1	)
                 return DataLoader(
                     __a     ,		batch_size=__a     ,		collate_fn=dataset.collate_fn     ,		shuffle=__a     ,		num_workers=self.num_workers     ,		sampler=__a     ,		)

           elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
                 lowerCamelCase					:					List[Any]        =	dataset.make_dynamic_sampler(
                     self.hparams.max_tokens_per_batch     ,		distributed=self.hparams.gpus > 1	)
                 return DataLoader(
                     __a     ,		batch_sampler=__a     ,		collate_fn=dataset.collate_fn     ,		num_workers=self.num_workers     ,		)
           else:
                 return DataLoader(
                     __a     ,		batch_size=__a     ,		collate_fn=dataset.collate_fn     ,		shuffle=__a     ,		num_workers=self.num_workers     ,		sampler=__a     ,		)

     def 		a__						(	self:						Optional[int]	)->    DataLoader:
           lowerCamelCase					:					Tuple        =	self.get_dataloader("""train"""     ,		batch_size=self.hparams.train_batch_size     ,		shuffle=__a	)
           return dataloader

     def 		a__						(	self:						List[str]	)->    DataLoader:
           return self.get_dataloader("""val"""     ,		batch_size=self.hparams.eval_batch_size	)

     def 		a__						(	self:						Dict	)->    DataLoader:
           return self.get_dataloader("""test"""     ,		batch_size=self.hparams.eval_batch_size	)







     @staticmethod
     def 		a__						(	__a:						str     ,		__a:						Optional[int]	)->    Dict:
           BaseTransformer.add_model_specific_args(__a     ,		__a	)
           add_generic_args(__a     ,		__a	)
           parser.add_argument(
               """--max_source_length"""     ,		default=1_024     ,		type=__a     ,		help=(
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated, sequences shorter will be padded."""
               )     ,		)
           parser.add_argument(
               """--max_target_length"""     ,		default=56     ,		type=__a     ,		help=(
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated, sequences shorter will be padded."""
               )     ,		)
           parser.add_argument(
               """--val_max_target_length"""     ,		default=142     ,		type=__a     ,		help=(
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated, sequences shorter will be padded."""
               )     ,		)
           parser.add_argument(
               """--test_max_target_length"""     ,		default=142     ,		type=__a     ,		help=(
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated, sequences shorter will be padded."""
               )     ,		)
           parser.add_argument("""--freeze_encoder"""     ,		action="""store_true"""	)
           parser.add_argument("""--freeze_embeds"""     ,		action="""store_true"""	)
           parser.add_argument("""--sortish_sampler"""     ,		action="""store_true"""     ,		default=__a	)
           parser.add_argument("""--overwrite_output_dir"""     ,		action="""store_true"""     ,		default=__a	)
           parser.add_argument("""--max_tokens_per_batch"""     ,		type=__a     ,		default=__a	)
           parser.add_argument("""--logger_name"""     ,		type=__a     ,		choices=["""default""", """wandb""", """wandb_shared"""]     ,		default="""default"""	)
           parser.add_argument("""--n_train"""     ,		type=__a     ,		default=-1     ,		required=__a     ,		help="""# examples. -1 means use all."""	)
           parser.add_argument("""--n_val"""     ,		type=__a     ,		default=500     ,		required=__a     ,		help="""# examples. -1 means use all."""	)
           parser.add_argument("""--n_test"""     ,		type=__a     ,		default=-1     ,		required=__a     ,		help="""# examples. -1 means use all."""	)
           parser.add_argument(
               """--task"""     ,		type=__a     ,		default="""summarization"""     ,		required=__a     ,		help="""# examples. -1 means use all."""	)
           parser.add_argument("""--label_smoothing"""     ,		type=__a     ,		default=0.0     ,		required=__a	)
           parser.add_argument("""--src_lang"""     ,		type=__a     ,		default=""""""     ,		required=__a	)
           parser.add_argument("""--tgt_lang"""     ,		type=__a     ,		default=""""""     ,		required=__a	)
           parser.add_argument("""--eval_beams"""     ,		type=__a     ,		default=__a     ,		required=__a	)
           parser.add_argument(
               """--val_metric"""     ,		type=__a     ,		default=__a     ,		required=__a     ,		choices=["""bleu""", """rouge2""", """loss""", None]	)
           parser.add_argument("""--eval_max_gen_length"""     ,		type=__a     ,		default=__a     ,		help="""never generate more than n tokens"""	)
           parser.add_argument("""--save_top_k"""     ,		type=__a     ,		default=1     ,		required=__a     ,		help="""How many checkpoints to save"""	)
           parser.add_argument(
               """--early_stopping_patience"""     ,		type=__a     ,		default=-1     ,		required=__a     ,		help=(
                   """-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
                   """ val_check_interval will effect it."""
               )     ,		)
           return parser


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													='''translation'''
     snake_case__	:					Dict													=['''loss''']
     snake_case__	:					Any													=['''bleu''']
     snake_case__	:					List[str]													='''bleu'''

     def __init__(	self:						Dict     ,		__a:						Union[str, Any]     ,		**__a:						Any	)->    Union[str, Any]:
           super().__init__(__a     ,		**__a	)
           lowerCamelCase					:					List[str]        =	hparams.src_lang
           lowerCamelCase					:					List[Any]        =	hparams.tgt_lang







     def 		a__						(	self:						List[str]     ,		__a:						Dict     ,		__a:						Dict	)->    dict:
           return calculate_bleu(__a     ,		__a	)




def   snake_case		(     UpperCamelCase__       :						List[str]			,				UpperCamelCase__       :						str=None    )   ->      SummarizationModule:
      Path(args.output_dir    ).mkdir(exist_ok=UpperCamelCase__    )
      check_output_dir(UpperCamelCase__			,				expected_items=3    )

      if model is None:
            if "summarization" in args.task:
                  lowerCamelCase					:					SummarizationModule        =	SummarizationModule(UpperCamelCase__    )
            else:
                  lowerCamelCase					:					SummarizationModule        =	TranslationModule(UpperCamelCase__    )
      lowerCamelCase					:					str        =	Path(args.data_dir    ).name
      if (
          args.logger_name == "default"
          or args.fast_dev_run
          or str(args.output_dir    ).startswith("""/tmp"""    )
          or str(args.output_dir    ).startswith("""/var"""    )
      ):
            lowerCamelCase					:					List[str]        =	True  # don't pollute wandb logs unnecessarily
      elif args.logger_name == "wandb":
            from pytorch_lightning.loggers import WandbLogger

            lowerCamelCase					:					Dict        =	os.environ.get("""WANDB_PROJECT"""			,				UpperCamelCase__    )
            lowerCamelCase					:					Optional[Any]        =	WandbLogger(name=model.output_dir.name			,				project=UpperCamelCase__    )

      elif args.logger_name == "wandb_shared":
            from pytorch_lightning.loggers import WandbLogger

            lowerCamelCase					:					Tuple        =	WandbLogger(name=model.output_dir.name			,				project=F'hf_{dataset}'    )

      if args.early_stopping_patience >= 0:
            lowerCamelCase					:					str        =	get_early_stopping_callback(model.val_metric			,				args.early_stopping_patience    )
      else:
            lowerCamelCase					:					int        =	False

      lowerCamelCase					:					Tuple        =	args.val_metric == """loss"""
      lowerCamelCase					:					pl.Trainer        =	generic_train(
          UpperCamelCase__			,				UpperCamelCase__			,				logging_callback=SeqaSeqLoggingCallback()			,				checkpoint_callback=get_checkpoint_callback(
              args.output_dir			,				model.val_metric			,				args.save_top_k			,				UpperCamelCase__    )			,				early_stopping_callback=UpperCamelCase__			,				logger=UpperCamelCase__			,				)
      pickle_save(model.hparams			,				model.output_dir / """hparams.pkl"""    )
      if not args.do_predict:
            return model

      lowerCamelCase					:					Optional[int]        =	""""""
      lowerCamelCase					:					Optional[Any]        =	sorted(glob.glob(os.path.join(args.output_dir			,				"""*.ckpt"""    )			,				recursive=UpperCamelCase__    )    )
      if checkpoints:
            lowerCamelCase					:					List[Any]        =	checkpoints[-1]
            lowerCamelCase					:					str        =	checkpoints[-1]
      trainer.logger.log_hyperparams(model.hparams    )

      # test() without a model tests using the best checkpoint automatically
      trainer.test()
      return model


if __name__ == "__main__":
    __lowerCamelCase      :Optional[Any]	    =		argparse.ArgumentParser()
    __lowerCamelCase      :int	    =		pl.Trainer.add_argparse_args(parser)
    __lowerCamelCase      :Optional[Any]	    =		SummarizationModule.add_model_specific_args(parser, os.getcwd())

    __lowerCamelCase      :Any	    =		parser.parse_args()

    main(args)








"""simple docstring"""


import os




def   snake_case		(     )   ->      Optional[Any]:
      with open(os.path.dirname(UpperCamelCase__    ) + """/grid.txt"""    ) as f:
            lowerCamelCase					:					int        =	[]  # noqa: E741
            for _ in range(20    ):
                  l.append([int(UpperCamelCase__    ) for x in f.readline().split()]    )

            lowerCamelCase					:					Union[str, Any]        =	0

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

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

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

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


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




