




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__					:  Union[str, Any]      							=				logging.getLogger(__name__)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "summarization"
 lowercase_							       =       ["loss"]
 lowercase_							       =       ROUGE_KEYS
 lowercase_							       =       "rouge2"



 def __init__(				self    :						Optional[Any]							,							_lowerCAmelCase    :						Optional[Any]							,							**_lowerCAmelCase    :						Optional[Any]		):
       if hparams.sortish_sampler and hparams.gpus > 1:
             SCREAMING_SNAKE_CASE_      		=     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__(_lowerCAmelCase							,							num_labels=_lowerCAmelCase							,							mode=self.mode							,							**_lowerCAmelCase		)
       use_task_specific_params(self.model							,							'summarization'		)
       save_git_info(self.hparams.output_dir		)
       SCREAMING_SNAKE_CASE_      		=     Path(self.output_dir		) / 'metrics.json'
       SCREAMING_SNAKE_CASE_      		=     Path(self.output_dir		) / 'hparams.pkl'
       pickle_save(self.hparams							,							self.hparams_save_path		)
       SCREAMING_SNAKE_CASE_      		=     0
       SCREAMING_SNAKE_CASE_      		=     defaultdict(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.config.model_type
       SCREAMING_SNAKE_CASE_      		=     self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

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

       SCREAMING_SNAKE_CASE_      		=     {
           '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()		)

       SCREAMING_SNAKE_CASE_      		=     get_git_info()['repo_sha']
       SCREAMING_SNAKE_CASE_      		=     hparams.num_workers
       SCREAMING_SNAKE_CASE_      		=     None  # default to config
       if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer							,							_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_      		=     self.tokenizer.lang_code_to_id[hparams.tgt_lang]
             SCREAMING_SNAKE_CASE_      		=     self.decoder_start_token_id
       SCREAMING_SNAKE_CASE_      		=     (
           SeqaSeqDataset if hasattr(self.tokenizer							,							'prepare_seq2seq_batch'		) else LegacySeqaSeqDataset
       )
       SCREAMING_SNAKE_CASE_      		=     False
       SCREAMING_SNAKE_CASE_      		=     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:
             SCREAMING_SNAKE_CASE_      		=     self.hparams.eval_max_gen_length
       else:
             SCREAMING_SNAKE_CASE_      		=     self.model.config.max_length
       SCREAMING_SNAKE_CASE_      		=     self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric



 def  lowerCAmelCase_    (				self    :						int							,							_lowerCAmelCase    :						Dict[str, torch.Tensor]		):
       SCREAMING_SNAKE_CASE_      		=     {
           k: self.tokenizer.batch_decode(v.tolist()		) if 'mask' not in k else v.shape for k, v in batch.items()
       }
       save_json(_lowerCAmelCase							,							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'		)

       SCREAMING_SNAKE_CASE_      		=     True
       return readable_batch



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						Optional[int]							,							**_lowerCAmelCase    :						Optional[int]		):
       return self.model(_lowerCAmelCase							,							**_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						List[int]		):
       SCREAMING_SNAKE_CASE_      		=     self.tokenizer.batch_decode(
           _lowerCAmelCase							,							skip_special_tokens=_lowerCAmelCase							,							clean_up_tokenization_spaces=_lowerCAmelCase		)
       return lmap(str.strip							,							_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						dict		):
       SCREAMING_SNAKE_CASE_      		=     self.tokenizer.pad_token_id
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     batch['input_ids'], batch['attention_mask']
       SCREAMING_SNAKE_CASE_      		=     batch['labels']
       if isinstance(self.model							,							_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_      		=     self.model._shift_right(_lowerCAmelCase		)
       else:
             SCREAMING_SNAKE_CASE_      		=     shift_tokens_right(_lowerCAmelCase							,							_lowerCAmelCase		)
       if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
             SCREAMING_SNAKE_CASE_      		=     decoder_input_ids
             self.save_readable_batch(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     self(_lowerCAmelCase							,							attention_mask=_lowerCAmelCase							,							decoder_input_ids=_lowerCAmelCase							,							use_cache=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     outputs['logits']
       if self.hparams.label_smoothing == 0:
             # Same behavior as modeling_bart.py, besides ignoring pad_token_id
             SCREAMING_SNAKE_CASE_      		=     nn.CrossEntropyLoss(ignore_index=_lowerCAmelCase		)

             assert lm_logits.shape[-1] == self.vocab_size
             SCREAMING_SNAKE_CASE_      		=     ce_loss_fct(lm_logits.view(-1							,							lm_logits.shape[-1]		)							,							tgt_ids.view(-1		)		)
       else:
             SCREAMING_SNAKE_CASE_      		=     nn.functional.log_softmax(_lowerCAmelCase							,							dim=-1		)
             SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     label_smoothed_nll_loss(
                 _lowerCAmelCase							,							_lowerCAmelCase							,							self.hparams.label_smoothing							,							ignore_index=_lowerCAmelCase		)
       return (loss,)



 @property
 def  lowerCAmelCase_    (				self    :						Any		):
       return self.tokenizer.pad_token_id



 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						int		):
       SCREAMING_SNAKE_CASE_      		=     self._step(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     dict(zip(self.loss_names							,							_lowerCAmelCase		)		)
       # tokens per batch
       SCREAMING_SNAKE_CASE_      		=     batch['input_ids'].ne(self.pad		).sum() + batch['labels'].ne(self.pad		).sum()
       SCREAMING_SNAKE_CASE_      		=     batch['input_ids'].shape[0]
       SCREAMING_SNAKE_CASE_      		=     batch['input_ids'].eq(self.pad		).sum()
       SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						List[str]							,							_lowerCAmelCase    :						List[str]							,							_lowerCAmelCase    :						int		):
       return self._generative_step(_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						Optional[Any]							,							_lowerCAmelCase    :						Union[str, Any]="val"		):
       self.step_count += 1
       SCREAMING_SNAKE_CASE_      		=     {k: torch.stack([x[k] for x in outputs]		).mean() for k in self.loss_names}
       SCREAMING_SNAKE_CASE_      		=     losses['loss']
       SCREAMING_SNAKE_CASE_      		=     {
           k: np.array([x[k] for x in outputs]		).mean() for k in self.metric_names + ['gen_time', 'gen_len']
       }
       SCREAMING_SNAKE_CASE_      		=     (
           generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
       )
       SCREAMING_SNAKE_CASE_      		=     torch.tensor(_lowerCAmelCase		).type_as(_lowerCAmelCase		)
       generative_metrics.update({k: v.item() for k, v in losses.items()}		)
       losses.update(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     {F"{prefix}_avg_{k}": x for k, x in losses.items()}
       SCREAMING_SNAKE_CASE_      		=     self.step_count
       self.metrics[prefix].append(_lowerCAmelCase		)  # callback writes this to self.metrics_save_path
       SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						Optional[int]		):
       return calculate_rouge(_lowerCAmelCase							,							_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						int							,							_lowerCAmelCase    :						dict		):
       SCREAMING_SNAKE_CASE_      		=     time.time()

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



 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						Optional[Any]							,							_lowerCAmelCase    :						Optional[Any]		):
       return self._generative_step(_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						str		):
       return self.validation_epoch_end(_lowerCAmelCase							,							prefix='test'		)



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.n_obs[type_path]
       SCREAMING_SNAKE_CASE_      		=     self.target_lens[type_path]
       SCREAMING_SNAKE_CASE_      		=     self.dataset_class(
           self.tokenizer							,							type_path=_lowerCAmelCase							,							n_obs=_lowerCAmelCase							,							max_target_length=_lowerCAmelCase							,							**self.dataset_kwargs							,							)
       return dataset



 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						bool = False		):
       SCREAMING_SNAKE_CASE_      		=     self.get_dataset(_lowerCAmelCase		)

       if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
             SCREAMING_SNAKE_CASE_      		=     dataset.make_sortish_sampler(_lowerCAmelCase							,							distributed=self.hparams.gpus > 1		)
             return DataLoader(
                 _lowerCAmelCase							,							batch_size=_lowerCAmelCase							,							collate_fn=dataset.collate_fn							,							shuffle=_lowerCAmelCase							,							num_workers=self.num_workers							,							sampler=_lowerCAmelCase							,							)

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



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     self.get_dataloader('train'							,							batch_size=self.hparams.train_batch_size							,							shuffle=_lowerCAmelCase		)
       return dataloader



 def  lowerCAmelCase_    (				self    :						Any		):
       return self.get_dataloader('val'							,							batch_size=self.hparams.eval_batch_size		)



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       return self.get_dataloader('test'							,							batch_size=self.hparams.eval_batch_size		)




 @staticmethod
 def  lowerCAmelCase_    (				_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						Tuple		):
       BaseTransformer.add_model_specific_args(_lowerCAmelCase							,							_lowerCAmelCase		)
       add_generic_args(_lowerCAmelCase							,							_lowerCAmelCase		)
       parser.add_argument(
           '--max_source_length'							,							default=1_024							,							type=_lowerCAmelCase							,							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=_lowerCAmelCase							,							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=_lowerCAmelCase							,							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=_lowerCAmelCase							,							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=_lowerCAmelCase		)
       parser.add_argument('--overwrite_output_dir'							,							action='store_true'							,							default=_lowerCAmelCase		)
       parser.add_argument('--max_tokens_per_batch'							,							type=_lowerCAmelCase							,							default=_lowerCAmelCase		)
       parser.add_argument('--logger_name'							,							type=_lowerCAmelCase							,							choices=['default', 'wandb', 'wandb_shared']							,							default='default'		)
       parser.add_argument('--n_train'							,							type=_lowerCAmelCase							,							default=-1							,							required=_lowerCAmelCase							,							help='# examples. -1 means use all.'		)
       parser.add_argument('--n_val'							,							type=_lowerCAmelCase							,							default=500							,							required=_lowerCAmelCase							,							help='# examples. -1 means use all.'		)
       parser.add_argument('--n_test'							,							type=_lowerCAmelCase							,							default=-1							,							required=_lowerCAmelCase							,							help='# examples. -1 means use all.'		)
       parser.add_argument(
           '--task'							,							type=_lowerCAmelCase							,							default='summarization'							,							required=_lowerCAmelCase							,							help='# examples. -1 means use all.'		)
       parser.add_argument('--label_smoothing'							,							type=_lowerCAmelCase							,							default=0.0							,							required=_lowerCAmelCase		)
       parser.add_argument('--src_lang'							,							type=_lowerCAmelCase							,							default=''							,							required=_lowerCAmelCase		)
       parser.add_argument('--tgt_lang'							,							type=_lowerCAmelCase							,							default=''							,							required=_lowerCAmelCase		)
       parser.add_argument('--eval_beams'							,							type=_lowerCAmelCase							,							default=_lowerCAmelCase							,							required=_lowerCAmelCase		)
       parser.add_argument(
           '--val_metric'							,							type=_lowerCAmelCase							,							default=_lowerCAmelCase							,							required=_lowerCAmelCase							,							choices=['bleu', 'rouge2', 'loss', None]		)
       parser.add_argument('--eval_max_gen_length'							,							type=_lowerCAmelCase							,							default=_lowerCAmelCase							,							help='never generate more than n tokens'		)
       parser.add_argument('--save_top_k'							,							type=_lowerCAmelCase							,							default=1							,							required=_lowerCAmelCase							,							help='How many checkpoints to save'		)
       parser.add_argument(
           '--early_stopping_patience'							,							type=_lowerCAmelCase							,							default=-1							,							required=_lowerCAmelCase							,							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 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "translation"
 lowercase_							       =       ["loss"]
 lowercase_							       =       ["bleu"]
 lowercase_							       =       "bleu"



 def __init__(				self    :						str							,							_lowerCAmelCase    :						List[Any]							,							**_lowerCAmelCase    :						Optional[int]		):
       super().__init__(_lowerCAmelCase							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     hparams.src_lang
       SCREAMING_SNAKE_CASE_      		=     hparams.tgt_lang




 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						Any		):
       return calculate_bleu(_lowerCAmelCase							,							_lowerCAmelCase		)


def    UpperCAmelCase_				( __UpperCAmelCase		:      Union[str, Any]     ,     __UpperCAmelCase		:      List[Any]=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:
                  SCREAMING_SNAKE_CASE_      		=     SummarizationModule(__UpperCAmelCase				)
            else:
                  SCREAMING_SNAKE_CASE_      		=     TranslationModule(__UpperCAmelCase				)
      SCREAMING_SNAKE_CASE_      		=     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'				)
      ):
            SCREAMING_SNAKE_CASE_      		=     True  # don't pollute wandb logs unnecessarily
      elif args.logger_name == "wandb":
            from pytorch_lightning.loggers import WandbLogger

            SCREAMING_SNAKE_CASE_      		=     os.environ.get('WANDB_PROJECT'     ,     __UpperCAmelCase				)
            SCREAMING_SNAKE_CASE_      		=     WandbLogger(name=model.output_dir.name     ,     project=__UpperCAmelCase				)

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

            SCREAMING_SNAKE_CASE_      		=     WandbLogger(name=model.output_dir.name     ,     project=f"hf_{dataset}"				)

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

      SCREAMING_SNAKE_CASE_      		=     args.val_metric == 'loss'
      SCREAMING_SNAKE_CASE_      		=     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

      SCREAMING_SNAKE_CASE_      		=     ''
      SCREAMING_SNAKE_CASE_      		=     sorted(glob.glob(os.path.join(args.output_dir     ,     '*.ckpt'				)     ,     recursive=__UpperCAmelCase				)				)
      if checkpoints:
            SCREAMING_SNAKE_CASE_      		=     checkpoints[-1]
            SCREAMING_SNAKE_CASE_      		=     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__					:  Tuple      							=				argparse.ArgumentParser()
   lowerCamelCase__					:  Optional[Any]      							=				pl.Trainer.add_argparse_args(parser)
   lowerCamelCase__					:  List[str]      							=				SummarizationModule.add_model_specific_args(parser, os.getcwd())

   lowerCamelCase__					:  Optional[int]      							=				parser.parse_args()

   main(args)





def    UpperCAmelCase_				( )   ->					list[list[int]]:
      return [list(range(10_00 - i     ,     -10_00 - i     ,     -1				)				) for i in range(10_00				)]


lowerCamelCase__					:  List[Any]      							=				generate_large_matrix()
lowerCamelCase__					:  List[Any]      							=				(
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[list[int]]				)   ->					None:
      assert all(row == sorted(__UpperCAmelCase     ,     reverse=__UpperCAmelCase				) for row in grid				)
      assert all(list(__UpperCAmelCase				) == sorted(__UpperCAmelCase     ,     reverse=__UpperCAmelCase				) for col in zip(*__UpperCAmelCase				)				)

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[int]				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     0
      SCREAMING_SNAKE_CASE_      		=     len(__UpperCAmelCase				) - 1

      # Edge cases such as no values or all numbers are negative.
      if not array or array[0] < 0:
            return 0

      while right + 1 > left:
            SCREAMING_SNAKE_CASE_      		=     (left + right) // 2
            SCREAMING_SNAKE_CASE_      		=     array[mid]

            # Num must be negative and the index must be greater than or equal to 0.
            if num < 0 and array[mid - 1] >= 0:
                  return mid

            if num >= 0:
                  SCREAMING_SNAKE_CASE_      		=     mid + 1
            else:
                  SCREAMING_SNAKE_CASE_      		=     mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
      return len(__UpperCAmelCase				)

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[list[int]]				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     0
      SCREAMING_SNAKE_CASE_      		=     len(grid[0]				)

      for i in range(len(__UpperCAmelCase				)				):
            SCREAMING_SNAKE_CASE_      		=     find_negative_index(grid[i][:bound]				)
            total += bound
      return (len(__UpperCAmelCase				) * len(grid[0]				)) - total

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[list[int]]				)   ->					int:
      return len([number for row in grid for number in row if number < 0]				)

def    UpperCAmelCase_				( __UpperCAmelCase		:      list[list[int]]				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     0
      for row in grid:
            for i, number in enumerate(__UpperCAmelCase				):
                  if number < 0:
                        total += len(__UpperCAmelCase				) - i
                        break
      return total

def    UpperCAmelCase_				( )   ->					None:
      from timeit import timeit

      print('Running benchmarks'				)
      SCREAMING_SNAKE_CASE_      		=     (
          'from __main__ import count_negatives_binary_search, '
          'count_negatives_brute_force, count_negatives_brute_force_with_break, grid'
      )
      for func in (
          "count_negatives_binary_search",  # took 0.7727 seconds
          "count_negatives_brute_force_with_break",  # took 4.6505 seconds
          "count_negatives_brute_force",  # took 12.8160 seconds
      ):
            SCREAMING_SNAKE_CASE_      		=     timeit(f"{func}(grid=grid)"     ,     setup=__UpperCAmelCase     ,     number=5_00				)
            print(f"{func}() took {time:0.4f} seconds"				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()