





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


_snake_case  :   Dict						   =  logging.getLogger(__name__)



class     _UpperCAmelCase   (   _snake_case   ):

  """simple docstring"""


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






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

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

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






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		Tuple					)							->						Dict[str, List[str]]:
     __lowerCAmelCase							     =     {
         k: self.tokenizer.batch_decode(v.tolist()					) if 'mask' not in k else v.shape for k, v in batch.items()
     }
     save_json(UpperCamelCase__ ,  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							     =     True
     return readable_batch






  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		str ,  **lowerCAmelCase_			:		Any					)							->						str:
     return self.model(UpperCamelCase__ ,  **UpperCamelCase__					)






  def     lowercase		(    self			:		Optional[int] ,  lowerCAmelCase_			:		Union[str, Any]					)							->						List[str]:
     __lowerCAmelCase							     =     self.tokenizer.batch_decode(
         UpperCamelCase__ ,  skip_special_tokens=UpperCamelCase__ ,  clean_up_tokenization_spaces=UpperCamelCase__					)
     return lmap(str.strip ,  UpperCamelCase__					)






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

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

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






  @property
  def     lowercase		(    self			:		Any					)							->						int:
     return self.tokenizer.pad_token_id






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Optional[int]					)							->						Dict:
     __lowerCAmelCase							     =     self._step(UpperCamelCase__					)

     __lowerCAmelCase							     =     dict(zip(self.loss_names ,  UpperCamelCase__					)					)
     # tokens per batch
     __lowerCAmelCase							     =     batch['input_ids'].ne(self.pad					).sum() + batch['labels'].ne(self.pad					).sum()
     __lowerCAmelCase							     =     batch['input_ids'].shape[0]
     __lowerCAmelCase							     =     batch['input_ids'].eq(self.pad					).sum()
     __lowerCAmelCase							     =     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     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Optional[int]					)							->						Dict:
     return self._generative_step(UpperCamelCase__					)






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






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		Optional[Any]					)							->						dict:
     __lowerCAmelCase							     =     time.time()

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






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		List[str]					)							->						Tuple:
     return self._generative_step(UpperCamelCase__					)






  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		str					)							->						Dict:
     return self.validation_epoch_end(UpperCamelCase__ ,  prefix='test'					)






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Any					)							->						SeqaSeqDataset:
     __lowerCAmelCase							     =     self.n_obs[type_path]
     __lowerCAmelCase							     =     self.target_lens[type_path]
     __lowerCAmelCase							     =     self.dataset_class(
         self.tokenizer ,  type_path=UpperCamelCase__ ,  n_obs=UpperCamelCase__ ,  max_target_length=UpperCamelCase__ ,  **self.dataset_kwargs ,  )
     return dataset






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		Tuple = False					)							->						DataLoader:
     __lowerCAmelCase							     =     self.get_dataset(UpperCamelCase__					)

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

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






  def     lowercase		(    self			:		Optional[Any]					)							->						DataLoader:
     __lowerCAmelCase							     =     self.get_dataloader('train' ,  batch_size=self.hparams.train_batch_size ,  shuffle=UpperCamelCase__					)
     return dataloader






  def     lowercase		(    self			:		str					)							->						DataLoader:
     return self.get_dataloader('val' ,  batch_size=self.hparams.eval_batch_size					)






  def     lowercase		(    self			:		List[Any]					)							->						DataLoader:
     return self.get_dataloader('test' ,  batch_size=self.hparams.eval_batch_size					)






  @staticmethod
  def     lowercase		(    lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		str					)							->						List[Any]:
     BaseTransformer.add_model_specific_args(UpperCamelCase__ ,  UpperCamelCase__					)
     add_generic_args(UpperCamelCase__ ,  UpperCamelCase__					)
     parser.add_argument(
         '--max_source_length' ,  default=1_0_2_4 ,  type=UpperCamelCase__ ,  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=5_6 ,  type=UpperCamelCase__ ,  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=1_4_2 ,  type=UpperCamelCase__ ,  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=1_4_2 ,  type=UpperCamelCase__ ,  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=UpperCamelCase__					)
     parser.add_argument('--overwrite_output_dir' ,  action='store_true' ,  default=UpperCamelCase__					)
     parser.add_argument('--max_tokens_per_batch' ,  type=UpperCamelCase__ ,  default=UpperCamelCase__					)
     parser.add_argument('--logger_name' ,  type=UpperCamelCase__ ,  choices=['default', 'wandb', 'wandb_shared'] ,  default='default'					)
     parser.add_argument('--n_train' ,  type=UpperCamelCase__ ,  default=-1 ,  required=UpperCamelCase__ ,  help='# examples. -1 means use all.'					)
     parser.add_argument('--n_val' ,  type=UpperCamelCase__ ,  default=5_0_0 ,  required=UpperCamelCase__ ,  help='# examples. -1 means use all.'					)
     parser.add_argument('--n_test' ,  type=UpperCamelCase__ ,  default=-1 ,  required=UpperCamelCase__ ,  help='# examples. -1 means use all.'					)
     parser.add_argument(
         '--task' ,  type=UpperCamelCase__ ,  default='summarization' ,  required=UpperCamelCase__ ,  help='# examples. -1 means use all.'					)
     parser.add_argument('--label_smoothing' ,  type=UpperCamelCase__ ,  default=0.0 ,  required=UpperCamelCase__					)
     parser.add_argument('--src_lang' ,  type=UpperCamelCase__ ,  default='' ,  required=UpperCamelCase__					)
     parser.add_argument('--tgt_lang' ,  type=UpperCamelCase__ ,  default='' ,  required=UpperCamelCase__					)
     parser.add_argument('--eval_beams' ,  type=UpperCamelCase__ ,  default=UpperCamelCase__ ,  required=UpperCamelCase__					)
     parser.add_argument(
         '--val_metric' ,  type=UpperCamelCase__ ,  default=UpperCamelCase__ ,  required=UpperCamelCase__ ,  choices=['bleu', 'rouge2', 'loss', None]					)
     parser.add_argument('--eval_max_gen_length' ,  type=UpperCamelCase__ ,  default=UpperCamelCase__ ,  help='never generate more than n tokens'					)
     parser.add_argument('--save_top_k' ,  type=UpperCamelCase__ ,  default=1 ,  required=UpperCamelCase__ ,  help='How many checkpoints to save'					)
     parser.add_argument(
         '--early_stopping_patience' ,  type=UpperCamelCase__ ,  default=-1 ,  required=UpperCamelCase__ ,  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     _UpperCAmelCase   (   _snake_case   ):

  """simple docstring"""


  a_      =			"""translation"""
  a_      =			["""loss"""]
  a_      =			["""bleu"""]
  a_      =			"""bleu"""






  def __init__(    self			:		List[Any] ,  lowerCAmelCase_			:		int ,  **lowerCAmelCase_			:		str					)							->						Optional[int]:
     super().__init__(UpperCamelCase__ ,  **UpperCamelCase__					)
     __lowerCAmelCase							     =     hparams.src_lang
     __lowerCAmelCase							     =     hparams.tgt_lang






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Optional[int]					)							->						dict:
     return calculate_bleu(UpperCamelCase__ ,  UpperCamelCase__					)





def   a_   (			lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      int=None     ):
   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(UpperCAmelCase__     )
      else:
         __lowerCAmelCase							     =     TranslationModule(UpperCAmelCase__     )
   __lowerCAmelCase							     =     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							     =     True  # don't pollute wandb logs unnecessarily
   elif args.logger_name == "wandb":
      from pytorch_lightning.loggers import WandbLogger

      __lowerCAmelCase							     =     os.environ.get('WANDB_PROJECT',     UpperCAmelCase__     )
      __lowerCAmelCase							     =     WandbLogger(name=model.output_dir.name,     project=UpperCAmelCase__     )

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

      __lowerCAmelCase							     =     WandbLogger(name=model.output_dir.name,     project=F"""hf_{dataset}"""     )

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

   __lowerCAmelCase							     =     args.val_metric == 'loss'
   __lowerCAmelCase							     =     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							     =     ''
   __lowerCAmelCase							     =     sorted(glob.glob(os.path.join(args.output_dir,     '*.ckpt'     ),     recursive=UpperCAmelCase__     )     )
   if checkpoints:
      __lowerCAmelCase							     =     checkpoints[-1]
      __lowerCAmelCase							     =     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__":
     _snake_case  :   str						   =  argparse.ArgumentParser()
     _snake_case  :   Union[str, Any]						   =  pl.Trainer.add_argparse_args(parser)
     _snake_case  :   List[str]						   =  SummarizationModule.add_model_specific_args(parser, os.getcwd())

     _snake_case  :   List[str]						   =  parser.parse_args()

     main(args)







from collections.abc import Sequence

def   a_   (			lowerCAmelCase_				:      Sequence[float],     lowerCAmelCase_				:      bool = False     ):
   if not arr:
      return 0

   __lowerCAmelCase							     =     0 if allow_empty_subarrays else float('-inf'     )
   __lowerCAmelCase							     =     0.0
   for num in arr:
      __lowerCAmelCase							     =     max(0 if allow_empty_subarrays else num,     curr_sum + num     )
      __lowerCAmelCase							     =     max(lowerCAmelCase_,     lowerCAmelCase_     )

   return max_sum


if __name__ == "__main__":
     from doctest import testmod

     testmod()

     _snake_case  :   Optional[Any]						   =  [-2, 1, -3, 4, -1, 2, 1, -5, 4]
     print(F"""{max_subarray_sum(nums) = }""")

