


"""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


__snake_case             =   logging.getLogger(__name__)







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Optional[int]							  =  '''summarization'''
   __UpperCAmelCase			:	Any							  =  ['''loss''']
   __UpperCAmelCase			:	Any							  =  ROUGE_KEYS
   __UpperCAmelCase			:	Tuple							  =  '''rouge2'''







   def __init__(     self						,     UpperCamelCase__						,     **UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




       if hparams.sortish_sampler and hparams.gpus > 1:
           snake_case   :				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__(UpperCamelCase__						,     num_labels=UpperCamelCase__						,     mode=self.mode						,     **UpperCamelCase__						)
       use_task_specific_params(self.model						,     "summarization"						)
       save_git_info(self.hparams.output_dir						)
       snake_case   :				Union[str, Any]				 =   Path(self.output_dir						) / "metrics.json"
       snake_case   :				Tuple				 =   Path(self.output_dir						) / "hparams.pkl"
       pickle_save(self.hparams						,     self.hparams_save_path						)
       snake_case   :				Any				 =   0
       snake_case   :				Tuple				 =   defaultdict(UpperCamelCase__						)
       snake_case   :				Union[str, Any]				 =   self.config.model_type
       snake_case   :				Dict				 =   self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

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

       snake_case   :				List[str]				 =   {
           "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()						)

       snake_case   :				Union[str, Any]				 =   get_git_info()["repo_sha"]
       snake_case   :				Any				 =   hparams.num_workers
       snake_case   :				List[str]				 =   None  # default to config
       if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer						,     UpperCamelCase__						):
           snake_case   :				Tuple				 =   self.tokenizer.lang_code_to_id[hparams.tgt_lang]
           snake_case   :				Any				 =   self.decoder_start_token_id
       snake_case   :				Dict				 =   (
           SeqaSeqDataset if hasattr(self.tokenizer						,     "prepare_seq2seq_batch"						) else LegacySeqaSeqDataset
       )
       snake_case   :				Optional[Any]				 =   False
       snake_case   :				List[Any]				 =   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:
           snake_case   :				List[Any]				 =   self.hparams.eval_max_gen_length
       else:
           snake_case   :				Tuple				 =   self.model.config.max_length
       snake_case   :				int				 =   self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Dict[str, List[str]]:

       '''simple docstring'''




       snake_case   :				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(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"						)

       snake_case   :				Optional[int]				 =   True
       return readable_batch







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     **UpperCamelCase__						)     ->		Any:

       '''simple docstring'''




       return self.model(UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				Any				 =   self.tokenizer.batch_decode(
           UpperCamelCase__						,     skip_special_tokens=UpperCamelCase__						,     clean_up_tokenization_spaces=UpperCamelCase__						)
       return lmap(str.strip						,     UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				int				 =   self.tokenizer.pad_token_id
       snake_case ,snake_case   :				int				 =   batch["input_ids"], batch["attention_mask"]
       snake_case   :				Dict				 =   batch["labels"]
       if isinstance(self.model						,     UpperCamelCase__						):
           snake_case   :				Optional[int]				 =   self.model._shift_right(UpperCamelCase__						)
       else:
           snake_case   :				List[str]				 =   shift_tokens_right(UpperCamelCase__						,     UpperCamelCase__						)
       if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
           snake_case   :				List[Any]				 =   decoder_input_ids
           self.save_readable_batch(UpperCamelCase__						)

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

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







   @property
   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       return self.tokenizer.pad_token_id







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Dict:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   self._step(UpperCamelCase__						)

       snake_case   :				List[str]				 =   dict(zip(self.loss_names						,     UpperCamelCase__						)						)
       # tokens per batch
       snake_case   :				Tuple				 =   batch["input_ids"].ne(self.pad						).sum() + batch["labels"].ne(self.pad						).sum()
       snake_case   :				Tuple				 =   batch["input_ids"].shape[0]
       snake_case   :				Dict				 =   batch["input_ids"].eq(self.pad						).sum()
       snake_case   :				Optional[int]				 =   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						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Dict:

       '''simple docstring'''




       return self._generative_step(UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__="val"						)     ->		Dict:

       '''simple docstring'''




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

       '''simple docstring'''




       return calculate_rouge(UpperCamelCase__						,     UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		dict:

       '''simple docstring'''




       snake_case   :				str				 =   time.time()

       # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
       snake_case   :				Optional[int]				 =   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						,     )
       snake_case   :				Tuple				 =   (time.time() - ta) / batch["input_ids"].shape[0]
       snake_case   :				List[str]				 =   self.ids_to_clean_text(UpperCamelCase__						)
       snake_case   :				List[str]				 =   self.ids_to_clean_text(batch["labels"]						)
       snake_case   :				Any				 =   self._step(UpperCamelCase__						)
       snake_case   :				int				 =   dict(zip(self.loss_names						,     UpperCamelCase__						)						)
       snake_case   :				Dict				 =   self.calc_generative_metrics(UpperCamelCase__						,     UpperCamelCase__						)
       snake_case   :				int				 =   np.mean(lmap(UpperCamelCase__						,     UpperCamelCase__						)						)
       base_metrics.update(gen_time=UpperCamelCase__						,     gen_len=UpperCamelCase__						,     preds=UpperCamelCase__						,     target=UpperCamelCase__						,     **UpperCamelCase__						)
       return base_metrics







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		int:

       '''simple docstring'''




       return self._generative_step(UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       return self.validation_epoch_end(UpperCamelCase__						,     prefix="test"						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		SeqaSeqDataset:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   self.n_obs[type_path]
       snake_case   :				Union[str, Any]				 =   self.target_lens[type_path]
       snake_case   :				List[Any]				 =   self.dataset_class(
           self.tokenizer						,     type_path=UpperCamelCase__						,     n_obs=UpperCamelCase__						,     max_target_length=UpperCamelCase__						,     **self.dataset_kwargs						,     )
       return dataset







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = False						)     ->		DataLoader:

       '''simple docstring'''




       snake_case   :				Dict				 =   self.get_dataset(UpperCamelCase__						)

       if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
           snake_case   :				Tuple				 =   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":
           snake_case   :				Tuple				 =   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 			lowerCamelCase		(     self						)     ->		DataLoader:

       '''simple docstring'''




       snake_case   :				Any				 =   self.get_dataloader("train"						,     batch_size=self.hparams.train_batch_size						,     shuffle=UpperCamelCase__						)
       return dataloader







   def 			lowerCamelCase		(     self						)     ->		DataLoader:

       '''simple docstring'''




       return self.get_dataloader("val"						,     batch_size=self.hparams.eval_batch_size						)







   def 			lowerCamelCase		(     self						)     ->		DataLoader:

       '''simple docstring'''




       return self.get_dataloader("test"						,     batch_size=self.hparams.eval_batch_size						)







   @staticmethod
   def 			lowerCamelCase		(     UpperCamelCase__						,     UpperCamelCase__						)     ->		Union[str, Any]:

       '''simple docstring'''




       BaseTransformer.add_model_specific_args(UpperCamelCase__						,     UpperCamelCase__						)
       add_generic_args(UpperCamelCase__						,     UpperCamelCase__						)
       parser.add_argument(
           "--max_source_length"						,     default=1024						,     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=56						,     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=142						,     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=142						,     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=500						,     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 _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Any							  =  '''translation'''
   __UpperCAmelCase			:	List[Any]							  =  ['''loss''']
   __UpperCAmelCase			:	str							  =  ['''bleu''']
   __UpperCAmelCase			:	List[Any]							  =  '''bleu'''







   def __init__(     self						,     UpperCamelCase__						,     **UpperCamelCase__						)     ->		Optional[int]:

       '''simple docstring'''




       super().__init__(UpperCamelCase__						,     **UpperCamelCase__						)
       snake_case   :				Optional[int]				 =   hparams.src_lang
       snake_case   :				Union[str, Any]				 =   hparams.tgt_lang







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		dict:

       '''simple docstring'''




       return calculate_bleu(UpperCamelCase__						,     UpperCamelCase__						)







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int=None			)     ->						SummarizationModule:


    """simple docstring"""





    Path(args.output_dir			).mkdir(exist_ok=lowercase			)
    check_output_dir(lowercase						,							expected_items=3			)

    if model is None:
        if "summarization" in args.task:
            snake_case   :				SummarizationModule				 =   SummarizationModule(lowercase			)
        else:
            snake_case   :				SummarizationModule				 =   TranslationModule(lowercase			)
    snake_case   :				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"			)
    ):
        snake_case   :				int				 =   True  # don't pollute wandb logs unnecessarily
    elif args.logger_name == "wandb":
        from pytorch_lightning.loggers import WandbLogger

        snake_case   :				Optional[int]				 =   os.environ.get("WANDB_PROJECT"						,							lowercase			)
        snake_case   :				str				 =   WandbLogger(name=model.output_dir.name						,							project=lowercase			)

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

        snake_case   :				Union[str, Any]				 =   WandbLogger(name=model.output_dir.name						,							project=F'hf_{dataset}'			)

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

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

    snake_case   :				Union[str, Any]				 =   ""
    snake_case   :				str				 =   sorted(glob.glob(os.path.join(args.output_dir						,							"*.ckpt"			)						,							recursive=lowercase			)			)
    if checkpoints:
        snake_case   :				Optional[int]				 =   checkpoints[-1]
        snake_case   :				Any				 =   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             =   argparse.ArgumentParser()
  __snake_case             =   pl.Trainer.add_argparse_args(parser)
  __snake_case             =   SummarizationModule.add_model_specific_args(parser, os.getcwd())

  __snake_case             =   parser.parse_args()

  main(args)









"""simple docstring"""



import math
import sys







def   __lowerCAmelCase						(						lowercase  :  int			)     ->						int:


    """simple docstring"""





    if number != int(lowercase			):
        raise ValueError("the value of input must be a natural number"			)
    if number < 0:
        raise ValueError("the value of input must not be a negative number"			)
    if number == 0:
        return 1
    snake_case   :				Optional[Any]				 =   [-1] * (number + 1)
    snake_case   :				str				 =   0
    for i in range(1						,							number + 1			):
        snake_case   :				List[Any]				 =   sys.maxsize
        snake_case   :				Union[str, Any]				 =   int(math.sqrt(lowercase			)			)
        for j in range(1						,							root + 1			):
            snake_case   :				List[str]				 =   1 + answers[i - (j**2)]
            snake_case   :				Optional[Any]				 =   min(lowercase						,							lowercase			)
        snake_case   :				Any				 =   answer
    return answers[number]


if __name__ == "__main__":
  import doctest

  doctest.testmod()





