






"""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


UpperCamelCase_	:				Any										= logging.getLogger(__name__)


class      _lowercase (     lowerCAmelCase						):
      _a   :     Tuple								=   '''summarization'''
      _a   :     str								=   ['''loss''']
      _a   :     Optional[int]								=   ROUGE_KEYS
      _a   :     List[str]								=   '''rouge2'''


      def __init__(    self				:	List[Any]							,   a				:	List[Any]							,   **a				:	Union[str, Any]						):





            """simple docstring"""







            if hparams.sortish_sampler and hparams.gpus > 1:
                  __snake_case      :    Optional[int]  			=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						)
            __snake_case      :    Dict  			=Path(self.output_dir						) / '''metrics.json'''
            __snake_case      :    Optional[int]  			=Path(self.output_dir						) / '''hparams.pkl'''
            pickle_save(self.hparams							,   self.hparams_save_path						)
            __snake_case      :    Optional[Any]  			=0
            __snake_case      :    List[Any]  			=defaultdict(a						)
            __snake_case      :    List[str]  			=self.config.model_type
            __snake_case      :    List[str]  			=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      :    int  			={
                '''train''': self.hparams.n_train,
                '''val''': self.hparams.n_val,
                '''test''': self.hparams.n_test,
            }
            __snake_case      :    List[str]  			={k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

            __snake_case      :    List[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()						)

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


      def _UpperCamelCase						(    self				:	int							,   a				:	Dict[str, torch.Tensor]						):





            """simple docstring"""







            __snake_case      :    Optional[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'''						)

            __snake_case      :    str  			=True
            return readable_batch


      def _UpperCamelCase						(    self				:	Union[str, Any]							,   a				:	Optional[Any]							,   **a				:	Optional[Any]						):





            """simple docstring"""







            return self.model(a							,   **a						)


      def _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	List[int]						):





            """simple docstring"""







            __snake_case      :    List[str]  			=self.tokenizer.batch_decode(
                a							,   skip_special_tokens=a							,   clean_up_tokenization_spaces=a						)
            return lmap(str.strip							,   a						)


      def _UpperCamelCase						(    self				:	Union[str, Any]							,   a				:	dict						):





            """simple docstring"""







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

            __snake_case      :    Union[str, Any]  			=self(a							,   attention_mask=a							,   decoder_input_ids=a							,   use_cache=a						)
            __snake_case      :    Tuple  			=outputs['''logits''']
            if self.hparams.label_smoothing == 0:
                  # Same behavior as modeling_bart.py, besides ignoring pad_token_id
                  __snake_case      :    Dict  			=nn.CrossEntropyLoss(ignore_index=a						)

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


      @property
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            return self.tokenizer.pad_token_id


      def _UpperCamelCase						(    self				:	List[str]							,   a				:	List[Any]							,   a				:	int						):





            """simple docstring"""







            __snake_case      :    int  			=self._step(a						)

            __snake_case      :    Any  			=dict(zip(self.loss_names							,   a						)						)
            # tokens per batch
            __snake_case      :    str  			=batch['''input_ids'''].ne(self.pad						).sum() + batch['''labels'''].ne(self.pad						).sum()
            __snake_case      :    Dict  			=batch['''input_ids'''].shape[0]
            __snake_case      :    Optional[int]  			=batch['''input_ids'''].eq(self.pad						).sum()
            __snake_case      :    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 _UpperCamelCase						(    self				:	List[str]							,   a				:	List[Any]							,   a				:	int						):





            """simple docstring"""







            return self._generative_step(a						)


      def _UpperCamelCase						(    self				:	Optional[int]							,   a				:	int							,   a				:	List[str]="val"						):





            """simple docstring"""







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





            """simple docstring"""







            return calculate_rouge(a							,   a						)


      def _UpperCamelCase						(    self				:	Any							,   a				:	dict						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=time.time()

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


      def _UpperCamelCase						(    self				:	Any							,   a				:	Dict							,   a				:	Union[str, Any]						):





            """simple docstring"""







            return self._generative_step(a						)


      def _UpperCamelCase						(    self				:	Optional[int]							,   a				:	Any						):





            """simple docstring"""







            return self.validation_epoch_end(a							,   prefix='''test'''						)


      def _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    List[str]  			=self.n_obs[type_path]
            __snake_case      :    List[str]  			=self.target_lens[type_path]
            __snake_case      :    Tuple  			=self.dataset_class(
                self.tokenizer							,   type_path=a							,   n_obs=a							,   max_target_length=a							,   **self.dataset_kwargs							,   )
            return dataset


      def _UpperCamelCase						(    self				:	List[str]							,   a				:	str							,   a				:	int							,   a				:	bool = False						):





            """simple docstring"""







            __snake_case      :    Union[str, Any]  			=self.get_dataset(a						)

            if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
                  __snake_case      :    Dict  			=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":
                  __snake_case      :    List[str]  			=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 _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    Dict  			=self.get_dataloader('''train'''							,   batch_size=self.hparams.train_batch_size							,   shuffle=a						)
            return dataloader


      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







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







      @staticmethod
      def _UpperCamelCase						(    a				:	str							,   a				:	Tuple						):





            """simple docstring"""







            BaseTransformer.add_model_specific_args(a							,   a						)
            add_generic_args(a							,   a						)
            parser.add_argument(
                '''--max_source_length'''							,   default=1_0_2_4							,   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=5_6							,   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=1_4_2							,   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=1_4_2							,   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=5_0_0							,   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      _lowercase (     lowerCAmelCase						):
      _a   :     Optional[Any]								=   '''translation'''
      _a   :     int								=   ['''loss''']
      _a   :     Any								=   ['''bleu''']
      _a   :     str								=   '''bleu'''


      def __init__(    self				:	Dict							,   a				:	Any							,   **a				:	Tuple						):





            """simple docstring"""







            super().__init__(a							,   **a						)
            __snake_case      :    List[Any]  			=hparams.src_lang
            __snake_case      :    List[Any]  			=hparams.tgt_lang







      def _UpperCamelCase						(    self				:	Optional[int]							,   a				:	List[Any]							,   a				:	Tuple						):





            """simple docstring"""







            return calculate_bleu(a							,   a						)



def     __lowercase					(    a :				int       ,		a :				List[Any]=None )					->					SummarizationModule:
      Path(args.output_dir ).mkdir(exist_ok=a )
      check_output_dir(a       ,		expected_items=3 )

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

            __snake_case      :    List[str]  			=os.environ.get('''WANDB_PROJECT'''       ,		a )
            __snake_case      :    Any  			=WandbLogger(name=model.output_dir.name       ,		project=a )

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

            __snake_case      :    List[Any]  			=WandbLogger(name=model.output_dir.name       ,		project=f'''hf_{dataset}''' )

      if args.early_stopping_patience >= 0:
            __snake_case      :    List[Any]  			=get_early_stopping_callback(model.val_metric       ,		args.early_stopping_patience )
      else:
            __snake_case      :    str  			=False

      __snake_case      :    List[Any]  			=args.val_metric == '''loss'''
      __snake_case      :    pl.Trainer  			=generic_train(
          a       ,		a       ,		logging_callback=SeqaSeqLoggingCallback()       ,		checkpoint_callback=get_checkpoint_callback(
              args.output_dir       ,		model.val_metric       ,		args.save_top_k       ,		a )       ,		early_stopping_callback=a       ,		logger=a       ,		)
      pickle_save(model.hparams       ,		model.output_dir / '''hparams.pkl''' )
      if not args.do_predict:
            return model

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

      UpperCamelCase_	:				Tuple										= parser.parse_args()

      main(args)










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase_	:				Union[str, Any]										= {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
    import sys

    UpperCamelCase_	:				List[Any]										= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


