


'''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


__magic_name__			=      logging.getLogger(__name__)



class 	__lowerCAmelCase		(							__lowercase    ):


  '''simple docstring'''







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






  def __init__(							self :       str	,_a :       int	,**_a :       Optional[int] ):

        '''simple docstring'''




        if hparams.sortish_sampler and hparams.gpus > 1:
              A_						:  Any          =				False
        elif hparams.max_tokens_per_batch is not None:
              if hparams.gpus > 1:
                    raise NotImplementedError("""Dynamic Batch size does not work for multi-gpu training""" )
              if hparams.sortish_sampler:
                    raise ValueError("""--sortish_sampler and --max_tokens_per_batch may not be used simultaneously""" )

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

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

        A_						:  Optional[int]          =				{
            """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() )

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






  def        _a							(							self :       str	,_a :       Dict[str, torch.Tensor] ):

        '''simple docstring'''




        A_						:  List[Any]          =				{
            k: self.tokenizer.batch_decode(v.tolist() ) if """mask""" not in k else v.shape for k, v in batch.items()
        }
        save_json(_A	,Path(self.output_dir ) / """text_batch.json""" )
        save_json({k: v.tolist() for k, v in batch.items()}	,Path(self.output_dir ) / """tok_batch.json""" )

        A_						:  Tuple          =				True
        return readable_batch






  def        _a							(							self :       Tuple	,_a :       Tuple	,**_a :       Dict ):

        '''simple docstring'''




        return self.model(_A	,**_A )






  def        _a							(							self :       Any	,_a :       List[int] ):

        '''simple docstring'''




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






  def        _a							(							self :       List[str]	,_a :       dict ):

        '''simple docstring'''




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

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

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






  @property
  def        _a							(							self :       Optional[Any] ):

        '''simple docstring'''




        return self.tokenizer.pad_token_id






  def        _a							(							self :       Optional[int]	,_a :       str	,_a :       Optional[Any] ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				self._step(_A )

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






  def        _a							(							self :       str	,_a :       List[str]	,_a :       Tuple ):

        '''simple docstring'''




        return self._generative_step(_A )






  def        _a							(							self :       Dict	,_a :       Optional[Any]	,_a :       Optional[int]="val" ):

        '''simple docstring'''




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






  def        _a							(							self :       Any	,_a :       str	,_a :       Dict ):

        '''simple docstring'''




        return calculate_rouge(_A	,_A )






  def        _a							(							self :       Tuple	,_a :       dict ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				time.time()

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






  def        _a							(							self :       int	,_a :       Tuple	,_a :       Tuple ):

        '''simple docstring'''




        return self._generative_step(_A )






  def        _a							(							self :       Any	,_a :       List[Any] ):

        '''simple docstring'''




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






  def        _a							(							self :       Any	,_a :       Union[str, Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				self.n_obs[type_path]
        A_						:  Any          =				self.target_lens[type_path]
        A_						:  List[Any]          =				self.dataset_class(
            self.tokenizer	,type_path=_A	,n_obs=_A	,max_target_length=_A	,**self.dataset_kwargs	,)
        return dataset






  def        _a							(							self :       Any	,_a :       str	,_a :       int	,_a :       bool = False ):

        '''simple docstring'''




        A_						:  Union[str, Any]          =				self.get_dataset(_A )

        if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
              A_						:  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":
              A_						:  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        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  Dict          =				self.get_dataloader("""train"""	,batch_size=self.hparams.train_batch_size	,shuffle=_A )
        return dataloader






  def        _a							(							self :       int ):

        '''simple docstring'''




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






  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




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






  @staticmethod
  def        _a							(							_a :       int	,_a :       List[Any] ):

        '''simple docstring'''




        BaseTransformer.add_model_specific_args(_A	,_A )
        add_generic_args(_A	,_A )
        parser.add_argument(
            """--max_source_length"""	,default=1024	,type=_A	,help=(
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )	,)
        parser.add_argument(
            """--max_target_length"""	,default=56	,type=_A	,help=(
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )	,)
        parser.add_argument(
            """--val_max_target_length"""	,default=142	,type=_A	,help=(
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )	,)
        parser.add_argument(
            """--test_max_target_length"""	,default=142	,type=_A	,help=(
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )	,)
        parser.add_argument("""--freeze_encoder"""	,action="""store_true""" )
        parser.add_argument("""--freeze_embeds"""	,action="""store_true""" )
        parser.add_argument("""--sortish_sampler"""	,action="""store_true"""	,default=_A )
        parser.add_argument("""--overwrite_output_dir"""	,action="""store_true"""	,default=_A )
        parser.add_argument("""--max_tokens_per_batch"""	,type=_A	,default=_A )
        parser.add_argument("""--logger_name"""	,type=_A	,choices=["""default""", """wandb""", """wandb_shared"""]	,default="""default""" )
        parser.add_argument("""--n_train"""	,type=_A	,default=-1	,required=_A	,help="""# examples. -1 means use all.""" )
        parser.add_argument("""--n_val"""	,type=_A	,default=500	,required=_A	,help="""# examples. -1 means use all.""" )
        parser.add_argument("""--n_test"""	,type=_A	,default=-1	,required=_A	,help="""# examples. -1 means use all.""" )
        parser.add_argument(
            """--task"""	,type=_A	,default="""summarization"""	,required=_A	,help="""# examples. -1 means use all.""" )
        parser.add_argument("""--label_smoothing"""	,type=_A	,default=0.0	,required=_A )
        parser.add_argument("""--src_lang"""	,type=_A	,default=""""""	,required=_A )
        parser.add_argument("""--tgt_lang"""	,type=_A	,default=""""""	,required=_A )
        parser.add_argument("""--eval_beams"""	,type=_A	,default=_A	,required=_A )
        parser.add_argument(
            """--val_metric"""	,type=_A	,default=_A	,required=_A	,choices=["""bleu""", """rouge2""", """loss""", None] )
        parser.add_argument("""--eval_max_gen_length"""	,type=_A	,default=_A	,help="""never generate more than n tokens""" )
        parser.add_argument("""--save_top_k"""	,type=_A	,default=1	,required=_A	,help="""How many checkpoints to save""" )
        parser.add_argument(
            """--early_stopping_patience"""	,type=_A	,default=-1	,required=_A	,help=(
                """-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
                """ val_check_interval will effect it."""
            )	,)
        return parser







class 	__lowerCAmelCase		(							__lowercase    ):


  '''simple docstring'''







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






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

        '''simple docstring'''




        super().__init__(_A	,**_A )
        A_						:  Optional[Any]          =				hparams.src_lang
        A_						:  str          =				hparams.tgt_lang






  def        _a							(							self :       List[Any]	,_a :       Dict	,_a :       List[str] ):

        '''simple docstring'''




        return calculate_bleu(_A	,_A )







def      lowerCamelCase					(      lowerCamelCase  :      Dict							,						lowerCamelCase  :      int=None):
      Path(args.output_dir).mkdir(exist_ok=__snake_case)
      check_output_dir(__snake_case							,						expected_items=3)

      if model is None:
            if "summarization" in args.task:
                  A_						:  List[str]          =				SummarizationModule(__snake_case)
            else:
                  A_						:  str          =				TranslationModule(__snake_case)
      A_						:  Union[str, Any]          =				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""")
      ):
            A_						:  int          =				True  # don't pollute wandb logs unnecessarily
      elif args.logger_name == "wandb":
            from pytorch_lightning.loggers import WandbLogger

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

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

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

      if args.early_stopping_patience >= 0:
            A_						:  Optional[Any]          =				get_early_stopping_callback(model.val_metric							,						args.early_stopping_patience)
      else:
            A_						:  Tuple          =				False

      A_						:  Optional[Any]          =				args.val_metric == """loss"""
      A_						:  Optional[int]          =				generic_train(
          __snake_case							,						__snake_case							,						logging_callback=SeqaSeqLoggingCallback()							,						checkpoint_callback=get_checkpoint_callback(
              args.output_dir							,						model.val_metric							,						args.save_top_k							,						__snake_case)							,						early_stopping_callback=__snake_case							,						logger=__snake_case							,						)
      pickle_save(model.hparams							,						model.output_dir / """hparams.pkl""")
      if not args.do_predict:
            return model

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

     __magic_name__			=      parser.parse_args()

     main(args)




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__			=      {}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['NllbTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__			=      ['NllbTokenizerFast']


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb import NllbTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb_fast import NllbTokenizerFast

else:
     import sys

     __magic_name__			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
