






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






class 		snake_case						(						UpperCAmelCase ):
    __magic_name__					  =  '''summarization'''
    __magic_name__					  =  ['''loss''']
    __magic_name__					  =  ROUGE_KEYS
    __magic_name__					  =  '''rouge2'''





    def __init__(							self			:      Union[str, Any]       ,	A			:      Tuple       ,	**A			:      Dict    ):


      '''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  :    List[Any]       =     Path(self.output_dir    ) / 'metrics.json'
      a  :    List[Any]       =     Path(self.output_dir    ) / 'hparams.pkl'
      pickle_save(self.hparams       ,	self.hparams_save_path    )
      a  :    Dict       =     0
      a  :    Optional[Any]       =     defaultdict(A    )
      a  :    Tuple       =     self.config.model_type
      a  :    Dict       =     self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

      a  :    dict       =     {
          "data_dir": self.hparams.data_dir,
          "max_source_length": self.hparams.max_source_length,
          "prefix": self.model.config.prefix or "",
      }
      a  :    Optional[int]       =     {
          'train': self.hparams.n_train,
          'val': self.hparams.n_val,
          'test': self.hparams.n_test,
      }
      a  :    List[Any]       =     {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  :    Tuple       =     get_git_info()['repo_sha']
      a  :    Optional[int]       =     hparams.num_workers
      a  :    List[Any]       =     None  # default to config
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer       ,	A    ):
        a  :    str       =     self.tokenizer.lang_code_to_id[hparams.tgt_lang]
        a  :    List[str]       =     self.decoder_start_token_id
      a  :    str       =     (
          SeqaSeqDataset if hasattr(self.tokenizer       ,	'prepare_seq2seq_batch'    ) else LegacySeqaSeqDataset
      )
      a  :    int       =     False
      a  :    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:
        a  :    Union[str, Any]       =     self.hparams.eval_max_gen_length
      else:
        a  :    Optional[Any]       =     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        lowerCamelCase__   (							self			:      Optional[int]       ,	A			:      Dict[str, torch.Tensor]    ):


      '''simple docstring'''




      a  :    Dict       =     {
          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        lowerCamelCase__   (							self			:      Tuple       ,	A			:      List[str]       ,	**A			:      Optional[Any]    ):


      '''simple docstring'''




      return self.model(A       ,	**A    )





    def        lowerCamelCase__   (							self			:      int       ,	A			:      List[int]    ):


      '''simple docstring'''




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





    def        lowerCamelCase__   (							self			:      Tuple       ,	A			:      dict    ):


      '''simple docstring'''




      a  :    List[Any]       =     self.tokenizer.pad_token_id
      a,  a  :    Tuple       =     batch['input_ids'], batch['attention_mask']
      a  :    List[Any]       =     batch['labels']
      if isinstance(self.model       ,	A    ):
        a  :    Optional[Any]       =     self.model._shift_right(A    )
      else:
        a  :    List[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  :    Any       =     self(A       ,	attention_mask=A       ,	decoder_input_ids=A       ,	use_cache=A    )
      a  :    Any       =     outputs['logits']
      if self.hparams.label_smoothing == 0:
        # Same behavior as modeling_bart.py, besides ignoring pad_token_id
        a  :    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  :    Any       =     nn.functional.log_softmax(A       ,	dim=-1    )
        a,  a  :    Any       =     label_smoothed_nll_loss(
            A       ,	A       ,	self.hparams.label_smoothing       ,	ignore_index=A    )
      return (loss,)





    @property
    def        lowerCamelCase__   (							self			:      Tuple    ):


      '''simple docstring'''




      return self.tokenizer.pad_token_id





    def        lowerCamelCase__   (							self			:      Tuple       ,	A			:      Dict       ,	A			:      Any    ):


      '''simple docstring'''




      a  :    List[str]       =     self._step(A    )

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


      '''simple docstring'''




      return self._generative_step(A    )





    def        lowerCamelCase__   (							self			:      Optional[Any]       ,	A			:      List[Any]       ,	A			:      Tuple="val"    ):


      '''simple docstring'''




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


      '''simple docstring'''




      return calculate_rouge(A       ,	A    )





    def        lowerCamelCase__   (							self			:      Optional[int]       ,	A			:      dict    ):


      '''simple docstring'''




      a  :    List[Any]       =     time.time()

      # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
      a  :    Dict       =     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[str]       =     (time.time() - ta) / batch['input_ids'].shape[0]
      a  :    List[str]       =     self.ids_to_clean_text(A    )
      a  :    List[str]       =     self.ids_to_clean_text(batch['labels']    )
      a  :    Union[str, Any]       =     self._step(A    )
      a  :    str       =     dict(zip(self.loss_names       ,	A    )    )
      a  :    Dict       =     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        lowerCamelCase__   (							self			:      str       ,	A			:      Any       ,	A			:      Dict    ):


      '''simple docstring'''




      return self._generative_step(A    )





    def        lowerCamelCase__   (							self			:      Tuple       ,	A			:      str    ):


      '''simple docstring'''




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





    def        lowerCamelCase__   (							self			:      Union[str, Any]       ,	A			:      List[str]    ):


      '''simple docstring'''




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





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


      '''simple docstring'''




      a  :    Optional[int]       =     self.get_dataset(A    )

      if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
        a  :    Tuple       =     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  :    Tuple       =     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        lowerCamelCase__   (							self			:      str    ):


      '''simple docstring'''




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





    def        lowerCamelCase__   (							self			:      Optional[int]    ):


      '''simple docstring'''




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





    def        lowerCamelCase__   (							self			:      List[Any]    ):


      '''simple docstring'''




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





    @staticmethod
    def        lowerCamelCase__   (							A			:      Union[str, Any]       ,	A			:      str    ):


      '''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 		snake_case						(						UpperCAmelCase ):
    __magic_name__					  =  '''translation'''
    __magic_name__					  =  ['''loss''']
    __magic_name__					  =  ['''bleu''']
    __magic_name__					  =  '''bleu'''





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


      '''simple docstring'''




      super().__init__(A       ,	**A    )
      a  :    Dict       =     hparams.src_lang
      a  :    Optional[int]       =     hparams.tgt_lang





    def        lowerCamelCase__   (							self			:      Tuple       ,	A			:      int       ,	A			:      List[Any]    ):


      '''simple docstring'''




      return calculate_bleu(A       ,	A    )




def     snake_case						(A_   :Tuple			,    A_   :List[Any]=None   ):







  '''simple docstring'''

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

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

    a  :    str       =     os.environ.get('WANDB_PROJECT'			,    A_   )
    a  :    Any       =     WandbLogger(name=model.output_dir.name			,    project=A_   )

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

    a  :    Optional[int]       =     WandbLogger(name=model.output_dir.name			,    project=f'''hf_{dataset}'''   )

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

  a  :    List[str]       =     args.val_metric == 'loss'
  a  :    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

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

   _UpperCamelCase       :					Tuple									=		parser.parse_args()

   main(args)












"""simple docstring"""


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase       :					Optional[Any]									=		{
    'configuration_informer': [
        'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase       :					Tuple									=		[
       'INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'InformerForPrediction',
       'InformerModel',
       'InformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_informer import (
          INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          InformerForPrediction,
          InformerModel,
          InformerPreTrainedModel,
      )

else:
   import sys

   _UpperCamelCase       :					List[str]									=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




