
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


lowercase__          =logging.getLogger(__name__)



class 		UpperCamelCase__  (	__SCREAMING_SNAKE_CASE   ):
   _SCREAMING_SNAKE_CASE				:							List[Any]     	=				"summarization"
   _SCREAMING_SNAKE_CASE				:							Any     	=				["loss"]
   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				ROUGE_KEYS
   _SCREAMING_SNAKE_CASE				:							List[Any]     	=				"rouge2"
   def __init__(self			: Optional[Any] ,    snake_case_			: str ,    **snake_case_			: List[str]       ):







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

    __a			:  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()       )

    __a			:  int		    =  get_git_info()['''repo_sha''']
    __a			:  Optional[Any]		    =  hparams.num_workers
    __a			:  Dict		    =  None  # default to config
    if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,    UpperCamelCase__       ):
     __a			:  List[Any]		    =  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			:  Union[str, Any]		    =  False
    __a			:  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:
     __a			:  Any		    =  self.hparams.eval_max_gen_length
    else:
     __a			:  List[str]		    =  self.model.config.max_length
    __a			:  List[Any]		    =  self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
   def 				lowerCAmelCase      (self			: Optional[int] ,    snake_case_			: Dict[str, torch.Tensor]       ):
    __a			:  List[str]		    =  {
        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'''       )

    __a			:  Dict		    =  True
    return readable_batch
   def 				lowerCAmelCase      (self			: Tuple ,    snake_case_			: List[Any] ,    **snake_case_			: Optional[int]       ):
    return self.model(UpperCamelCase__ ,    **UpperCamelCase__       )
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: List[int]       ):
    __a			:  Tuple		    =  self.tokenizer.batch_decode(
        UpperCamelCase__ ,    skip_special_tokens=UpperCamelCase__ ,    clean_up_tokenization_spaces=UpperCamelCase__       )
    return lmap(str.strip ,    UpperCamelCase__       )
   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: dict       ):
    __a			:  Any		    =  self.tokenizer.pad_token_id
    __a			:  Optional[int]		    =  batch['''input_ids'''], batch['''attention_mask''']
    __a			:  Optional[int]		    =  batch['''labels''']
    if isinstance(self.model ,    UpperCamelCase__       ):
     __a			:  Optional[Any]		    =  self.model._shift_right(UpperCamelCase__       )
    else:
     __a			:  Dict		    =  shift_tokens_right(UpperCamelCase__ ,    UpperCamelCase__       )
    if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
     __a			:  Any		    =  decoder_input_ids
     self.save_readable_batch(UpperCamelCase__       )

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

     assert lm_logits.shape[-1] == self.vocab_size
     __a			:  Tuple		    =  ce_loss_fct(lm_logits.view(-1 ,    lm_logits.shape[-1]       ) ,    tgt_ids.view(-1       )       )
    else:
     __a			:  Union[str, Any]		    =  nn.functional.log_softmax(UpperCamelCase__ ,    dim=-1       )
     __a			:  Tuple		    =  label_smoothed_nll_loss(
         UpperCamelCase__ ,    UpperCamelCase__ ,    self.hparams.label_smoothing ,    ignore_index=UpperCamelCase__       )
    return (loss,)
   @property
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    return self.tokenizer.pad_token_id
   def 				lowerCAmelCase      (self			: Dict ,    snake_case_			: str ,    snake_case_			: List[Any]       ):
    __a			:  int		    =  self._step(UpperCamelCase__       )

    __a			:  List[Any]		    =  dict(zip(self.loss_names ,    UpperCamelCase__       )       )
    # tokens per batch
    __a			:  int		    =  batch['''input_ids'''].ne(self.pad       ).sum() + batch['''labels'''].ne(self.pad       ).sum()
    __a			:  Dict		    =  batch['''input_ids'''].shape[0]
    __a			:  Tuple		    =  batch['''input_ids'''].eq(self.pad       ).sum()
    __a			:  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			: Dict ,    snake_case_			: Any ,    snake_case_			: str       ):
    return self._generative_step(UpperCamelCase__       )
   def 				lowerCAmelCase      (self			: List[Any] ,    snake_case_			: List[str] ,    snake_case_			: str="val"       ):
    self.step_count += 1
    __a			:  Dict		    =  {k: torch.stack([x[k] for x in outputs]       ).mean() for k in self.loss_names}
    __a			:  Optional[int]		    =  losses['''loss''']
    __a			:  str		    =  {
        k: np.array([x[k] for x in outputs]       ).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
    }
    __a			:  List[str]		    =  (
        generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
    )
    __a			:  torch.FloatTensor		    =  torch.tensor(UpperCamelCase__       ).type_as(UpperCamelCase__       )
    generative_metrics.update({k: v.item() for k, v in losses.items()}       )
    losses.update(UpperCamelCase__       )
    __a			:  Union[str, Any]		    =  {f"{prefix}_avg_{k}": x for k, x in losses.items()}
    __a			:  Dict		    =  self.step_count
    self.metrics[prefix].append(UpperCamelCase__       )  # 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 				lowerCAmelCase      (self			: Optional[Any] ,    snake_case_			: List[str] ,    snake_case_			: List[str]       ):
    return calculate_rouge(UpperCamelCase__ ,    UpperCamelCase__       )
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: dict       ):
    __a			:  Dict		    =  time.time()

    # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
    __a			:  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 ,    )
    __a			:  Union[str, Any]		    =  (time.time() - ta) / batch['''input_ids'''].shape[0]
    __a			:  List[str]		    =  self.ids_to_clean_text(UpperCamelCase__       )
    __a			:  List[str]		    =  self.ids_to_clean_text(batch['''labels''']       )
    __a			:  str		    =  self._step(UpperCamelCase__       )
    __a			:  Optional[int]		    =  dict(zip(self.loss_names ,    UpperCamelCase__       )       )
    __a			:  Dict		    =  self.calc_generative_metrics(UpperCamelCase__ ,    UpperCamelCase__       )
    __a			:  Dict		    =  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			: str ,    snake_case_			: int ,    snake_case_			: Optional[Any]       ):
    return self._generative_step(UpperCamelCase__       )
   def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: int       ):
    return self.validation_epoch_end(UpperCamelCase__ ,    prefix='''test'''       )
   def 				lowerCAmelCase      (self			: List[str] ,    snake_case_			: int       ):
    __a			:  str		    =  self.n_obs[type_path]
    __a			:  Tuple		    =  self.target_lens[type_path]
    __a			:  Any		    =  self.dataset_class(
        self.tokenizer ,    type_path=UpperCamelCase__ ,    n_obs=UpperCamelCase__ ,    max_target_length=UpperCamelCase__ ,    **self.dataset_kwargs ,    )
    return dataset
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: str ,    snake_case_			: int ,    snake_case_			: bool = False       ):
    __a			:  Tuple		    =  self.get_dataset(UpperCamelCase__       )

    if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
     __a			:  Dict		    =  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":
     __a			:  Union[str, Any]		    =  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			: str       ):
    __a			:  List[Any]		    =  self.get_dataloader('''train''' ,    batch_size=self.hparams.train_batch_size ,    shuffle=UpperCamelCase__       )
    return dataloader
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    return self.get_dataloader('''val''' ,    batch_size=self.hparams.eval_batch_size       )
   def 				lowerCAmelCase      (self			: Any       ):
    return self.get_dataloader('''test''' ,    batch_size=self.hparams.eval_batch_size       )







   @staticmethod
   def 				lowerCAmelCase      (snake_case_			: List[str] ,    snake_case_			: Optional[Any]       ):
    BaseTransformer.add_model_specific_args(UpperCamelCase__ ,    UpperCamelCase__       )
    add_generic_args(UpperCamelCase__ ,    UpperCamelCase__       )
    parser.add_argument(
        '''--max_source_length''' ,    default=1_0_2_4 ,    type=UpperCamelCase__ ,    help=(
            '''The maximum total input sequence length after tokenization. Sequences longer '''
            '''than this will be truncated, sequences shorter will be padded.'''
        ) ,    )
    parser.add_argument(
        '''--max_target_length''' ,    default=5_6 ,    type=UpperCamelCase__ ,    help=(
            '''The maximum total input sequence length after tokenization. Sequences longer '''
            '''than this will be truncated, sequences shorter will be padded.'''
        ) ,    )
    parser.add_argument(
        '''--val_max_target_length''' ,    default=1_4_2 ,    type=UpperCamelCase__ ,    help=(
            '''The maximum total input sequence length after tokenization. Sequences longer '''
            '''than this will be truncated, sequences shorter will be padded.'''
        ) ,    )
    parser.add_argument(
        '''--test_max_target_length''' ,    default=1_4_2 ,    type=UpperCamelCase__ ,    help=(
            '''The maximum total input sequence length after tokenization. Sequences longer '''
            '''than this will be truncated, sequences shorter will be padded.'''
        ) ,    )
    parser.add_argument('''--freeze_encoder''' ,    action='''store_true'''       )
    parser.add_argument('''--freeze_embeds''' ,    action='''store_true'''       )
    parser.add_argument('''--sortish_sampler''' ,    action='''store_true''' ,    default=UpperCamelCase__       )
    parser.add_argument('''--overwrite_output_dir''' ,    action='''store_true''' ,    default=UpperCamelCase__       )
    parser.add_argument('''--max_tokens_per_batch''' ,    type=UpperCamelCase__ ,    default=UpperCamelCase__       )
    parser.add_argument('''--logger_name''' ,    type=UpperCamelCase__ ,    choices=['''default''', '''wandb''', '''wandb_shared'''] ,    default='''default'''       )
    parser.add_argument('''--n_train''' ,    type=UpperCamelCase__ ,    default=-1 ,    required=UpperCamelCase__ ,    help='''# examples. -1 means use all.'''       )
    parser.add_argument('''--n_val''' ,    type=UpperCamelCase__ ,    default=5_0_0 ,    required=UpperCamelCase__ ,    help='''# examples. -1 means use all.'''       )
    parser.add_argument('''--n_test''' ,    type=UpperCamelCase__ ,    default=-1 ,    required=UpperCamelCase__ ,    help='''# examples. -1 means use all.'''       )
    parser.add_argument(
        '''--task''' ,    type=UpperCamelCase__ ,    default='''summarization''' ,    required=UpperCamelCase__ ,    help='''# examples. -1 means use all.'''       )
    parser.add_argument('''--label_smoothing''' ,    type=UpperCamelCase__ ,    default=0.0 ,    required=UpperCamelCase__       )
    parser.add_argument('''--src_lang''' ,    type=UpperCamelCase__ ,    default='''''' ,    required=UpperCamelCase__       )
    parser.add_argument('''--tgt_lang''' ,    type=UpperCamelCase__ ,    default='''''' ,    required=UpperCamelCase__       )
    parser.add_argument('''--eval_beams''' ,    type=UpperCamelCase__ ,    default=UpperCamelCase__ ,    required=UpperCamelCase__       )
    parser.add_argument(
        '''--val_metric''' ,    type=UpperCamelCase__ ,    default=UpperCamelCase__ ,    required=UpperCamelCase__ ,    choices=['''bleu''', '''rouge2''', '''loss''', None]       )
    parser.add_argument('''--eval_max_gen_length''' ,    type=UpperCamelCase__ ,    default=UpperCamelCase__ ,    help='''never generate more than n tokens'''       )
    parser.add_argument('''--save_top_k''' ,    type=UpperCamelCase__ ,    default=1 ,    required=UpperCamelCase__ ,    help='''How many checkpoints to save'''       )
    parser.add_argument(
        '''--early_stopping_patience''' ,    type=UpperCamelCase__ ,    default=-1 ,    required=UpperCamelCase__ ,    help=(
            '''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
            ''' val_check_interval will effect it.'''
        ) ,    )
    return parser





class 		UpperCamelCase__  (	__SCREAMING_SNAKE_CASE   ):
   _SCREAMING_SNAKE_CASE				:							List[Any]     	=				"translation"
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				["loss"]
   _SCREAMING_SNAKE_CASE				:							str     	=				["bleu"]
   _SCREAMING_SNAKE_CASE				:							str     	=				"bleu"
   def __init__(self			: str ,    snake_case_			: int ,    **snake_case_			: Union[str, Any]       ):
    super().__init__(UpperCamelCase__ ,    **UpperCamelCase__       )
    __a			:  int		    =  hparams.src_lang
    __a			:  List[str]		    =  hparams.tgt_lang







   def 				lowerCAmelCase      (self			: Tuple ,    snake_case_			: Union[str, Any] ,    snake_case_			: Optional[int]       ):
    return calculate_bleu(UpperCamelCase__ ,    UpperCamelCase__       )





def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Union[str, Any]=None  ):
 Path(args.output_dir  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__  )
 check_output_dir(SCREAMING_SNAKE_CASE__  ,       expected_items=3  )

 if model is None:
  if "summarization" in args.task:
   __a			:  SummarizationModule		    =  SummarizationModule(SCREAMING_SNAKE_CASE__  )
  else:
   __a			:  SummarizationModule		    =  TranslationModule(SCREAMING_SNAKE_CASE__  )
 __a			:  List[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'''  )
 ):
  __a			:  List[Any]		    =  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'''  ,       SCREAMING_SNAKE_CASE__  )
  __a			:  Any		    =  WandbLogger(name=model.output_dir.name  ,       project=SCREAMING_SNAKE_CASE__  )

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

  __a			:  Tuple		    =  WandbLogger(name=model.output_dir.name  ,       project=f"hf_{dataset}"  )

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

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

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

      lowercase__          =parser.parse_args()

      main(args)


from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
