





import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


snake_case 				=       logging.getLogger(__name__)





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : List[Any]      =  '''summarization'''
       SCREAMING_SNAKE_CASE_      : List[Any]      =  ['''loss''']
       SCREAMING_SNAKE_CASE_      : Any      =  ROUGE_KEYS
       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  '''rouge2'''







       def __init__(    self	:	Dict						,__A	:	Tuple						,**__A	:	str  )						->					Optional[Any]:
         if hparams.sortish_sampler and hparams.gpus > 1:
           _lowercase							= 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  )
         _lowercase							= Path(self.output_dir  ) / 'metrics.json'
         _lowercase							= Path(self.output_dir  ) / 'hparams.pkl'
         pickle_save(self.hparams						,self.hparams_save_path  )
         _lowercase							= 0
         _lowercase							= defaultdict(__A  )
         _lowercase							= self.config.model_type
         _lowercase							= self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

         _lowercase							= {
             "data_dir": self.hparams.data_dir,
             "max_source_length": self.hparams.max_source_length,
             "prefix": self.model.config.prefix or "",
         }
         _lowercase							= {
             'train': self.hparams.n_train,
             'val': self.hparams.n_val,
             'test': self.hparams.n_test,
         }
         _lowercase							= {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

         _lowercase							= {
             '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()  )

         _lowercase							= get_git_info()['repo_sha']
         _lowercase							= hparams.num_workers
         _lowercase							= None  # default to config
         if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer						,__A  ):
           _lowercase							= self.tokenizer.lang_code_to_id[hparams.tgt_lang]
           _lowercase							= self.decoder_start_token_id
         _lowercase							= (
             SeqaSeqDataset if hasattr(self.tokenizer						,'prepare_seq2seq_batch'  ) else LegacySeqaSeqDataset
         )
         _lowercase							= False
         _lowercase							= 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:
           _lowercase							= self.hparams.eval_max_gen_length
         else:
           _lowercase							= self.model.config.max_length
         _lowercase							= self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Dict[str, torch.Tensor]  )						->					Dict[str, List[str]]:
         _lowercase							= {
             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'  )

         _lowercase							= True
         return readable_batch







       def    __UpperCAmelCase (    self	:	Any						,__A	:	Dict						,**__A	:	Optional[int]  )						->					Optional[int]:
         return self.model(__A						,**__A  )







       def    __UpperCAmelCase (    self	:	List[str]						,__A	:	List[int]  )						->					Union[str, Any]:
         _lowercase							= self.tokenizer.batch_decode(
             __A						,skip_special_tokens=__A						,clean_up_tokenization_spaces=__A  )
         return lmap(str.strip						,__A  )







       def    __UpperCAmelCase (    self	:	Any						,__A	:	dict  )						->					Tuple:
         _lowercase							= self.tokenizer.pad_token_id
         _lowercase						,		_lowercase							= batch['input_ids'], batch['attention_mask']
         _lowercase							= batch['labels']
         if isinstance(self.model						,__A  ):
           _lowercase							= self.model._shift_right(__A  )
         else:
           _lowercase							= shift_tokens_right(__A						,__A  )
         if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
           _lowercase							= decoder_input_ids
           self.save_readable_batch(__A  )

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

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







       @property
       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					int:
         return self.tokenizer.pad_token_id







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	Optional[Any]						,__A	:	Tuple  )						->					Dict:
         _lowercase							= self._step(__A  )

         _lowercase							= dict(zip(self.loss_names						,__A  )  )
         # tokens per batch
         _lowercase							= batch['input_ids'].ne(self.pad  ).sum() + batch['labels'].ne(self.pad  ).sum()
         _lowercase							= batch['input_ids'].shape[0]
         _lowercase							= batch['input_ids'].eq(self.pad  ).sum()
         _lowercase							= 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	:	int						,__A	:	Any						,__A	:	Tuple  )						->					Dict:
         return self._generative_step(__A  )







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







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	dict  )						->					dict:
         _lowercase							= time.time()

         # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
         _lowercase							= 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						,)
         _lowercase							= (time.time() - ta) / batch['input_ids'].shape[0]
         _lowercase							= self.ids_to_clean_text(__A  )
         _lowercase							= self.ids_to_clean_text(batch['labels']  )
         _lowercase							= self._step(__A  )
         _lowercase							= dict(zip(self.loss_names						,__A  )  )
         _lowercase							= self.calc_generative_metrics(__A						,__A  )
         _lowercase							= 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	:	Dict						,__A	:	str						,__A	:	Dict  )						->					Tuple:
         return self._generative_step(__A  )







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	Tuple  )						->					Optional[int]:
         return self.validation_epoch_end(__A						,prefix='test'  )







       def    __UpperCAmelCase (    self	:	str						,__A	:	Union[str, Any]  )						->					SeqaSeqDataset:
         _lowercase							= self.n_obs[type_path]
         _lowercase							= self.target_lens[type_path]
         _lowercase							= self.dataset_class(
             self.tokenizer						,type_path=__A						,n_obs=__A						,max_target_length=__A						,**self.dataset_kwargs						,)
         return dataset







       def    __UpperCAmelCase (    self	:	int						,__A	:	str						,__A	:	int						,__A	:	bool = False  )						->					DataLoader:
         _lowercase							= self.get_dataset(__A  )

         if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
           _lowercase							= 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":
           _lowercase							= 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]  )						->					DataLoader:
         _lowercase							= self.get_dataloader('train'						,batch_size=self.hparams.train_batch_size						,shuffle=__A  )
         return dataloader







       def    __UpperCAmelCase (    self	:	Dict  )						->					DataLoader:
         return self.get_dataloader('val'						,batch_size=self.hparams.eval_batch_size  )







       def    __UpperCAmelCase (    self	:	List[str]  )						->					DataLoader:
         return self.get_dataloader('test'						,batch_size=self.hparams.eval_batch_size  )







       @staticmethod
       def    __UpperCAmelCase (    __A	:	str						,__A	:	List[Any]  )						->					Dict:
         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 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  '''translation'''
       SCREAMING_SNAKE_CASE_      : Optional[Any]      =  ['''loss''']
       SCREAMING_SNAKE_CASE_      : Any      =  ['''bleu''']
       SCREAMING_SNAKE_CASE_      : str      =  '''bleu'''







       def __init__(    self	:	Tuple						,__A	:	Union[str, Any]						,**__A	:	List[str]  )						->					List[str]:
         super().__init__(__A						,**__A  )
         _lowercase							= hparams.src_lang
         _lowercase							= hparams.tgt_lang







       def    __UpperCAmelCase (    self	:	int						,__A	:	str						,__A	:	str  )						->					dict:
         return calculate_bleu(__A						,__A  )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]			,							snake_case__  :Optional[Any]=None     )      ->						SummarizationModule:
  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:
      _lowercase							= SummarizationModule(snake_case__     )
    else:
      _lowercase							= TranslationModule(snake_case__     )
  _lowercase							= 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'     )
  ):
    _lowercase							= True  # don't pollute wandb logs unnecessarily
  elif args.logger_name == "wandb":
    from pytorch_lightning.loggers import WandbLogger

    _lowercase							= os.environ.get('WANDB_PROJECT'			,							snake_case__     )
    _lowercase							= WandbLogger(name=model.output_dir.name			,							project=snake_case__     )

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

    _lowercase							= WandbLogger(name=model.output_dir.name			,							project=F"""hf_{dataset}"""     )

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

  _lowercase							= args.val_metric == 'loss'
  _lowercase							= 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

  _lowercase							= ''
  _lowercase							= sorted(glob.glob(os.path.join(args.output_dir			,							'*.ckpt'     )			,							recursive=snake_case__     )     )
  if checkpoints:
    _lowercase							= checkpoints[-1]
    _lowercase							= checkpoints[-1]
  trainer.logger.log_hyperparams(model.hparams     )

  # test() without a model tests using the best checkpoint automatically
  trainer.test()
  return model


if __name__ == "__main__":
      snake_case 				=       argparse.ArgumentParser()
      snake_case 				=       pl.Trainer.add_argparse_args(parser)
      snake_case 				=       SummarizationModule.add_model_specific_args(parser, os.getcwd())

      snake_case 				=       parser.parse_args()

      main(args)






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


snake_case 				=       logging.get_logger(__name__)





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       def __init__(    self	:	Dict						,*__A	:	Optional[int]						,**__A	:	List[Any]  )						->					None:
         warnings.warn(
             'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use DonutImageProcessor instead.'						,__A						,)
         super().__init__(*__A						,**__A  )