


'''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


lowerCAmelCase						=					logging.getLogger(__name__)






class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					='summarization'
       __UpperCAmelCase					=['loss']
       __UpperCAmelCase					=ROUGE_KEYS
       __UpperCAmelCase					='rouge2'






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

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

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






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      Dict[str, List[str]]:
            _A				=							{
                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				=							True
            return readable_batch






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     **_UpperCamelCase   )->      Dict:
            return self.model(_UpperCamelCase			,     **_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      Tuple:
            _A				=							self.tokenizer.batch_decode(
                _UpperCamelCase			,     skip_special_tokens=_UpperCamelCase			,     clean_up_tokenization_spaces=_UpperCamelCase   )
            return lmap(str.strip			,     _UpperCamelCase   )






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

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

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






       @property
       def   UpperCamelCase						( self   )->      int:
            return self.tokenizer.pad_token_id






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      Dict:
            _A				=							self._step(_UpperCamelCase   )

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






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






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      dict:
            _A				=							time.time()

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






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      Tuple:
            return self._generative_step(_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      Optional[int]:
            return self.validation_epoch_end(_UpperCamelCase			,     prefix='test'   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      SeqaSeqDataset:
            _A				=							self.n_obs[type_path]
            _A				=							self.target_lens[type_path]
            _A				=							self.dataset_class(
                self.tokenizer			,     type_path=_UpperCamelCase			,     n_obs=_UpperCamelCase			,     max_target_length=_UpperCamelCase			,     **self.dataset_kwargs			,     )
            return dataset






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase = False   )->      DataLoader:
            _A				=							self.get_dataset(_UpperCamelCase   )

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






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






       def   UpperCamelCase						( self   )->      DataLoader:
            return self.get_dataloader('test'			,     batch_size=self.hparams.eval_batch_size   )






       @staticmethod
       def   UpperCamelCase						( _UpperCamelCase			,     _UpperCamelCase   )->      List[Any]:
            BaseTransformer.add_model_specific_args(_UpperCamelCase			,     _UpperCamelCase   )
            add_generic_args(_UpperCamelCase			,     _UpperCamelCase   )
            parser.add_argument(
                '--max_source_length'			,     default=1024			,     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=56			,     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=142			,     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=142			,     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=500			,     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        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					='translation'
       __UpperCAmelCase					=['loss']
       __UpperCAmelCase					=['bleu']
       __UpperCAmelCase					='bleu'






       def __init__( self			,     _UpperCamelCase			,     **_UpperCamelCase   )->      List[str]:
            super().__init__(_UpperCamelCase			,     **_UpperCamelCase   )
            _A				=							hparams.src_lang
            _A				=							hparams.tgt_lang






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase   )->      dict:
            return calculate_bleu(_UpperCamelCase			,     _UpperCamelCase   )






def 					lowerCamelCase_ ( __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       List[str]=None		)					->	SummarizationModule:







     """simple docstring"""
     Path(args.output_dir		).mkdir(exist_ok=__UpperCamelCase		)
     check_output_dir(__UpperCamelCase							,    expected_items=3		)

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

          _A				=							os.environ.get('WANDB_PROJECT'							,    __UpperCamelCase		)
          _A				=							WandbLogger(name=model.output_dir.name							,    project=__UpperCamelCase		)

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

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

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

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

     _A				=							''
     _A				=							sorted(glob.glob(os.path.join(args.output_dir							,    '*.ckpt'		)							,    recursive=__UpperCamelCase		)		)
     if checkpoints:
          _A				=							checkpoints[-1]
          _A				=							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__":
    lowerCAmelCase						=					argparse.ArgumentParser()
    lowerCAmelCase						=					pl.Trainer.add_argparse_args(parser)
    lowerCAmelCase						=					SummarizationModule.add_model_specific_args(parser, os.getcwd())

    lowerCAmelCase						=					parser.parse_args()

    main(args)









'''simple docstring'''
from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip





