

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       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       List[str]														=     """summarization"""
    __lowerCamelCase						:       Union[str, Any]														=     ["""loss"""]
    __lowerCamelCase						:       List[Any]														=     ROUGE_KEYS
    __lowerCamelCase						:       Dict														=     """rouge2"""





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

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

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





    def       snake_case_				(      self						,   a):
         lowercase__    :   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')

         lowercase__    :   List[str]      =       True
         return readable_batch





    def       snake_case_				(      self						,   a						,   **a):
         return self.model(a						,   **a)





    def       snake_case_				(      self						,   a):
         lowercase__    :   Optional[Any]      =       self.tokenizer.batch_decode(
             a						,   skip_special_tokens=a						,   clean_up_tokenization_spaces=a)
         return lmap(str.strip						,   a)





    def       snake_case_				(      self						,   a):
         lowercase__    :   Dict      =       self.tokenizer.pad_token_id
         lowercase__      , lowercase__    :   Dict      =       batch['input_ids'], batch['attention_mask']
         lowercase__    :   List[str]      =       batch['labels']
         if isinstance(self.model						,   a):
              lowercase__    :   Optional[int]      =       self.model._shift_right(a)
         else:
              lowercase__    :   List[str]      =       shift_tokens_right(a						,   a)
         if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
              lowercase__    :   Tuple      =       decoder_input_ids
              self.save_readable_batch(a)

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

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





    @property
    def       snake_case_				(      self):
         return self.tokenizer.pad_token_id





    def       snake_case_				(      self						,   a						,   a):
         lowercase__    :   Optional[Any]      =       self._step(a)

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





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





    def       snake_case_				(      self						,   a):
         lowercase__    :   str      =       time.time()

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





    def       snake_case_				(      self						,   a						,   a):
         return self._generative_step(a)





    def       snake_case_				(      self						,   a):
         return self.validation_epoch_end(a						,   prefix='test')





    def       snake_case_				(      self						,   a):
         lowercase__    :   int      =       self.n_obs[type_path]
         lowercase__    :   str      =       self.target_lens[type_path]
         lowercase__    :   str      =       self.dataset_class(
             self.tokenizer						,   type_path=a						,   n_obs=a						,   max_target_length=a						,   **self.dataset_kwargs						,   )
         return dataset





    def       snake_case_				(      self						,   a						,   a						,   a = False):
         lowercase__    :   Optional[Any]      =       self.get_dataset(a)

         if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
              lowercase__    :   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":
              lowercase__    :   Any      =       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       snake_case_				(      self):
         lowercase__    :   int      =       self.get_dataloader('train'						,   batch_size=self.hparams.train_batch_size						,   shuffle=a)
         return dataloader





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





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





    @staticmethod
    def       snake_case_				(      a						,   a):
         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       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       Optional[Any]														=     """translation"""
    __lowerCamelCase						:       Optional[int]														=     ["""loss"""]
    __lowerCamelCase						:       Tuple														=     ["""bleu"""]
    __lowerCamelCase						:       str														=     """bleu"""





    def __init__(      self						,   a						,   **a):
         super().__init__(a						,   **a)
         lowercase__    :   Optional[int]      =       hparams.src_lang
         lowercase__    :   Union[str, Any]      =       hparams.tgt_lang





    def       snake_case_				(      self						,   a						,   a):
         return calculate_bleu(a						,   a)



def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Tuple	,     SCREAMING_SNAKE_CASE_		:    str=None			):



     '''simple docstring'''





     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:
               lowercase__    :   SummarizationModule      =       SummarizationModule(SCREAMING_SNAKE_CASE_			)
          else:
               lowercase__    :   SummarizationModule      =       TranslationModule(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Tuple      =       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__    :   List[str]      =       True  # don't pollute wandb logs unnecessarily
     elif args.logger_name == "wandb":
          from pytorch_lightning.loggers import WandbLogger

          lowercase__    :   Optional[int]      =       os.environ.get('WANDB_PROJECT'	,     SCREAMING_SNAKE_CASE_			)
          lowercase__    :   Optional[Any]      =       WandbLogger(name=model.output_dir.name	,     project=SCREAMING_SNAKE_CASE_			)

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

          lowercase__    :   Optional[Any]      =       WandbLogger(name=model.output_dir.name	,     project=f"""hf_{dataset}"""			)

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

     lowercase__    :   int      =       args.val_metric == 'loss'
     lowercase__    :   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

     lowercase__    :   Union[str, Any]      =       ''
     lowercase__    :   Dict      =       sorted(glob.glob(os.path.join(args.output_dir	,     '*.ckpt'			)	,     recursive=SCREAMING_SNAKE_CASE_			)			)
     if checkpoints:
          lowercase__    :   int      =       checkpoints[-1]
          lowercase__    :   List[Any]      =       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)




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if len(SCREAMING_SNAKE_CASE_			) != len(SCREAMING_SNAKE_CASE_			):
          raise ValueError('String lengths must match!'			)

     lowercase__    :   Union[str, Any]      =       0

     for chara, chara in zip(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			):
          if chara != chara:
               count += 1

     return count


if __name__ == "__main__":
       import doctest

       doctest.testmod()

