'''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


__snake_case							: int			=     logging.getLogger(__name__)
class 				lowercase_     (  _A    ):
    a_   							= """summarization"""
    a_   							= ["""loss"""]
    a_   							= ROUGE_KEYS
    a_   							= """rouge2"""







    def __init__(						self							,  UpperCamelCase__							,  **UpperCamelCase__					)					->							Union[str, Any]:





     """simple docstring"""

     if hparams.sortish_sampler and hparams.gpus > 1:
      UpperCAmelCase_												=       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					)
     UpperCAmelCase_												=       Path(self.output_dir					) / "metrics.json"
     UpperCAmelCase_												=       Path(self.output_dir					) / "hparams.pkl"
     pickle_save(self.hparams							,  self.hparams_save_path					)
     UpperCAmelCase_												=       0
     UpperCAmelCase_												=       defaultdict(_UpperCamelCase					)
     UpperCAmelCase_												=       self.config.model_type
     UpperCAmelCase_												=       self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

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

     UpperCAmelCase_												=       {
         "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()					)

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







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__					)					->							Dict[str, List[str]]:





     """simple docstring"""

     UpperCAmelCase_												=       {
         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"					)

     UpperCAmelCase_												=       True
     return readable_batch







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__							,  **UpperCamelCase__					)					->							Dict:





     """simple docstring"""

     return self.model(_UpperCamelCase							,  **_UpperCamelCase					)







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__					)					->							Tuple:





     """simple docstring"""

     UpperCAmelCase_												=       self.tokenizer.batch_decode(
         _UpperCamelCase							,  skip_special_tokens=_UpperCamelCase							,  clean_up_tokenization_spaces=_UpperCamelCase					)
     return lmap(str.strip							,  _UpperCamelCase					)







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__					)					->							Tuple:





     """simple docstring"""

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

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

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







    @property
    def 	lowerCamelCase_     (						self					)					->							int:





     """simple docstring"""

     return self.tokenizer.pad_token_id







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__							,  UpperCamelCase__					)					->							Dict:





     """simple docstring"""

     UpperCAmelCase_												=       self._step(_UpperCamelCase					)

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





     """simple docstring"""

     return self._generative_step(_UpperCamelCase					)







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__							,  UpperCamelCase__="val"					)					->							Dict:





     """simple docstring"""

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





     """simple docstring"""

     return calculate_rouge(_UpperCamelCase							,  _UpperCamelCase					)







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__					)					->							dict:





     """simple docstring"""

     UpperCAmelCase_												=       time.time()

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





     """simple docstring"""

     return self._generative_step(_UpperCamelCase					)







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__					)					->							Optional[int]:





     """simple docstring"""

     return self.validation_epoch_end(_UpperCamelCase							,  prefix="test"					)







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__					)					->							SeqaSeqDataset:





     """simple docstring"""

     UpperCAmelCase_												=       self.n_obs[type_path]
     UpperCAmelCase_												=       self.target_lens[type_path]
     UpperCAmelCase_												=       self.dataset_class(
         self.tokenizer							,  type_path=_UpperCamelCase							,  n_obs=_UpperCamelCase							,  max_target_length=_UpperCamelCase							,  **self.dataset_kwargs							,  )
     return dataset







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__							,  UpperCamelCase__							,  UpperCamelCase__ = False					)					->							DataLoader:





     """simple docstring"""

     UpperCAmelCase_												=       self.get_dataset(_UpperCamelCase					)

     if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
      UpperCAmelCase_												=       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":
      UpperCAmelCase_												=       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					)					->							DataLoader:





     """simple docstring"""

     UpperCAmelCase_												=       self.get_dataloader("train"							,  batch_size=self.hparams.train_batch_size							,  shuffle=_UpperCamelCase					)
     return dataloader







    def 	lowerCamelCase_     (						self					)					->							DataLoader:





     """simple docstring"""

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







    def 	lowerCamelCase_     (						self					)					->							DataLoader:





     """simple docstring"""

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







    @staticmethod
    def 	lowerCamelCase_     (						UpperCamelCase__							,  UpperCamelCase__					)					->							List[Any]:





     """simple docstring"""

     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 				lowercase_     (  _A    ):
    a_   							= """translation"""
    a_   							= ["""loss"""]
    a_   							= ["""bleu"""]
    a_   							= """bleu"""







    def __init__(						self							,  UpperCamelCase__							,  **UpperCamelCase__					)					->							List[str]:





     """simple docstring"""

     super().__init__(_UpperCamelCase							,  **_UpperCamelCase					)
     UpperCAmelCase_												=       hparams.src_lang
     UpperCAmelCase_												=       hparams.tgt_lang







    def 	lowerCamelCase_     (						self							,  UpperCamelCase__							,  UpperCamelCase__					)					->							dict:





     """simple docstring"""

     return calculate_bleu(_UpperCamelCase							,  _UpperCamelCase					)



def lowerCamelCase__    (							A_							,      A_=None							):
 Path(args.output_dir							).mkdir(exist_ok=__UpperCamelCase							)
 check_output_dir(__UpperCamelCase							,      expected_items=3							)

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

  UpperCAmelCase_												=       os.environ.get("WANDB_PROJECT"							,      __UpperCamelCase							)
  UpperCAmelCase_												=       WandbLogger(name=model.output_dir.name							,      project=__UpperCamelCase							)

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

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

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

 UpperCAmelCase_												=       args.val_metric == "loss"
 UpperCAmelCase_												=       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

 UpperCAmelCase_												=       ""
 UpperCAmelCase_												=       sorted(glob.glob(os.path.join(args.output_dir							,      "*.ckpt"							)							,      recursive=__UpperCamelCase							)							)
 if checkpoints:
  UpperCAmelCase_												=       checkpoints[-1]
  UpperCAmelCase_												=       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							: Tuple			=     argparse.ArgumentParser()
      __snake_case							: Optional[Any]			=     pl.Trainer.add_argparse_args(parser)
      __snake_case							: List[str]			=     SummarizationModule.add_model_specific_args(parser, os.getcwd())

      __snake_case							: Optional[int]			=     parser.parse_args()

      main(args)




'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       int		)					->	bool:







     """simple docstring"""
     return str(__UpperCamelCase		) == str(__UpperCamelCase		)[::-1]

def 					lowerCamelCase_ ( __UpperCamelCase					:       int		)					->	int:







     """simple docstring"""
     return int(__UpperCamelCase		) + int(str(__UpperCamelCase		)[::-1]		)

def 					lowerCamelCase_ ( __UpperCamelCase					:       int = 1_0_0_0_0		)					->	int:







     """simple docstring"""
     _A				=							[]
     for num in range(1							,    __UpperCamelCase		):
          _A				=							0
          _A				=							num
          while iterations < 5_0:
               _A				=							sum_reverse(__UpperCamelCase		)
               iterations += 1
               if is_palindrome(__UpperCamelCase		):
                    break
          else:
               lychrel_nums.append(__UpperCamelCase		)
     return len(__UpperCamelCase		)


if __name__ == "__main__":
    print(F'{solution() = }')





