



'''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


SCREAMING_SNAKE_CASE__          =  logging.getLogger(__name__)


class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:Any     =       "summarization"
       lowerCAmelCase__	:Tuple     =       ["loss"]
       lowerCAmelCase__	:List[Any]     =       ROUGE_KEYS
       lowerCAmelCase__	:Union[str, Any]     =       "rouge2"



       def __init__(					self  ,UpperCAmelCase_  ,**UpperCAmelCase_   )	->				Tuple:
           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__(UpperCAmelCase_  ,num_labels=UpperCAmelCase_  ,mode=self.mode  ,**UpperCAmelCase_   )
           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(UpperCAmelCase_   )
           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  ,UpperCAmelCase_   ):
               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 				_a   (					self  ,UpperCAmelCase_   )	->				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(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"   )

           lowercase__									=    True
           return readable_batch



       def 				_a   (					self  ,UpperCAmelCase_  ,**UpperCAmelCase_   )	->				Any:
           return self.model(UpperCAmelCase_  ,**UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Optional[Any]:
           lowercase__									=    self.tokenizer.batch_decode(
               UpperCAmelCase_  ,skip_special_tokens=UpperCAmelCase_  ,clean_up_tokenization_spaces=UpperCAmelCase_   )
           return lmap(str.strip  ,UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Tuple:
           lowercase__									=    self.tokenizer.pad_token_id
           lowercase__							,							lowercase__									=    batch["input_ids"], batch["attention_mask"]
           lowercase__									=    batch["labels"]
           if isinstance(self.model  ,UpperCAmelCase_   ):
               lowercase__									=    self.model._shift_right(UpperCAmelCase_   )
           else:
               lowercase__									=    shift_tokens_right(UpperCAmelCase_  ,UpperCAmelCase_   )
           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(UpperCAmelCase_   )

           lowercase__									=    self(UpperCAmelCase_  ,attention_mask=UpperCAmelCase_  ,decoder_input_ids=UpperCAmelCase_  ,use_cache=UpperCAmelCase_   )
           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=UpperCAmelCase_   )

               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(UpperCAmelCase_  ,dim=-1   )
               lowercase__							,							lowercase__									=    label_smoothed_nll_loss(
                   UpperCAmelCase_  ,UpperCAmelCase_  ,self.hparams.label_smoothing  ,ignore_index=UpperCAmelCase_   )
           return (loss,)



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



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Dict:
           lowercase__									=    self._step(UpperCAmelCase_   )

           lowercase__									=    dict(zip(self.loss_names  ,UpperCAmelCase_   )   )
           # 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 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Dict:
           return self._generative_step(UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_="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(UpperCAmelCase_   ).type_as(UpperCAmelCase_   )
           generative_metrics.update({k: v.item() for k, v in losses.items()}   )
           losses.update(UpperCAmelCase_   )
           lowercase__									=    {F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
           lowercase__									=    self.step_count
           self.metrics[prefix].append(UpperCAmelCase_   )  # 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 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Dict:
           return calculate_rouge(UpperCAmelCase_  ,UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				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=UpperCAmelCase_  ,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(UpperCAmelCase_   )
           lowercase__									=    self.ids_to_clean_text(batch["labels"]   )
           lowercase__									=    self._step(UpperCAmelCase_   )
           lowercase__									=    dict(zip(self.loss_names  ,UpperCAmelCase_   )   )
           lowercase__									=    self.calc_generative_metrics(UpperCAmelCase_  ,UpperCAmelCase_   )
           lowercase__									=    np.mean(lmap(UpperCAmelCase_  ,UpperCAmelCase_   )   )
           base_metrics.update(gen_time=UpperCAmelCase_  ,gen_len=UpperCAmelCase_  ,preds=UpperCAmelCase_  ,target=UpperCAmelCase_  ,**UpperCAmelCase_   )
           return base_metrics



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				Optional[Any]:
           return self._generative_step(UpperCAmelCase_   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				List[str]:
           return self.validation_epoch_end(UpperCAmelCase_  ,prefix="test"   )



       def 				_a   (					self  ,UpperCAmelCase_   )	->				SeqaSeqDataset:
           lowercase__									=    self.n_obs[type_path]
           lowercase__									=    self.target_lens[type_path]
           lowercase__									=    self.dataset_class(
               self.tokenizer  ,type_path=UpperCAmelCase_  ,n_obs=UpperCAmelCase_  ,max_target_length=UpperCAmelCase_  ,**self.dataset_kwargs  ,)
           return dataset



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_  ,UpperCAmelCase_ = False   )	->				DataLoader:
           lowercase__									=    self.get_dataset(UpperCAmelCase_   )

           if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
               lowercase__									=    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":
               lowercase__									=    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 				_a   (					self   )	->				DataLoader:
           lowercase__									=    self.get_dataloader("train"  ,batch_size=self.hparams.train_batch_size  ,shuffle=UpperCAmelCase_   )
           return dataloader



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



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







       @staticmethod
       def 				_a   (					UpperCAmelCase_  ,UpperCAmelCase_   )	->				Dict:
           BaseTransformer.add_model_specific_args(UpperCAmelCase_  ,UpperCAmelCase_   )
           add_generic_args(UpperCAmelCase_  ,UpperCAmelCase_   )
           parser.add_argument(
               "--max_source_length"  ,default=1_024  ,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 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:Optional[Any]     =       "translation"
       lowerCAmelCase__	:Optional[int]     =       ["loss"]
       lowerCAmelCase__	:Any     =       ["bleu"]
       lowerCAmelCase__	:List[Any]     =       "bleu"



       def __init__(					self  ,UpperCAmelCase_  ,**UpperCAmelCase_   )	->				Optional[Any]:
           super().__init__(UpperCAmelCase_  ,**UpperCAmelCase_   )
           lowercase__									=    hparams.src_lang
           lowercase__									=    hparams.tgt_lang







       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				dict:
           return calculate_bleu(UpperCAmelCase_  ,UpperCAmelCase_   )





def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    List[str]=None							):





    '''simple docstring'''


    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__":
 SCREAMING_SNAKE_CASE__          =  argparse.ArgumentParser()
 SCREAMING_SNAKE_CASE__          =  pl.Trainer.add_argparse_args(parser)
 SCREAMING_SNAKE_CASE__          =  SummarizationModule.add_model_specific_args(parser, os.getcwd())

 SCREAMING_SNAKE_CASE__          =  parser.parse_args()

 main(args)









'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    int = 50_000_000							):





    '''simple docstring'''


    lowercase__									=    set()
    lowercase__									=    int((limit - 24) ** (1 / 2)							)

    lowercase__									=    set(range(3  ,prime_square_limit + 1  ,2							)							)
    primes.add(2							)
    for p in range(3  ,prime_square_limit + 1  ,2							):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p  ,prime_square_limit + 1  ,_snake_case							)							)							)

    for primea in primes:
        lowercase__									=    primea * primea
        for primea in primes:
            lowercase__									=    primea * primea * primea
            if square + cube >= limit - 16:
                break
            for primea in primes:
                lowercase__									=    primea * primea * primea * primea
                lowercase__									=    square + cube + tetr
                if total >= limit:
                    break
                ret.add(_snake_case							)

    return len(_snake_case							)


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




