


"""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


__magic_name__						: List[str]    =				logging.getLogger(__name__)




class 			__snake_case   (lowerCamelCase  ):
							__a					 =       '''summarization'''
							__a					 =       ['''loss''']
							__a					 =       ROUGE_KEYS
							__a					 =       '''rouge2'''




							def __init__(     self:    Dict     ,							A_:    int     ,							**A_:    Dict   ):
										if hparams.sortish_sampler and hparams.gpus > 1:
													__lowerCamelCase          =      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   )
										__lowerCamelCase          =      Path(self.output_dir   ) / """metrics.json"""
										__lowerCamelCase          =      Path(self.output_dir   ) / """hparams.pkl"""
										pickle_save(self.hparams     ,							self.hparams_save_path   )
										__lowerCamelCase          =      0
										__lowerCamelCase          =      defaultdict(A_   )
										__lowerCamelCase          =      self.config.model_type
										__lowerCamelCase          =      self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

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

										__lowerCamelCase          =      {
										    """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()   )

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




							def   __a   (     self:    Any     ,							A_:    Dict[str, torch.Tensor]   ):
										__lowerCamelCase          =      {
										    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"""   )

										__lowerCamelCase          =      True
										return readable_batch




							def   __a   (     self:    Optional[Any]     ,							A_:    Optional[int]     ,							**A_:    str   ):
										return self.model(A_     ,							**A_   )




							def   __a   (     self:    Union[str, Any]     ,							A_:    List[int]   ):
										__lowerCamelCase          =      self.tokenizer.batch_decode(
										    A_     ,							skip_special_tokens=A_     ,							clean_up_tokenization_spaces=A_   )
										return lmap(str.strip     ,							A_   )




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

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

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




							@property
							def   __a   (     self:    Any   ):
										return self.tokenizer.pad_token_id




							def   __a   (     self:    Optional[Any]     ,							A_:    str     ,							A_:    int   ):
										__lowerCamelCase          =      self._step(A_   )

										__lowerCamelCase          =      dict(zip(self.loss_names     ,							A_   )   )
										# tokens per batch
										__lowerCamelCase          =      batch["""input_ids"""].ne(self.pad   ).sum() + batch["""labels"""].ne(self.pad   ).sum()
										__lowerCamelCase          =      batch["""input_ids"""].shape[0]
										__lowerCamelCase          =      batch["""input_ids"""].eq(self.pad   ).sum()
										__lowerCamelCase          =      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:    int     ,							A_:    str     ,							A_:    Optional[int]   ):
										return self._generative_step(A_   )




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




							def   __a   (     self:    List[Any]     ,							A_:    dict   ):
										__lowerCamelCase          =      time.time()

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




							def   __a   (     self:    Union[str, Any]     ,							A_:    Any     ,							A_:    Any   ):
										return self._generative_step(A_   )




							def   __a   (     self:    Union[str, Any]     ,							A_:    int   ):
										return self.validation_epoch_end(A_     ,							prefix="""test"""   )




							def   __a   (     self:    Tuple     ,							A_:    Union[str, Any]   ):
										__lowerCamelCase          =      self.n_obs[type_path]
										__lowerCamelCase          =      self.target_lens[type_path]
										__lowerCamelCase          =      self.dataset_class(
										    self.tokenizer     ,							type_path=A_     ,							n_obs=A_     ,							max_target_length=A_     ,							**self.dataset_kwargs     ,							)
										return dataset




							def   __a   (     self:    Optional[int]     ,							A_:    str     ,							A_:    int     ,							A_:    bool = False   ):
										__lowerCamelCase          =      self.get_dataset(A_   )

										if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
													__lowerCamelCase          =      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":
													__lowerCamelCase          =      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   __a   (     self:    Tuple   ):
										__lowerCamelCase          =      self.get_dataloader("""train"""     ,							batch_size=self.hparams.train_batch_size     ,							shuffle=A_   )
										return dataloader




							def   __a   (     self:    Any   ):
										return self.get_dataloader("""val"""     ,							batch_size=self.hparams.eval_batch_size   )




							def   __a   (     self:    List[Any]   ):
										return self.get_dataloader("""test"""     ,							batch_size=self.hparams.eval_batch_size   )




							@staticmethod
							def   __a   (     A_:    Dict     ,							A_:    str   ):
										BaseTransformer.add_model_specific_args(A_     ,							A_   )
										add_generic_args(A_     ,							A_   )
										parser.add_argument(
										    """--max_source_length"""     ,							default=10_24     ,							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=1_42     ,							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=1_42     ,							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=5_00     ,							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 			__snake_case   (lowerCamelCase  ):
							__a					 =       '''translation'''
							__a					 =       ['''loss''']
							__a					 =       ['''bleu''']
							__a					 =       '''bleu'''




							def __init__(     self:    str     ,							A_:    Dict     ,							**A_:    str   ):
										super().__init__(A_     ,							**A_   )
										__lowerCamelCase          =      hparams.src_lang
										__lowerCamelCase          =      hparams.tgt_lang




							def   __a   (     self:    List[str]     ,							A_:    int     ,							A_:    Dict   ):
										return calculate_bleu(A_     ,							A_   )





def       a_ (	lowercase__ :List[Any],			lowercase__ :Union[str, Any]=None     ):
			Path(args.output_dir     ).mkdir(exist_ok=lowercase__     )
			check_output_dir(lowercase__,			expected_items=3     )

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

						__lowerCamelCase          =      os.environ.get("""WANDB_PROJECT""",			lowercase__     )
						__lowerCamelCase          =      WandbLogger(name=model.output_dir.name,			project=lowercase__     )

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

						__lowerCamelCase          =      WandbLogger(name=model.output_dir.name,			project=f'hf_{dataset}'     )

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

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

			__lowerCamelCase          =      """"""
			__lowerCamelCase          =      sorted(glob.glob(os.path.join(args.output_dir,			"""*.ckpt"""     ),			recursive=lowercase__     )     )
			if checkpoints:
						__lowerCamelCase          =      checkpoints[-1]
						__lowerCamelCase          =      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__":
					__magic_name__						: Optional[Any]    =				argparse.ArgumentParser()
					__magic_name__						: Union[str, Any]    =				pl.Trainer.add_argparse_args(parser)
					__magic_name__						: int    =				SummarizationModule.add_model_specific_args(parser, os.getcwd())

					__magic_name__						: str    =				parser.parse_args()

					main(args)








"""simple docstring"""




def       a_ (	lowercase__ :int = 10**9     ):
			__lowerCamelCase          =      1
			__lowerCamelCase          =      2

			__lowerCamelCase          =      0
			__lowerCamelCase          =      0
			__lowerCamelCase          =      0
			while perimeter <= max_perimeter:
						perimeters_sum += perimeter

						prev_value += 2 * value
						value += prev_value

						__lowerCamelCase          =      2 * value + 2 if i % 2 == 0 else 2 * value - 2
						i += 1

			return perimeters_sum


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




