
"""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


UpperCAmelCase__   :					Optional[int]												=				logging.getLogger(__name__)



class 			lowerCAmelCase_		(a__						):


    """simple docstring"""



    __UpperCamelCase   :		Optional[int]       =     '''summarization'''
    __UpperCamelCase   :		int       =     ['''loss''']
    __UpperCamelCase   :		Dict       =     ROUGE_KEYS
    __UpperCamelCase   :		Any       =     '''rouge2'''



    def __init__(self     , SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)      ->	int:



           """simple docstring"""




           if hparams.sortish_sampler and hparams.gpus > 1:
                  SCREAMING_SNAKE_CASE__		: Union[str, 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__(SCREAMING_SNAKE_CASE__     , num_labels=SCREAMING_SNAKE_CASE__     , mode=self.mode     , **SCREAMING_SNAKE_CASE__					)
           use_task_specific_params(self.model     , """summarization"""					)
           save_git_info(self.hparams.output_dir					)
           SCREAMING_SNAKE_CASE__		: str     =     Path(self.output_dir					) / """metrics.json"""
           SCREAMING_SNAKE_CASE__		: Any     =     Path(self.output_dir					) / """hparams.pkl"""
           pickle_save(self.hparams     , self.hparams_save_path					)
           SCREAMING_SNAKE_CASE__		: Any     =     0
           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     defaultdict(SCREAMING_SNAKE_CASE__					)
           SCREAMING_SNAKE_CASE__		: Any     =     self.config.model_type
           SCREAMING_SNAKE_CASE__		: Dict     =     self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

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

           SCREAMING_SNAKE_CASE__		: 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()					)

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



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Dict[str, List[str]]:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: int     =     {
               k: self.tokenizer.batch_decode(v.tolist()					) if """mask""" not in k else v.shape for k, v in batch.items()
           }
           save_json(SCREAMING_SNAKE_CASE__     , 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"""					)

           SCREAMING_SNAKE_CASE__		: str     =     True
           return readable_batch



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)      ->	int:



           """simple docstring"""




           return self.model(SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Dict:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.tokenizer.batch_decode(
               SCREAMING_SNAKE_CASE__     , skip_special_tokens=SCREAMING_SNAKE_CASE__     , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__					)
           return lmap(str.strip     , SCREAMING_SNAKE_CASE__					)



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	Tuple:



           """simple docstring"""




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

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

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



    @property
    def 			__magic_name__					(self					)      ->	int:



           """simple docstring"""




           return self.tokenizer.pad_token_id



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Dict:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Dict     =     self._step(SCREAMING_SNAKE_CASE__					)

           SCREAMING_SNAKE_CASE__		: Dict     =     dict(zip(self.loss_names     , SCREAMING_SNAKE_CASE__					)					)
           # tokens per batch
           SCREAMING_SNAKE_CASE__		: List[Any]     =     batch["""input_ids"""].ne(self.pad					).sum() + batch["""labels"""].ne(self.pad					).sum()
           SCREAMING_SNAKE_CASE__		: int     =     batch["""input_ids"""].shape[0]
           SCREAMING_SNAKE_CASE__		: int     =     batch["""input_ids"""].eq(self.pad					).sum()
           SCREAMING_SNAKE_CASE__		: int     =     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 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Dict:



           """simple docstring"""




           return self._generative_step(SCREAMING_SNAKE_CASE__					)



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__="val"					)      ->	Dict:



           """simple docstring"""




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



           """simple docstring"""




           return calculate_rouge(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	dict:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: int     =     time.time()

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



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Union[str, Any]:



           """simple docstring"""




           return self._generative_step(SCREAMING_SNAKE_CASE__					)



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	List[str]:



           """simple docstring"""




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



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	SeqaSeqDataset:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.n_obs[type_path]
           SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.target_lens[type_path]
           SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.dataset_class(
               self.tokenizer     , type_path=SCREAMING_SNAKE_CASE__     , n_obs=SCREAMING_SNAKE_CASE__     , max_target_length=SCREAMING_SNAKE_CASE__     , **self.dataset_kwargs     , )
           return dataset



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = False					)      ->	DataLoader:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: Tuple     =     self.get_dataset(SCREAMING_SNAKE_CASE__					)

           if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
                  SCREAMING_SNAKE_CASE__		: Tuple     =     dataset.make_sortish_sampler(SCREAMING_SNAKE_CASE__     , distributed=self.hparams.gpus > 1					)
                  return DataLoader(
                      SCREAMING_SNAKE_CASE__     , batch_size=SCREAMING_SNAKE_CASE__     , collate_fn=dataset.collate_fn     , shuffle=SCREAMING_SNAKE_CASE__     , num_workers=self.num_workers     , sampler=SCREAMING_SNAKE_CASE__     , )

           elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
                  SCREAMING_SNAKE_CASE__		: int     =     dataset.make_dynamic_sampler(
                      self.hparams.max_tokens_per_batch     , distributed=self.hparams.gpus > 1					)
                  return DataLoader(
                      SCREAMING_SNAKE_CASE__     , batch_sampler=SCREAMING_SNAKE_CASE__     , collate_fn=dataset.collate_fn     , num_workers=self.num_workers     , )
           else:
                  return DataLoader(
                      SCREAMING_SNAKE_CASE__     , batch_size=SCREAMING_SNAKE_CASE__     , collate_fn=dataset.collate_fn     , shuffle=SCREAMING_SNAKE_CASE__     , num_workers=self.num_workers     , sampler=SCREAMING_SNAKE_CASE__     , )



    def 			__magic_name__					(self					)      ->	DataLoader:



           """simple docstring"""




           SCREAMING_SNAKE_CASE__		: int     =     self.get_dataloader("""train"""     , batch_size=self.hparams.train_batch_size     , shuffle=SCREAMING_SNAKE_CASE__					)
           return dataloader



    def 			__magic_name__					(self					)      ->	DataLoader:



           """simple docstring"""




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



    def 			__magic_name__					(self					)      ->	DataLoader:



           """simple docstring"""




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



    @staticmethod
    def 			__magic_name__					(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	int:



           """simple docstring"""




           BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
           add_generic_args(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
           parser.add_argument(
               """--max_source_length"""     , default=10_24     , type=SCREAMING_SNAKE_CASE__     , 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=SCREAMING_SNAKE_CASE__     , 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=SCREAMING_SNAKE_CASE__     , 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=SCREAMING_SNAKE_CASE__     , 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=SCREAMING_SNAKE_CASE__					)
           parser.add_argument("""--overwrite_output_dir"""     , action="""store_true"""     , default=SCREAMING_SNAKE_CASE__					)
           parser.add_argument("""--max_tokens_per_batch"""     , type=SCREAMING_SNAKE_CASE__     , default=SCREAMING_SNAKE_CASE__					)
           parser.add_argument("""--logger_name"""     , type=SCREAMING_SNAKE_CASE__     , choices=["""default""", """wandb""", """wandb_shared"""]     , default="""default"""					)
           parser.add_argument("""--n_train"""     , type=SCREAMING_SNAKE_CASE__     , default=-1     , required=SCREAMING_SNAKE_CASE__     , help="""# examples. -1 means use all."""					)
           parser.add_argument("""--n_val"""     , type=SCREAMING_SNAKE_CASE__     , default=5_00     , required=SCREAMING_SNAKE_CASE__     , help="""# examples. -1 means use all."""					)
           parser.add_argument("""--n_test"""     , type=SCREAMING_SNAKE_CASE__     , default=-1     , required=SCREAMING_SNAKE_CASE__     , help="""# examples. -1 means use all."""					)
           parser.add_argument(
               """--task"""     , type=SCREAMING_SNAKE_CASE__     , default="""summarization"""     , required=SCREAMING_SNAKE_CASE__     , help="""# examples. -1 means use all."""					)
           parser.add_argument("""--label_smoothing"""     , type=SCREAMING_SNAKE_CASE__     , default=0.0     , required=SCREAMING_SNAKE_CASE__					)
           parser.add_argument("""--src_lang"""     , type=SCREAMING_SNAKE_CASE__     , default=""""""     , required=SCREAMING_SNAKE_CASE__					)
           parser.add_argument("""--tgt_lang"""     , type=SCREAMING_SNAKE_CASE__     , default=""""""     , required=SCREAMING_SNAKE_CASE__					)
           parser.add_argument("""--eval_beams"""     , type=SCREAMING_SNAKE_CASE__     , default=SCREAMING_SNAKE_CASE__     , required=SCREAMING_SNAKE_CASE__					)
           parser.add_argument(
               """--val_metric"""     , type=SCREAMING_SNAKE_CASE__     , default=SCREAMING_SNAKE_CASE__     , required=SCREAMING_SNAKE_CASE__     , choices=["""bleu""", """rouge2""", """loss""", None]					)
           parser.add_argument("""--eval_max_gen_length"""     , type=SCREAMING_SNAKE_CASE__     , default=SCREAMING_SNAKE_CASE__     , help="""never generate more than n tokens"""					)
           parser.add_argument("""--save_top_k"""     , type=SCREAMING_SNAKE_CASE__     , default=1     , required=SCREAMING_SNAKE_CASE__     , help="""How many checkpoints to save"""					)
           parser.add_argument(
               """--early_stopping_patience"""     , type=SCREAMING_SNAKE_CASE__     , default=-1     , required=SCREAMING_SNAKE_CASE__     , help=(
                   """-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So"""
                   """ val_check_interval will effect it."""
               )     , )
           return parser






class 			lowerCAmelCase_		(a__						):


    """simple docstring"""



    __UpperCamelCase   :		Union[str, Any]       =     '''translation'''
    __UpperCamelCase   :		Optional[Any]       =     ['''loss''']
    __UpperCamelCase   :		Optional[int]       =     ['''bleu''']
    __UpperCamelCase   :		Tuple       =     '''bleu'''



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



           """simple docstring"""




           super().__init__(SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)
           SCREAMING_SNAKE_CASE__		: Optional[Any]     =     hparams.src_lang
           SCREAMING_SNAKE_CASE__		: int     =     hparams.tgt_lang



    def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	dict:



           """simple docstring"""




           return calculate_bleu(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)






def lowercase_						(     _snake_case     ,_snake_case=None		):
       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:
                     SCREAMING_SNAKE_CASE__		: SummarizationModule     =     SummarizationModule(_snake_case		)
              else:
                     SCREAMING_SNAKE_CASE__		: SummarizationModule     =     TranslationModule(_snake_case		)
       SCREAMING_SNAKE_CASE__		: List[str]     =     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"""		)
       ):
              SCREAMING_SNAKE_CASE__		: List[Any]     =     True  # don't pollute wandb logs unnecessarily
       elif args.logger_name == "wandb":
              from pytorch_lightning.loggers import WandbLogger

              SCREAMING_SNAKE_CASE__		: Optional[int]     =     os.environ.get("""WANDB_PROJECT"""     ,_snake_case		)
              SCREAMING_SNAKE_CASE__		: Optional[Any]     =     WandbLogger(name=model.output_dir.name     ,project=_snake_case		)

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

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

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

       SCREAMING_SNAKE_CASE__		: Any     =     args.val_metric == """loss"""
       SCREAMING_SNAKE_CASE__		: pl.Trainer     =     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

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

     UpperCAmelCase__   :					Any												=				parser.parse_args()

     main(args)






"""simple docstring"""




from math import pi, sqrt, tan

def lowercase_						(     _snake_case		):
							if side_length < 0:
														raise ValueError("""surface_area_cube() only accepts non-negative values"""		)
							return 6 * side_length**2

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							if length < 0 or breadth < 0 or height < 0:
														raise ValueError("""surface_area_cuboid() only accepts non-negative values"""		)
							return 2 * ((length * breadth) + (breadth * height) + (length * height))

def lowercase_						(     _snake_case		):
							if radius < 0:
														raise ValueError("""surface_area_sphere() only accepts non-negative values"""		)
							return 4 * pi * radius**2

def lowercase_						(     _snake_case		):
							if radius < 0:
														raise ValueError("""surface_area_hemisphere() only accepts non-negative values"""		)
							return 3 * pi * radius**2

def lowercase_						(     _snake_case     ,_snake_case		):
							if radius < 0 or height < 0:
														raise ValueError("""surface_area_cone() only accepts non-negative values"""		)
							return pi * radius * (radius + (height**2 + radius**2) ** 0.5)

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							if radius_a < 0 or radius_a < 0 or height < 0:
														raise ValueError(
														    """surface_area_conical_frustum() only accepts non-negative values"""		)
							SCREAMING_SNAKE_CASE__		: int     =     (height**2 + (radius_a - radius_a) ** 2) ** 0.5
							return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)

def lowercase_						(     _snake_case     ,_snake_case		):
							if radius < 0 or height < 0:
														raise ValueError("""surface_area_cylinder() only accepts non-negative values"""		)
							return 2 * pi * radius * (height + radius)

def lowercase_						(     _snake_case     ,_snake_case		):
							if torus_radius < 0 or tube_radius < 0:
														raise ValueError("""surface_area_torus() only accepts non-negative values"""		)
							if torus_radius < tube_radius:
														raise ValueError(
														    """surface_area_torus() does not support spindle or self intersecting tori"""		)
							return 4 * pow(_snake_case     ,2		) * torus_radius * tube_radius

def lowercase_						(     _snake_case     ,_snake_case		):
							if length < 0 or width < 0:
														raise ValueError("""area_rectangle() only accepts non-negative values"""		)
							return length * width

def lowercase_						(     _snake_case		):
							if side_length < 0:
														raise ValueError("""area_square() only accepts non-negative values"""		)
							return side_length**2

def lowercase_						(     _snake_case     ,_snake_case		):
							if base < 0 or height < 0:
														raise ValueError("""area_triangle() only accepts non-negative values"""		)
							return (base * height) / 2

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							if sidea < 0 or sidea < 0 or sidea < 0:
														raise ValueError("""area_triangle_three_sides() only accepts non-negative values"""		)
							elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
														raise ValueError("""Given three sides do not form a triangle"""		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     (sidea + sidea + sidea) / 2
							SCREAMING_SNAKE_CASE__		: List[Any]     =     sqrt(
							    semi_perimeter
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea)		)
							return area

def lowercase_						(     _snake_case     ,_snake_case		):
							if base < 0 or height < 0:
														raise ValueError("""area_parallelogram() only accepts non-negative values"""		)
							return base * height

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							if basea < 0 or basea < 0 or height < 0:
														raise ValueError("""area_trapezium() only accepts non-negative values"""		)
							return 1 / 2 * (basea + basea) * height

def lowercase_						(     _snake_case		):
							if radius < 0:
														raise ValueError("""area_circle() only accepts non-negative values"""		)
							return pi * radius**2

def lowercase_						(     _snake_case     ,_snake_case		):
							if radius_x < 0 or radius_y < 0:
														raise ValueError("""area_ellipse() only accepts non-negative values"""		)
							return pi * radius_x * radius_y

def lowercase_						(     _snake_case     ,_snake_case		):
							if diagonal_a < 0 or diagonal_a < 0:
														raise ValueError("""area_rhombus() only accepts non-negative values"""		)
							return 1 / 2 * diagonal_a * diagonal_a

def lowercase_						(     _snake_case     ,_snake_case		):
							if not isinstance(_snake_case     ,_snake_case		) or sides < 3:
														raise ValueError(
														    """area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"""		)
							elif length < 0:
														raise ValueError(
														    """area_reg_polygon() only accepts non-negative values as \
length of a side"""		)
							return (sides * length**2) / (4 * tan(pi / sides		))
							return (sides * length**2) / (4 * tan(pi / sides		))


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

					print('[DEMO] Areas of various geometric shapes: \n')
					print(f"""Rectangle: {area_rectangle(1_0, 2_0) = }""")
					print(f"""Square: {area_square(1_0) = }""")
					print(f"""Triangle: {area_triangle(1_0, 1_0) = }""")
					print(f"""Triangle: {area_triangle_three_sides(5, 1_2, 1_3) = }""")
					print(f"""Parallelogram: {area_parallelogram(1_0, 2_0) = }""")
					print(f"""Rhombus: {area_rhombus(1_0, 2_0) = }""")
					print(f"""Trapezium: {area_trapezium(1_0, 2_0, 3_0) = }""")
					print(f"""Circle: {area_circle(2_0) = }""")
					print(f"""Ellipse: {area_ellipse(1_0, 2_0) = }""")
					print('\nSurface Areas of various geometric shapes: \n')
					print(f"""Cube: {surface_area_cube(2_0) = }""")
					print(f"""Cuboid: {surface_area_cuboid(1_0, 2_0, 3_0) = }""")
					print(f"""Sphere: {surface_area_sphere(2_0) = }""")
					print(f"""Hemisphere: {surface_area_hemisphere(2_0) = }""")
					print(f"""Cone: {surface_area_cone(1_0, 2_0) = }""")
					print(f"""Conical Frustum: {surface_area_conical_frustum(1_0, 2_0, 3_0) = }""")
					print(f"""Cylinder: {surface_area_cylinder(1_0, 2_0) = }""")
					print(f"""Torus: {surface_area_torus(2_0, 1_0) = }""")
					print(f"""Equilateral Triangle: {area_reg_polygon(3, 1_0) = }""")
					print(f"""Square: {area_reg_polygon(4, 1_0) = }""")
					print(f"""Reqular Pentagon: {area_reg_polygon(5, 1_0) = }""")




