



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



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'summarization'
	SCREAMING_SNAKE_CASE					   =			['loss']
	SCREAMING_SNAKE_CASE					   =			ROUGE_KEYS
	SCREAMING_SNAKE_CASE					   =			'rouge2'

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

				"""simple docstring"""

				if hparams.sortish_sampler and hparams.gpus > 1:
							__lowerCAmelCase   :						Optional[int]				= 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)
				__lowerCAmelCase   :						int				= Path(self.output_dir) / "metrics.json"
				__lowerCAmelCase   :						Dict				= Path(self.output_dir) / "hparams.pkl"
				pickle_save(self.hparams ,    self.hparams_save_path)
				__lowerCAmelCase   :						Any				= 0
				__lowerCAmelCase   :						Optional[int]				= defaultdict(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						List[Any]				= self.config.model_type
				__lowerCAmelCase   :						Optional[Any]				= self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

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

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	Dict[str, torch.Tensor])       -> Dict[str, List[str]]:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= {
				    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")

				__lowerCAmelCase   :						Any				= True
				return readable_batch

	def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	Dict ,    **_SCREAMING_SNAKE_CASE:	Optional[int])       -> Optional[Any]:

				"""simple docstring"""

				return self.model(_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	List[int])       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= 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 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	dict)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= self.tokenizer.pad_token_id
				__lowerCAmelCase		,			__lowerCAmelCase   :						List[str]				= batch["input_ids"], batch["attention_mask"]
				__lowerCAmelCase   :						Optional[int]				= batch["labels"]
				if isinstance(self.model ,    _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase   :						Dict				= self.model._shift_right(_SCREAMING_SNAKE_CASE)
				else:
							__lowerCAmelCase   :						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
							__lowerCAmelCase   :						str				= decoder_input_ids
							self.save_readable_batch(_SCREAMING_SNAKE_CASE)

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

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

	@property
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> int:

				"""simple docstring"""

				return self.tokenizer.pad_token_id

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	Tuple)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= self._step(_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				return self._generative_step(_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	List[Any]="val")       -> Dict:

				"""simple docstring"""

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

				"""simple docstring"""

				return calculate_rouge(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	dict)       -> dict:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= time.time()

				# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
				__lowerCAmelCase   :						str				= 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 ,    )
				__lowerCAmelCase   :						List[Any]				= (time.time() - ta) / batch["input_ids"].shape[0]
				__lowerCAmelCase   :						List[str]				= self.ids_to_clean_text(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						List[str]				= self.ids_to_clean_text(batch["labels"])
				__lowerCAmelCase   :						List[str]				= self._step(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						str				= dict(zip(self.loss_names ,    _SCREAMING_SNAKE_CASE))
				__lowerCAmelCase   :						Dict				= self.calc_generative_metrics(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Dict				= 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 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	List[Any])       -> Dict:

				"""simple docstring"""

				return self._generative_step(_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	Dict)       -> Dict:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	Optional[Any])       -> SeqaSeqDataset:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.n_obs[type_path]
				__lowerCAmelCase   :						Optional[int]				= self.target_lens[type_path]
				__lowerCAmelCase   :						List[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 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	bool = False)       -> DataLoader:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= self.get_dataset(_SCREAMING_SNAKE_CASE)

				if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
							__lowerCAmelCase   :						Optional[int]				= 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":
							__lowerCAmelCase   :						Optional[Any]				= 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 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> DataLoader:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= self.get_dataloader("train" ,    batch_size=self.hparams.train_batch_size ,    shuffle=_SCREAMING_SNAKE_CASE)
				return dataloader

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> DataLoader:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> DataLoader:

				"""simple docstring"""

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







	@staticmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Optional[int])       -> 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=1024 ,    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=142 ,    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=142 ,    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=500 ,    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     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'translation'
	SCREAMING_SNAKE_CASE					   =			['loss']
	SCREAMING_SNAKE_CASE					   =			['bleu']
	SCREAMING_SNAKE_CASE					   =			'bleu'

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

				"""simple docstring"""

				super().__init__(_SCREAMING_SNAKE_CASE ,    **_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Union[str, Any]				= hparams.src_lang
				__lowerCAmelCase   :						Union[str, Any]				= hparams.tgt_lang







	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    _SCREAMING_SNAKE_CASE:	Tuple ,    _SCREAMING_SNAKE_CASE:	Tuple)       -> dict:

				"""simple docstring"""

				return calculate_bleu(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)






def     _lowercase       (						__snake_case      ,__snake_case=None			)		->     SummarizationModule:
			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:
									__lowerCAmelCase   :						SummarizationModule				= SummarizationModule(__snake_case			)
						else:
									__lowerCAmelCase   :						SummarizationModule				= TranslationModule(__snake_case			)
			__lowerCAmelCase   :						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"			)
			):
						__lowerCAmelCase   :						Union[str, Any]				= True  # don't pollute wandb logs unnecessarily
			elif args.logger_name == "wandb":
						from pytorch_lightning.loggers import WandbLogger

						__lowerCAmelCase   :						str				= os.environ.get("WANDB_PROJECT"      ,__snake_case			)
						__lowerCAmelCase   :						List[Any]				= WandbLogger(name=model.output_dir.name      ,project=__snake_case			)

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

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

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

			__lowerCAmelCase   :						Tuple				= args.val_metric == "loss"
			__lowerCAmelCase   :						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

			__lowerCAmelCase   :						Any				= ""
			__lowerCAmelCase   :						Optional[Any]				= sorted(glob.glob(os.path.join(args.output_dir      ,"*.ckpt"			)      ,recursive=__snake_case			)			)
			if checkpoints:
						__lowerCAmelCase   :						Optional[Any]				= checkpoints[-1]
						__lowerCAmelCase   :						Any				= checkpoints[-1]
			trainer.logger.log_hyperparams(model.hparams			)

			# test() without a model tests using the best checkpoint automatically
			trainer.test()
			return model


if __name__ == "__main__":
	__snake_case							:     Tuple   =					argparse.ArgumentParser()
	__snake_case							:     Optional[int]   =					pl.Trainer.add_argparse_args(parser)
	__snake_case							:     Any   =					SummarizationModule.add_model_specific_args(parser, os.getcwd())

	__snake_case							:     str   =					parser.parse_args()

	main(args)




"""simple docstring"""






import string
from math import logaa


def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			__lowerCAmelCase   :						int				= document.translate(
			    str.maketrans(""      ,""      ,string.punctuation			)			).replace("\n"      ,""			)
			__lowerCAmelCase   :						Dict				= document_without_punctuation.split(" "			)  # word tokenization
			return len([word for word in tokenize_document if word.lower() == term.lower()]			)


def     _lowercase       (						__snake_case      ,__snake_case			)		->     tuple[int, int]:
			__lowerCAmelCase   :						Optional[Any]				= corpus.lower().translate(
			    str.maketrans(""      ,""      ,string.punctuation			)			)  # strip all punctuation and replace it with ''
			__lowerCAmelCase   :						List[str]				= corpus_without_punctuation.split("\n"			)
			__lowerCAmelCase   :						str				= term.lower()
			return (len([doc for doc in docs if term in doc]			), len(__snake_case			))


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case=False			)		->     float:
			if smoothing:
						if n == 0:
									raise ValueError("log10(0) is undefined."			)
						return round(1 + logaa(n / (1 + df)			)      ,3			)

			if df == 0:
						raise ZeroDivisionError("df must be > 0"			)
			elif n == 0:
						raise ValueError("log10(0) is undefined."			)
			return round(logaa(n / df			)      ,3			)





def     _lowercase       (						__snake_case      ,__snake_case			)		->     float:
			return round(tf * idf      ,3			)