




'''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       =		logging.getLogger(__name__)
class 		SCREAMING_SNAKE_CASE			(     _a							):




				'''simple docstring'''





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







				def __init__(   self  ,						SCREAMING_SNAKE_CASE__  ,						**SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							if hparams.sortish_sampler and hparams.gpus > 1:
										snake_case:       str        =       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				)
							snake_case:       str        =       Path(self.output_dir				) / 'metrics.json'
							snake_case:       int        =       Path(self.output_dir				) / 'hparams.pkl'
							pickle_save(self.hparams  ,						self.hparams_save_path				)
							snake_case:       Optional[int]        =       0
							snake_case:       str        =       defaultdict(SCREAMING_SNAKE_CASE__				)
							snake_case:       int        =       self.config.model_type
							snake_case:       int        =       self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

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

							snake_case:       List[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()				)

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







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							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'				)

							snake_case:       Dict        =       True
							return readable_batch







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						**SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							return self.model(SCREAMING_SNAKE_CASE__  ,						**SCREAMING_SNAKE_CASE__				)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       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        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







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

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

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







				@property
				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







							return self.tokenizer.pad_token_id







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Any        =       self._step(SCREAMING_SNAKE_CASE__				)

							snake_case:       int        =       dict(zip(self.loss_names  ,						SCREAMING_SNAKE_CASE__				)				)
							# tokens per batch
							snake_case:       str        =       batch['input_ids'].ne(self.pad				).sum() + batch['labels'].ne(self.pad				).sum()
							snake_case:       Optional[Any]        =       batch['input_ids'].shape[0]
							snake_case:       List[str]        =       batch['input_ids'].eq(self.pad				).sum()
							snake_case:       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        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							return self._generative_step(SCREAMING_SNAKE_CASE__				)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__="val"				):
							'''simple docstring'''







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







							return calculate_rouge(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Optional[Any]        =       time.time()

							# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
							snake_case:       Dict        =       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  ,						)
							snake_case:       Any        =       (time.time() - ta) / batch['input_ids'].shape[0]
							snake_case:       int        =       self.ids_to_clean_text(SCREAMING_SNAKE_CASE__				)
							snake_case:       Any        =       self.ids_to_clean_text(batch['labels']				)
							snake_case:       Optional[int]        =       self._step(SCREAMING_SNAKE_CASE__				)
							snake_case:       Any        =       dict(zip(self.loss_names  ,						SCREAMING_SNAKE_CASE__				)				)
							snake_case:       int        =       self.calc_generative_metrics(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				)
							snake_case:       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        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							return self._generative_step(SCREAMING_SNAKE_CASE__				)







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							snake_case:       Any        =       self.n_obs[type_path]
							snake_case:       int        =       self.target_lens[type_path]
							snake_case:       int        =       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        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ = False				):
							'''simple docstring'''







							snake_case:       Dict        =       self.get_dataset(SCREAMING_SNAKE_CASE__				)

							if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
										snake_case:       Dict        =       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":
										snake_case:       str        =       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        _UpperCamelCase							(   self				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self				):
							'''simple docstring'''







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







				@staticmethod
				def        _UpperCamelCase							(   SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
							'''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 		SCREAMING_SNAKE_CASE			(     _a							):




				'''simple docstring'''





				__UpperCamelCase     				=   '''translation'''
				__UpperCamelCase     				=   ['''loss''']
				__UpperCamelCase     				=   ['''bleu''']
				__UpperCamelCase     				=   '''bleu'''







				def __init__(   self  ,						SCREAMING_SNAKE_CASE__  ,						**SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







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







				def        _UpperCamelCase							(   self  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				):
							'''simple docstring'''







							return calculate_bleu(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__				)






def       lowerCAmelCase_     (					__A						:							Dict ,			__A						:							int=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:
									snake_case:       List[Any]        =       SummarizationModule(_snake_case )
						else:
									snake_case:       Optional[int]        =       TranslationModule(_snake_case )
			snake_case:       Tuple        =       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' )
			):
						snake_case:       Optional[int]        =       True  # don't pollute wandb logs unnecessarily
			elif args.logger_name == "wandb":
						from pytorch_lightning.loggers import WandbLogger

						snake_case:       List[str]        =       os.environ.get('WANDB_PROJECT' ,			_snake_case )
						snake_case:       Tuple        =       WandbLogger(name=model.output_dir.name ,			project=_snake_case )

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

						snake_case:       List[Any]        =       WandbLogger(name=model.output_dir.name ,			project=f"""hf_{dataset}""" )

			if args.early_stopping_patience >= 0:
						snake_case:       List[Any]        =       get_early_stopping_callback(model.val_metric ,			args.early_stopping_patience )
			else:
						snake_case:       Any        =       False

			snake_case:       Union[str, Any]        =       args.val_metric == 'loss'
			snake_case:       Dict        =       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

			snake_case:       Optional[int]        =       ''
			snake_case:       Union[str, Any]        =       sorted(glob.glob(os.path.join(args.output_dir ,			'*.ckpt' ) ,			recursive=_snake_case ) )
			if checkpoints:
						snake_case:       Tuple        =       checkpoints[-1]
						snake_case:       Tuple        =       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       =		argparse.ArgumentParser()
			__UpperCAmelCase       =		pl.Trainer.add_argparse_args(parser)
			__UpperCAmelCase       =		SummarizationModule.add_model_specific_args(parser, os.getcwd())

			__UpperCAmelCase       =		parser.parse_args()

			main(args)







def        _snake_case   (_snake_case				:       list      ,    _snake_case				:       int      ,    _snake_case				:       int = 0      ,    _snake_case				:       int = 0)					->	int:
   _lowercase              =right or len(_snake_case) - 1
   if left > right:
      return -1
   elif list_data[left] == key:
      return left
   elif list_data[right] == key:
      return right
   else:
      return search(_snake_case      ,    _snake_case      ,    left + 1      ,    right - 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


