





'''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


lowerCamelCase__    	=     logging.getLogger(__name__)






class 	lowerCAmelCase__			(			a__	):
			lowerCAmelCase :	Optional[int]          =     """summarization"""
			lowerCAmelCase :	int          =     ["""loss"""]
			lowerCAmelCase :	Dict          =     ROUGE_KEYS
			lowerCAmelCase :	Optional[Any]          =     """rouge2"""




			def __init__(		self  :     Optional[Any]	,  lowerCamelCase__  :     Union[str, Any]	,  **lowerCamelCase__  :     Optional[int] )   ->Tuple:



					'''simple docstring'''







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

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

					_UpperCAmelCase		:       str								=      {
					    "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() )

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




			def 							lowerCAmelCase__    (		self  :     List[Any]	,  lowerCamelCase__  :     Union[str, Any] )   ->Dict[str, List[str]]:



					'''simple docstring'''







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

					_UpperCAmelCase		:       Any								=      True
					return readable_batch




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     Tuple	,  **lowerCamelCase__  :     str )   ->int:



					'''simple docstring'''







					return self.model(lowerCAmelCase__	,  **lowerCAmelCase__ )




			def 							lowerCAmelCase__    (		self  :     Dict	,  lowerCamelCase__  :     Dict )   ->int:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      self.tokenizer.batch_decode(
					    lowerCAmelCase__	,  skip_special_tokens=lowerCAmelCase__	,  clean_up_tokenization_spaces=lowerCAmelCase__ )
					return lmap(str.strip	,  lowerCAmelCase__ )




			def 							lowerCAmelCase__    (		self  :     Any	,  lowerCamelCase__  :     Optional[Any] )   ->Tuple:



					'''simple docstring'''







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

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

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




			@property
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->int:



					'''simple docstring'''







					return self.tokenizer.pad_token_id




			def 							lowerCAmelCase__    (		self  :     Optional[int]	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     Union[str, Any] )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      self._step(lowerCAmelCase__ )

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



					'''simple docstring'''







					return self._generative_step(lowerCAmelCase__ )




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     List[str]="val" )   ->Dict:



					'''simple docstring'''







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



					'''simple docstring'''







					return calculate_rouge(lowerCAmelCase__	,  lowerCAmelCase__ )




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     Union[str, Any] )   ->dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      time.time()

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




			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     int )   ->List[Any]:



					'''simple docstring'''







					return self._generative_step(lowerCAmelCase__ )




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     List[Any] )   ->Optional[Any]:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     Dict )   ->SeqaSeqDataset:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      self.n_obs[type_path]
					_UpperCAmelCase		:       List[str]								=      self.target_lens[type_path]
					_UpperCAmelCase		:       Any								=      self.dataset_class(
					    self.tokenizer	,  type_path=lowerCAmelCase__	,  n_obs=lowerCAmelCase__	,  max_target_length=lowerCAmelCase__	,  **self.dataset_kwargs	,  )
					return dataset




			def 							lowerCAmelCase__    (		self  :     Any	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     int = False )   ->DataLoader:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      self.get_dataset(lowerCAmelCase__ )

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

					elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
							_UpperCAmelCase		:       Union[str, Any]								=      dataset.make_dynamic_sampler(
							    self.hparams.max_tokens_per_batch	,  distributed=self.hparams.gpus > 1 )
							return DataLoader(
							    lowerCAmelCase__	,  batch_sampler=lowerCAmelCase__	,  collate_fn=dataset.collate_fn	,  num_workers=self.num_workers	,  )
					else:
							return DataLoader(
							    lowerCAmelCase__	,  batch_size=lowerCAmelCase__	,  collate_fn=dataset.collate_fn	,  shuffle=lowerCAmelCase__	,  num_workers=self.num_workers	,  sampler=lowerCAmelCase__	,  )




			def 							lowerCAmelCase__    (		self  :     List[str] )   ->DataLoader:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      self.get_dataloader("train"	,  batch_size=self.hparams.train_batch_size	,  shuffle=lowerCAmelCase__ )
					return dataloader




			def 							lowerCAmelCase__    (		self  :     List[str] )   ->DataLoader:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->DataLoader:



					'''simple docstring'''







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






			@staticmethod
			def 							lowerCAmelCase__    (		lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     Any )   ->Optional[int]:



					'''simple docstring'''







					BaseTransformer.add_model_specific_args(lowerCAmelCase__	,  lowerCAmelCase__ )
					add_generic_args(lowerCAmelCase__	,  lowerCAmelCase__ )
					parser.add_argument(
					    "--max_source_length"	,  default=10_24	,  type=lowerCAmelCase__	,  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=lowerCAmelCase__	,  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=lowerCAmelCase__	,  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=lowerCAmelCase__	,  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=lowerCAmelCase__ )
					parser.add_argument("--overwrite_output_dir"	,  action="store_true"	,  default=lowerCAmelCase__ )
					parser.add_argument("--max_tokens_per_batch"	,  type=lowerCAmelCase__	,  default=lowerCAmelCase__ )
					parser.add_argument("--logger_name"	,  type=lowerCAmelCase__	,  choices=["default", "wandb", "wandb_shared"]	,  default="default" )
					parser.add_argument("--n_train"	,  type=lowerCAmelCase__	,  default=-1	,  required=lowerCAmelCase__	,  help="# examples. -1 means use all." )
					parser.add_argument("--n_val"	,  type=lowerCAmelCase__	,  default=5_00	,  required=lowerCAmelCase__	,  help="# examples. -1 means use all." )
					parser.add_argument("--n_test"	,  type=lowerCAmelCase__	,  default=-1	,  required=lowerCAmelCase__	,  help="# examples. -1 means use all." )
					parser.add_argument(
					    "--task"	,  type=lowerCAmelCase__	,  default="summarization"	,  required=lowerCAmelCase__	,  help="# examples. -1 means use all." )
					parser.add_argument("--label_smoothing"	,  type=lowerCAmelCase__	,  default=0.0	,  required=lowerCAmelCase__ )
					parser.add_argument("--src_lang"	,  type=lowerCAmelCase__	,  default=""	,  required=lowerCAmelCase__ )
					parser.add_argument("--tgt_lang"	,  type=lowerCAmelCase__	,  default=""	,  required=lowerCAmelCase__ )
					parser.add_argument("--eval_beams"	,  type=lowerCAmelCase__	,  default=lowerCAmelCase__	,  required=lowerCAmelCase__ )
					parser.add_argument(
					    "--val_metric"	,  type=lowerCAmelCase__	,  default=lowerCAmelCase__	,  required=lowerCAmelCase__	,  choices=["bleu", "rouge2", "loss", None] )
					parser.add_argument("--eval_max_gen_length"	,  type=lowerCAmelCase__	,  default=lowerCAmelCase__	,  help="never generate more than n tokens" )
					parser.add_argument("--save_top_k"	,  type=lowerCAmelCase__	,  default=1	,  required=lowerCAmelCase__	,  help="How many checkpoints to save" )
					parser.add_argument(
					    "--early_stopping_patience"	,  type=lowerCAmelCase__	,  default=-1	,  required=lowerCAmelCase__	,  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__	):
			lowerCAmelCase :	Tuple          =     """translation"""
			lowerCAmelCase :	Optional[int]          =     ["""loss"""]
			lowerCAmelCase :	Tuple          =     ["""bleu"""]
			lowerCAmelCase :	Tuple          =     """bleu"""




			def __init__(		self  :     Optional[int]	,  lowerCamelCase__  :     Optional[Any]	,  **lowerCamelCase__  :     str )   ->Optional[Any]:



					'''simple docstring'''







					super().__init__(lowerCAmelCase__	,  **lowerCAmelCase__ )
					_UpperCAmelCase		:       Optional[Any]								=      hparams.src_lang
					_UpperCAmelCase		:       Union[str, Any]								=      hparams.tgt_lang






			def 							lowerCAmelCase__    (		self  :     int	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     Dict )   ->dict:



					'''simple docstring'''







					return calculate_bleu(lowerCAmelCase__	,  lowerCAmelCase__ )






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase=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:
						_UpperCAmelCase		:       SummarizationModule								=      SummarizationModule(_lowercase   )
				else:
						_UpperCAmelCase		:       SummarizationModule								=      TranslationModule(_lowercase   )
		_UpperCAmelCase		:       int								=      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"   )
		):
				_UpperCAmelCase		:       Tuple								=      True  # don't pollute wandb logs unnecessarily
		elif args.logger_name == "wandb":
				from pytorch_lightning.loggers import WandbLogger

				_UpperCAmelCase		:       Optional[int]								=      os.environ.get("WANDB_PROJECT"      , _lowercase   )
				_UpperCAmelCase		:       Dict								=      WandbLogger(name=model.output_dir.name      , project=_lowercase   )

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

				_UpperCAmelCase		:       str								=      WandbLogger(name=model.output_dir.name      , project=F"""hf_{dataset}"""   )

		if args.early_stopping_patience >= 0:
				_UpperCAmelCase		:       Tuple								=      get_early_stopping_callback(model.val_metric      , args.early_stopping_patience   )
		else:
				_UpperCAmelCase		:       Optional[int]								=      False

		_UpperCAmelCase		:       Optional[Any]								=      args.val_metric == "loss"
		_UpperCAmelCase		:       pl.Trainer								=      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

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

						lowerCamelCase__    	=     parser.parse_args()

						main(args)







'''simple docstring'''


from __future__ import annotations

lowerCamelCase__    	=     {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}






class 	lowerCAmelCase__			:




			def __init__(		self  :     int	,  lowerCamelCase__  :     dict[str, list[str]]	,  lowerCamelCase__  :     str )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      graph
					# mapping node to its parent in resulting breadth first tree
					_UpperCAmelCase		:       dict[str, str | None]								=      {}
					_UpperCAmelCase		:       List[Any]								=      source_vertex




			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->None:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      {self.source_vertex}
					_UpperCAmelCase		:       List[Any]								=      None
					_UpperCAmelCase		:       List[str]								=      [self.source_vertex]  # first in first out queue

					while queue:
							_UpperCAmelCase		:       int								=      queue.pop(0 )
							for adjacent_vertex in self.graph[vertex]:
									if adjacent_vertex not in visited:
											visited.add(lowerCamelCase__ )
											_UpperCAmelCase		:       List[Any]								=      vertex
											queue.append(lowerCamelCase__ )






			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     str )   ->str:



					'''simple docstring'''







					if target_vertex == self.source_vertex:
							return self.source_vertex

					_UpperCAmelCase		:       int								=      self.parent.get(lowerCamelCase__ )
					if target_vertex_parent is None:
							_UpperCAmelCase		:       Tuple								=      (
							    F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
							)
							raise ValueError(lowerCamelCase__ )

					return self.shortest_path(lowerCamelCase__ ) + F"""->{target_vertex}"""


if __name__ == "__main__":
						lowerCamelCase__    	=     Graph(graph, 'G')
						g.breath_first_search()
						print(g.shortest_path('D'))
						print(g.shortest_path('G'))
						print(g.shortest_path('Foo'))



