


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


_A		:				List[Any]									=       logging.getLogger(__name__)



class        a					(			a__   ):
     UpperCAmelCase__ :  List[str]						      =	"summarization"
     UpperCAmelCase__ :  Optional[Any]						      =	["loss"]
     UpperCAmelCase__ :  List[Any]						      =	ROUGE_KEYS
     UpperCAmelCase__ :  List[str]						      =	"rouge2"





     def __init__(    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	**SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        if hparams.sortish_sampler and hparams.gpus > 1:
           __lowerCamelCase:				Tuple     =	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							)
        __lowerCamelCase:				Optional[Any]     =	Path(self.output_dir							) / """metrics.json"""
        __lowerCamelCase:				str     =	Path(self.output_dir							) / """hparams.pkl"""
        pickle_save(self.hparams    ,	self.hparams_save_path							)
        __lowerCamelCase:				Tuple     =	0
        __lowerCamelCase:				Tuple     =	defaultdict(lowerCamelCase_							)
        __lowerCamelCase:				List[str]     =	self.config.model_type
        __lowerCamelCase:				Tuple     =	self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

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

        __lowerCamelCase:				Optional[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()							)

        __lowerCamelCase:				Optional[int]     =	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    ,	lowerCamelCase_							):
           __lowerCamelCase:				Union[str, Any]     =	self.tokenizer.lang_code_to_id[hparams.tgt_lang]
           __lowerCamelCase:				int     =	self.decoder_start_token_id
        __lowerCamelCase:				Any     =	(
            SeqaSeqDataset if hasattr(self.tokenizer    ,	"""prepare_seq2seq_batch"""							) else LegacySeqaSeqDataset
        )
        __lowerCamelCase:				Any     =	False
        __lowerCamelCase:				Union[str, Any]     =	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:				Optional[Any]     =	self.hparams.eval_max_gen_length
        else:
           __lowerCamelCase:				List[str]     =	self.model.config.max_length
        __lowerCamelCase:				List[str]     =	self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        __lowerCamelCase:				Union[str, Any]     =	{
            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"""							)

        __lowerCamelCase:				List[str]     =	True
        return readable_batch





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	**SCREAMING_SNAKE_CASE_					:				List[Any]							):
        return self.model(lowerCamelCase_    ,	**lowerCamelCase_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        __lowerCamelCase:				Tuple     =	self.tokenizer.batch_decode(
            lowerCamelCase_    ,	skip_special_tokens=lowerCamelCase_    ,	clean_up_tokenization_spaces=lowerCamelCase_							)
        return lmap(str.strip    ,	lowerCamelCase_							)





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

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

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





     @property
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        return self.tokenizer.pad_token_id





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        __lowerCamelCase:				int     =	self._step(lowerCamelCase_							)

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Any="val"							):
        self.step_count += 1
        __lowerCamelCase:				List[Any]     =	{k: torch.stack([x[k] for x in outputs]							).mean() for k in self.loss_names}
        __lowerCamelCase:				List[Any]     =	losses["""loss"""]
        __lowerCamelCase:				List[str]     =	{
            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:				Optional[int]     =	torch.tensor(lowerCamelCase_							).type_as(lowerCamelCase_							)
        generative_metrics.update({k: v.item() for k, v in losses.items()}							)
        losses.update(lowerCamelCase_							)
        __lowerCamelCase:				Dict     =	{F'{prefix}_avg_{k}': x for k, x in losses.items()}
        __lowerCamelCase:				Tuple     =	self.step_count
        self.metrics[prefix].append(lowerCamelCase_							)  # callback writes this to self.metrics_save_path
        __lowerCamelCase:				List[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					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
        return calculate_rouge(lowerCamelCase_    ,	lowerCamelCase_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        __lowerCamelCase:				Tuple     =	time.time()

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        return self._generative_step(lowerCamelCase_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        return self.validation_epoch_end(lowerCamelCase_    ,	prefix="""test"""							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
        __lowerCamelCase:				Optional[Any]     =	self.n_obs[type_path]
        __lowerCamelCase:				str     =	self.target_lens[type_path]
        __lowerCamelCase:				List[str]     =	self.dataset_class(
            self.tokenizer    ,	type_path=lowerCamelCase_    ,	n_obs=lowerCamelCase_    ,	max_target_length=lowerCamelCase_    ,	**self.dataset_kwargs    ,	)
        return dataset





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				str = False							):
        __lowerCamelCase:				Optional[Any]     =	self.get_dataset(lowerCamelCase_							)

        if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
           __lowerCamelCase:				str     =	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":
           __lowerCamelCase:				Dict     =	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  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				str     =	self.get_dataloader("""train"""    ,	batch_size=self.hparams.train_batch_size    ,	shuffle=lowerCamelCase_							)
        return dataloader





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        return self.get_dataloader("""val"""    ,	batch_size=self.hparams.eval_batch_size							)





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







     @staticmethod
     def  SCREAMING_SNAKE_CASE__       (    SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        BaseTransformer.add_model_specific_args(lowerCamelCase_    ,	lowerCamelCase_							)
        add_generic_args(lowerCamelCase_    ,	lowerCamelCase_							)
        parser.add_argument(
            """--max_source_length"""    ,	default=1024    ,	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=142    ,	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=142    ,	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=500    ,	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        a					(			a__   ):
     UpperCAmelCase__ :  Optional[int]						      =	"translation"
     UpperCAmelCase__ :  Tuple						      =	["loss"]
     UpperCAmelCase__ :  str						      =	["bleu"]
     UpperCAmelCase__ :  Optional[int]						      =	"bleu"





     def __init__(    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	**SCREAMING_SNAKE_CASE_					:				Any							):
        super().__init__(lowerCamelCase_    ,	**lowerCamelCase_							)
        __lowerCamelCase:				Any     =	hparams.src_lang
        __lowerCamelCase:				Union[str, Any]     =	hparams.tgt_lang







     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        return calculate_bleu(lowerCamelCase_    ,	lowerCamelCase_							)






def     __lowerCAmelCase						(							snake_case	:    Dict						,      snake_case	:    Union[str, Any]=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:				Optional[int]     =	SummarizationModule(snake_case )
      else:
         __lowerCamelCase:				int     =	TranslationModule(snake_case )
   __lowerCamelCase:				Optional[Any]     =	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:				Any     =	True  # don't pollute wandb logs unnecessarily
   elif args.logger_name == "wandb":
      from pytorch_lightning.loggers import WandbLogger

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

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

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

   if args.early_stopping_patience >= 0:
      __lowerCamelCase:				str     =	get_early_stopping_callback(model.val_metric						,      args.early_stopping_patience )
   else:
      __lowerCamelCase:				Optional[Any]     =	False

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

   _A		:				Optional[Any]									=       parser.parse_args()

   main(args)






from __future__ import annotations

from typing import Any



class        a					:





     def __init__(    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				float = 0							):
        __lowerCamelCase   ,     __lowerCamelCase:				Dict     =	row, column
        __lowerCamelCase:				Optional[Any]     =	[[default_value for c in range(SCREAMING_SNAKE_CASE_							)] for r in range(SCREAMING_SNAKE_CASE_							)]





     def __str__(    self					:				Union[str, Any]							):
        __lowerCamelCase:				str     =	F'Matrix consist of {self.row} rows and {self.column} columns\n'

        # Make string identifier
        __lowerCamelCase:				Optional[int]     =	0
        for row_vector in self.array:
           for obj in row_vector:
              __lowerCamelCase:				int     =	max(SCREAMING_SNAKE_CASE_    ,	len(str(SCREAMING_SNAKE_CASE_							)							)							)
        __lowerCamelCase:				Union[str, Any]     =	F'%{max_element_length}s'

        # Make string and return
        def single_line(SCREAMING_SNAKE_CASE_					:				list[float]							) -> str:
           nonlocal string_format_identifier
           __lowerCamelCase:				Any     =	"""["""
           line += ", ".join(string_format_identifier % (obj,) for obj in row_vector							)
           line += "]"
           return line

        s += "\n".join(single_line(SCREAMING_SNAKE_CASE_							) for row_vector in self.array							)
        return s





     def __repr__(    self					:				Union[str, Any]							):
        return str(self							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				tuple[int, int]							):
        if not (isinstance(SCREAMING_SNAKE_CASE_    ,	(list, tuple)							) and len(SCREAMING_SNAKE_CASE_							) == 2):
           return False
        elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
           return False
        else:
           return True





     def __getitem__(    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				tuple[int, int]							):
        assert self.validate_indicies(SCREAMING_SNAKE_CASE_							)
        return self.array[loc[0]][loc[1]]





     def __setitem__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				tuple[int, int]    ,	SCREAMING_SNAKE_CASE_					:				float							):
        assert self.validate_indicies(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				List[Any]     =	value





     def __add__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Matrix							):
        assert isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        assert self.row == another.row and self.column == another.column

        # Add
        __lowerCamelCase:				Any     =	Matrix(self.row    ,	self.column							)
        for r in range(self.row							):
           for c in range(self.column							):
              __lowerCamelCase:				Tuple     =	self[r, c] + another[r, c]
        return result





     def __neg__(    self					:				Any							):
        __lowerCamelCase:				List[str]     =	Matrix(self.row    ,	self.column							)
        for r in range(self.row							):
           for c in range(self.column							):
              __lowerCamelCase:				int     =	-self[r, c]
        return result





     def __sub__(    self					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				Matrix							):
        return self + (-another)





     def __mul__(    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				int | float | Matrix							):

        if isinstance(SCREAMING_SNAKE_CASE_    ,	(int, float)							):  # Scalar multiplication
           __lowerCamelCase:				List[str]     =	Matrix(self.row    ,	self.column							)
           for r in range(self.row							):
              for c in range(self.column							):
                 __lowerCamelCase:				List[Any]     =	self[r, c] * another
           return result
        elif isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							):  # Matrix multiplication
           assert self.column == another.row
           __lowerCamelCase:				Optional[int]     =	Matrix(self.row    ,	another.column							)
           for r in range(self.row							):
              for c in range(another.column							):
                 for i in range(self.column							):
                    result[r, c] += self[r, i] * another[i, c]
           return result
        else:
           __lowerCamelCase:				int     =	F'Unsupported type given for another ({type(SCREAMING_SNAKE_CASE_							)})'
           raise TypeError(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				int     =	Matrix(self.column    ,	self.row							)
        for r in range(self.row							):
           for c in range(self.column							):
              __lowerCamelCase:				List[str]     =	self[r, c]
        return result







     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				Matrix    ,	SCREAMING_SNAKE_CASE_					:				Matrix							):
        assert isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							) and isinstance(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        assert self.row == self.column == u.row == v.row  # u, v should be column vector
        assert u.column == v.column == 1  # u, v should be column vector

        # Calculate
        __lowerCamelCase:				Optional[Any]     =	v.transpose()
        __lowerCamelCase:				Any     =	(v_t * self * u)[0, 0] + 1
        if numerator_factor == 0:
           return None  # It's not invertable
        return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



   def     __lowerCAmelCase						(							)       ->							None:
      # a^(-1)
      __lowerCamelCase:				int     =	Matrix(3						,      3						,      0 )
      for i in range(3 ):
         __lowerCamelCase:				List[Any]     =	1
      print(f'a^(-1) is {ainv}' )
      # u, v
      __lowerCamelCase:				List[str]     =	Matrix(3						,      1						,      0 )
      __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Optional[Any]     =	1, 2, -3
      __lowerCamelCase:				Dict     =	Matrix(3						,      1						,      0 )
      __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Union[str, Any]     =	4, -2, 5
      print(f'u is {u}' )
      print(f'v is {v}' )
      print(f'uv^T is {u * v.transpose()}' )
      # Sherman Morrison
      print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(snake_case						,      snake_case )}' )



   def     __lowerCAmelCase						(							)       ->							None:
      import doctest

      doctest.testmod()







   testa()


