

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


_SCREAMING_SNAKE_CASE	:    str       =     logging.getLogger(__name__)






class 			UpperCAmelCase__						(			__lowerCamelCase			):






  """simple docstring"""


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






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






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

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

       SCREAMING_SNAKE_CASE__       =       {
           '''train''': self.hparams.max_target_length,
           '''val''': self.hparams.val_max_target_length,
           '''test''': self.hparams.test_max_target_length,
       }
       assert self.target_lens["train"] <= self.target_lens["val"], f'''target_lens: {self.target_lens}'''
       assert self.target_lens["train"] <= self.target_lens["test"], f'''target_lens: {self.target_lens}'''
       if self.hparams.freeze_embeds:
            freeze_embeds(self.model   )
       if self.hparams.freeze_encoder:
            freeze_params(self.model.get_encoder()   )
            assert_all_frozen(self.model.get_encoder()   )

       SCREAMING_SNAKE_CASE__       =       get_git_info()['''repo_sha''']
       SCREAMING_SNAKE_CASE__       =       hparams.num_workers
       SCREAMING_SNAKE_CASE__       =       None  # default to config
       if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer      ,  __lowerCamelCase   ):
            SCREAMING_SNAKE_CASE__       =       self.tokenizer.lang_code_to_id[hparams.tgt_lang]
            SCREAMING_SNAKE_CASE__       =       self.decoder_start_token_id
       SCREAMING_SNAKE_CASE__       =       (
           SeqaSeqDataset if hasattr(self.tokenizer      ,  '''prepare_seq2seq_batch'''   ) else LegacySeqaSeqDataset
       )
       SCREAMING_SNAKE_CASE__       =       False
       SCREAMING_SNAKE_CASE__       =       self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
       if self.hparams.eval_max_gen_length is not None:
            SCREAMING_SNAKE_CASE__       =       self.hparams.eval_max_gen_length
       else:
            SCREAMING_SNAKE_CASE__       =       self.model.config.max_length
       SCREAMING_SNAKE_CASE__       =       self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric






  def  lowercase_						(				self		:						int      ,  __lowerCamelCase		:						Dict[str, torch.Tensor]   )						->				Dict[str, List[str]]:
       SCREAMING_SNAKE_CASE__       =       {
           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'''   )

       SCREAMING_SNAKE_CASE__       =       True
       return readable_batch






  def  lowercase_						(				self		:						List[Any]      ,  __lowerCamelCase		:						List[str]      ,  **__lowerCamelCase		:						int   )						->				Tuple:
       return self.model(__lowerCamelCase      ,  **__lowerCamelCase   )






  def  lowercase_						(				self		:						List[str]      ,  __lowerCamelCase		:						List[int]   )						->				int:
       SCREAMING_SNAKE_CASE__       =       self.tokenizer.batch_decode(
           __lowerCamelCase      ,  skip_special_tokens=__lowerCamelCase      ,  clean_up_tokenization_spaces=__lowerCamelCase   )
       return lmap(str.strip      ,  __lowerCamelCase   )






  def  lowercase_						(				self		:						Optional[int]      ,  __lowerCamelCase		:						dict   )						->				Tuple:
       SCREAMING_SNAKE_CASE__       =       self.tokenizer.pad_token_id
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__       =       batch['''input_ids'''], batch['''attention_mask''']
       SCREAMING_SNAKE_CASE__       =       batch['''labels''']
       if isinstance(self.model      ,  __lowerCamelCase   ):
            SCREAMING_SNAKE_CASE__       =       self.model._shift_right(__lowerCamelCase   )
       else:
            SCREAMING_SNAKE_CASE__       =       shift_tokens_right(__lowerCamelCase      ,  __lowerCamelCase   )
       if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
            SCREAMING_SNAKE_CASE__       =       decoder_input_ids
            self.save_readable_batch(__lowerCamelCase   )

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

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






  @property
  def  lowercase_						(				self		:						List[Any]   )						->				int:
       return self.tokenizer.pad_token_id






  def  lowercase_						(				self		:						Union[str, Any]      ,  __lowerCamelCase		:						Dict      ,  __lowerCamelCase		:						List[Any]   )						->				Dict:
       SCREAMING_SNAKE_CASE__       =       self._step(__lowerCamelCase   )

       SCREAMING_SNAKE_CASE__       =       dict(zip(self.loss_names      ,  __lowerCamelCase   )   )
       # tokens per batch
       SCREAMING_SNAKE_CASE__       =       batch['''input_ids'''].ne(self.pad   ).sum() + batch['''labels'''].ne(self.pad   ).sum()
       SCREAMING_SNAKE_CASE__       =       batch['''input_ids'''].shape[0]
       SCREAMING_SNAKE_CASE__       =       batch['''input_ids'''].eq(self.pad   ).sum()
       SCREAMING_SNAKE_CASE__       =       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  lowercase_						(				self		:						List[str]      ,  __lowerCamelCase		:						List[str]      ,  __lowerCamelCase		:						Tuple   )						->				Dict:
       return self._generative_step(__lowerCamelCase   )






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






  def  lowercase_						(				self		:						Optional[int]      ,  __lowerCamelCase		:						dict   )						->				dict:
       SCREAMING_SNAKE_CASE__       =       time.time()

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






  def  lowercase_						(				self		:						List[str]      ,  __lowerCamelCase		:						Dict      ,  __lowerCamelCase		:						Any   )						->				Union[str, Any]:
       return self._generative_step(__lowerCamelCase   )






  def  lowercase_						(				self		:						Tuple      ,  __lowerCamelCase		:						Union[str, Any]   )						->				Optional[int]:
       return self.validation_epoch_end(__lowerCamelCase      ,  prefix='''test'''   )






  def  lowercase_						(				self		:						Tuple      ,  __lowerCamelCase		:						List[str]   )						->				SeqaSeqDataset:
       SCREAMING_SNAKE_CASE__       =       self.n_obs[type_path]
       SCREAMING_SNAKE_CASE__       =       self.target_lens[type_path]
       SCREAMING_SNAKE_CASE__       =       self.dataset_class(
           self.tokenizer      ,  type_path=__lowerCamelCase      ,  n_obs=__lowerCamelCase      ,  max_target_length=__lowerCamelCase      ,  **self.dataset_kwargs      ,  )
       return dataset






  def  lowercase_						(				self		:						str      ,  __lowerCamelCase		:						str      ,  __lowerCamelCase		:						int      ,  __lowerCamelCase		:						bool = False   )						->				DataLoader:
       SCREAMING_SNAKE_CASE__       =       self.get_dataset(__lowerCamelCase   )

       if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
            SCREAMING_SNAKE_CASE__       =       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":
            SCREAMING_SNAKE_CASE__       =       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  lowercase_						(				self		:						Tuple   )						->				DataLoader:
       SCREAMING_SNAKE_CASE__       =       self.get_dataloader('''train'''      ,  batch_size=self.hparams.train_batch_size      ,  shuffle=__lowerCamelCase   )
       return dataloader






  def  lowercase_						(				self		:						Optional[Any]   )						->				DataLoader:
       return self.get_dataloader('''val'''      ,  batch_size=self.hparams.eval_batch_size   )






  def  lowercase_						(				self		:						Tuple   )						->				DataLoader:
       return self.get_dataloader('''test'''      ,  batch_size=self.hparams.eval_batch_size   )







  @staticmethod
  def  lowercase_						(				__lowerCamelCase		:						Union[str, Any]      ,  __lowerCamelCase		:						Any   )						->				Optional[int]:
       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 			UpperCAmelCase__						(			__lowerCamelCase			):






  """simple docstring"""


  a 	=   'translation'
  a 	=   ['loss']
  a 	=   ['bleu']
  a 	=   'bleu'






  def __init__(				self		:						str      ,  __lowerCamelCase		:						List[Any]      ,  **__lowerCamelCase		:						Union[str, Any]   )						->				Union[str, Any]:
       super().__init__(__lowerCamelCase      ,  **__lowerCamelCase   )
       SCREAMING_SNAKE_CASE__       =       hparams.src_lang
       SCREAMING_SNAKE_CASE__       =       hparams.tgt_lang







  def  lowercase_						(				self		:						Union[str, Any]      ,  __lowerCamelCase		:						Dict      ,  __lowerCamelCase		:						Tuple   )						->				dict:
       return calculate_bleu(__lowerCamelCase      ,  __lowerCamelCase   )



def 							UpperCAmelCase_  (		_A ,						_A=None							):





     '''simple docstring'''




     Path(args.output_dir							).mkdir(exist_ok=_A							)
     check_output_dir(_A ,						expected_items=3							)

     if model is None:
          if "summarization" in args.task:
               SCREAMING_SNAKE_CASE__       =       SummarizationModule(_A							)
          else:
               SCREAMING_SNAKE_CASE__       =       TranslationModule(_A							)
     SCREAMING_SNAKE_CASE__       =       Path(args.data_dir							).name
     if (
         args.logger_name == "default"
         or args.fast_dev_run
         or str(args.output_dir							).startswith('''/tmp'''							)
         or str(args.output_dir							).startswith('''/var'''							)
     ):
          SCREAMING_SNAKE_CASE__       =       True  # don't pollute wandb logs unnecessarily
     elif args.logger_name == "wandb":
          from pytorch_lightning.loggers import WandbLogger

          SCREAMING_SNAKE_CASE__       =       os.environ.get('''WANDB_PROJECT''' ,						_A							)
          SCREAMING_SNAKE_CASE__       =       WandbLogger(name=model.output_dir.name ,						project=_A							)

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

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

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

     SCREAMING_SNAKE_CASE__       =       args.val_metric == '''loss'''
     SCREAMING_SNAKE_CASE__       =       generic_train(
         _A ,						_A ,						logging_callback=SeqaSeqLoggingCallback() ,						checkpoint_callback=get_checkpoint_callback(
             args.output_dir ,						model.val_metric ,						args.save_top_k ,						_A							) ,						early_stopping_callback=_A ,						logger=_A ,						)
     pickle_save(model.hparams ,						model.output_dir / '''hparams.pkl'''							)
     if not args.do_predict:
          return model

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

       _SCREAMING_SNAKE_CASE	:    Any       =     parser.parse_args()

       main(args)




from __future__ import annotations





class     lowerCamelCase      :







	def __init__(     self      :List[Any]   ,     lowercase      :list[list[int]]			)		->      Dict:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= TypeError(
				    '''Matrices must be formed from a list of zero or more lists containing at '''
				    '''least one and the same number of values, each of which must be of type '''
				    '''int or float.'''			)
				if len(lowercase			) != 0:
							SCREAMING_SNAKE_CASE     						= len(rows[0]			)
							if cols == 0:
										raise error
							for row in rows:
										if len(lowercase			) != cols:
													raise error
										for value in row:
													if not isinstance(lowercase   ,     (int, float)			):
																raise error
							SCREAMING_SNAKE_CASE     						= rows
				else:
							SCREAMING_SNAKE_CASE     						= []







	def  snake_case__     (     self      :List[str]			)		->      list[list[int]]:







				"""simple docstring"""






				return [[row[i] for row in self.rows] for i in range(len(self.rows[0]			)			)]







	@property
	def  snake_case__     (     self      :Any			)		->      int:







				"""simple docstring"""






				return len(self.rows			)







	@property
	def  snake_case__     (     self      :List[Any]			)		->      int:







				"""simple docstring"""






				return len(self.rows[0]			)







	@property
	def  snake_case__     (     self      :Dict			)		->      tuple[int, int]:







				"""simple docstring"""






				return (self.num_rows, self.num_columns)







	@property
	def  snake_case__     (     self      :Any			)		->      bool:







				"""simple docstring"""






				return self.order[0] == self.order[1]







	def  snake_case__     (     self      :Tuple			)		->      Matrix:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [
				    [0 if column_num != row_num else 1 for column_num in range(self.num_rows			)]
				    for row_num in range(self.num_rows			)
				]
				return Matrix(lowercase			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      int:







				"""simple docstring"""






				if not self.is_square:
							return 0
				if self.order == (0, 0):
							return 1
				if self.order == (1, 1):
							return int(self.rows[0][0]			)
				if self.order == (2, 2):
							return int(
							    (self.rows[0][0] * self.rows[1][1])
							    - (self.rows[0][1] * self.rows[1][0])			)
				else:
							return sum(
							    self.rows[0][column] * self.cofactors().rows[0][column]
							    for column in range(self.num_columns			)			)







	def  snake_case__     (     self      :int			)		->      bool:







				"""simple docstring"""






				return bool(self.determinant()			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [
				    [
				        self.rows[other_row][other_column]
				        for other_column in range(self.num_columns			)
				        if other_column != column
				    ]
				    for other_row in range(self.num_rows			)
				    if other_row != row
				]
				return Matrix(lowercase			).determinant()







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :int   ,     lowercase      :int			)		->      int:







				"""simple docstring"""






				if (row + column) % 2 == 0:
							return self.get_minor(lowercase   ,     lowercase			)
				return -1 * self.get_minor(lowercase   ,     lowercase			)







	def  snake_case__     (     self      :List[Any]			)		->      Matrix:







				"""simple docstring"""






				return Matrix(
				    [
				        [self.get_minor(lowercase   ,     lowercase			) for column in range(self.num_columns			)]
				        for row in range(self.num_rows			)
				    ]			)







	def  snake_case__     (     self      :Dict			)		->      Matrix:







				"""simple docstring"""






				return Matrix(
				    [
				        [
				            self.minors().rows[row][column]
				            if (row + column) % 2 == 0
				            else self.minors().rows[row][column] * -1
				            for column in range(self.minors().num_columns			)
				        ]
				        for row in range(self.minors().num_rows			)
				    ]			)







	def  snake_case__     (     self      :str			)		->      Matrix:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [
				    [self.cofactors().rows[column][row] for column in range(self.num_columns			)]
				    for row in range(self.num_rows			)
				]
				return Matrix(lowercase			)







	def  snake_case__     (     self      :Any			)		->      Matrix:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.determinant()
				if not determinant:
							raise TypeError('''Only matrices with a non-zero determinant have an inverse'''			)
				return self.adjugate() * (1 / determinant)







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







				"""simple docstring"""






				return str(self.rows			)







	def __str__(     self      :List[str]			)		->      str:







				"""simple docstring"""






				if self.num_rows == 0:
							return "[]"
				if self.num_rows == 1:
							return "[[" + ". ".join(str(self.rows[0]			)			) + "]]"
				return (
				    "["
				    + "\n ".join(
				        [
				            '''[''' + '''. '''.join([str(lowercase			) for value in row]			) + '''.]'''
				            for row in self.rows
				        ]			)
				    + "]"
				)







	def  snake_case__     (     self      :int   ,     lowercase      :list[int]   ,     lowercase      :int | None = None			)		->      None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= TypeError('''Row must be a list containing all ints and/or floats'''			)
				if not isinstance(lowercase   ,     lowercase			):
							raise type_error
				for value in row:
							if not isinstance(lowercase   ,     (int, float)			):
										raise type_error
				if len(lowercase			) != self.num_columns:
							raise ValueError(
							    '''Row must be equal in length to the other rows in the matrix'''			)
				if position is None:
							self.rows.append(lowercase			)
				else:
							SCREAMING_SNAKE_CASE     						= self.rows[0:position] + [row] + self.rows[position:]







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :list[int]   ,     lowercase      :int | None = None			)		->      None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= TypeError(
				    '''Column must be a list containing all ints and/or floats'''			)
				if not isinstance(lowercase   ,     lowercase			):
							raise type_error
				for value in column:
							if not isinstance(lowercase   ,     (int, float)			):
										raise type_error
				if len(lowercase			) != self.num_rows:
							raise ValueError(
							    '''Column must be equal in length to the other columns in the matrix'''			)
				if position is None:
							SCREAMING_SNAKE_CASE     						= [self.rows[i] + [column[i]] for i in range(self.num_rows			)]
				else:
							SCREAMING_SNAKE_CASE     						= [
							    self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
							    for i in range(self.num_rows			)
							]







	def __eq__(     self      :Union[str, Any]   ,     lowercase      :object			)		->      bool:







				"""simple docstring"""






				if not isinstance(lowercase   ,     lowercase			):
							return NotImplemented
				return self.rows == other.rows







	def __ne__(     self      :int   ,     lowercase      :object			)		->      bool:







				"""simple docstring"""






				return not self == other







	def __neg__(     self      :int			)		->      Matrix:







				"""simple docstring"""






				return self * -1







	def __add__(     self      :Optional[Any]   ,     lowercase      :Matrix			)		->      Matrix:







				"""simple docstring"""






				if self.order != other.order:
							raise ValueError('''Addition requires matrices of the same order'''			)
				return Matrix(
				    [
				        [self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns			)]
				        for i in range(self.num_rows			)
				    ]			)







	def __sub__(     self      :Optional[Any]   ,     lowercase      :Matrix			)		->      Matrix:







				"""simple docstring"""






				if self.order != other.order:
							raise ValueError('''Subtraction requires matrices of the same order'''			)
				return Matrix(
				    [
				        [self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns			)]
				        for i in range(self.num_rows			)
				    ]			)







	def __mul__(     self      :Optional[int]   ,     lowercase      :Matrix | int | float			)		->      Matrix:







				"""simple docstring"""






				if isinstance(lowercase   ,     (int, float)			):
							return Matrix(
							    [[int(element * other			) for element in row] for row in self.rows]			)
				elif isinstance(lowercase   ,     lowercase			):
							if self.num_columns != other.num_rows:
										raise ValueError(
										    '''The number of columns in the first matrix must '''
										    '''be equal to the number of rows in the second'''			)
							return Matrix(
							    [
							        [Matrix.dot_product(lowercase   ,     lowercase			) for column in other.columns()]
							        for row in self.rows
							    ]			)
				else:
							raise TypeError(
							    '''A Matrix can only be multiplied by an int, float, or another matrix'''			)







	def __pow__(     self      :str   ,     lowercase      :int			)		->      Matrix:







				"""simple docstring"""






				if not isinstance(lowercase   ,     lowercase			):
							raise TypeError('''A Matrix can only be raised to the power of an int'''			)
				if not self.is_square:
							raise ValueError('''Only square matrices can be raised to a power'''			)
				if other == 0:
							return self.identity()
				if other < 0:
							if self.is_invertable():
										return self.inverse() ** (-other)
							raise ValueError(
							    '''Only invertable matrices can be raised to a negative power'''			)
				SCREAMING_SNAKE_CASE     						= self
				for _ in range(other - 1			):
							result *= self
				return result







	@classmethod
	def  snake_case__     (     cls      :List[Any]   ,     lowercase      :list[int]   ,     lowercase      :list[int]			)		->      int:







				"""simple docstring"""






				return sum(row[i] * column[i] for i in range(len(lowercase			)			)			)


if __name__ == "__main__":
							import doctest

							doctest.testmod()