


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   :					Any	=      logging.getLogger(__name__)







class     _lowercase				(							UpperCAmelCase__):
       A__      		=    "summarization"
       A__      		=    ["loss"]
       A__      		=    ROUGE_KEYS
       A__      		=    "rouge2"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    Optional[Any]      ,   **__lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



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

             lowerCamelCase__	:       Dict					=						{
                 "data_dir": self.hparams.data_dir,
                 "max_source_length": self.hparams.max_source_length,
                 "prefix": self.model.config.prefix or "",
             }
             lowerCamelCase__	:       str					=						{
                 "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__	:       List[Any]					=						{
                 "train": self.hparams.max_target_length,
                 "val": self.hparams.val_max_target_length,
                 "test": self.hparams.test_max_target_length,
             }
             assert self.target_lens["train"] <= self.target_lens["val"], f"target_lens: {self.target_lens}"
             assert self.target_lens["train"] <= self.target_lens["test"], f"target_lens: {self.target_lens}"
             if self.hparams.freeze_embeds:
                   freeze_embeds(self.model )
             if self.hparams.freeze_encoder:
                   freeze_params(self.model.get_encoder() )
                   assert_all_frozen(self.model.get_encoder() )

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





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    Dict[str, torch.Tensor] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[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__	:       Optional[Any]					=						True
             return readable_batch





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    List[str]      ,   **__lowerCamelCase							:    Optional[Any] ):


             '''simple docstring'''



             return self.model(__lowerCAmelCase      ,   **__lowerCAmelCase )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    List[int] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						self.tokenizer.batch_decode(
                 __lowerCAmelCase      ,   skip_special_tokens=__lowerCAmelCase      ,   clean_up_tokenization_spaces=__lowerCAmelCase )
             return lmap(str.strip      ,   __lowerCAmelCase )





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    dict ):


             '''simple docstring'''



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

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

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





       @property
       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             return self.tokenizer.pad_token_id





       def 		lowerCAmelCase  (     self							:    Any      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self._step(__lowerCAmelCase )

             lowerCamelCase__	:       int					=						dict(zip(self.loss_names      ,   __lowerCAmelCase ) )
             # tokens per batch
             lowerCamelCase__	:       Tuple					=						batch["input_ids"].ne(self.pad ).sum() + batch["labels"].ne(self.pad ).sum()
             lowerCamelCase__	:       int					=						batch["input_ids"].shape[0]
             lowerCamelCase__	:       int					=						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 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    List[str] ):


             '''simple docstring'''



             return self._generative_step(__lowerCAmelCase )





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    str="val" ):


             '''simple docstring'''



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


             '''simple docstring'''



             return calculate_rouge(__lowerCAmelCase      ,   __lowerCAmelCase )





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    dict ):


             '''simple docstring'''



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

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


             '''simple docstring'''



             return self._generative_step(__lowerCAmelCase )





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						self.n_obs[type_path]
             lowerCamelCase__	:       Tuple					=						self.target_lens[type_path]
             lowerCamelCase__	:       Any					=						self.dataset_class(
                 self.tokenizer      ,   type_path=__lowerCAmelCase      ,   n_obs=__lowerCAmelCase      ,   max_target_length=__lowerCAmelCase      ,   **self.dataset_kwargs      ,   )
             return dataset





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    bool = False ):


             '''simple docstring'''



             lowerCamelCase__	:       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__	:       Tuple					=						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							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.get_dataloader("train"      ,   batch_size=self.hparams.train_batch_size      ,   shuffle=__lowerCAmelCase )
             return dataloader





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



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





       @staticmethod
       def 		lowerCAmelCase  (     __lowerCamelCase							:    str      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             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     _lowercase				(							UpperCAmelCase__):
       A__      		=    "translation"
       A__      		=    ["loss"]
       A__      		=    ["bleu"]
       A__      		=    "bleu"





       def __init__(     self							:    Union[str, Any]      ,   __lowerCamelCase							:    str      ,   **__lowerCamelCase							:    Tuple ):


             '''simple docstring'''



             super().__init__(__lowerCAmelCase      ,   **__lowerCAmelCase )
             lowerCamelCase__	:       List[Any]					=						hparams.src_lang
             lowerCamelCase__	:       Optional[int]					=						hparams.tgt_lang





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Optional[int] ):


             '''simple docstring'''



             return calculate_bleu(__lowerCAmelCase      ,   __lowerCAmelCase )



def    lowercase_	(	_A    :					Tuple					,      _A    :					int=None		):

      """simple docstring"""




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

      if model is None:
            if "summarization" in args.task:
                  lowerCamelCase__	:       str					=						SummarizationModule(UpperCAmelCase__		)
            else:
                  lowerCamelCase__	:       Any					=						TranslationModule(UpperCAmelCase__		)
      lowerCamelCase__	:       Dict					=						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__	:       List[Any]					=						True  # don't pollute wandb logs unnecessarily
      elif args.logger_name == "wandb":
            from pytorch_lightning.loggers import WandbLogger

            lowerCamelCase__	:       Dict					=						os.environ.get("WANDB_PROJECT"					,      UpperCAmelCase__		)
            lowerCamelCase__	:       str					=						WandbLogger(name=model.output_dir.name					,      project=UpperCAmelCase__		)

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

            lowerCamelCase__	:       List[str]					=						WandbLogger(name=model.output_dir.name					,      project=F"hf_{dataset}"		)

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

      lowerCamelCase__	:       Optional[int]					=						args.val_metric == "loss"
      lowerCamelCase__	:       Tuple					=						generic_train(
          UpperCAmelCase__					,      UpperCAmelCase__					,      logging_callback=SeqaSeqLoggingCallback()					,      checkpoint_callback=get_checkpoint_callback(
              args.output_dir					,      model.val_metric					,      args.save_top_k					,      UpperCAmelCase__		)					,      early_stopping_callback=UpperCAmelCase__					,      logger=UpperCAmelCase__					,      )
      pickle_save(model.hparams					,      model.output_dir / "hparams.pkl"		)
      if not args.do_predict:
            return model

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

    A   :					Any	=      parser.parse_args()

    main(args)






import cva
import numpy as np







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Union[str, Any]      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             if k in (0.0_4, 0.0_6):
                   lowerCamelCase__	:       int					=						k
                   lowerCamelCase__	:       List[str]					=						window_size
             else:
                   raise ValueError("invalid k value" )





       def __str__(     self							:    str ):


             '''simple docstring'''



             return str(self.k )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						cva.imread(__lowerCamelCase      ,   0 )
             lowerCamelCase__					,     lowerCamelCase__	:       Any					=						img.shape
             lowerCamelCase__	:       list[list[int]]					=						[]
             lowerCamelCase__	:       List[Any]					=						img.copy()
             lowerCamelCase__	:       int					=						cva.cvtColor(__lowerCamelCase      ,   cva.COLOR_GRAY2RGB )
             lowerCamelCase__					,     lowerCamelCase__	:       int					=						np.gradient(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						dx**2
             lowerCamelCase__	:       Optional[Any]					=						dy**2
             lowerCamelCase__	:       int					=						dx * dy
             lowerCamelCase__	:       Union[str, Any]					=						0.0_4
             lowerCamelCase__	:       Any					=						self.window_size // 2
             for y in range(__lowerCamelCase      ,   h - offset ):
                   for x in range(__lowerCamelCase      ,   w - offset ):
                         lowerCamelCase__	:       Optional[Any]					=						ixx[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       Optional[Any]					=						iyy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       str					=						ixy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()

                         lowerCamelCase__	:       Optional[Any]					=						(wxx * wyy) - (wxy**2)
                         lowerCamelCase__	:       List[str]					=						wxx + wyy
                         lowerCamelCase__	:       List[Any]					=						det - k * (trace**2)
                         # Can change the value
                         if r > 0.5:
                               corner_list.append([x, y, r] )
                               color_img.itemset((y, x, 0)      ,   0 )
                               color_img.itemset((y, x, 1)      ,   0 )
                               color_img.itemset((y, x, 2)      ,   255 )
             return color_img, corner_list


if __name__ == "__main__":
    A   :					Tuple	=      HarrisCorner(0.0_4, 3)
    A,						A   :					Optional[int]	=      edge_detect.detect("path_to_image")
    cva.imwrite("detect.png", color_img)


