

'''simple docstring'''







import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)



'''simple docstring'''







import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


__lowerCamelCase						    =      logging.getLogger(__name__)


class    A__ ( _snake_case     ):
    lowercase		   =						"summarization"
    lowercase		   =						["loss"]
    lowercase		   =						ROUGE_KEYS
    lowercase		   =						"rouge2"



    def __init__(   self						,			UpperCamelCase__						,			**UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           if hparams.sortish_sampler and hparams.gpus > 1:
                  A_		   = 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__(UpperCamelCase__						,			num_labels=UpperCamelCase__						,			mode=self.mode						,			**UpperCamelCase__    )
           use_task_specific_params(self.model						,			"""summarization"""    )
           save_git_info(self.hparams.output_dir    )
           A_		   = Path(self.output_dir    ) / """metrics.json"""
           A_		   = Path(self.output_dir    ) / """hparams.pkl"""
           pickle_save(self.hparams						,			self.hparams_save_path    )
           A_		   = 0
           A_		   = defaultdict(UpperCamelCase__    )
           A_		   = self.config.model_type
           A_		   = self.config.tgt_vocab_size if self.model_type == """fsmt""" else self.config.vocab_size

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

           A_		   = {
               """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()    )

           A_		   = get_git_info()["""repo_sha"""]
           A_		   = hparams.num_workers
           A_		   = None  # default to config
           if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer						,			UpperCamelCase__    ):
                  A_		   = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
                  A_		   = self.decoder_start_token_id
           A_		   = (
               SeqaSeqDataset if hasattr(self.tokenizer						,			"""prepare_seq2seq_batch"""    ) else LegacySeqaSeqDataset
           )
           A_		   = False
           A_		   = 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:
                  A_		   = self.hparams.eval_max_gen_length
           else:
                  A_		   = self.model.config.max_length
           A_		   = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Dict[str, List[str]]:





           '''simple docstring'''
           A_		   = {
               k: self.tokenizer.batch_decode(v.tolist()    ) if """mask""" not in k else v.shape for k, v in batch.items()
           }
           save_json(UpperCamelCase__						,			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"""    )

           A_		   = True
           return readable_batch



    def    snake_case_     (   self						,			UpperCamelCase__						,			**UpperCamelCase__    )  -> str:





           '''simple docstring'''
           return self.model(UpperCamelCase__						,			**UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> List[str]:





           '''simple docstring'''
           A_		   = self.tokenizer.batch_decode(
               UpperCamelCase__						,			skip_special_tokens=UpperCamelCase__						,			clean_up_tokenization_spaces=UpperCamelCase__    )
           return lmap(str.strip						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Tuple:





           '''simple docstring'''
           A_		   = self.tokenizer.pad_token_id
           A_   ,     A_		   = batch["""input_ids"""], batch["""attention_mask"""]
           A_		   = batch["""labels"""]
           if isinstance(self.model						,			UpperCamelCase__    ):
                  A_		   = self.model._shift_right(UpperCamelCase__    )
           else:
                  A_		   = shift_tokens_right(UpperCamelCase__						,			UpperCamelCase__    )
           if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
                  A_		   = decoder_input_ids
                  self.save_readable_batch(UpperCamelCase__    )

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

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



    @property
    def    snake_case_     (   self    )  -> int:





           '''simple docstring'''
           return self.tokenizer.pad_token_id



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           A_		   = self._step(UpperCamelCase__    )

           A_		   = dict(zip(self.loss_names						,			UpperCamelCase__    )    )
           # tokens per batch
           A_		   = batch["""input_ids"""].ne(self.pad    ).sum() + batch["""labels"""].ne(self.pad    ).sum()
           A_		   = batch["""input_ids"""].shape[0]
           A_		   = batch["""input_ids"""].eq(self.pad    ).sum()
           A_		   = 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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           return self._generative_step(UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__="val"    )  -> Dict:





           '''simple docstring'''
           self.step_count += 1
           A_		   = {k: torch.stack([x[k] for x in outputs]    ).mean() for k in self.loss_names}
           A_		   = losses["""loss"""]
           A_		   = {
               k: np.array([x[k] for x in outputs]    ).mean() for k in self.metric_names + ["""gen_time""", """gen_len"""]
           }
           A_		   = (
               generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
           )
           A_		   = torch.tensor(UpperCamelCase__    ).type_as(UpperCamelCase__    )
           generative_metrics.update({k: v.item() for k, v in losses.items()}    )
           losses.update(UpperCamelCase__    )
           A_		   = {f'''{prefix}_avg_{k}''': x for k, x in losses.items()}
           A_		   = self.step_count
           self.metrics[prefix].append(UpperCamelCase__    )  # callback writes this to self.metrics_save_path
           A_		   = 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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           return calculate_rouge(UpperCamelCase__						,			UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> dict:





           '''simple docstring'''
           A_		   = time.time()

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



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> Tuple:





           '''simple docstring'''
           return self._generative_step(UpperCamelCase__    )



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> Dict:





           '''simple docstring'''
           return self.validation_epoch_end(UpperCamelCase__						,			prefix="""test"""    )



    def    snake_case_     (   self						,			UpperCamelCase__    )  -> SeqaSeqDataset:





           '''simple docstring'''
           A_		   = self.n_obs[type_path]
           A_		   = self.target_lens[type_path]
           A_		   = self.dataset_class(
               self.tokenizer						,			type_path=UpperCamelCase__						,			n_obs=UpperCamelCase__						,			max_target_length=UpperCamelCase__						,			**self.dataset_kwargs						,			)
           return dataset



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__ = False    )  -> DataLoader:





           '''simple docstring'''
           A_		   = self.get_dataset(UpperCamelCase__    )

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

           elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
                  A_		   = dataset.make_dynamic_sampler(
                      self.hparams.max_tokens_per_batch						,			distributed=self.hparams.gpus > 1    )
                  return DataLoader(
                      UpperCamelCase__						,			batch_sampler=UpperCamelCase__						,			collate_fn=dataset.collate_fn						,			num_workers=self.num_workers						,			)
           else:
                  return DataLoader(
                      UpperCamelCase__						,			batch_size=UpperCamelCase__						,			collate_fn=dataset.collate_fn						,			shuffle=UpperCamelCase__						,			num_workers=self.num_workers						,			sampler=UpperCamelCase__						,			)



    def    snake_case_     (   self    )  -> DataLoader:





           '''simple docstring'''
           A_		   = self.get_dataloader("""train"""						,			batch_size=self.hparams.train_batch_size						,			shuffle=UpperCamelCase__    )
           return dataloader



    def    snake_case_     (   self    )  -> DataLoader:





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



    def    snake_case_     (   self    )  -> DataLoader:





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







    @staticmethod
    def    snake_case_     (   UpperCamelCase__						,			UpperCamelCase__    )  -> List[Any]:





           '''simple docstring'''
           BaseTransformer.add_model_specific_args(UpperCamelCase__						,			UpperCamelCase__    )
           add_generic_args(UpperCamelCase__						,			UpperCamelCase__    )
           parser.add_argument(
               """--max_source_length"""						,			default=1024						,			type=UpperCamelCase__						,			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=UpperCamelCase__						,			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=UpperCamelCase__						,			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=UpperCamelCase__						,			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=UpperCamelCase__    )
           parser.add_argument("""--overwrite_output_dir"""						,			action="""store_true"""						,			default=UpperCamelCase__    )
           parser.add_argument("""--max_tokens_per_batch"""						,			type=UpperCamelCase__						,			default=UpperCamelCase__    )
           parser.add_argument("""--logger_name"""						,			type=UpperCamelCase__						,			choices=["""default""", """wandb""", """wandb_shared"""]						,			default="""default"""    )
           parser.add_argument("""--n_train"""						,			type=UpperCamelCase__						,			default=-1						,			required=UpperCamelCase__						,			help="""# examples. -1 means use all."""    )
           parser.add_argument("""--n_val"""						,			type=UpperCamelCase__						,			default=500						,			required=UpperCamelCase__						,			help="""# examples. -1 means use all."""    )
           parser.add_argument("""--n_test"""						,			type=UpperCamelCase__						,			default=-1						,			required=UpperCamelCase__						,			help="""# examples. -1 means use all."""    )
           parser.add_argument(
               """--task"""						,			type=UpperCamelCase__						,			default="""summarization"""						,			required=UpperCamelCase__						,			help="""# examples. -1 means use all."""    )
           parser.add_argument("""--label_smoothing"""						,			type=UpperCamelCase__						,			default=0.0						,			required=UpperCamelCase__    )
           parser.add_argument("""--src_lang"""						,			type=UpperCamelCase__						,			default=""""""						,			required=UpperCamelCase__    )
           parser.add_argument("""--tgt_lang"""						,			type=UpperCamelCase__						,			default=""""""						,			required=UpperCamelCase__    )
           parser.add_argument("""--eval_beams"""						,			type=UpperCamelCase__						,			default=UpperCamelCase__						,			required=UpperCamelCase__    )
           parser.add_argument(
               """--val_metric"""						,			type=UpperCamelCase__						,			default=UpperCamelCase__						,			required=UpperCamelCase__						,			choices=["""bleu""", """rouge2""", """loss""", None]    )
           parser.add_argument("""--eval_max_gen_length"""						,			type=UpperCamelCase__						,			default=UpperCamelCase__						,			help="""never generate more than n tokens"""    )
           parser.add_argument("""--save_top_k"""						,			type=UpperCamelCase__						,			default=1						,			required=UpperCamelCase__						,			help="""How many checkpoints to save"""    )
           parser.add_argument(
               """--early_stopping_patience"""						,			type=UpperCamelCase__						,			default=-1						,			required=UpperCamelCase__						,			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__ ( _snake_case     ):
    lowercase		   =						"translation"
    lowercase		   =						["loss"]
    lowercase		   =						["bleu"]
    lowercase		   =						"bleu"



    def __init__(   self						,			UpperCamelCase__						,			**UpperCamelCase__    )  -> Optional[int]:





           '''simple docstring'''
           super().__init__(UpperCamelCase__						,			**UpperCamelCase__    )
           A_		   = hparams.src_lang
           A_		   = hparams.tgt_lang







    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__    )  -> dict:





           '''simple docstring'''
           return calculate_bleu(UpperCamelCase__						,			UpperCamelCase__    )

def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__=None			)     ->     SummarizationModule:
       Path(args.output_dir			).mkdir(exist_ok=UpperCAmelCase__			)
       check_output_dir(UpperCAmelCase__,    expected_items=3			)

       if model is None:
              if "summarization" in args.task:
                     A_		   = SummarizationModule(UpperCAmelCase__			)
              else:
                     A_		   = TranslationModule(UpperCAmelCase__			)
       A_		   = 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"""			)
       ):
              A_		   = True  # don't pollute wandb logs unnecessarily
       elif args.logger_name == "wandb":
              from pytorch_lightning.loggers import WandbLogger

              A_		   = os.environ.get("""WANDB_PROJECT""",    UpperCAmelCase__			)
              A_		   = WandbLogger(name=model.output_dir.name,    project=UpperCAmelCase__			)

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

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

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

       A_		   = args.val_metric == """loss"""
       A_		   = 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

       A_		   = """"""
       A_		   = sorted(glob.glob(os.path.join(args.output_dir,    """*.ckpt"""			),    recursive=UpperCAmelCase__			)			)
       if checkpoints:
              A_		   = checkpoints[-1]
              A_		   = checkpoints[-1]
       trainer.logger.log_hyperparams(model.hparams			)

       # test() without a model tests using the best checkpoint automatically
       trainer.test()
       return model


if __name__ == "__main__":
      __lowerCamelCase						    =      argparse.ArgumentParser()
      __lowerCamelCase						    =      pl.Trainer.add_argparse_args(parser)
      __lowerCamelCase						    =      SummarizationModule.add_model_specific_args(parser, os.getcwd())

      __lowerCamelCase						    =      parser.parse_args()

      main(args)
