






"""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


UpperCAmelCase_   : List[str]   			= logging.getLogger(__name__)




class 							lowerCAmelCase__				(			__snake_case ):






      '''simple docstring'''

      __UpperCamelCase	 =  """summarization"""
      __UpperCamelCase	 =  ["""loss"""]
      __UpperCamelCase	 =  ROUGE_KEYS
      __UpperCamelCase	 =  """rouge2"""
      def __init__(						self     :  Tuple						,	lowercase_     :  Union[str, Any]						,	**lowercase_     :  List[str]):




          '''simple docstring'''


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

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

          SCREAMING_SNAKE_CASE_      :  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())

          SCREAMING_SNAKE_CASE_      :  str    						=		get_git_info()["repo_sha"]
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		hparams.num_workers
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		None  # default to config
          if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer						,	UpperCamelCase__):
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self.tokenizer.lang_code_to_id[hparams.tgt_lang]
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.decoder_start_token_id
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		(
              SeqaSeqDataset if hasattr(self.tokenizer						,	'''prepare_seq2seq_batch''') else LegacySeqaSeqDataset
          )
          SCREAMING_SNAKE_CASE_      :  Dict    						=		False
          SCREAMING_SNAKE_CASE_      :  int    						=		self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
          if self.hparams.eval_max_gen_length is not None:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.hparams.eval_max_gen_length
          else:
              SCREAMING_SNAKE_CASE_      :  Dict    						=		self.model.config.max_length
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  Dict[str, torch.Tensor]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		{
              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''')

          SCREAMING_SNAKE_CASE_      :  Any    						=		True
          return readable_batch
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  str						,	**lowercase_     :  Optional[int]):




          '''simple docstring'''


          return self.model(UpperCamelCase__						,	**UpperCamelCase__)
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  List[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  int    						=		self.tokenizer.batch_decode(
              UpperCamelCase__						,	skip_special_tokens=UpperCamelCase__						,	clean_up_tokenization_spaces=UpperCamelCase__)
          return lmap(str.strip						,	UpperCamelCase__)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  dict):




          '''simple docstring'''


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

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

              assert lm_logits.shape[-1] == self.vocab_size
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		ce_loss_fct(lm_logits.view(-1						,	lm_logits.shape[-1])						,	tgt_ids.view(-1))
          else:
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		nn.functional.log_softmax(UpperCamelCase__						,	dim=-1)
              SCREAMING_SNAKE_CASE_      :  int    						=		label_smoothed_nll_loss(
                  UpperCamelCase__						,	UpperCamelCase__						,	self.hparams.label_smoothing						,	ignore_index=UpperCamelCase__)
          return (loss,)
      @property
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          return self.tokenizer.pad_token_id
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]						,	lowercase_     :  List[Any]						,	lowercase_     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self._step(UpperCamelCase__)

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




          '''simple docstring'''


          return self._generative_step(UpperCamelCase__)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple						,	lowercase_     :  Any						,	lowercase_     :  List[str]="val"):




          '''simple docstring'''


          self.step_count += 1
          SCREAMING_SNAKE_CASE_      :  int    						=		{k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		losses["loss"]
          SCREAMING_SNAKE_CASE_      :  Any    						=		{
              k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "gen_len"]
          }
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		(
              generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
          )
          SCREAMING_SNAKE_CASE_      :  torch.FloatTensor    						=		torch.tensor(UpperCamelCase__).type_as(UpperCamelCase__)
          generative_metrics.update({k: v.item() for k, v in losses.items()})
          losses.update(UpperCamelCase__)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		{F'{prefix}_avg_{k}': x for k, x in losses.items()}
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.step_count
          self.metrics[prefix].append(UpperCamelCase__)  # callback writes this to self.metrics_save_path
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		flatten_list([x['''preds'''] for x in outputs])
          return {
              "log": all_metrics,
              "preds": preds,
              F'{prefix}_loss': loss,
              F'{prefix}_{self.val_metric}': metric_tensor,
          }
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  int						,	lowercase_     :  List[Any]):




          '''simple docstring'''


          return calculate_rouge(UpperCamelCase__						,	UpperCamelCase__)
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		time.time()

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




          '''simple docstring'''


          return self._generative_step(UpperCamelCase__)
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]						,	lowercase_     :  List[str]):




          '''simple docstring'''


          return self.validation_epoch_end(UpperCamelCase__						,	prefix='''test''')
      def _SCREAMING_SNAKE_CASE (						self     :  Union[str, Any]						,	lowercase_     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.n_obs[type_path]
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.target_lens[type_path]
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.dataset_class(
              self.tokenizer						,	type_path=UpperCamelCase__						,	n_obs=UpperCamelCase__						,	max_target_length=UpperCamelCase__						,	**self.dataset_kwargs						,	)
          return dataset
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  str						,	lowercase_     :  int						,	lowercase_     :  bool = False):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_dataset(UpperCamelCase__)

          if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		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":
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		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 _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		self.get_dataloader('''train'''						,	batch_size=self.hparams.train_batch_size						,	shuffle=UpperCamelCase__)
          return dataloader
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]):




          '''simple docstring'''


          return self.get_dataloader('''val'''						,	batch_size=self.hparams.eval_batch_size)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


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



      @staticmethod
      def _SCREAMING_SNAKE_CASE (						lowercase_     :  str						,	lowercase_     :  Tuple):




          '''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 							lowerCAmelCase__				(			__snake_case ):






      '''simple docstring'''

      __UpperCamelCase	 =  """translation"""
      __UpperCamelCase	 =  ["""loss"""]
      __UpperCamelCase	 =  ["""bleu"""]
      __UpperCamelCase	 =  """bleu"""
      def __init__(						self     :  Dict						,	lowercase_     :  List[Any]						,	**lowercase_     :  int):




          '''simple docstring'''


          super().__init__(UpperCamelCase__						,	**UpperCamelCase__)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		hparams.src_lang
          SCREAMING_SNAKE_CASE_      :  str    						=		hparams.tgt_lang



      def _SCREAMING_SNAKE_CASE (						self     :  str						,	lowercase_     :  List[str]						,	lowercase_     :  Optional[int]):




          '''simple docstring'''


          return calculate_bleu(UpperCamelCase__						,	UpperCamelCase__)



def 					_A			(__a    ,				__a=None							) ->       SummarizationModule:
    """simple docstring"""






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

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

        SCREAMING_SNAKE_CASE_      :  str    						=		os.environ.get('''WANDB_PROJECT'''    ,				__SCREAMING_SNAKE_CASE							)
        SCREAMING_SNAKE_CASE_      :  int    						=		WandbLogger(name=model.output_dir.name    ,				project=__SCREAMING_SNAKE_CASE							)

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

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

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

    SCREAMING_SNAKE_CASE_      :  List[str]    						=		args.val_metric == "loss"
    SCREAMING_SNAKE_CASE_      :  pl.Trainer    						=		generic_train(
        __SCREAMING_SNAKE_CASE    ,				__SCREAMING_SNAKE_CASE    ,				logging_callback=SeqaSeqLoggingCallback()    ,				checkpoint_callback=get_checkpoint_callback(
            args.output_dir    ,				model.val_metric    ,				args.save_top_k    ,				__SCREAMING_SNAKE_CASE							)    ,				early_stopping_callback=__SCREAMING_SNAKE_CASE    ,				logger=__SCREAMING_SNAKE_CASE    ,				)
    pickle_save(model.hparams    ,				model.output_dir / '''hparams.pkl'''							)
    if not args.do_predict:
        return model

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

  UpperCAmelCase_   : Any   			= parser.parse_args()

  main(args)





"""simple docstring"""


import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Optional[int]:
		# Construct model
		if gpta_config_file == "":
				__lowerCAmelCase:       Optional[int]          =			GPTaConfig()
		else:
				__lowerCAmelCase:       List[str]          =			GPTaConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		__lowerCAmelCase:       Optional[int]          =			GPTaModel(__SCREAMING_SNAKE_CASE )

		# Load weights from numpy
		load_tf_weights_in_gpta(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )

		# Save pytorch-model
		__lowerCAmelCase:       str          =			pytorch_dump_folder_path + "/" + WEIGHTS_NAME
		__lowerCAmelCase:       List[Any]          =			pytorch_dump_folder_path + "/" + CONFIG_NAME
		print(F"Save PyTorch model to {pytorch_weights_dump_path}" )
		torch.save(model.state_dict()					, __SCREAMING_SNAKE_CASE )
		print(F"Save configuration file to {pytorch_config_dump_path}" )
		with open(__SCREAMING_SNAKE_CASE					, "w"					, encoding="utf-8" ) as f:
				f.write(config.to_json_string() )


if __name__ == "__main__":
			__A						   =			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			parser.add_argument(
			    "--gpt2_config_file",
			    default="",
			    type=str,
			    help=(
			        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
			        "This specifies the model architecture."
			    ),
			)
			__A						   =			parser.parse_args()
			convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


