





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__  			=logging.getLogger(__name__)





class       lowerCAmelCase__(			_snake_case     ):






       '''simple docstring'''


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




       def __init__(   self     ,    __lowerCamelCase     ,    **__lowerCamelCase				)      ->  Optional[Any]:
            if hparams.sortish_sampler and hparams.gpus > 1:
                 _SCREAMING_SNAKE_CASE							:						Optional[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__(_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							:						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							:						List[Any]         =				defaultdict(_lowerCamelCase				)
            _SCREAMING_SNAKE_CASE							:						Dict         =				self.config.model_type
            _SCREAMING_SNAKE_CASE							:						int         =				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							:						Optional[int]         =				{
                '''train''': self.hparams.n_train,
                '''val''': self.hparams.n_val,
                '''test''': self.hparams.n_test,
            }
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				{k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

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




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase				)      ->  str:
            _SCREAMING_SNAKE_CASE							:						int         =				{
                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							:						str         =				True
            return readable_batch




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    **__lowerCamelCase				)      ->  List[str]:
            return self.model(_lowerCamelCase     ,    **_lowerCamelCase				)




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase				)      ->  Optional[int]:
            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				self.tokenizer.batch_decode(
                _lowerCamelCase     ,    skip_special_tokens=_lowerCamelCase     ,    clean_up_tokenization_spaces=_lowerCamelCase				)
            return lmap(str.strip     ,    _lowerCamelCase				)




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase				)      ->  List[Any]:
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				self.tokenizer.pad_token_id
            _SCREAMING_SNAKE_CASE							:						Tuple         =				batch['''input_ids'''], batch['''attention_mask''']
            _SCREAMING_SNAKE_CASE							:						List[str]         =				batch['''labels''']
            if isinstance(self.model     ,    _lowerCamelCase				):
                 _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				self.model._shift_right(_lowerCamelCase				)
            else:
                 _SCREAMING_SNAKE_CASE							:						int         =				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							:						Any         =				decoder_input_ids
                 self.save_readable_batch(_lowerCamelCase				)

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

                 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[int]         =				nn.functional.log_softmax(_lowerCamelCase     ,    dim=-1				)
                 _SCREAMING_SNAKE_CASE							:						Any         =				label_smoothed_nll_loss(
                     _lowerCamelCase     ,    _lowerCamelCase     ,    self.hparams.label_smoothing     ,    ignore_index=_lowerCamelCase				)
            return (loss,)




       @property
       def      UpperCamelCase_      (   self				)      ->  Any:
            return self.tokenizer.pad_token_id




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase				)      ->  Optional[int]:
            _SCREAMING_SNAKE_CASE							:						List[str]         =				self._step(_lowerCamelCase				)

            _SCREAMING_SNAKE_CASE							:						Dict         =				dict(zip(self.loss_names     ,    _lowerCamelCase				)				)
            # tokens per batch
            _SCREAMING_SNAKE_CASE							:						str         =				batch['''input_ids'''].ne(self.pad				).sum() + batch['''labels'''].ne(self.pad				).sum()
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				batch['''input_ids'''].shape[0]
            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				batch['''input_ids'''].eq(self.pad				).sum()
            _SCREAMING_SNAKE_CASE							:						int         =				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      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase				)      ->  Dict:
            return self._generative_step(_lowerCamelCase				)




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




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase				)      ->  Optional[int]:
            _SCREAMING_SNAKE_CASE							:						Dict         =				time.time()

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




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase				)      ->  int:
            return self._generative_step(_lowerCamelCase				)




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase				)      ->  List[Any]:
            return self.validation_epoch_end(_lowerCamelCase     ,    prefix="test"				)




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase				)      ->  Optional[int]:
            _SCREAMING_SNAKE_CASE							:						Any         =				self.n_obs[type_path]
            _SCREAMING_SNAKE_CASE							:						Union[str, Any]         =				self.target_lens[type_path]
            _SCREAMING_SNAKE_CASE							:						Optional[int]         =				self.dataset_class(
                self.tokenizer     ,    type_path=_lowerCamelCase     ,    n_obs=_lowerCamelCase     ,    max_target_length=_lowerCamelCase     ,    **self.dataset_kwargs     ,    )
            return dataset




       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase     ,    __lowerCamelCase = False				)      ->  Optional[Any]:
            _SCREAMING_SNAKE_CASE							:						List[str]         =				self.get_dataset(_lowerCamelCase				)

            if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
                 _SCREAMING_SNAKE_CASE							:						Dict         =				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							:						Any         =				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      UpperCamelCase_      (   self				)      ->  Dict:
            _SCREAMING_SNAKE_CASE							:						List[Any]         =				self.get_dataloader("train"     ,    batch_size=self.hparams.train_batch_size     ,    shuffle=_lowerCamelCase				)
            return dataloader




       def      UpperCamelCase_      (   self				)      ->  str:
            return self.get_dataloader("val"     ,    batch_size=self.hparams.eval_batch_size				)




       def      UpperCamelCase_      (   self				)      ->  Optional[int]:
            return self.get_dataloader("test"     ,    batch_size=self.hparams.eval_batch_size				)







       @staticmethod
       def      UpperCamelCase_      (   __lowerCamelCase     ,    __lowerCamelCase				)      ->  List[str]:
            BaseTransformer.add_model_specific_args(_lowerCamelCase     ,    _lowerCamelCase				)
            add_generic_args(_lowerCamelCase     ,    _lowerCamelCase				)
            parser.add_argument(
                "--max_source_length"     ,    default=1_0_2_4     ,    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=5_6     ,    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=1_4_2     ,    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=1_4_2     ,    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=5_0_0     ,    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       lowerCAmelCase__(			_snake_case     ):






       '''simple docstring'''


       __snake_case			      =     'translation'
       __snake_case			      =     ['loss']
       __snake_case			      =     ['bleu']
       __snake_case			      =     'bleu'




       def __init__(   self     ,    __lowerCamelCase     ,    **__lowerCamelCase				)      ->  str:
            super().__init__(_lowerCamelCase     ,    **_lowerCamelCase				)
            _SCREAMING_SNAKE_CASE							:						str         =				hparams.src_lang
            _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				hparams.tgt_lang







       def      UpperCamelCase_      (   self     ,    __lowerCamelCase     ,    __lowerCamelCase				)      ->  Optional[Any]:
            return calculate_bleu(_lowerCamelCase     ,    _lowerCamelCase				)






def      lowerCamelCase__      (__lowerCamelCase,							__lowerCamelCase=None						):
     Path(args.output_dir						).mkdir(exist_ok=UpperCAmelCase_						)
     check_output_dir(UpperCAmelCase_,							expected_items=3						)

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

          _SCREAMING_SNAKE_CASE							:						List[Any]         =				os.environ.get("WANDB_PROJECT",							UpperCAmelCase_						)
          _SCREAMING_SNAKE_CASE							:						int         =				WandbLogger(name=model.output_dir.name,							project=UpperCAmelCase_						)

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

          _SCREAMING_SNAKE_CASE							:						Optional[Any]         =				WandbLogger(name=model.output_dir.name,							project=f"""hf_{dataset}"""						)

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

     _SCREAMING_SNAKE_CASE							:						Tuple         =				args.val_metric == '''loss'''
     _SCREAMING_SNAKE_CASE							:						pl.Trainer         =				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

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

       UpperCamelCase__  			=parser.parse_args()

       main(args)



import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.distilbert.modeling_flax_distilbert import (
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertModel,
     )







class 						_snake_case    ( unittest.TestCase				):




 def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=13   ,    _lowerCamelCase=7   ,    _lowerCamelCase=True   ,    _lowerCamelCase=True   ,    _lowerCamelCase=True   ,    _lowerCamelCase=True   ,    _lowerCamelCase=99   ,    _lowerCamelCase=32   ,    _lowerCamelCase=5   ,    _lowerCamelCase=4   ,    _lowerCamelCase=37   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=512   ,    _lowerCamelCase=16   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=4   ,    ):
       a		:Optional[Any]									=			parent
       a		:str									=			batch_size
       a		:Tuple									=			seq_length
       a		:List[Any]									=			is_training
       a		:Optional[int]									=			use_attention_mask
       a		:List[str]									=			use_token_type_ids
       a		:str									=			use_labels
       a		:Optional[Any]									=			vocab_size
       a		:Optional[int]									=			hidden_size
       a		:Tuple									=			num_hidden_layers
       a		:Union[str, Any]									=			num_attention_heads
       a		:int									=			intermediate_size
       a		:int									=			hidden_act
       a		:int									=			hidden_dropout_prob
       a		:Union[str, Any]									=			attention_probs_dropout_prob
       a		:str									=			max_position_embeddings
       a		:Dict									=			type_vocab_size
       a		:str									=			type_sequence_label_size
       a		:List[str]									=			initializer_range
       a		:Optional[Any]									=			num_choices




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size       )

       a		:Any									=			None
       if self.use_attention_mask:
             a		:Any									=			random_attention_mask([self.batch_size, self.seq_length]       )

       a		:Any									=			DistilBertConfig(
           vocab_size=self.vocab_size   ,    dim=self.hidden_size   ,    n_layers=self.num_hidden_layers   ,    n_heads=self.num_attention_heads   ,    hidden_dim=self.intermediate_size   ,    hidden_act=self.hidden_act   ,    dropout=self.hidden_dropout_prob   ,    attention_dropout=self.attention_probs_dropout_prob   ,    max_position_embeddings=self.max_position_embeddings   ,    initializer_range=self.initializer_range   ,    tie_weights_=_lowerCamelCase   ,    )

       return config, input_ids, attention_mask




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			self.prepare_config_and_inputs()
       a				,							a				,							a		:str									=			config_and_inputs
       a		:List[Any]									=			{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict







@require_flax
class 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			(
     (
         FlaxDistilBertModel,
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			FlaxDistilBertModelTester(self       )




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       for model_class_name in self.all_model_classes:
             a		:int									=			model_class_name.from_pretrained('''distilbert-base-uncased'''       )
             a		:List[str]									=			model(np.ones((1, 1)       )       )
             self.assertIsNotNone(_lowerCamelCase       )







@require_flax
class 						_snake_case    ( unittest.TestCase				):




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''       )
       a		:Optional[Any]									=			np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
       a		:List[Any]									=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
       a		:List[str]									=			model(_lowerCamelCase   ,    attention_mask=_lowerCamelCase       )[0]
       a		:Union[str, Any]									=			(1, 11, 768)
       self.assertEqual(output.shape   ,    _lowerCamelCase       )
       a		:int									=			np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]       )

       self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]   ,    _lowerCamelCase   ,    atol=1e-4       )       )





