




"""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    :   int   			=						logging.getLogger(__name__)






class 						snake_case  (  lowerCAmelCase_	):
    __magic_name__   					=					"""summarization"""
    __magic_name__   					=					["""loss"""]
    __magic_name__   					=					ROUGE_KEYS
    __magic_name__   					=					"""rouge2"""





    def __init__(							self     : List[str]    ,		A     : Optional[int]    ,		**A     : int      ):


      '''simple docstring'''




      if hparams.sortish_sampler and hparams.gpus > 1:
        a					:      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__(__SCREAMING_SNAKE_CASE    ,		num_labels=__SCREAMING_SNAKE_CASE    ,		mode=self.mode    ,		**__SCREAMING_SNAKE_CASE      )
      use_task_specific_params(self.model    ,		'summarization'      )
      save_git_info(self.hparams.output_dir      )
      a					:      Any    =       Path(self.output_dir      ) / 'metrics.json'
      a					:      Optional[Any]    =       Path(self.output_dir      ) / 'hparams.pkl'
      pickle_save(self.hparams    ,		self.hparams_save_path      )
      a					:      int    =       0
      a					:      str    =       defaultdict(__SCREAMING_SNAKE_CASE      )
      a					:      List[str]    =       self.config.model_type
      a					:      Any    =       self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size

      a					:      List[str]    =       {
          'data_dir': self.hparams.data_dir,
          'max_source_length': self.hparams.max_source_length,
          'prefix': self.model.config.prefix or '',
      }
      a					:      Optional[Any]    =       {
          'train': self.hparams.n_train,
          'val': self.hparams.n_val,
          'test': self.hparams.n_test,
      }
      a					:      Dict    =       {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

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





    def     lowerCamelCase__				(							self     : str    ,		A     : Tuple      ):


      '''simple docstring'''




      a					:      Tuple    =       {
          k: self.tokenizer.batch_decode(v.tolist()      ) if 'mask' not in k else v.shape for k, v in batch.items()
      }
      save_json(__SCREAMING_SNAKE_CASE    ,		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					:      List[str]    =       True
      return readable_batch





    def     lowerCamelCase__				(							self     : Dict    ,		A     : List[Any]    ,		**A     : Any      ):


      '''simple docstring'''




      return self.model(__SCREAMING_SNAKE_CASE    ,		**__SCREAMING_SNAKE_CASE      )





    def     lowerCamelCase__				(							self     : int    ,		A     : List[str]      ):


      '''simple docstring'''




      a					:      List[Any]    =       self.tokenizer.batch_decode(
          __SCREAMING_SNAKE_CASE    ,		skip_special_tokens=__SCREAMING_SNAKE_CASE    ,		clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE      )
      return lmap(str.strip    ,		__SCREAMING_SNAKE_CASE      )





    def     lowerCamelCase__				(							self     : str    ,		A     : Optional[Any]      ):


      '''simple docstring'''




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

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

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





    @property
    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      return self.tokenizer.pad_token_id





    def     lowerCamelCase__				(							self     : Dict    ,		A     : List[Any]    ,		A     : Optional[int]      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       self._step(__SCREAMING_SNAKE_CASE      )

      a					:      int    =       dict(zip(self.loss_names    ,		__SCREAMING_SNAKE_CASE      )      )
      # tokens per batch
      a					:      List[Any]    =       batch['input_ids'].ne(self.pad      ).sum() + batch['labels'].ne(self.pad      ).sum()
      a					:      Union[str, Any]    =       batch['input_ids'].shape[0]
      a					:      Tuple    =       batch['input_ids'].eq(self.pad      ).sum()
      a					:      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     : List[str]    ,		A     : Tuple    ,		A     : Any      ):


      '''simple docstring'''




      return self._generative_step(__SCREAMING_SNAKE_CASE      )





    def     lowerCamelCase__				(							self     : Tuple    ,		A     : List[str]    ,		A     : int="val"      ):


      '''simple docstring'''




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


      '''simple docstring'''




      return calculate_rouge(__SCREAMING_SNAKE_CASE    ,		__SCREAMING_SNAKE_CASE      )





    def     lowerCamelCase__				(							self     : str    ,		A     : Any      ):


      '''simple docstring'''




      a					:      Optional[int]    =       time.time()

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





    def     lowerCamelCase__				(							self     : Optional[Any]    ,		A     : Optional[Any]    ,		A     : List[str]      ):


      '''simple docstring'''




      return self._generative_step(__SCREAMING_SNAKE_CASE      )





    def     lowerCamelCase__				(							self     : Any    ,		A     : Optional[int]      ):


      '''simple docstring'''




      return self.validation_epoch_end(__SCREAMING_SNAKE_CASE    ,		prefix='test'      )





    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Optional[int]      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       self.n_obs[type_path]
      a					:      Dict    =       self.target_lens[type_path]
      a					:      List[Any]    =       self.dataset_class(
          self.tokenizer    ,		type_path=__SCREAMING_SNAKE_CASE    ,		n_obs=__SCREAMING_SNAKE_CASE    ,		max_target_length=__SCREAMING_SNAKE_CASE    ,		**self.dataset_kwargs    ,		)
      return dataset





    def     lowerCamelCase__				(							self     : List[Any]    ,		A     : str    ,		A     : str    ,		A     : str = False      ):


      '''simple docstring'''




      a					:      Optional[int]    =       self.get_dataset(__SCREAMING_SNAKE_CASE      )

      if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
        a					:      Optional[Any]    =       dataset.make_sortish_sampler(__SCREAMING_SNAKE_CASE    ,		distributed=self.hparams.gpus > 1      )
        return DataLoader(
            __SCREAMING_SNAKE_CASE    ,		batch_size=__SCREAMING_SNAKE_CASE    ,		collate_fn=dataset.collate_fn    ,		shuffle=__SCREAMING_SNAKE_CASE    ,		num_workers=self.num_workers    ,		sampler=__SCREAMING_SNAKE_CASE    ,		)

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





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       self.get_dataloader('train'    ,		batch_size=self.hparams.train_batch_size    ,		shuffle=__SCREAMING_SNAKE_CASE      )
      return dataloader





    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




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





    @staticmethod
    def     lowerCamelCase__				(							A     : int    ,		A     : Tuple      ):


      '''simple docstring'''




      BaseTransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE    ,		__SCREAMING_SNAKE_CASE      )
      add_generic_args(__SCREAMING_SNAKE_CASE    ,		__SCREAMING_SNAKE_CASE      )
      parser.add_argument(
          '--max_source_length'    ,		default=1_0_2_4    ,		type=__SCREAMING_SNAKE_CASE    ,		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=__SCREAMING_SNAKE_CASE    ,		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=__SCREAMING_SNAKE_CASE    ,		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=__SCREAMING_SNAKE_CASE    ,		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=__SCREAMING_SNAKE_CASE      )
      parser.add_argument('--overwrite_output_dir'    ,		action='store_true'    ,		default=__SCREAMING_SNAKE_CASE      )
      parser.add_argument('--max_tokens_per_batch'    ,		type=__SCREAMING_SNAKE_CASE    ,		default=__SCREAMING_SNAKE_CASE      )
      parser.add_argument('--logger_name'    ,		type=__SCREAMING_SNAKE_CASE    ,		choices=['default', 'wandb', 'wandb_shared']    ,		default='default'      )
      parser.add_argument('--n_train'    ,		type=__SCREAMING_SNAKE_CASE    ,		default=-1    ,		required=__SCREAMING_SNAKE_CASE    ,		help='# examples. -1 means use all.'      )
      parser.add_argument('--n_val'    ,		type=__SCREAMING_SNAKE_CASE    ,		default=5_0_0    ,		required=__SCREAMING_SNAKE_CASE    ,		help='# examples. -1 means use all.'      )
      parser.add_argument('--n_test'    ,		type=__SCREAMING_SNAKE_CASE    ,		default=-1    ,		required=__SCREAMING_SNAKE_CASE    ,		help='# examples. -1 means use all.'      )
      parser.add_argument(
          '--task'    ,		type=__SCREAMING_SNAKE_CASE    ,		default='summarization'    ,		required=__SCREAMING_SNAKE_CASE    ,		help='# examples. -1 means use all.'      )
      parser.add_argument('--label_smoothing'    ,		type=__SCREAMING_SNAKE_CASE    ,		default=0.0    ,		required=__SCREAMING_SNAKE_CASE      )
      parser.add_argument('--src_lang'    ,		type=__SCREAMING_SNAKE_CASE    ,		default=''    ,		required=__SCREAMING_SNAKE_CASE      )
      parser.add_argument('--tgt_lang'    ,		type=__SCREAMING_SNAKE_CASE    ,		default=''    ,		required=__SCREAMING_SNAKE_CASE      )
      parser.add_argument('--eval_beams'    ,		type=__SCREAMING_SNAKE_CASE    ,		default=__SCREAMING_SNAKE_CASE    ,		required=__SCREAMING_SNAKE_CASE      )
      parser.add_argument(
          '--val_metric'    ,		type=__SCREAMING_SNAKE_CASE    ,		default=__SCREAMING_SNAKE_CASE    ,		required=__SCREAMING_SNAKE_CASE    ,		choices=['bleu', 'rouge2', 'loss', None]      )
      parser.add_argument('--eval_max_gen_length'    ,		type=__SCREAMING_SNAKE_CASE    ,		default=__SCREAMING_SNAKE_CASE    ,		help='never generate more than n tokens'      )
      parser.add_argument('--save_top_k'    ,		type=__SCREAMING_SNAKE_CASE    ,		default=1    ,		required=__SCREAMING_SNAKE_CASE    ,		help='How many checkpoints to save'      )
      parser.add_argument(
          '--early_stopping_patience'    ,		type=__SCREAMING_SNAKE_CASE    ,		default=-1    ,		required=__SCREAMING_SNAKE_CASE    ,		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 						snake_case  (  lowerCAmelCase_	):
    __magic_name__   					=					"""translation"""
    __magic_name__   					=					["""loss"""]
    __magic_name__   					=					["""bleu"""]
    __magic_name__   					=					"""bleu"""





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


      '''simple docstring'''




      super().__init__(__SCREAMING_SNAKE_CASE    ,		**__SCREAMING_SNAKE_CASE      )
      a					:      Union[str, Any]    =       hparams.src_lang
      a					:      int    =       hparams.tgt_lang





    def     lowerCamelCase__				(							self     : Any    ,		A     : Dict    ,		A     : Union[str, Any]      ):


      '''simple docstring'''




      return calculate_bleu(__SCREAMING_SNAKE_CASE    ,		__SCREAMING_SNAKE_CASE      )




def       snake_case  (A_				:Union[str, Any]     ,      A_				:int=None				):







  '''simple docstring'''

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

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

    a					:      List[Any]    =       os.environ.get('WANDB_PROJECT'     ,      A_				)
    a					:      Tuple    =       WandbLogger(name=model.output_dir.name     ,      project=A_				)

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

    a					:      Optional[int]    =       WandbLogger(name=model.output_dir.name     ,      project=f'''hf_{dataset}'''				)

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

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

  a					:      Optional[Any]    =       ''
  a					:      Union[str, Any]    =       sorted(glob.glob(os.path.join(args.output_dir     ,      '*.ckpt'				)     ,      recursive=A_				)				)
  if checkpoints:
    a					:      Dict    =       checkpoints[-1]
    a					:      Tuple    =       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    :   Optional[Any]   			=						argparse.ArgumentParser()
   _UpperCamelCase    :   str   			=						pl.Trainer.add_argparse_args(parser)
   _UpperCamelCase    :   Dict   			=						SummarizationModule.add_model_specific_args(parser, os.getcwd())

   _UpperCamelCase    :   Tuple   			=						parser.parse_args()

   main(args)












"""simple docstring"""


import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase    :   List[str]   			=						get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
@require_tokenizers
class 						snake_case  (  UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					XLNetTokenizer
    __magic_name__   					=					XLNetTokenizerFast
    __magic_name__   					=					True
    __magic_name__   					=					True





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      super().setUp()

      # We have a SentencePiece fixture for testing
      a					:      Tuple    =       XLNetTokenizer(A    ,		keep_accents=A      )
      tokenizer.sanitize_special_tokens()
      tokenizer.save_pretrained(self.tmpdirname      )





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Optional[int]    =       '<s>'
      a					:      int    =       1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(A      )    ,		A      )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(A      )    ,		A      )





    def     lowerCamelCase__				(							self     : Tuple      ):


      '''simple docstring'''




      a					:      str    =       list(self.get_tokenizer().get_vocab().keys()      )

      self.assertEqual(vocab_keys[0]    ,		'<unk>'      )
      self.assertEqual(vocab_keys[1]    ,		'<s>'      )
      self.assertEqual(vocab_keys[-1]    ,		'<eod>'      )
      self.assertEqual(len(A      )    ,		1_0_0_6      )





    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      self.assertEqual(self.get_tokenizer().vocab_size    ,		1_0_0_0      )





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       XLNetTokenizer(A    ,		keep_accents=A      )

      a					:      List[str]    =       tokenizer.tokenize('This is a test'      )
      self.assertListEqual(A    ,		['▁This', '▁is', '▁a', '▁t', 'est']      )

      self.assertListEqual(tokenizer.convert_tokens_to_ids(A      )    ,		[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]      )

      a					:      List[str]    =       tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
      self.assertListEqual(
          A    ,		[
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '9',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              'é',
              '.',
          ]    ,		)
      a					:      Union[str, Any]    =       tokenizer.convert_tokens_to_ids(A      )
      self.assertListEqual(A    ,		[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]      )

      a					:      Tuple    =       tokenizer.convert_ids_to_tokens(A      )
      self.assertListEqual(
          A    ,		[
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '<unk>',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              '<unk>',
              '.',
          ]    ,		)





    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      a					:      Optional[int]    =       XLNetTokenizer(A    ,		do_lower_case=A      )
      a					:      Any    =       tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
      self.assertListEqual(
          A    ,		[
              SPIECE_UNDERLINE + '',
              'i',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '9',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              'se',
              '.',
          ]    ,		)
      self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )    ,		['▁he', 'll', 'o']      )





    def     lowerCamelCase__				(							self     : int      ):


      '''simple docstring'''




      a					:      str    =       XLNetTokenizer(A    ,		do_lower_case=A      )
      a					:      List[Any]    =       tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
      self.assertListEqual(
          A    ,		[
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '9',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              'se',
              '.',
          ]    ,		)





    @slow
    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Dict    =       XLNetTokenizer.from_pretrained('xlnet-base-cased'      )

      a					:      int    =       tokenizer.encode('sequence builders'    ,		add_special_tokens=A      )
      a					:      List[Any]    =       tokenizer.encode('multi-sequence build'    ,		add_special_tokens=A      )

      a					:      Optional[int]    =       tokenizer.build_inputs_with_special_tokens(A      )
      a					:      Optional[Any]    =       tokenizer.build_inputs_with_special_tokens(A    ,		A      )

      assert encoded_sentence == text + [4, 3]
      assert encoded_pair == text + [4] + text_a + [4, 3]





    @slow
    def     lowerCamelCase__				(							self     : Any      ):


      '''simple docstring'''




      a					:      Union[str, Any]    =       {'input_ids': [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 9, 4, 3]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=A    ,		model_name='xlnet-base-cased'    ,		revision='c841166438c31ec7ca9a106dee7bb312b73ae511'    ,		)




