'''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


__a:			Any	       =       logging.getLogger(__name__)



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   "summarization"
       SCREAMING_SNAKE_CASE 		=   ["loss"]
       SCREAMING_SNAKE_CASE 		=   ROUGE_KEYS
       SCREAMING_SNAKE_CASE 		=   "rouge2"

       def __init__(						self     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  Optional[Any]:
        if hparams.sortish_sampler and hparams.gpus > 1:
         lowercase__    :   List[Any]						= False
        elif hparams.max_tokens_per_batch is not None:
         if hparams.gpus > 1:
          raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training'''			)
         if hparams.sortish_sampler:
          raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously'''			)

        super().__init__(__lowerCAmelCase     ,       num_labels=__lowerCAmelCase     ,       mode=self.mode     ,       **__lowerCAmelCase			)
        use_task_specific_params(self.model     ,       '''summarization'''			)
        save_git_info(self.hparams.output_dir			)
        lowercase__    :   List[Any]						= Path(self.output_dir			) / '''metrics.json'''
        lowercase__    :   Dict						= Path(self.output_dir			) / '''hparams.pkl'''
        pickle_save(self.hparams     ,       self.hparams_save_path			)
        lowercase__    :   int						= 0
        lowercase__    :   Optional[int]						= defaultdict(__lowerCAmelCase			)
        lowercase__    :   int						= self.config.model_type
        lowercase__    :   int						= self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size

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

        lowercase__    :   Union[str, 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()			)

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Dict[str, List[str]]:
        lowercase__    :   Optional[Any]						= {
            k: self.tokenizer.batch_decode(v.tolist()			) if '''mask''' not in k else v.shape for k, v in batch.items()
        }
        save_json(__lowerCAmelCase     ,       Path(self.output_dir			) / '''text_batch.json'''			)
        save_json({k: v.tolist() for k, v in batch.items()}     ,       Path(self.output_dir			) / '''tok_batch.json'''			)

        lowercase__    :   Optional[int]						= True
        return readable_batch

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  str:
        return self.model(__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  int:
        lowercase__    :   Any						= self.tokenizer.batch_decode(
            __lowerCAmelCase     ,       skip_special_tokens=__lowerCAmelCase     ,       clean_up_tokenization_spaces=__lowerCAmelCase			)
        return lmap(str.strip     ,       __lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Tuple:
        lowercase__    :   Any						= self.tokenizer.pad_token_id
        lowercase__			,							lowercase__    :   Dict						= batch['''input_ids'''], batch['''attention_mask''']
        lowercase__    :   str						= batch['''labels''']
        if isinstance(self.model     ,       __lowerCAmelCase			):
         lowercase__    :   int						= self.model._shift_right(__lowerCAmelCase			)
        else:
         lowercase__    :   Tuple						= shift_tokens_right(__lowerCAmelCase     ,       __lowerCAmelCase			)
        if not self.already_saved_batch:  # This would be slightly better if it only happened on rank zero
         lowercase__    :   Optional[Any]						= decoder_input_ids
         self.save_readable_batch(__lowerCAmelCase			)

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

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

       @property
       def _lowerCAmelCase(						self			)  ->  int:
        return self.tokenizer.pad_token_id

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Dict:
        lowercase__    :   Union[str, Any]						= self._step(__lowerCAmelCase			)

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase="val"			)  ->  Dict:
        self.step_count += 1
        lowercase__    :   int						= {k: torch.stack([x[k] for x in outputs]			).mean() for k in self.loss_names}
        lowercase__    :   List[Any]						= losses['''loss''']
        lowercase__    :   str						= {
            k: np.array([x[k] for x in outputs]			).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
        }
        lowercase__    :   Dict						= (
            generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
        )
        lowercase__    :   torch.FloatTensor						= torch.tensor(__lowerCAmelCase			).type_as(__lowerCAmelCase			)
        generative_metrics.update({k: v.item() for k, v in losses.items()}			)
        losses.update(__lowerCAmelCase			)
        lowercase__    :   Optional[int]						= {F"""{prefix}_avg_{k}""": x for k, x in losses.items()}
        lowercase__    :   Optional[Any]						= self.step_count
        self.metrics[prefix].append(__lowerCAmelCase			)  # callback writes this to self.metrics_save_path
        lowercase__    :   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 _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Dict:
        return calculate_rouge(__lowerCAmelCase     ,       __lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  dict:
        lowercase__    :   List[Any]						= time.time()

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[str]:
        return self._generative_step(__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  int:
        return self.validation_epoch_end(__lowerCAmelCase     ,       prefix='''test'''			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  SeqaSeqDataset:
        lowercase__    :   Optional[int]						= self.n_obs[type_path]
        lowercase__    :   str						= self.target_lens[type_path]
        lowercase__    :   Union[str, Any]						= self.dataset_class(
            self.tokenizer     ,       type_path=__lowerCAmelCase     ,       n_obs=__lowerCAmelCase     ,       max_target_length=__lowerCAmelCase     ,       **self.dataset_kwargs     ,       )
        return dataset

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase = False			)  ->  DataLoader:
        lowercase__    :   int						= self.get_dataset(__lowerCAmelCase			)

        if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
         lowercase__    :   Optional[Any]						= 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":
         lowercase__    :   Optional[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 _lowerCAmelCase(						self			)  ->  DataLoader:
        lowercase__    :   List[str]						= self.get_dataloader('''train'''     ,       batch_size=self.hparams.train_batch_size     ,       shuffle=__lowerCAmelCase			)
        return dataloader

       def _lowerCAmelCase(						self			)  ->  DataLoader:
        return self.get_dataloader('''val'''     ,       batch_size=self.hparams.eval_batch_size			)

       def _lowerCAmelCase(						self			)  ->  DataLoader:
        return self.get_dataloader('''test'''     ,       batch_size=self.hparams.eval_batch_size			)


       @staticmethod
       def _lowerCAmelCase(						__lowerCAmelCase     ,       __lowerCAmelCase			)  ->  List[str]:
        BaseTransformer.add_model_specific_args(__lowerCAmelCase     ,       __lowerCAmelCase			)
        add_generic_args(__lowerCAmelCase     ,       __lowerCAmelCase			)
        parser.add_argument(
            '''--max_source_length'''     ,       default=1024     ,       type=__lowerCAmelCase     ,       help=(
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            )     ,       )
        parser.add_argument(
            '''--max_target_length'''     ,       default=56     ,       type=__lowerCAmelCase     ,       help=(
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            )     ,       )
        parser.add_argument(
            '''--val_max_target_length'''     ,       default=142     ,       type=__lowerCAmelCase     ,       help=(
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            )     ,       )
        parser.add_argument(
            '''--test_max_target_length'''     ,       default=142     ,       type=__lowerCAmelCase     ,       help=(
                '''The maximum total input sequence length after tokenization. Sequences longer '''
                '''than this will be truncated, sequences shorter will be padded.'''
            )     ,       )
        parser.add_argument('''--freeze_encoder'''     ,       action='''store_true'''			)
        parser.add_argument('''--freeze_embeds'''     ,       action='''store_true'''			)
        parser.add_argument('''--sortish_sampler'''     ,       action='''store_true'''     ,       default=__lowerCAmelCase			)
        parser.add_argument('''--overwrite_output_dir'''     ,       action='''store_true'''     ,       default=__lowerCAmelCase			)
        parser.add_argument('''--max_tokens_per_batch'''     ,       type=__lowerCAmelCase     ,       default=__lowerCAmelCase			)
        parser.add_argument('''--logger_name'''     ,       type=__lowerCAmelCase     ,       choices=['''default''', '''wandb''', '''wandb_shared''']     ,       default='''default'''			)
        parser.add_argument('''--n_train'''     ,       type=__lowerCAmelCase     ,       default=-1     ,       required=__lowerCAmelCase     ,       help='''# examples. -1 means use all.'''			)
        parser.add_argument('''--n_val'''     ,       type=__lowerCAmelCase     ,       default=500     ,       required=__lowerCAmelCase     ,       help='''# examples. -1 means use all.'''			)
        parser.add_argument('''--n_test'''     ,       type=__lowerCAmelCase     ,       default=-1     ,       required=__lowerCAmelCase     ,       help='''# examples. -1 means use all.'''			)
        parser.add_argument(
            '''--task'''     ,       type=__lowerCAmelCase     ,       default='''summarization'''     ,       required=__lowerCAmelCase     ,       help='''# examples. -1 means use all.'''			)
        parser.add_argument('''--label_smoothing'''     ,       type=__lowerCAmelCase     ,       default=0.0     ,       required=__lowerCAmelCase			)
        parser.add_argument('''--src_lang'''     ,       type=__lowerCAmelCase     ,       default=''''''     ,       required=__lowerCAmelCase			)
        parser.add_argument('''--tgt_lang'''     ,       type=__lowerCAmelCase     ,       default=''''''     ,       required=__lowerCAmelCase			)
        parser.add_argument('''--eval_beams'''     ,       type=__lowerCAmelCase     ,       default=__lowerCAmelCase     ,       required=__lowerCAmelCase			)
        parser.add_argument(
            '''--val_metric'''     ,       type=__lowerCAmelCase     ,       default=__lowerCAmelCase     ,       required=__lowerCAmelCase     ,       choices=['''bleu''', '''rouge2''', '''loss''', None]			)
        parser.add_argument('''--eval_max_gen_length'''     ,       type=__lowerCAmelCase     ,       default=__lowerCAmelCase     ,       help='''never generate more than n tokens'''			)
        parser.add_argument('''--save_top_k'''     ,       type=__lowerCAmelCase     ,       default=1     ,       required=__lowerCAmelCase     ,       help='''How many checkpoints to save'''			)
        parser.add_argument(
            '''--early_stopping_patience'''     ,       type=__lowerCAmelCase     ,       default=-1     ,       required=__lowerCAmelCase     ,       help=(
                '''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
                ''' val_check_interval will effect it.'''
            )     ,       )
        return parser



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   "translation"
       SCREAMING_SNAKE_CASE 		=   ["loss"]
       SCREAMING_SNAKE_CASE 		=   ["bleu"]
       SCREAMING_SNAKE_CASE 		=   "bleu"

       def __init__(						self     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  Any:
        super().__init__(__lowerCAmelCase     ,       **__lowerCAmelCase			)
        lowercase__    :   Tuple						= hparams.src_lang
        lowercase__    :   List[Any]						= hparams.tgt_lang


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  dict:
        return calculate_bleu(__lowerCAmelCase     ,       __lowerCAmelCase			)


def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase=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:
   lowercase__    :   SummarizationModule						= SummarizationModule(UpperCAmelCase					)
  else:
   lowercase__    :   SummarizationModule						= TranslationModule(UpperCAmelCase					)
 lowercase__    :   Optional[int]						= 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'''					)
 ):
  lowercase__    :   int						= True  # don't pollute wandb logs unnecessarily
 elif args.logger_name == "wandb":
  from pytorch_lightning.loggers import WandbLogger

  lowercase__    :   str						= os.environ.get('''WANDB_PROJECT'''		,					UpperCAmelCase					)
  lowercase__    :   List[str]						= WandbLogger(name=model.output_dir.name		,					project=UpperCAmelCase					)

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

  lowercase__    :   Tuple						= WandbLogger(name=model.output_dir.name		,					project=F"""hf_{dataset}"""					)

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

 lowercase__    :   Dict						= args.val_metric == '''loss'''
 lowercase__    :   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

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

  __a:			List[Any]	       =       parser.parse_args()

  main(args)


'''simple docstring'''

import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   ProphetNetTokenizer
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self			)  ->  Any:
        super().setUp()

        lowercase__    :   Tuple						= [
            '''[UNK]''',
            '''[CLS]''',
            '''[SEP]''',
            '''[PAD]''',
            '''[MASK]''',
            '''want''',
            '''##want''',
            '''##ed''',
            '''wa''',
            '''un''',
            '''runn''',
            '''##ing''',
            ''',''',
            '''low''',
            '''lowest''',
        ]
        lowercase__    :   List[str]						= os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''vocab_file''']			)
        with open(self.vocab_file     ,       '''w'''     ,       encoding='''utf-8'''			) as vocab_writer:
         vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]			)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        lowercase__    :   Union[str, Any]						= '''UNwant\u00E9d,running'''
        lowercase__    :   List[Any]						= '''unwanted, running'''
        return input_text, output_text

       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   List[str]						= self.tokenizer_class(self.vocab_file			)

        lowercase__    :   Union[str, Any]						= tokenizer.tokenize('''UNwant\u00E9d,running'''			)
        self.assertListEqual(__lowerCAmelCase     ,       ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']			)
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase			)     ,       [9, 6, 7, 12, 10, 11]			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   List[str]						= BasicTokenizer()

        self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz'''			)     ,       ['''ah''', '''\u535A''', '''\u63A8''', '''zz''']			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   List[Any]						= BasicTokenizer(do_lower_case=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''			)     ,       ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']			)
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''			)     ,       ['''hello''']			)

       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Optional[int]						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       strip_accents=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''			)     ,       ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?''']			)
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''			)     ,       ['''h\u00E9llo''']			)

       def _lowerCAmelCase(						self			)  ->  Tuple:
        lowercase__    :   str						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       strip_accents=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''			)     ,       ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']			)
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''			)     ,       ['''hello''']			)

       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   Union[str, Any]						= BasicTokenizer(do_lower_case=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''			)     ,       ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']			)
        self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''			)     ,       ['''hello''']			)

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   int						= BasicTokenizer(do_lower_case=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''			)     ,       ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   Tuple						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       strip_accents=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''			)     ,       ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']			)

       def _lowerCAmelCase(						self			)  ->  Any:
        lowercase__    :   Tuple						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       strip_accents=__lowerCAmelCase			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''			)     ,       ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']			)

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Optional[int]						= BasicTokenizer(do_lower_case=__lowerCAmelCase     ,       never_split=['''[UNK]''']			)

        self.assertListEqual(
            tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]'''			)     ,       ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]''']			)

       def _lowerCAmelCase(						self			)  ->  int:
        lowercase__    :   Union[str, Any]						= ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

        lowercase__    :   Union[str, Any]						= {}
        for i, token in enumerate(__lowerCAmelCase			):
         lowercase__    :   List[Any]						= i
        lowercase__    :   Dict						= WordpieceTokenizer(vocab=__lowerCAmelCase     ,       unk_token='''[UNK]'''			)

        self.assertListEqual(tokenizer.tokenize(''''''			)     ,       []			)

        self.assertListEqual(tokenizer.tokenize('''unwanted running'''			)     ,       ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing''']			)

        self.assertListEqual(tokenizer.tokenize('''unwantedX running'''			)     ,       ['''[UNK]''', '''runn''', '''##ing''']			)

       @require_torch
       def _lowerCAmelCase(						self			)  ->  Union[str, Any]:
        lowercase__    :   Union[str, Any]						= self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased'''			)

        lowercase__    :   Optional[int]						= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        lowercase__    :   Dict						= [1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
        lowercase__    :   int						= tokenizer(__lowerCAmelCase     ,       padding=__lowerCAmelCase     ,       return_tensors='''pt'''			)
        self.assertIsInstance(__lowerCAmelCase     ,       __lowerCAmelCase			)
        lowercase__    :   Optional[int]						= list(batch.input_ids.numpy()[0]			)
        self.assertListEqual(__lowerCAmelCase     ,       __lowerCAmelCase			)

        self.assertEqual((2, 9)     ,       batch.input_ids.shape			)
        self.assertEqual((2, 9)     ,       batch.attention_mask.shape			)

       def _lowerCAmelCase(						self			)  ->  str:
        self.assertTrue(_is_whitespace(''' '''			)			)
        self.assertTrue(_is_whitespace('''\t'''			)			)
        self.assertTrue(_is_whitespace('''\r'''			)			)
        self.assertTrue(_is_whitespace('''\n'''			)			)
        self.assertTrue(_is_whitespace('''\u00A0'''			)			)

        self.assertFalse(_is_whitespace('''A'''			)			)
        self.assertFalse(_is_whitespace('''-'''			)			)

       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        self.assertTrue(_is_control('''\u0005'''			)			)

        self.assertFalse(_is_control('''A'''			)			)
        self.assertFalse(_is_control(''' '''			)			)
        self.assertFalse(_is_control('''\t'''			)			)
        self.assertFalse(_is_control('''\r'''			)			)

       def _lowerCAmelCase(						self			)  ->  Dict:
        self.assertTrue(_is_punctuation('''-'''			)			)
        self.assertTrue(_is_punctuation('''$'''			)			)
        self.assertTrue(_is_punctuation('''`'''			)			)
        self.assertTrue(_is_punctuation('''.'''			)			)

        self.assertFalse(_is_punctuation('''A'''			)			)
        self.assertFalse(_is_punctuation(''' '''			)			)


       @slow
       def _lowerCAmelCase(						self			)  ->  Optional[Any]:
        lowercase__    :   List[Any]						= self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased'''			)

        lowercase__    :   List[str]						= tokenizer.encode('''sequence builders'''     ,       add_special_tokens=__lowerCAmelCase			)
        lowercase__    :   Optional[int]						= tokenizer.encode('''multi-sequence build'''     ,       add_special_tokens=__lowerCAmelCase			)

        lowercase__    :   Tuple						= tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase			)
        lowercase__    :   List[str]						= tokenizer.build_inputs_with_special_tokens(__lowerCAmelCase     ,       __lowerCAmelCase			)

        assert encoded_sentence == text + [102]
        assert encoded_pair == text + [102] + text_a + [102]

