
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


lowercase__		:List[Any]   =    logging.getLogger(__name__)







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''summarization'''
							lowercase_       :       Optional[Any]       					=['''loss''']
							lowercase_       :       Union[str, Any]       					=ROUGE_KEYS
							lowercase_       :       Optional[int]       					='''rouge2'''




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

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

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




							def  A__      (				self   ,A__):
												lowercase							=						{
												    k: self.tokenizer.batch_decode(v.tolist()) if '''mask''' not in k else v.shape for k, v in batch.items()
												}
												save_json(A__   ,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							=						True
												return readable_batch




							def  A__      (				self   ,A__   ,**A__):
												return self.model(A__   ,**A__)




							def  A__      (				self   ,A__):
												lowercase							=						self.tokenizer.batch_decode(
												    A__   ,skip_special_tokens=A__   ,clean_up_tokenization_spaces=A__)
												return lmap(str.strip   ,A__)




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

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

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




							@property
							def  A__      (				self):
												return self.tokenizer.pad_token_id




							def  A__      (				self   ,A__   ,A__):
												lowercase							=						self._step(A__)

												lowercase							=						dict(zip(self.loss_names   ,A__))
												# tokens per batch
												lowercase							=						batch['''input_ids'''].ne(self.pad).sum() + batch['''labels'''].ne(self.pad).sum()
												lowercase							=						batch['''input_ids'''].shape[0]
												lowercase							=						batch['''input_ids'''].eq(self.pad).sum()
												lowercase							=						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  A__      (				self   ,A__   ,A__):
												return self._generative_step(A__)




							def  A__      (				self   ,A__   ,A__="val"):
												self.step_count += 1
												lowercase							=						{k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
												lowercase							=						losses['''loss''']
												lowercase							=						{
												    k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
												}
												lowercase							=						(
												    generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
												)
												lowercase							=						torch.tensor(A__).type_as(A__)
												generative_metrics.update({k: v.item() for k, v in losses.items()})
												losses.update(A__)
												lowercase							=						{f'{prefix}_avg_{k}': x for k, x in losses.items()}
												lowercase							=						self.step_count
												self.metrics[prefix].append(A__)  # callback writes this to self.metrics_save_path
												lowercase							=						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  A__      (				self   ,A__   ,A__):
												return calculate_rouge(A__   ,A__)




							def  A__      (				self   ,A__):
												lowercase							=						time.time()

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




							def  A__      (				self   ,A__   ,A__):
												return self._generative_step(A__)




							def  A__      (				self   ,A__):
												return self.validation_epoch_end(A__   ,prefix='''test''')




							def  A__      (				self   ,A__):
												lowercase							=						self.n_obs[type_path]
												lowercase							=						self.target_lens[type_path]
												lowercase							=						self.dataset_class(
												    self.tokenizer   ,type_path=A__   ,n_obs=A__   ,max_target_length=A__   ,**self.dataset_kwargs   ,)
												return dataset




							def  A__      (				self   ,A__   ,A__   ,A__ = False):
												lowercase							=						self.get_dataset(A__)

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

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




							def  A__      (				self):
												lowercase							=						self.get_dataloader('''train'''   ,batch_size=self.hparams.train_batch_size   ,shuffle=A__)
												return dataloader




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




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





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




							def __init__(				self   ,A__   ,**A__):
												super().__init__(A__   ,**A__)
												lowercase							=						hparams.src_lang
												lowercase							=						hparams.tgt_lang





							def  A__      (				self   ,A__   ,A__):
												return calculate_bleu(A__   ,A__)



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=None		):
					'''simple docstring'''
					Path(args.output_dir		).mkdir(exist_ok=lowerCAmelCase__		)
					check_output_dir(lowerCAmelCase__ ,			expected_items=3		)

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

										lowercase							=						os.environ.get('''WANDB_PROJECT''' ,			lowerCAmelCase__		)
										lowercase							=						WandbLogger(name=model.output_dir.name ,			project=lowerCAmelCase__		)

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

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

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

					lowercase							=						args.val_metric == '''loss'''
					lowercase							=						generic_train(
					    lowerCAmelCase__ ,			lowerCAmelCase__ ,			logging_callback=SeqaSeqLoggingCallback() ,			checkpoint_callback=get_checkpoint_callback(
					        args.output_dir ,			model.val_metric ,			args.save_top_k ,			lowerCAmelCase__		) ,			early_stopping_callback=lowerCAmelCase__ ,			logger=lowerCAmelCase__ ,			)
					pickle_save(model.hparams ,			model.output_dir / '''hparams.pkl'''		)
					if not args.do_predict:
										return model

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

							lowercase__		:Union[str, Any]   =    parser.parse_args()

							main(args)






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


lowercase__		:List[Any]   =    get_tests_dir("fixtures/test_sentencepiece.model")







@require_sentencepiece
@require_tokenizers
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       str       					=XLNetTokenizer
							lowercase_       :       Dict       					=XLNetTokenizerFast
							lowercase_       :       str       					=True
							lowercase_       :       str       					=True




							def  A__      (				self):
												super().setUp()

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




							def  A__      (				self):
												lowercase							=						'''<s>'''
												lowercase							=						1

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




							def  A__      (				self):
												lowercase							=						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  A__      (				self):
												self.assertEqual(self.get_tokenizer().vocab_size   ,1_0_0_0)




							def  A__      (				self):
												lowercase							=						XLNetTokenizer(A__   ,keep_accents=A__)

												lowercase							=						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])

												lowercase							=						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''',
												        '''.''',
												    ]   ,)
												lowercase							=						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])

												lowercase							=						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  A__      (				self):
												lowercase							=						XLNetTokenizer(A__   ,do_lower_case=A__)
												lowercase							=						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  A__      (				self):
												lowercase							=						XLNetTokenizer(A__   ,do_lower_case=A__)
												lowercase							=						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  A__      (				self):
												lowercase							=						XLNetTokenizer.from_pretrained('''xlnet-base-cased''')

												lowercase							=						tokenizer.encode('''sequence builders'''   ,add_special_tokens=A__)
												lowercase							=						tokenizer.encode('''multi-sequence build'''   ,add_special_tokens=A__)

												lowercase							=						tokenizer.build_inputs_with_special_tokens(A__)
												lowercase							=						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  A__      (				self):
												# fmt: off
												lowercase							=						{'''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'''   ,)




