






import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader

from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
    ROUGE_KEYS,
    LegacySeqaSeqDataset,
    SeqaSeqDataset,
    assert_all_frozen,
    calculate_bleu,
    calculate_rouge,
    check_output_dir,
    flatten_list,
    freeze_embeds,
    freeze_params,
    get_git_info,
    label_smoothed_nll_loss,
    lmap,
    pickle_save,
    save_git_info,
    save_json,
    use_task_specific_params,
)


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train  # noqa


__UpperCAmelCase								=						logging.getLogger(__name__)



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    List[Any]       						=  '''summarization'''
		_snake_case    :    Optional[int]       						=  ['''loss''']
		_snake_case    :    str       						=  ROUGE_KEYS
		_snake_case    :    Tuple       						=  '''rouge2'''



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

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

							UpperCAmelCase_    :      str            =						{
							    '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()   )

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Dict[str, List[str]]:
							UpperCAmelCase_    :      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(_UpperCamelCase				,		Path(self.output_dir   ) / 'text_batch.json'   )
							save_json({k: v.tolist() for k, v in batch.items()}				,		Path(self.output_dir   ) / 'tok_batch.json'   )

							UpperCAmelCase_    :      Union[str, Any]            =						True
							return readable_batch



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		**_UpperCamelCase   )	->							List[str]:
							return self.model(_UpperCamelCase				,		**_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							List[Any]:
							UpperCAmelCase_    :      Dict            =						self.tokenizer.batch_decode(
							    _UpperCamelCase				,		skip_special_tokens=_UpperCamelCase				,		clean_up_tokenization_spaces=_UpperCamelCase   )
							return lmap(str.strip				,		_UpperCamelCase   )



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

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

												assert lm_logits.shape[-1] == self.vocab_size
												UpperCAmelCase_    :      Optional[int]            =						ce_loss_fct(lm_logits.view(-1				,		lm_logits.shape[-1]   )				,		tgt_ids.view(-1   )   )
							else:
												UpperCAmelCase_    :      Optional[Any]            =						nn.functional.log_softmax(_UpperCamelCase				,		dim=-1   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						label_smoothed_nll_loss(
												    _UpperCamelCase				,		_UpperCamelCase				,		self.hparams.label_smoothing				,		ignore_index=_UpperCamelCase   )
							return (loss,)



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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							UpperCAmelCase_    :      List[Any]            =						self._step(_UpperCamelCase   )

							UpperCAmelCase_    :      Optional[Any]            =						dict(zip(self.loss_names				,		_UpperCamelCase   )   )
							# tokens per batch
							UpperCAmelCase_    :      Tuple            =						batch['input_ids'].ne(self.pad   ).sum() + batch['labels'].ne(self.pad   ).sum()
							UpperCAmelCase_    :      Dict            =						batch['input_ids'].shape[0]
							UpperCAmelCase_    :      Optional[int]            =						batch['input_ids'].eq(self.pad   ).sum()
							UpperCAmelCase_    :      List[str]            =						batch['input_ids'].eq(self.pad   ).float().mean()
							# TODO(SS): make a wandb summary metric for this
							return {"loss": loss_tensors[0], "log": logs}



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							return self._generative_step(_UpperCamelCase   )



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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							dict:
							UpperCAmelCase_    :      Dict            =						time.time()

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							int:
							return self._generative_step(_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Dict:
							return self.validation_epoch_end(_UpperCamelCase				,		prefix='test'   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							SeqaSeqDataset:
							UpperCAmelCase_    :      int            =						self.n_obs[type_path]
							UpperCAmelCase_    :      List[str]            =						self.target_lens[type_path]
							UpperCAmelCase_    :      List[str]            =						self.dataset_class(
							    self.tokenizer				,		type_path=_UpperCamelCase				,		n_obs=_UpperCamelCase				,		max_target_length=_UpperCamelCase				,		**self.dataset_kwargs				,		)
							return dataset



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = False   )	->							DataLoader:
							UpperCAmelCase_    :      List[str]            =						self.get_dataset(_UpperCamelCase   )

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

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



		def 					__UpperCAmelCase			(							self   )	->							DataLoader:
							UpperCAmelCase_    :      List[Any]            =						self.get_dataloader('train'				,		batch_size=self.hparams.train_batch_size				,		shuffle=_UpperCamelCase   )
							return dataloader



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



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



		@staticmethod
		def 					__UpperCAmelCase			(							_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							BaseTransformer.add_model_specific_args(_UpperCamelCase				,		_UpperCamelCase   )
							add_generic_args(_UpperCamelCase				,		_UpperCamelCase   )
							parser.add_argument(
							    '--max_source_length'				,		default=1_0_2_4				,		type=_UpperCamelCase				,		help=(
							        'The maximum total input sequence length after tokenization. Sequences longer '
							        'than this will be truncated, sequences shorter will be padded.'
							    )				,		)
							parser.add_argument(
							    '--max_target_length'				,		default=5_6				,		type=_UpperCamelCase				,		help=(
							        'The maximum total input sequence length after tokenization. Sequences longer '
							        'than this will be truncated, sequences shorter will be padded.'
							    )				,		)
							parser.add_argument(
							    '--val_max_target_length'				,		default=1_4_2				,		type=_UpperCamelCase				,		help=(
							        'The maximum total input sequence length after tokenization. Sequences longer '
							        'than this will be truncated, sequences shorter will be padded.'
							    )				,		)
							parser.add_argument(
							    '--test_max_target_length'				,		default=1_4_2				,		type=_UpperCamelCase				,		help=(
							        'The maximum total input sequence length after tokenization. Sequences longer '
							        'than this will be truncated, sequences shorter will be padded.'
							    )				,		)
							parser.add_argument('--freeze_encoder'				,		action='store_true'   )
							parser.add_argument('--freeze_embeds'				,		action='store_true'   )
							parser.add_argument('--sortish_sampler'				,		action='store_true'				,		default=_UpperCamelCase   )
							parser.add_argument('--overwrite_output_dir'				,		action='store_true'				,		default=_UpperCamelCase   )
							parser.add_argument('--max_tokens_per_batch'				,		type=_UpperCamelCase				,		default=_UpperCamelCase   )
							parser.add_argument('--logger_name'				,		type=_UpperCamelCase				,		choices=['default', 'wandb', 'wandb_shared']				,		default='default'   )
							parser.add_argument('--n_train'				,		type=_UpperCamelCase				,		default=-1				,		required=_UpperCamelCase				,		help='# examples. -1 means use all.'   )
							parser.add_argument('--n_val'				,		type=_UpperCamelCase				,		default=5_0_0				,		required=_UpperCamelCase				,		help='# examples. -1 means use all.'   )
							parser.add_argument('--n_test'				,		type=_UpperCamelCase				,		default=-1				,		required=_UpperCamelCase				,		help='# examples. -1 means use all.'   )
							parser.add_argument(
							    '--task'				,		type=_UpperCamelCase				,		default='summarization'				,		required=_UpperCamelCase				,		help='# examples. -1 means use all.'   )
							parser.add_argument('--label_smoothing'				,		type=_UpperCamelCase				,		default=0.0				,		required=_UpperCamelCase   )
							parser.add_argument('--src_lang'				,		type=_UpperCamelCase				,		default=''				,		required=_UpperCamelCase   )
							parser.add_argument('--tgt_lang'				,		type=_UpperCamelCase				,		default=''				,		required=_UpperCamelCase   )
							parser.add_argument('--eval_beams'				,		type=_UpperCamelCase				,		default=_UpperCamelCase				,		required=_UpperCamelCase   )
							parser.add_argument(
							    '--val_metric'				,		type=_UpperCamelCase				,		default=_UpperCamelCase				,		required=_UpperCamelCase				,		choices=['bleu', 'rouge2', 'loss', None]   )
							parser.add_argument('--eval_max_gen_length'				,		type=_UpperCamelCase				,		default=_UpperCamelCase				,		help='never generate more than n tokens'   )
							parser.add_argument('--save_top_k'				,		type=_UpperCamelCase				,		default=1				,		required=_UpperCamelCase				,		help='How many checkpoints to save'   )
							parser.add_argument(
							    '--early_stopping_patience'				,		type=_UpperCamelCase				,		default=-1				,		required=_UpperCamelCase				,		help=(
							        '-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
							        ' val_check_interval will effect it.'
							    )				,		)
							return parser





class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    int       						=  '''translation'''
		_snake_case    :    Dict       						=  ['''loss''']
		_snake_case    :    str       						=  ['''bleu''']
		_snake_case    :    str       						=  '''bleu'''



		def __init__(							self				,		_UpperCamelCase				,		**_UpperCamelCase   )	->							List[str]:
							super().__init__(_UpperCamelCase				,		**_UpperCamelCase   )
							UpperCAmelCase_    :      List[str]            =						hparams.src_lang
							UpperCAmelCase_    :      List[str]            =						hparams.tgt_lang



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							dict:
							return calculate_bleu(_UpperCamelCase				,		_UpperCamelCase   )





def      lowercase__      (     __snake_case    :							Dict     ,       __snake_case    :							List[Any]=None  ):


					'''simple docstring'''


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

					if model is None:
										if "summarization" in args.task:
															UpperCAmelCase_    :      SummarizationModule            =						SummarizationModule(__snake_case  )
										else:
															UpperCAmelCase_    :      SummarizationModule            =						TranslationModule(__snake_case  )
					UpperCAmelCase_    :      List[str]            =						Path(args.data_dir  ).name
					if (
					    args.logger_name == "default"
					    or args.fast_dev_run
					    or str(args.output_dir  ).startswith('/tmp'  )
					    or str(args.output_dir  ).startswith('/var'  )
					):
										UpperCAmelCase_    :      Dict            =						True  # don't pollute wandb logs unnecessarily
					elif args.logger_name == "wandb":
										from pytorch_lightning.loggers import WandbLogger

										UpperCAmelCase_    :      Optional[Any]            =						os.environ.get('WANDB_PROJECT'     ,       __snake_case  )
										UpperCAmelCase_    :      int            =						WandbLogger(name=model.output_dir.name     ,       project=__snake_case  )

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

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

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

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

					UpperCAmelCase_    :      Tuple            =						''
					UpperCAmelCase_    :      List[Any]            =						sorted(glob.glob(os.path.join(args.output_dir     ,       '*.ckpt'  )     ,       recursive=__snake_case  )  )
					if checkpoints:
										UpperCAmelCase_    :      int            =						checkpoints[-1]
										UpperCAmelCase_    :      Optional[Any]            =						checkpoints[-1]
					trainer.logger.log_hyperparams(model.hparams  )

					# test() without a model tests using the best checkpoint automatically
					trainer.test()
					return model


if __name__ == "__main__":
		__UpperCAmelCase								=						argparse.ArgumentParser()
		__UpperCAmelCase								=						pl.Trainer.add_argparse_args(parser)
		__UpperCAmelCase								=						SummarizationModule.add_model_specific_args(parser, os.getcwd())

		__UpperCAmelCase								=						parser.parse_args()

		main(args)











'''simple docstring'''




import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = LongformerTokenizer
	lowercase          = True
	lowercase          = LongformerTokenizerFast
	lowercase          = True





	def  _lowercase(  self    )			->			List[Any]:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								UpperCAmelCase     :		List[str]							     =		[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """<unk>""",
								]
								UpperCAmelCase     :		int							     =		dict(zip(A		,		range(len(A    )    )    )    )
								UpperCAmelCase     :		Any							     =		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
								UpperCAmelCase     :		Dict							     =		{"""unk_token""": """<unk>"""}

								UpperCAmelCase     :		int							     =		os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES["""vocab_file"""]    )
								UpperCAmelCase     :		Optional[int]							     =		os.path.join(self.tmpdirname		,		VOCAB_FILES_NAMES["""merges_file"""]    )
								with open(self.vocab_file		,		"""w"""		,		encoding="""utf-8"""    ) as fp:
															fp.write(json.dumps(A    ) + """\n"""    )
								with open(self.merges_file		,		"""w"""		,		encoding="""utf-8"""    ) as fp:
															fp.write("""\n""".join(A    )    )





	def  _lowercase(  self		,		**A    )			->			Optional[Any]:
								kwargs.update(self.special_tokens_map    )
								return self.tokenizer_class.from_pretrained(self.tmpdirname		,		**A    )





	def  _lowercase(  self		,		**A    )			->			int:
								kwargs.update(self.special_tokens_map    )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,		**A    )





	def  _lowercase(  self		,		A    )			->			Optional[int]:
								UpperCAmelCase     :		Optional[Any]							     =		"""lower newer"""
								UpperCAmelCase     :		Optional[int]							     =		"""lower newer"""
								return input_text, output_text





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		Tuple							     =		self.tokenizer_class(self.vocab_file		,		self.merges_file		,		**self.special_tokens_map    )
								UpperCAmelCase     :		Dict							     =		"""lower newer"""
								UpperCAmelCase     :		int							     =		["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
								UpperCAmelCase     :		Tuple							     =		tokenizer.tokenize(A    )  # , add_prefix_space=True)
								self.assertListEqual(A		,		A    )

								UpperCAmelCase     :		Any							     =		tokens + [tokenizer.unk_token]
								UpperCAmelCase     :		Tuple							     =		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(A    )		,		A    )





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		str							     =		self.get_tokenizer()

								self.assertListEqual(tokenizer.encode("""Hello world!"""		,		add_special_tokens=A    )		,		[0, 31414, 232, 328, 2]    )
								self.assertListEqual(
								    tokenizer.encode("""Hello world! cécé herlolip 418"""		,		add_special_tokens=A    )		,		[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]		,		)





	@slow
	def  _lowercase(  self    )			->			Optional[int]:
								UpperCAmelCase     :		Any							     =		self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096"""    )

								UpperCAmelCase     :		List[Any]							     =		tokenizer.encode("""sequence builders"""		,		add_special_tokens=A    )
								UpperCAmelCase     :		Optional[Any]							     =		tokenizer.encode("""multi-sequence build"""		,		add_special_tokens=A    )

								UpperCAmelCase     :		List[str]							     =		tokenizer.encode(
								    """sequence builders"""		,		add_special_tokens=A		,		add_prefix_space=A    )
								UpperCAmelCase     :		List[str]							     =		tokenizer.encode(
								    """sequence builders"""		,		"""multi-sequence build"""		,		add_special_tokens=A		,		add_prefix_space=A    )

								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.build_inputs_with_special_tokens(A    )
								UpperCAmelCase     :		Any							     =		tokenizer.build_inputs_with_special_tokens(A		,		A    )

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode





	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		str							     =		self.get_tokenizer()

								UpperCAmelCase     :		List[Any]							     =		"""Encode this sequence."""
								UpperCAmelCase     :		List[str]							     =		tokenizer.byte_encoder[""" """.encode("""utf-8"""    )[0]]

								# Testing encoder arguments
								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.encode(A		,		add_special_tokens=A		,		add_prefix_space=A    )
								UpperCAmelCase     :		Dict							     =		tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
								self.assertNotEqual(A		,		A    )

								UpperCAmelCase     :		Tuple							     =		tokenizer.encode(A		,		add_special_tokens=A		,		add_prefix_space=A    )
								UpperCAmelCase     :		int							     =		tokenizer.convert_ids_to_tokens(encoded[0]    )[0]
								self.assertEqual(A		,		A    )

								tokenizer.add_special_tokens({"""bos_token""": """<s>"""}    )
								UpperCAmelCase     :		int							     =		tokenizer.encode(A		,		add_special_tokens=A    )
								UpperCAmelCase     :		List[Any]							     =		tokenizer.convert_ids_to_tokens(encoded[1]    )[0]
								self.assertNotEqual(A		,		A    )

								# Testing spaces after special tokens
								UpperCAmelCase     :		Union[str, Any]							     =		"""<mask>"""
								tokenizer.add_special_tokens(
								    {"""mask_token""": AddedToken(A		,		lstrip=A		,		rstrip=A    )}    )  # mask token has a left space
								UpperCAmelCase     :		str							     =		tokenizer.convert_tokens_to_ids(A    )

								UpperCAmelCase     :		Union[str, Any]							     =		"""Encode <mask> sequence"""
								UpperCAmelCase     :		Union[str, Any]							     =		"""Encode <mask>sequence"""

								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.encode(A    )
								UpperCAmelCase     :		Union[str, Any]							     =		encoded.index(A    )
								UpperCAmelCase     :		List[str]							     =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
								self.assertEqual(A		,		A    )

								UpperCAmelCase     :		Tuple							     =		tokenizer.encode(A    )
								UpperCAmelCase     :		Optional[int]							     =		encoded.index(A    )
								UpperCAmelCase     :		Optional[int]							     =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]    )[0]
								self.assertNotEqual(A		,		A    )





	def  _lowercase(  self    )			->			Optional[int]:
								pass





	def  _lowercase(  self    )			->			Any:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																						UpperCAmelCase     :		str							     =		self.rust_tokenizer_class.from_pretrained(A		,		**A    )
																						UpperCAmelCase     :		int							     =		self.tokenizer_class.from_pretrained(A		,		**A    )
																						UpperCAmelCase     :		Dict							     =		"""A, <mask> AllenNLP sentence."""
																						UpperCAmelCase     :		Dict							     =		tokenizer_r.encode_plus(A		,		add_special_tokens=A		,		return_token_type_ids=A    )
																						UpperCAmelCase     :		Tuple							     =		tokenizer_p.encode_plus(A		,		add_special_tokens=A		,		return_token_type_ids=A    )

																						# token_type_ids should put 0 everywhere
																						self.assertEqual(sum(tokens_r["""token_type_ids"""]    )		,		sum(tokens_p["""token_type_ids"""]    )    )

																						# attention_mask should put 1 everywhere, so sum over length should be 1
																						self.assertEqual(
																						    sum(tokens_r["""attention_mask"""]    ) / len(tokens_r["""attention_mask"""]    )		,		sum(tokens_p["""attention_mask"""]    ) / len(tokens_p["""attention_mask"""]    )		,		)

																						UpperCAmelCase     :		List[Any]							     =		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]    )
																						UpperCAmelCase     :		int							     =		tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]    )

																						# Rust correctly handles the space before the mask while python doesnt
																						self.assertSequenceEqual(tokens_p["""input_ids"""]		,		[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]    )
																						self.assertSequenceEqual(tokens_r["""input_ids"""]		,		[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]    )

																						self.assertSequenceEqual(
																						    A		,		["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )
																						self.assertSequenceEqual(
																						    A		,		["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]    )





	def  _lowercase(  self    )			->			List[Any]:
								for trim_offsets, add_prefix_space in itertools.product([True, False]		,		repeat=2    ):
															UpperCAmelCase     :		Optional[int]							     =		self.rust_tokenizer_class.from_pretrained(
															    self.tmpdirname		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )

															UpperCAmelCase     :		Optional[int]							     =		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()    )
															UpperCAmelCase     :		Optional[int]							     =		json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()    )

															self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]		,		A    )

															self.assertEqual(post_processor_state["""add_prefix_space"""]		,		A    )
															self.assertEqual(post_processor_state["""trim_offsets"""]		,		A    )






	def  _lowercase(  self    )			->			Optional[Any]:
								# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
								# `trim_offsets`
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
																						UpperCAmelCase     :		Union[str, Any]							     =		"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																						UpperCAmelCase     :		int							     =		f'''{text_of_1_token} {text_of_1_token}'''

																						UpperCAmelCase     :		List[Any]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		str							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ) + 1, len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Optional[Any]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		Dict							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ) + 1, len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		int							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		List[Any]							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ), len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Any							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		str							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(len(A    ), len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Optional[Any]							     =		f''' {text}'''

																						# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																						#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																						# )
																						# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																						# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																						# self.assertEqual(
																						#     encoding.offset_mapping[1],
																						#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																						# )

																						UpperCAmelCase     :		Any							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		str							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(1, 1 + len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(1 + len(A    ) + 1, 1 + len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Optional[int]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		Union[str, Any]							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, 1 + len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(1 + len(A    ), 1 + len(A    ) + 1 + len(A    ))		,		)

																						UpperCAmelCase     :		Union[str, Any]							     =		self.rust_tokenizer_class.from_pretrained(
																						    A		,		use_fast=A		,		add_prefix_space=A		,		trim_offsets=A    )
																						UpperCAmelCase     :		Optional[int]							     =		tokenizer_r(A		,		return_offsets_mapping=A		,		add_special_tokens=A    )
																						self.assertEqual(encoding.offset_mapping[0]		,		(0, 1 + len(A    ))    )
																						self.assertEqual(
																						    encoding.offset_mapping[1]		,		(1 + len(A    ), 1 + len(A    ) + 1 + len(A    ))		,		)

