




"""simple docstring"""







import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

a								=							logging.getLogger()
a								=							torch.cuda.is_available()
a								=							{
    'max_tokens_per_batch': None,
    'supervise_forward': True,
    'normalize_hidden': True,
    'label_smoothing': 0.2,
    'eval_max_gen_length': None,
    'eval_beams': 1,
    'val_metric': 'loss',
    'save_top_k': 1,
    'adafactor': True,
    'early_stopping_patience': 2,
    'logger_name': 'default',
    'length_penalty': 0.5,
    'cache_dir': '',
    'task': 'summarization',
    'num_workers': 2,
    'alpha_hid': 0,
    'freeze_embeds': True,
    'enc_only': False,
    'tgt_suffix': '',
    'resume_from_checkpoint': None,
    'sortish_sampler': True,
    'student_decoder_layers': 1,
    'val_check_interval': 1.0,
    'output_dir': '',
    'fp16': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    'no_teacher': False,
    'fp16_opt_level': 'O1',
    'gpus': 1 if CUDA_AVAILABLE else 0,
    'n_tpu_cores': 0,
    'max_grad_norm': 1.0,
    'do_train': True,
    'do_predict': True,
    'accumulate_grad_batches': 1,
    'server_ip': '',
    'server_port': '',
    'seed': 4_2,
    'model_name_or_path': 'sshleifer/bart-tiny-random',
    'config_name': '',
    'tokenizer_name': 'facebook/bart-large',
    'do_lower_case': False,
    'learning_rate': 0.3,
    'lr_scheduler': 'linear',
    'weight_decay': 0.0,
    'adam_epsilon': 1e-08,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 1_2,
    'max_target_length': 1_2,
    'val_max_target_length': 1_2,
    'test_max_target_length': 1_2,
    'fast_dev_run': False,
    'no_cache': False,
    'n_train': -1,
    'n_val': -1,
    'n_test': -1,
    'student_encoder_layers': 1,
    'freeze_encoder': False,
    'auto_scale_batch_size': False,
    'overwrite_output_dir': False,
    'student': None,
}





def 							lowercase   (snake_case__				:	Path ,    snake_case__				:	list	)       ->					List[Any]:


				'''simple docstring'''






				lowerCAmelCase		=			"""\n""".join(snake_case__	)
				Path(snake_case__	).open("""w"""	).writelines(snake_case__	)


a								=							[' Sam ate lunch today.', 'Sams lunch ingredients.']
a								=							['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
a								=							'patrickvonplaten/t5-tiny-random'
a								=							'sshleifer/t5-tinier-random'
a								=							'sshleifer/bart-tiny-random'
a								=							'sshleifer/tiny-mbart'
a								=							'sshleifer/tiny-marian-en-de'
a								=							'stas/tiny-wmt19-en-de'


a								=							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





def 							lowercase   (snake_case__				:	Union[str, Any]	)       ->					Optional[Any]:


				'''simple docstring'''






				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(snake_case__ ,    f'''{split}.source'''	) ,    snake_case__	)
								_dump_articles(os.path.join(snake_case__ ,    f'''{split}.target'''	) ,    snake_case__	)
				return tmp_dir






class  SCREAMING_SNAKE_CASE__   (   _a   ):


						@classmethod
						def 					__lowercase	(							cls			:     Optional[int]   ):
										logging.disable(logging.CRITICAL   )  # remove noisy download output from tracebacks
										return cls


						@slow
						@require_torch_gpu
						def 					__lowercase	(							self			:     List[Any]   ):
										lowerCAmelCase		=			list_models()
										lowerCAmelCase		=			"""sshleifer"""
										lowerCAmelCase		=			[x.modelId for x in model_list if x.modelId.startswith(lowerCAmelCase   )]
										lowerCAmelCase		=			["""sshleifer/blenderbot-3B""", """sshleifer/blenderbot-90M"""]
										lowerCAmelCase		=			[]
										for m in model_ids:
														if m in allowed_to_be_broken:
																		continue
														try:
																		AutoConfig.from_pretrained(lowerCAmelCase   )
														except Exception:
																		failures.append(lowerCAmelCase   )
										assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''


						def 					__lowercase	(							self			:     List[str]   ):
										lowerCAmelCase		=			{"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
										self._test_distiller_cli(lowerCAmelCase   )


						def 					__lowercase	(							self			:     List[Any]   ):
										lowerCAmelCase		=			{
										    """student_encoder_layers""": 2,
										    """student_decoder_layers""": 1,
										    """max_epochs""": 4,
										    """val_check_interval""": 0.25,
										    """alpha_hid""": 2.0,
										    """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
										}
										lowerCAmelCase		=			self._test_distiller_cli(lowerCAmelCase  ,  check_contents=lowerCAmelCase   )

										lowerCAmelCase		=			list(Path(model.output_dir   ).glob("""*.ckpt"""   )   )
										self.assertEqual(1  ,  len(lowerCAmelCase   )   )
										lowerCAmelCase		=			list(Path(model.output_dir   ).glob("""**/*.bin"""   )   )
										self.assertEqual(len(lowerCAmelCase   )  ,  2   )
										lowerCAmelCase		=			lmap(str.strip  ,  Path(model.hparams.data_dir   ).joinpath("""test.source"""   ).open().readlines()   )
										lowerCAmelCase		=			tempfile.mktemp()  # XXX: not being cleaned up
										generate_summaries_or_translations(lowerCAmelCase  ,  lowerCAmelCase  ,  str(model.output_dir / """best_tfmr"""   )   )
										self.assertTrue(Path(lowerCAmelCase   ).exists()   )

										lowerCAmelCase		=			self.get_auto_remove_tmp_dir()
										convert_pl_to_hf(ckpts[0]  ,  transformer_ckpts[0].parent  ,  lowerCAmelCase   )
										assert os.path.exists(os.path.join(lowerCAmelCase  ,  """pytorch_model.bin"""   )   )


						def 					__lowercase	(							self			:     List[Any]   ):
										lowerCAmelCase		=			AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase   )
										lowerCAmelCase	,  lowerCAmelCase		=			model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
										lowerCAmelCase		=			torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]  ,  dtype=torch.long  ,  device=model.device   )
										lowerCAmelCase		=			target_ids[:, :-1].contiguous()  # Why this line?
										lowerCAmelCase		=			target_ids[:, 1:].clone()  # why clone?
										lowerCAmelCase		=			model(
										    lowerCAmelCase  ,  attention_mask=lowerCAmelCase  ,  decoder_input_ids=lowerCAmelCase  ,  labels=lowerCAmelCase  ,  use_cache=lowerCAmelCase   ).loss

										lowerCAmelCase		=			model(lowerCAmelCase  ,  attention_mask=lowerCAmelCase  ,  decoder_input_ids=lowerCAmelCase  ,  use_cache=lowerCAmelCase   ).logits

										lowerCAmelCase		=			nn.functional.log_softmax(lowerCAmelCase  ,  dim=-1   )
										lowerCAmelCase	,  lowerCAmelCase		=			label_smoothed_nll_loss(
										    lowerCAmelCase  ,  lowerCAmelCase  ,  0.1  ,  ignore_index=model.config.pad_token_id   )
										with self.assertRaises(lowerCAmelCase   ):
														# TODO: understand why this breaks
														self.assertEqual(lowerCAmelCase  ,  lowerCAmelCase   )


						def 					__lowercase	(							self			:     List[Any]   ):
										lowerCAmelCase		=			{
										    """student_encoder_layers""": 2,
										    """student_decoder_layers""": 1,
										    """num_train_epochs""": 4,
										    """val_check_interval""": 0.25,
										    """alpha_hid""": 2.0,
										    """task""": """translation""",
										    """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
										    """tokenizer_name""": MBART_TINY,
										    """teacher""": MBART_TINY,
										    """src_lang""": """en_XX""",
										    """tgt_lang""": """ro_RO""",
										}
										lowerCAmelCase		=			self._test_distiller_cli(lowerCAmelCase  ,  check_contents=lowerCAmelCase   )
										assert model.model.config.model_type == "mbart"

										lowerCAmelCase		=			list(Path(model.output_dir   ).glob("""*.ckpt"""   )   )
										self.assertEqual(1  ,  len(lowerCAmelCase   )   )
										lowerCAmelCase		=			list(Path(model.output_dir   ).glob("""**/*.bin"""   )   )
										lowerCAmelCase		=			list(Path(model.output_dir   ).glob("""best_tfmr/*"""   )   )
										assert len(lowerCAmelCase   ) > 2
										self.assertEqual(len(lowerCAmelCase   )  ,  2   )


						def 					__lowercase	(							self			:     Union[str, Any]   ):
										lowerCAmelCase		=			{
										    """student_encoder_layers""": 1,
										    """student_decoder_layers""": 1,
										    """alpha_hid""": 2.0,
										    """teacher""": T5_TINY,
										    """model_name_or_path""": T5_TINY,
										    """tokenizer_name""": T5_TINY,
										}
										self._test_distiller_cli(lowerCAmelCase   )


						def 					__lowercase	(							self			:     str   ):
										lowerCAmelCase		=			{
										    """teacher""": T5_TINY,
										    """student""": T5_TINIER,
										    """model_name_or_path""": T5_TINIER,
										    """tokenizer_name""": T5_TINIER,
										}
										self._test_distiller_cli(lowerCAmelCase   )




						def 					__lowercase	(							self			:     List[str]  ,  lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     Optional[Any]=True   ):
										lowerCAmelCase		=			{
										    """label_smoothing""": 0.0,
										    """early_stopping_patience""": -1,
										    """train_batch_size""": 1,
										    """eval_batch_size""": 2,
										    """max_epochs""": 2,
										    """alpha_mlm""": 0.2,
										    """alpha_ce""": 0.8,
										    """do_predict""": True,
										    """model_name_or_path""": """sshleifer/tinier_bart""",
										    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
										    """val_check_interval""": 0.5,
										}
										default_updates.update(lowerCAmelCase   )
										lowerCAmelCase		=			CHEAP_ARGS.copy()
										lowerCAmelCase		=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
										lowerCAmelCase		=			self.get_auto_remove_tmp_dir()

										args_d.update(data_dir=lowerCAmelCase  ,  output_dir=lowerCAmelCase  ,  **lowerCAmelCase   )
										lowerCAmelCase		=			distill_main(argparse.Namespace(**lowerCAmelCase   )   )
										if not check_contents:
														return model
										lowerCAmelCase		=			os.listdir(lowerCAmelCase   )
										lowerCAmelCase		=			{os.path.basename(lowerCAmelCase   ) for p in contents}
										lowerCAmelCase		=			[p for p in contents if p.endswith("""ckpt"""   )]
										assert len(lowerCAmelCase   ) > 0

										self.assertIn("""test_generations.txt"""  ,  lowerCAmelCase   )
										self.assertIn("""test_results.txt"""  ,  lowerCAmelCase   )

										lowerCAmelCase		=			load_json(model.metrics_save_path   )
										lowerCAmelCase		=			metrics["""val"""][-1]
										self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]  ,  0.01   )
										self.assertGreaterEqual(1.0  ,  last_step_stats["""val_avg_gen_time"""]   )
										self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']  ,  lowerCAmelCase   )
										lowerCAmelCase		=			int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1   )
										self.assertEqual(len(metrics["""val"""]   )  ,  lowerCAmelCase   )
										self.assertEqual(len(metrics["""test"""]   )  ,  1   )
										return model






class  SCREAMING_SNAKE_CASE__   (   _a   ):


						@parameterized.expand(
						    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]  ,  )
						def 					__lowercase	(							self			:     int  ,  lowerCAmelCase			:     Dict   ):
										lowerCAmelCase		=			CHEAP_ARGS.copy()
										lowerCAmelCase		=			"""translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
										lowerCAmelCase		=			0.1 if task == """translation""" else 0

										lowerCAmelCase		=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
										lowerCAmelCase		=			self.get_auto_remove_tmp_dir()
										args_d.update(
										    data_dir=lowerCAmelCase  ,  model_name_or_path=lowerCAmelCase  ,  tokenizer_name=lowerCAmelCase  ,  train_batch_size=2  ,  eval_batch_size=2  ,  output_dir=lowerCAmelCase  ,  do_predict=lowerCAmelCase  ,  task=lowerCAmelCase  ,  src_lang="""en_XX"""  ,  tgt_lang="""ro_RO"""  ,  freeze_encoder=lowerCAmelCase  ,  freeze_embeds=lowerCAmelCase  ,  )
										assert "n_train" in args_d
										lowerCAmelCase		=			argparse.Namespace(**lowerCAmelCase   )
										lowerCAmelCase		=			main(lowerCAmelCase   )

										lowerCAmelCase		=			module.model.get_input_embeddings()
										assert not input_embeds.weight.requires_grad
										if model == T5_TINY:
														lowerCAmelCase		=			module.model.lm_head
														assert not lm_head.weight.requires_grad
														assert (lm_head.weight == input_embeds.weight).all().item()
										elif model == FSMT_TINY:
														lowerCAmelCase		=			module.model.model
														lowerCAmelCase		=			fsmt.decoder.embed_positions
														assert not embed_pos.weight.requires_grad
														assert not fsmt.decoder.embed_tokens.weight.requires_grad
														# check that embeds are not the same
														assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
										else:
														lowerCAmelCase		=			module.model.model
														lowerCAmelCase		=			bart.decoder.embed_positions
														assert not embed_pos.weight.requires_grad
														assert not bart.shared.weight.requires_grad
														# check that embeds are the same
														assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
														assert bart.decoder.embed_tokens == bart.shared

										lowerCAmelCase		=			load_json(module.output_dir / """text_batch.json"""   )
										assert isinstance(lowerCAmelCase  ,  lowerCAmelCase   )
										assert len(lowerCAmelCase   ) >= 4


						def 					__lowercase	(							self			:     List[str]   ):
										lowerCAmelCase		=			CHEAP_ARGS.copy()

										lowerCAmelCase		=			"""summarization"""
										lowerCAmelCase		=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )

										args_d.update(
										    data_dir=lowerCAmelCase  ,  tokenizer_name=lowerCAmelCase  ,  train_batch_size=2  ,  eval_batch_size=2  ,  do_predict=lowerCAmelCase  ,  task=lowerCAmelCase  ,  src_lang="""en_XX"""  ,  tgt_lang="""ro_RO"""  ,  freeze_encoder=lowerCAmelCase  ,  freeze_embeds=lowerCAmelCase  ,  )

										# test models whose config includes the extra_model_args
										lowerCAmelCase		=			BART_TINY
										lowerCAmelCase		=			self.get_auto_remove_tmp_dir()
										lowerCAmelCase		=			args_d.copy()
										args_da.update(
										    model_name_or_path=lowerCAmelCase  ,  output_dir=lowerCAmelCase  ,  )
										lowerCAmelCase		=			("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
										for p in extra_model_params:
														lowerCAmelCase		=			0.5
										lowerCAmelCase		=			argparse.Namespace(**lowerCAmelCase   )
										lowerCAmelCase		=			main(lowerCAmelCase   )
										for p in extra_model_params:
														assert getattr(model.config  ,  lowerCAmelCase   ) == 0.5, f'''failed to override the model config for param {p}'''

										# test models whose config doesn't include the extra_model_args
										lowerCAmelCase		=			T5_TINY
										lowerCAmelCase		=			self.get_auto_remove_tmp_dir()
										lowerCAmelCase		=			args_d.copy()
										args_da.update(
										    model_name_or_path=lowerCAmelCase  ,  output_dir=lowerCAmelCase  ,  )
										lowerCAmelCase		=			"""encoder_layerdrop"""
										lowerCAmelCase		=			0.5
										lowerCAmelCase		=			argparse.Namespace(**lowerCAmelCase   )
										with pytest.raises(lowerCAmelCase   ) as excinfo:
														lowerCAmelCase		=			main(lowerCAmelCase   )
										assert str(excinfo.value   ) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''




						def 					__lowercase	(							self			:     Dict   ):
										lowerCAmelCase		=			CHEAP_ARGS.copy()

										lowerCAmelCase		=			"""summarization"""
										lowerCAmelCase		=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )

										lowerCAmelCase		=			BART_TINY
										lowerCAmelCase		=			self.get_auto_remove_tmp_dir()

										args_d.update(
										    data_dir=lowerCAmelCase  ,  model_name_or_path=lowerCAmelCase  ,  output_dir=lowerCAmelCase  ,  tokenizer_name=lowerCAmelCase  ,  train_batch_size=2  ,  eval_batch_size=2  ,  do_predict=lowerCAmelCase  ,  task=lowerCAmelCase  ,  src_lang="""en_XX"""  ,  tgt_lang="""ro_RO"""  ,  freeze_encoder=lowerCAmelCase  ,  freeze_embeds=lowerCAmelCase  ,  )

										# emulate finetune.py
										lowerCAmelCase		=			argparse.ArgumentParser()
										lowerCAmelCase		=			pl.Trainer.add_argparse_args(lowerCAmelCase   )
										lowerCAmelCase		=			SummarizationModule.add_model_specific_args(lowerCAmelCase  ,  os.getcwd()   )
										lowerCAmelCase		=			{"""--help""": True}

										# --help test
										with pytest.raises(lowerCAmelCase   ) as excinfo:
														with CaptureStdout() as cs:
																		lowerCAmelCase		=			parser.parse_args(lowerCAmelCase   )
														assert False, "--help is expected to sys.exit"
										assert excinfo.type == SystemExit
										lowerCAmelCase		=			lightning_base.arg_to_scheduler_metavar
										assert expected in cs.out, "--help is expected to list the supported schedulers"

										# --lr_scheduler=non_existing_scheduler test
										lowerCAmelCase		=			"""non_existing_scheduler"""
										lowerCAmelCase		=			{f'''--lr_scheduler={unsupported_param}'''}
										with pytest.raises(lowerCAmelCase   ) as excinfo:
														with CaptureStderr() as cs:
																		lowerCAmelCase		=			parser.parse_args(lowerCAmelCase   )
														assert False, "invalid argument is expected to sys.exit"
										assert excinfo.type == SystemExit
										lowerCAmelCase		=			f'''invalid choice: \'{unsupported_param}\''''
										assert expected in cs.err, f'''should have bailed on invalid choice of scheduler {unsupported_param}'''

										# --lr_scheduler=existing_scheduler test
										lowerCAmelCase		=			"""cosine"""
										lowerCAmelCase		=			args_d.copy()
										lowerCAmelCase		=			supported_param
										lowerCAmelCase		=			argparse.Namespace(**lowerCAmelCase   )
										lowerCAmelCase		=			main(lowerCAmelCase   )
										assert (
										    getattr(model.hparams  ,  """lr_scheduler"""   ) == supported_param
										), f'''lr_scheduler={supported_param} shouldn\'t fail'''











"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


a								=							logging.get_logger(__name__)

a								=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

a								=							{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

a								=							{
    'allenai/longformer-base-4096': 4_0_9_6,
    'allenai/longformer-large-4096': 4_0_9_6,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_0_9_6,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_0_9_6,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_0_9_6,
}






class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  VOCAB_FILES_NAMES
						_a        =  PRETRAINED_VOCAB_FILES_MAP
						_a        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a        =  ['input_ids', 'attention_mask']
						_a        =  LongformerTokenizer


						def __init__(							self			:     Optional[Any]  ,  lowerCAmelCase			:     Tuple=None  ,  lowerCAmelCase			:     Any=None  ,  lowerCAmelCase			:     List[Any]=None  ,  lowerCAmelCase			:     Optional[Any]="replace"  ,  lowerCAmelCase			:     Union[str, Any]="<s>"  ,  lowerCAmelCase			:     int="</s>"  ,  lowerCAmelCase			:     Any="</s>"  ,  lowerCAmelCase			:     Union[str, Any]="<s>"  ,  lowerCAmelCase			:     List[str]="<unk>"  ,  lowerCAmelCase			:     Optional[Any]="<pad>"  ,  lowerCAmelCase			:     List[str]="<mask>"  ,  lowerCAmelCase			:     Tuple=False  ,  lowerCAmelCase			:     List[str]=True  ,  **lowerCAmelCase			:     Optional[int]  ,  ):
										super().__init__(
										    lowerCAmelCase  ,  lowerCAmelCase  ,  tokenizer_file=lowerCAmelCase  ,  errors=lowerCAmelCase  ,  bos_token=lowerCAmelCase  ,  eos_token=lowerCAmelCase  ,  sep_token=lowerCAmelCase  ,  cls_token=lowerCAmelCase  ,  unk_token=lowerCAmelCase  ,  pad_token=lowerCAmelCase  ,  mask_token=lowerCAmelCase  ,  add_prefix_space=lowerCAmelCase  ,  trim_offsets=lowerCAmelCase  ,  **lowerCAmelCase  ,  )

										lowerCAmelCase		=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
										if pre_tok_state.get("""add_prefix_space"""  ,  lowerCAmelCase   ) != add_prefix_space:
														lowerCAmelCase		=			getattr(lowerCAmelCase  ,  pre_tok_state.pop("""type"""   )   )
														lowerCAmelCase		=			add_prefix_space
														lowerCAmelCase		=			pre_tok_class(**lowerCAmelCase   )

										lowerCAmelCase		=			add_prefix_space

										lowerCAmelCase		=			"""post_processor"""
										lowerCAmelCase		=			getattr(self.backend_tokenizer  ,  lowerCAmelCase  ,  lowerCAmelCase   )
										if tokenizer_component_instance:
														lowerCAmelCase		=			json.loads(tokenizer_component_instance.__getstate__()   )

														# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
														if "sep" in state:
																		lowerCAmelCase		=			tuple(state["""sep"""]   )
														if "cls" in state:
																		lowerCAmelCase		=			tuple(state["""cls"""]   )

														lowerCAmelCase		=			False

														if state.get("""add_prefix_space"""  ,  lowerCAmelCase   ) != add_prefix_space:
																		lowerCAmelCase		=			add_prefix_space
																		lowerCAmelCase		=			True

														if state.get("""trim_offsets"""  ,  lowerCAmelCase   ) != trim_offsets:
																		lowerCAmelCase		=			trim_offsets
																		lowerCAmelCase		=			True

														if changes_to_apply:
																		lowerCAmelCase		=			getattr(lowerCAmelCase  ,  state.pop("""type"""   )   )
																		lowerCAmelCase		=			component_class(**lowerCAmelCase   )
																		setattr(self.backend_tokenizer  ,  lowerCAmelCase  ,  lowerCAmelCase   )


						@property
						def 					__lowercase	(							self			:     List[Any]   ):
										if self._mask_token is None:
														if self.verbose:
																		logger.error("""Using mask_token, but it is not set yet."""   )
														return None
										return str(self._mask_token   )


						@mask_token.setter
						def 					__lowercase	(							self			:     List[Any]  ,  lowerCAmelCase			:     Dict   ):
										lowerCAmelCase		=			AddedToken(lowerCAmelCase  ,  lstrip=lowerCAmelCase  ,  rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase  ,  lowerCAmelCase   ) else value
										lowerCAmelCase		=			value


						def 					__lowercase	(							self			:     Tuple  ,  *lowerCAmelCase			:     Optional[int]  ,  **lowerCAmelCase			:     Optional[Any]   ):
										lowerCAmelCase		=			kwargs.get("""is_split_into_words"""  ,  lowerCAmelCase   )
										assert self.add_prefix_space or not is_split_into_words, (
										    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     str  ,  *lowerCAmelCase			:     str  ,  **lowerCAmelCase			:     str   ):
										lowerCAmelCase		=			kwargs.get("""is_split_into_words"""  ,  lowerCAmelCase   )

										assert self.add_prefix_space or not is_split_into_words, (
										    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     Optional[str] = None   ):
										lowerCAmelCase		=			self._tokenizer.model.save(lowerCAmelCase  ,  name=lowerCAmelCase   )
										return tuple(lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     Optional[int]=None   ):
										lowerCAmelCase		=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
														return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




						def 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     List[int]  ,  lowerCAmelCase			:     Optional[List[int]] = None   ):
										lowerCAmelCase		=			[self.sep_token_id]
										lowerCAmelCase		=			[self.cls_token_id]

										if token_ids_a is None:
														return len(cls + token_ids_a + sep   ) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]





