




"""simple docstring"""







import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


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






class  SCREAMING_SNAKE_CASE__   (   a_   ):
						_a        =  ['loss', 'ce_loss', 'mlm_loss', 'hid_loss_enc', 'hid_loss_dec']


						def __init__(							self			:     Tuple  ,  lowerCAmelCase			:     Union[str, Any]   ):
										assert Path(hparams.data_dir   ).exists()
										lowerCAmelCase		=			Path(hparams.output_dir   )
										self.output_dir.mkdir(exist_ok=snake_case__   )

										lowerCAmelCase		=			self.output_dir.joinpath("""student"""   )

										lowerCAmelCase		=			str(snake_case__   )  # Tell lightning we are training the student
										lowerCAmelCase		=			AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher   ).eval()
										use_task_specific_params(snake_case__  ,  hparams.task   )  # We copy good generation parameters to student by default
										if hparams.student is not None:
														lowerCAmelCase		=			AutoModelForSeqaSeqLM.from_pretrained(hparams.student   )
														use_task_specific_params(snake_case__  ,  hparams.task   )
														lowerCAmelCase	,  lowerCAmelCase		=			None, None
										else:
														lowerCAmelCase	,  lowerCAmelCase	,  lowerCAmelCase		=			create_student_by_copying_alternating_layers(
														    snake_case__  ,  e=hparams.student_encoder_layers  ,  d=hparams.student_decoder_layers  ,  save_path=snake_case__   )

										if hparams.length_penalty != -1:
														lowerCAmelCase		=			hparams.length_penalty
										lowerCAmelCase		=			hparams.teacher  # Use teacher's tokenizer
										super().__init__(snake_case__  ,  model=snake_case__  ,  config=student.config   )
										assert student.config.model_type == teacher.config.model_type, (
										    f'''teacher, student model types should be the same, got {student.config.model_type} !='''
										    f''' {teacher.config.model_type}'''
										)

										if student.config.model_type == "t5":
														lowerCAmelCase		=			len(student.get_encoder().block   )
														lowerCAmelCase		=			len(student.get_decoder().block   )
														lowerCAmelCase		=			len(teacher.get_encoder().block   )
														lowerCAmelCase		=			len(teacher.get_decoder().block   )
										else:
														lowerCAmelCase		=			student.config.encoder_layers
														lowerCAmelCase		=			student.config.decoder_layers
														lowerCAmelCase		=			teacher.config.encoder_layers
														lowerCAmelCase		=			teacher.config.decoder_layers

										lowerCAmelCase		=			not (hparams.student is None or hparams.teacher == hparams.student)
										lowerCAmelCase		=			(not self.different_base_models) and hparams.alpha_hid > 0
										lowerCAmelCase		=			self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
										# self.different_encoder determines whether we need to run the teacher encoder
										lowerCAmelCase		=			teacher
										freeze_params(self.teacher   )

										if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
														try:
																		del self.teacher.model.encoder
														except AttributeError:  # T5
																		del self.teacher.encoder

										if e_layer_ids is None:
														lowerCAmelCase		=			list(range(snake_case__   )   )
										if d_layer_ids is None:
														lowerCAmelCase		=			list(range(snake_case__   )   )

										lowerCAmelCase	,  lowerCAmelCase		=			e_layer_ids, d_layer_ids  # type: List[int], List[int]

										if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
														if hparams.supervise_forward:
																		lowerCAmelCase		=			get_layers_to_supervise(
																		    n_student=len(self.e_layer_ids   )  ,  n_teacher=snake_case__   )
																		lowerCAmelCase		=			get_layers_to_supervise(
																		    n_student=len(self.d_layer_ids   )  ,  n_teacher=snake_case__   )
														else:  # student layer should emulate hidden states of the teacher layer it was copied from
																		lowerCAmelCase		=			self.e_layer_ids
																		lowerCAmelCase		=			self.d_layer_ids
										else:
														lowerCAmelCase		=			None
														lowerCAmelCase		=			None

										lowerCAmelCase		=			nn.KLDivLoss(reduction="""batchmean"""   )
										lowerCAmelCase		=			2.0
										lowerCAmelCase		=			hparams.alpha_mlm
										lowerCAmelCase		=			hparams.alpha_ce
										lowerCAmelCase		=			hparams.alpha_hid
										gc.collect()
										torch.cuda.empty_cache()


						def 					__lowercase	(							self			:     int  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     List[Any]   ):
										lowerCAmelCase		=			mask[:, :, None].expand_as(snake_case__   )
										lowerCAmelCase		=			s_logits.size(-1   )
										lowerCAmelCase		=			torch.masked_select(snake_case__  ,  snake_case__   )  # (bs * seq_length * voc_size) modulo the 1s in mask
										lowerCAmelCase		=			torch.masked_select(snake_case__  ,  snake_case__   )  # (bs * seq_length * voc_size) modulo the 1s in mask
										lowerCAmelCase		=			s_logits_slct.view(-1  ,  snake_case__   )  # (bs * seq_length, voc_size) modulo the 1s in mask
										lowerCAmelCase		=			t_logits_slct.view(-1  ,  snake_case__   )  # (bs * seq_length, voc_size) modulo the 1s in mask
										assert t_logits_slct.size() == s_logits_slct.size()
										lowerCAmelCase		=			(
										    self.ce_loss_fct(
										        nn.functional.log_softmax(s_logits_slct / self.temperature  ,  dim=-1   )  ,  nn.functional.softmax(t_logits_slct / self.temperature  ,  dim=-1   )  ,  )
										    * (self.temperature) ** 2
										)
										return loss_ce


						@staticmethod
						def 					__lowercase	(							lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     Optional[Any]   ):
										SummarizationModule.add_model_specific_args(snake_case__  ,  snake_case__   )
										add_distill_args(snake_case__   )
										return parser


						def 					__lowercase	(							self			:     Dict  ,  lowerCAmelCase			:     Optional[int]   ):
										lowerCAmelCase		=			self.tokenizer.pad_token_id
										lowerCAmelCase	,  lowerCAmelCase	,  lowerCAmelCase		=			batch["""input_ids"""], batch["""attention_mask"""], batch["""labels"""]
										if isinstance(self.model  ,  snake_case__   ):
														lowerCAmelCase		=			self.model._shift_right(snake_case__   )
										else:
														lowerCAmelCase		=			shift_tokens_right(snake_case__  ,  snake_case__   )

										# noinspection PyCallingNonCallable
										lowerCAmelCase		=			self(
										    snake_case__  ,  attention_mask=snake_case__  ,  decoder_input_ids=snake_case__  ,  output_hidden_states=self.do_calc_hidden_loss  ,  output_attentions=snake_case__  ,  use_cache=snake_case__  ,  )
										lowerCAmelCase		=			student_outputs["""logits"""]

										# Same cross entropy vs. label smoothing logic as finetune.py
										assert lm_logits.shape[-1] == self.model.config.vocab_size
										if self.hparams.label_smoothing == 0:
														# Same behavior as modeling_bart.py, besides ignoring pad_token_id
														lowerCAmelCase		=			nn.CrossEntropyLoss(ignore_index=snake_case__   )
														lowerCAmelCase		=			loss_fct(lm_logits.view(-1  ,  lm_logits.shape[-1]   )  ,  labels.view(-1   )   )
										else:
														lowerCAmelCase		=			nn.functional.log_softmax(snake_case__  ,  dim=-1   )
														lowerCAmelCase	,  lowerCAmelCase		=			label_smoothed_nll_loss(
														    snake_case__  ,  snake_case__  ,  self.hparams.label_smoothing  ,  ignore_index=snake_case__   )

										def zero_tensor():
														return torch.tensor(0.0   ).type_as(snake_case__   )

										lowerCAmelCase		=			student_outputs[
										    """encoder_last_hidden_state"""
										]  # use this unless self.different_base_models
										lowerCAmelCase	,  lowerCAmelCase		=			zero_tensor(), zero_tensor()
										if self.different_encoder:  # compute encoder hidden state loss
														lowerCAmelCase		=			self.teacher.get_encoder()(
														    snake_case__  ,  attention_mask=snake_case__  ,  output_hidden_states=self.do_calc_hidden_loss  ,  )
														if self.different_base_models:
																		lowerCAmelCase		=			all_teacher_encoder_outputs["""last_hidden_state"""]
														elif self.do_calc_hidden_loss:
																		lowerCAmelCase		=			self.calc_hidden_loss(
																		    snake_case__  ,  student_outputs["""encoder_hidden_states"""]  ,  all_teacher_encoder_outputs["""hidden_states"""]  ,  self.e_matches  ,  normalize_hidden=self.hparams.normalize_hidden  ,  )

										lowerCAmelCase		=			self.teacher(
										    snake_case__  ,  attention_mask=snake_case__  ,  encoder_outputs=(teacher_enc_outputs,)  ,  decoder_input_ids=snake_case__  ,  output_hidden_states=self.do_calc_hidden_loss  ,  use_cache=snake_case__  ,  )
										lowerCAmelCase		=			decoder_input_ids.ne(snake_case__   )
										lowerCAmelCase		=			self.calc_ce_loss(snake_case__  ,  snake_case__  ,  teacher_outputs["""logits"""]   )
										if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
														lowerCAmelCase		=			self.calc_hidden_loss(
														    snake_case__  ,  student_outputs["""decoder_hidden_states"""]  ,  teacher_outputs["""decoder_hidden_states"""]  ,  self.d_matches  ,  normalize_hidden=self.hparams.normalize_hidden  ,  )

										lowerCAmelCase		=			(
										    self.alpha_ce * loss_ce
										    + self.alpha_mlm * student_lm_loss
										    + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
										)
										return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec




						@staticmethod
						def 					__lowercase	(							lowerCAmelCase			:     Optional[int]  ,  lowerCAmelCase			:     Dict  ,  lowerCAmelCase			:     Any  ,  lowerCAmelCase			:     Any  ,  lowerCAmelCase			:     List[str]   ):
										lowerCAmelCase		=			"""expected list or tuple for hidden_states, got tensor of shape: """
										assert not isinstance(snake_case__  ,  torch.Tensor   ), f'''{msg}{hidden_states.shape}'''
										assert not isinstance(snake_case__  ,  torch.Tensor   ), f'''{msg}{hidden_states_T.shape}'''
										lowerCAmelCase		=			attention_mask.to(hidden_states[0]   )
										lowerCAmelCase		=			mask.sum() * hidden_states[0].size(-1   )
										lowerCAmelCase		=			torch.stack([hidden_states[i] for i in range(len(snake_case__   )   )]   )
										lowerCAmelCase		=			torch.stack([hidden_states_T[j] for j in matches]   )
										assert student_states.shape == teacher_states.shape, f'''{student_states.shape} != {teacher_states.shape}'''
										if normalize_hidden:
														lowerCAmelCase		=			nn.functional.layer_norm(snake_case__  ,  student_states.shape[1:]   )
														lowerCAmelCase		=			nn.functional.layer_norm(snake_case__  ,  teacher_states.shape[1:]   )
										lowerCAmelCase		=			nn.functional.mse_loss(snake_case__  ,  snake_case__  ,  reduction="""none"""   )
										lowerCAmelCase		=			(mse * mask.unsqueeze(0   ).unsqueeze(-1   )).sum() / valid_count
										return masked_mse





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


				'''simple docstring'''






				parser.add_argument("""--teacher""" ,    type=_lowerCAmelCase	)
				parser.add_argument("""--alpha_ce""" ,    default=0.8 ,    type=_lowerCAmelCase	)
				parser.add_argument("""--alpha_mlm""" ,    default=0.2 ,    type=_lowerCAmelCase	)
				parser.add_argument("""--alpha_hid""" ,    default=0.0 ,    type=_lowerCAmelCase ,    required=_lowerCAmelCase	)
				parser.add_argument("""--student""" ,    type=_lowerCAmelCase ,    required=_lowerCAmelCase	)
				parser.add_argument("""--student_decoder_layers""" ,    default=12 ,    type=_lowerCAmelCase ,    required=_lowerCAmelCase	)
				parser.add_argument("""--student_encoder_layers""" ,    default=12 ,    type=_lowerCAmelCase ,    required=_lowerCAmelCase	)
				parser.add_argument("""--no_teacher""" ,    action="""store_true""" ,    default=_lowerCAmelCase	)
				parser.add_argument("""--length_penalty""" ,    type=_lowerCAmelCase ,    default=-1	)
				parser.add_argument("""--supervise_forward""" ,    action="""store_true""" ,    default=_lowerCAmelCase	)
				parser.add_argument("""--normalize_hidden""" ,    action="""store_true""" ,    default=_lowerCAmelCase	)






class  SCREAMING_SNAKE_CASE__   (   a_   ):
						_a        =  'translation'
						_a        =  ['bleu']
						_a        =  'bleu'


						def __init__(							self			:     Optional[Any]  ,  lowerCAmelCase			:     Dict  ,  **lowerCAmelCase			:     Optional[int]   ):
										super().__init__(snake_case__  ,  **snake_case__   )
										assert hparams.src_lang is not None
										assert hparams.tgt_lang is not None
										lowerCAmelCase		=			hparams.src_lang
										lowerCAmelCase		=			hparams.tgt_lang
										if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer  ,  snake_case__   ):
														lowerCAmelCase		=			self.tokenizer.lang_code_to_id[hparams.tgt_lang]


						def 					__lowercase	(							self			:     List[str]  ,  lowerCAmelCase			:     Tuple  ,  lowerCAmelCase			:     str   ):
										return calculate_bleu(snake_case__  ,  snake_case__   )




						@staticmethod
						def 					__lowercase	(							lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     Optional[Any]   ):
										TranslationModule.add_model_specific_args(snake_case__  ,  snake_case__   )
										add_distill_args(snake_case__   )
										return parser





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


				'''simple docstring'''






				if args.no_teacher:
								lowerCAmelCase		=			TranslationModule if """translation""" in args.task else SummarizationModule
				else:  # DISTILL WITH TEACHER
								lowerCAmelCase		=			TranslationDistiller if """translation""" in args.task else SummarizationDistiller
				lowerCAmelCase		=			module_cls.__name__
				print(f'''using module {args.setup_cls}'''	)
				lowerCAmelCase		=			module_cls(_lowerCAmelCase	)
				return model





def 							lowercase   (snake_case__				:	List[str]	)       ->					Any:


				'''simple docstring'''






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

				lowerCAmelCase		=			create_module(_lowerCAmelCase	)
				return ft_main(_lowerCAmelCase ,    model=_lowerCAmelCase	)


if __name__ == "__main__":
		a								=							argparse.ArgumentParser()
		a								=							pl.Trainer.add_argparse_args(parser)
		a								=							SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
		a								=							parser.parse_args()

		distill_main(args)











"""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]





