






"""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 	a					(       _a     ):
    UpperCAmelCase_			:					Tuple     					=['''loss''', '''ce_loss''', '''mlm_loss''', '''hid_loss_enc''', '''hid_loss_dec''']







    def __init__(      self							,		_lowerCamelCase	):
      assert Path(hparams.data_dir	).exists()
      lowercase							   =	Path(hparams.output_dir	)
      self.output_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__	)

      lowercase							   =	self.output_dir.joinpath('student'	)

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

      if hparams.length_penalty != -1:
        lowercase							   =	hparams.length_penalty
      lowercase							   =	hparams.teacher  # Use teacher's tokenizer
      super().__init__(SCREAMING_SNAKE_CASE__							,		model=SCREAMING_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":
        lowercase							   =	len(student.get_encoder().block	)
        lowercase							   =	len(student.get_decoder().block	)
        lowercase							   =	len(teacher.get_encoder().block	)
        lowercase							   =	len(teacher.get_decoder().block	)
      else:
        lowercase							   =	student.config.encoder_layers
        lowercase							   =	student.config.decoder_layers
        lowercase							   =	teacher.config.encoder_layers
        lowercase							   =	teacher.config.decoder_layers

      lowercase							   =	not (hparams.student is None or hparams.teacher == hparams.student)
      lowercase							   =	(not self.different_base_models) and hparams.alpha_hid > 0
      lowercase							   =	self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
      # self.different_encoder determines whether we need to run the teacher encoder
      lowercase							   =	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:
        lowercase							   =	list(range(SCREAMING_SNAKE_CASE__	)	)
      if d_layer_ids is None:
        lowercase							   =	list(range(SCREAMING_SNAKE_CASE__	)	)

      lowercase							   =	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:
          lowercase							   =	get_layers_to_supervise(
              n_student=len(self.e_layer_ids	)							,		n_teacher=SCREAMING_SNAKE_CASE__	)
          lowercase							   =	get_layers_to_supervise(
              n_student=len(self.d_layer_ids	)							,		n_teacher=SCREAMING_SNAKE_CASE__	)
        else:  # student layer should emulate hidden states of the teacher layer it was copied from
          lowercase							   =	self.e_layer_ids
          lowercase							   =	self.d_layer_ids
      else:
        lowercase							   =	None
        lowercase							   =	None

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







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	mask[:, :, None].expand_as(SCREAMING_SNAKE_CASE__	)
      lowercase							   =	s_logits.size(-1	)
      lowercase							   =	torch.masked_select(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__	)  # (bs * seq_length * voc_size) modulo the 1s in mask
      lowercase							   =	torch.masked_select(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__	)  # (bs * seq_length * voc_size) modulo the 1s in mask
      lowercase							   =	s_logits_slct.view(-1							,		SCREAMING_SNAKE_CASE__	)  # (bs * seq_length, voc_size) modulo the 1s in mask
      lowercase							   =	t_logits_slct.view(-1							,		SCREAMING_SNAKE_CASE__	)  # (bs * seq_length, voc_size) modulo the 1s in mask
      assert t_logits_slct.size() == s_logits_slct.size()
      lowercase							   =	(
          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       UpperCamelCase_				(      _lowerCamelCase							,		_lowerCamelCase	):
      SummarizationModule.add_model_specific_args(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__	)
      add_distill_args(SCREAMING_SNAKE_CASE__	)
      return parser







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	self.tokenizer.pad_token_id
      lowercase							   =	batch["input_ids"], batch["attention_mask"], batch["labels"]
      if isinstance(self.model							,		SCREAMING_SNAKE_CASE__	):
        lowercase							   =	self.model._shift_right(SCREAMING_SNAKE_CASE__	)
      else:
        lowercase							   =	shift_tokens_right(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__	)

      # noinspection PyCallingNonCallable
      lowercase							   =	self(
          SCREAMING_SNAKE_CASE__							,		attention_mask=SCREAMING_SNAKE_CASE__							,		decoder_input_ids=SCREAMING_SNAKE_CASE__							,		output_hidden_states=self.do_calc_hidden_loss							,		output_attentions=SCREAMING_SNAKE_CASE__							,		use_cache=SCREAMING_SNAKE_CASE__							,		)
      lowercase							   =	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
        lowercase							   =	nn.CrossEntropyLoss(ignore_index=SCREAMING_SNAKE_CASE__	)
        lowercase							   =	loss_fct(lm_logits.view(-1							,		lm_logits.shape[-1]	)							,		labels.view(-1	)	)
      else:
        lowercase							   =	nn.functional.log_softmax(SCREAMING_SNAKE_CASE__							,		dim=-1	)
        lowercase							   =	label_smoothed_nll_loss(
            SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		self.hparams.label_smoothing							,		ignore_index=SCREAMING_SNAKE_CASE__	)

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

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

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

      lowercase							   =	(
          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       UpperCamelCase_				(      _lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase							   =	"expected list or tuple for hidden_states, got tensor of shape: "
      assert not isinstance(SCREAMING_SNAKE_CASE__							,		torch.Tensor	), F'{msg}{hidden_states.shape}'
      assert not isinstance(SCREAMING_SNAKE_CASE__							,		torch.Tensor	), F'{msg}{hidden_states_T.shape}'
      lowercase							   =	attention_mask.to(hidden_states[0]	)
      lowercase							   =	mask.sum() * hidden_states[0].size(-1	)
      lowercase							   =	torch.stack([hidden_states[i] for i in range(len(SCREAMING_SNAKE_CASE__	)	)]	)
      lowercase							   =	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:
        lowercase							   =	nn.functional.layer_norm(SCREAMING_SNAKE_CASE__							,		student_states.shape[1:]	)
        lowercase							   =	nn.functional.layer_norm(SCREAMING_SNAKE_CASE__							,		teacher_states.shape[1:]	)
      lowercase							   =	nn.functional.mse_loss(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		reduction='none'	)
      lowercase							   =	(mse * mask.unsqueeze(0	).unsqueeze(-1	)).sum() / valid_count
      return masked_mse


def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  Union[str, Any]   ):





  '''simple docstring'''





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

class 	a					(       _a     ):
    UpperCAmelCase_			:					str     					='''translation'''
    UpperCAmelCase_			:					List[Any]     					=['''bleu''']
    UpperCAmelCase_			:					Union[str, Any]     					='''bleu'''







    def __init__(      self							,		_lowerCamelCase							,		**_lowerCamelCase	):
      super().__init__(SCREAMING_SNAKE_CASE__							,		**SCREAMING_SNAKE_CASE__	)
      assert hparams.src_lang is not None
      assert hparams.tgt_lang is not None
      lowercase							   =	hparams.src_lang
      lowercase							   =	hparams.tgt_lang
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer							,		SCREAMING_SNAKE_CASE__	):
        lowercase							   =	self.tokenizer.lang_code_to_id[hparams.tgt_lang]







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase	):
      return calculate_bleu(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__	)







    @staticmethod
    def       UpperCamelCase_				(      _lowerCamelCase							,		_lowerCamelCase	):
      TranslationModule.add_model_specific_args(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__	)
      add_distill_args(SCREAMING_SNAKE_CASE__	)
      return parser


def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  str   ):





  '''simple docstring'''





  if args.no_teacher:
    lowercase							   =	TranslationModule if "translation" in args.task else SummarizationModule
  else:  # DISTILL WITH TEACHER
    lowercase							   =	TranslationDistiller if "translation" in args.task else SummarizationDistiller
  lowercase							   =	module_cls.__name__
  print(f'using module {args.setup_cls}'   )
  lowercase							   =	module_cls(__snake_case   )
  return model
def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  List[str]   ):





  '''simple docstring'''





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

  lowercase							   =	create_module(__snake_case   )
  return ft_main(__snake_case		, model=__snake_case   )


if __name__ == "__main__":
    _UpperCamelCase		:  Tuple						=		argparse.ArgumentParser()
    _UpperCamelCase		:  List[Any]						=		pl.Trainer.add_argparse_args(parser)
    _UpperCamelCase		:  Optional[Any]						=		SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
    _UpperCamelCase		:  int						=		parser.parse_args()

    distill_main(args)









"""simple docstring"""



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase		:  Optional[Any]						=		logging.get_logger(__name__)


_UpperCamelCase		:  Any						=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all MVP models at https://huggingface.co/models?filter=mvp
_UpperCamelCase		:  Optional[int]						=		{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
}

_UpperCamelCase		:  Union[str, Any]						=		{
    'RUCAIBox/mvp': 1_0_2_4,
}
@lru_cache()
def 						_SCREAMING_SNAKE_CASE			(   ):





  '''simple docstring'''





  lowercase							   =	(
      list(range(ord('!'   )		, ord('~'   ) + 1   )   ) + list(range(ord('¡'   )		, ord('¬'   ) + 1   )   ) + list(range(ord('®'   )		, ord('ÿ'   ) + 1   )   )
  )
  lowercase							   =	bs[:]
  lowercase							   =	0
  for b in range(2**8   ):
    if b not in bs:
      bs.append(__snake_case   )
      cs.append(2**8 + n   )
      n += 1
  lowercase							   =	[chr(__snake_case   ) for n in cs]
  return dict(zip(__snake_case		, __snake_case   )   )
def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  int   ):





  '''simple docstring'''





  lowercase							   =	set()
  lowercase							   =	word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)   )
    lowercase							   =	char
  return pairs



class 	a					(       a_     ):
    UpperCAmelCase_			:					Tuple     					=VOCAB_FILES_NAMES
    UpperCAmelCase_			:					int     					=PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_			:					Optional[int]     					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_			:					Any     					=["input_ids", "attention_mask"]







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase="replace"							,		_lowerCamelCase="<s>"							,		_lowerCamelCase="</s>"							,		_lowerCamelCase="</s>"							,		_lowerCamelCase="<s>"							,		_lowerCamelCase="<unk>"							,		_lowerCamelCase="<pad>"							,		_lowerCamelCase="<mask>"							,		_lowerCamelCase=False							,		**_lowerCamelCase							,		):
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else bos_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else eos_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else sep_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else cls_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else unk_token
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else mask_token

      super().__init__(
          errors=_lowerCamelCase							,		bos_token=_lowerCamelCase							,		eos_token=_lowerCamelCase							,		unk_token=_lowerCamelCase							,		sep_token=_lowerCamelCase							,		cls_token=_lowerCamelCase							,		pad_token=_lowerCamelCase							,		mask_token=_lowerCamelCase							,		add_prefix_space=_lowerCamelCase							,		**_lowerCamelCase							,		)
      with open(_lowerCamelCase							,		encoding='utf-8'	) as vocab_handle:
        lowercase							   =	json.load(_lowerCamelCase	)
      lowercase							   =	{v: k for k, v in self.encoder.items()}
      lowercase							   =	errors  # how to handle errors in decoding
      lowercase							   =	bytes_to_unicode()
      lowercase							   =	{v: k for k, v in self.byte_encoder.items()}
      with open(_lowerCamelCase							,		encoding='utf-8'	) as merges_handle:
        lowercase							   =	merges_handle.read().split('\n'	)[1:-1]
      lowercase							   =	[tuple(merge.split()	) for merge in bpe_merges]
      lowercase							   =	dict(zip(_lowerCamelCase							,		range(len(_lowerCamelCase	)	)	)	)
      lowercase							   =	{}
      lowercase							   =	add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      lowercase							   =	re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)







    @property
    def       UpperCamelCase_				(      self	):
      return len(self.encoder	)







    def       UpperCamelCase_				(      self	):
      return dict(self.encoder							,		**self.added_tokens_encoder	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      if token in self.cache:
        return self.cache[token]
      lowercase							   =	tuple(_lowerCamelCase	)
      lowercase							   =	get_pairs(_lowerCamelCase	)

      if not pairs:
        return token

      while True:
        lowercase							   =	min(_lowerCamelCase							,		key=lambda _lowerCamelCase	: self.bpe_ranks.get(_lowerCamelCase							,		float('inf'	)	)	)
        if bigram not in self.bpe_ranks:
          break
        lowercase   ,					lowercase							   =	bigram
        lowercase							   =	[]
        lowercase							   =	0
        while i < len(_lowerCamelCase	):
          try:
            lowercase							   =	word.index(_lowerCamelCase							,		_lowerCamelCase	)
          except ValueError:
            new_word.extend(word[i:]	)
            break
          else:
            new_word.extend(word[i:j]	)
            lowercase							   =	j

          if word[i] == first and i < len(_lowerCamelCase	) - 1 and word[i + 1] == second:
            new_word.append(first + second	)
            i += 2
          else:
            new_word.append(word[i]	)
            i += 1
        lowercase							   =	tuple(_lowerCamelCase	)
        lowercase							   =	new_word
        if len(_lowerCamelCase	) == 1:
          break
        else:
          lowercase							   =	get_pairs(_lowerCamelCase	)
      lowercase							   =	' '.join(_lowerCamelCase	)
      lowercase							   =	word
      return word







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	[]
      for token in re.findall(self.pat							,		_lowerCamelCase	):
        lowercase							   =	''.join(
            self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCamelCase	).split(' '	)	)
      return bpe_tokens







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      return self.encoder.get(_lowerCamelCase							,		self.encoder.get(self.unk_token	)	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      return self.decoder.get(_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	''.join(_lowerCamelCase	)
      lowercase							   =	bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'							,		errors=self.errors	)
      return text







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):
      if not os.path.isdir(_lowerCamelCase	):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
        return
      lowercase							   =	os.path.join(
          _lowerCamelCase							,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
      lowercase							   =	os.path.join(
          _lowerCamelCase							,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

      with open(_lowerCamelCase							,		'w'							,		encoding='utf-8'	) as f:
        f.write(json.dumps(self.encoder							,		indent=2							,		sort_keys=_lowerCamelCase							,		ensure_ascii=_lowerCamelCase	) + '\n'	)

      lowercase							   =	0
      with open(_lowerCamelCase							,		'w'							,		encoding='utf-8'	) as writer:
        writer.write('#version: 0.2\n'	)
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,		key=lambda _lowerCamelCase	: kv[1]	):
          if index != token_index:
            logger.warning(
                F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                ' Please check that the tokenizer is not corrupted!'	)
            lowercase							   =	token_index
          writer.write(' '.join(_lowerCamelCase	) + '\n'	)
          index += 1

      return vocab_file, merge_file







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):
      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      lowercase							   =	[self.cls_token_id]
      lowercase							   =	[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None							,		_lowerCamelCase = False	):
      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=_lowerCamelCase							,		token_ids_a=_lowerCamelCase							,		already_has_special_tokens=_lowerCamelCase	)

      if token_ids_a is None:
        return [1] + ([0] * len(_lowerCamelCase	)) + [1]
      return [1] + ([0] * len(_lowerCamelCase	)) + [1, 1] + ([0] * len(_lowerCamelCase	)) + [1]







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):
      lowercase							   =	[self.sep_token_id]
      lowercase							   =	[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]







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		**_lowerCamelCase	):
      lowercase							   =	kwargs.pop('add_prefix_space'							,		self.add_prefix_space	)
      if (is_split_into_words or add_prefix_space) and (len(_lowerCamelCase	) > 0 and not text[0].isspace()):
        lowercase							   =	' ' + text
      return (text, kwargs)


