





'''simple docstring'''





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


UpperCAmelCase_					:   Any 							=						logging.get_logger(__name__)






def 							_lowerCAmelCase (  _a   :					Union[tf.Tensor, np.ndarray]			)	->			Tuple:
     if isinstance(a_					,   np.ndarray			):
          return list(tensor.shape			)

     lowerCAmelCase_					:       int    =		tf.shape(a_			)

     if tensor.shape == tf.TensorShape(a_			):
          return dynamic

     lowerCAmelCase_					:       Union[str, Any]    =		tensor.shape.as_list()

     return [dynamic[i] if s is None else s for i, s in enumerate(a_			)]






def 							_lowerCAmelCase (  _a   :					tf.Tensor					,   _a   :					Optional[int] = None					,   _a   :					Optional[str] = None			)	->			List[str]:
     return tf.nn.softmax(logits=logits + 1E-9					,   axis=a_					,   name=a_			)






def 							_lowerCAmelCase (  _a   :					Dict					,   _a   :					Optional[int]					,   _a   :					Union[str, Any]					,   _a   :					str=1E-5					,   _a   :					List[Any]=-1			)	->			Optional[int]:
     # This is a very simplified functional layernorm, designed to duplicate
     # the functionality of PyTorch nn.functional.layer_norm when this is needed to port
     # models in Transformers.

     if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(a_					,   a_			):
          raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis."""			)

     # Get mean and variance on the axis to be normalized
     lowerCAmelCase_					:       Any    =		tf.nn.moments(a_					,   axes=[axis]					,   keepdims=a_			)

     if axis != -1:
          # Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
          # on every dimension except axis
          lowerCAmelCase_					:       Dict    =		[1] * inputs.shape.rank
          lowerCAmelCase_					:       Tuple    =		shape_list(a_			)[axis]
          lowerCAmelCase_					:       List[Any]    =		tf.reshape(a_					,   a_			)
          lowerCAmelCase_					:       str    =		tf.reshape(a_					,   a_			)

     # Compute layer normalization using the batch_normalization
     # function.
     lowerCAmelCase_					:       Dict    =		tf.nn.batch_normalization(
         a_					,   a_					,   a_					,   offset=a_					,   scale=a_					,   variance_epsilon=a_					,   )
     return outputs






def 							_lowerCAmelCase (  _a   :					Dict					,   _a   :					List[str]=0					,   _a   :					Dict=-1			)	->			Tuple:
     # Replicates the behavior of torch.flatten in TF

     # If end_dim or start_dim is negative, count them from the end
     if end_dim < 0:
          end_dim += input.shape.rank
     if start_dim < 0:
          start_dim += input.shape.rank

     if start_dim == end_dim:
          return input

     lowerCAmelCase_					:       str    =		tf.shape(a_			)
     lowerCAmelCase_					:       int    =		tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]			)
     lowerCAmelCase_					:       int    =		tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]					,   axis=0			)
     return tf.reshape(a_					,   a_			)






def 							_lowerCAmelCase (  _a   :					tf.Tensor			)	->			Tuple:
     if not isinstance(a_					,   tf.Tensor			):
          lowerCAmelCase_					:       Optional[int]    =		tf.convert_to_tensor(a_			)  # Catches stray NumPy inputs
     if encoder_attention_mask.shape.rank == 3:
          lowerCAmelCase_					:       int    =		encoder_attention_mask[:, None, :, :]
     if encoder_attention_mask.shape.rank == 2:
          lowerCAmelCase_					:       List[Any]    =		encoder_attention_mask[:, None, None, :]
     # T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
     # Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
     # /transformer/transformer_layers.py#L270
     # encoder_extended_attention_mask = (encoder_extended_attention_mask ==
     # encoder_extended_attention_mask.transpose(-1, -2))
     lowerCAmelCase_					:       Tuple    =		(
         tf.cast(1					,   encoder_attention_mask.dtype			) - encoder_extended_attention_mask
     ) * encoder_extended_attention_mask.dtype.min

     return encoder_extended_attention_mask






def 							_lowerCAmelCase (  _a   :					tf.Tensor					,   _a   :					int					,   _a   :					str = "input_ids"			)	->			Tuple:
     tf.debugging.assert_less(
         a_					,   tf.cast(a_					,   dtype=tensor.dtype			)					,   message=(
             F'The maximum value of {tensor_name} ({tf.math.reduce_max(a_			)}) must be smaller than the embedding '
             F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
         )					,   )






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					Dict					,   _a   :					Any			)	->			Tuple:
     lowerCAmelCase_					:       Optional[Any]    =		6_45_12
     # Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
     # because in that case even chunking the array would not make the saving
     # possible.
     lowerCAmelCase_					:       str    =		[x for x in data if len(a_			) > HDF5_OBJECT_HEADER_LIMIT]

     # Expecting this to never be true.
     if bad_attributes:
          raise RuntimeError(
              """The following attributes cannot be saved to HDF5 file because """
              F'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
              F'bytes: {bad_attributes}'			)

     lowerCAmelCase_					:       Optional[int]    =		np.asarray(a_			)

     lowerCAmelCase_					:       Union[str, Any]    =		1
     lowerCAmelCase_					:       Tuple    =		np.array_split(a_					,   a_			)

     # This will never loop forever thanks to the test above.
     while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data			):
          num_chunks += 1
          lowerCAmelCase_					:       List[str]    =		np.array_split(a_					,   a_			)

     if num_chunks > 1:
          for chunk_id, chunk_data in enumerate(a_			):
               lowerCAmelCase_					:       Any    =		chunk_data
     else:
          lowerCAmelCase_					:       List[Any]    =		data






def 							_lowerCAmelCase (  _a   :					str					,   _a   :					List[Any]			)	->			str:
     if name in group.attrs:
          lowerCAmelCase_					:       Union[str, Any]    =		[n.decode("""utf8"""			) if hasattr(a_					,   """decode"""			) else n for n in group.attrs[name]]
     else:
          lowerCAmelCase_					:       str    =		[]
          lowerCAmelCase_					:       List[Any]    =		0
          while "%s%d" % (name, chunk_id) in group.attrs:
               data.extend(
                   [n.decode("""utf8"""			) if hasattr(a_					,   """decode"""			) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]]			)
               chunk_id += 1
     return data







def 							_lowerCAmelCase (  _a   :					Optional[Any]			)	->			Dict:

     def _expand_single_ad_tensor(_a   :					Any			):
          if isinstance(a_					,   tf.Tensor			) and t.shape.rank == 1:
               return tf.expand_dims(a_					,   axis=-1			)
          return t

     return tf.nest.map_structure(_expand_single_ad_tensor					,   a_			)







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


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

UpperCAmelCase_					:   str 							=						"""▁"""

UpperCAmelCase_					:   int 							=						{"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase_					:   Optional[Any] 							=						{
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"""
        ),
    }
}

UpperCAmelCase_					:   Union[str, Any] 							=						{
    """facebook/nllb-200-distilled-600M""": 10_24,
}

# fmt: off
UpperCAmelCase_					:   Tuple 							=						["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  VOCAB_FILES_NAMES
  __UpperCamelCase	     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __UpperCamelCase	     =  PRETRAINED_VOCAB_FILES_MAP
  __UpperCamelCase	     =  ["""input_ids""", """attention_mask"""]

  __UpperCamelCase	     =  []
  __UpperCamelCase	     =  []







  def __init__(	self    ,						_lowercase    ,						_lowercase="<s>"    ,						_lowercase="</s>"    ,						_lowercase="</s>"    ,						_lowercase="<s>"    ,						_lowercase="<unk>"    ,						_lowercase="<pad>"    ,						_lowercase="<mask>"    ,						_lowercase=None    ,						_lowercase=None    ,						_lowercase=None    ,						_lowercase = None    ,						_lowercase=None    ,						_lowercase=False    ,						**_lowercase    ,						):
       # Mask token behave like a normal word, i.e. include the space before it
       lowerCAmelCase_					:       Dict    =		AddedToken(_lowercase    ,						lstrip=_lowercase    ,						rstrip=_lowercase						) if isinstance(_lowercase    ,						_lowercase						) else mask_token

       lowerCAmelCase_					:       Optional[Any]    =		{} if sp_model_kwargs is None else sp_model_kwargs
       lowerCAmelCase_					:       str    =		legacy_behaviour

       super().__init__(
           bos_token=_lowercase    ,						eos_token=_lowercase    ,						unk_token=_lowercase    ,						sep_token=_lowercase    ,						cls_token=_lowercase    ,						pad_token=_lowercase    ,						mask_token=_lowercase    ,						tokenizer_file=_lowercase    ,						src_lang=_lowercase    ,						tgt_lang=_lowercase    ,						additional_special_tokens=_lowercase    ,						sp_model_kwargs=self.sp_model_kwargs    ,						legacy_behaviour=_lowercase    ,						**_lowercase    ,						)

       lowerCAmelCase_					:       Tuple    =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.Load(str(_lowercase						)						)
       lowerCAmelCase_					:       int    =		vocab_file
       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
       # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
       # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

       # Mimic fairseq token-to-id alignment for the first 4 token
       lowerCAmelCase_					:       Optional[Any]    =		{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       lowerCAmelCase_					:       Dict    =		1

       lowerCAmelCase_					:       Tuple    =		len(self.sp_model						)
       lowerCAmelCase_					:       Any    =		{
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_lowercase						)
       }
       lowerCAmelCase_					:       Optional[int]    =		{v: k for k, v in self.lang_code_to_id.items()}
       lowerCAmelCase_					:       List[str]    =		len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id						)
       lowerCAmelCase_					:       Optional[Any]    =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}
       lowerCAmelCase_					:       Tuple    =		list(self.lang_code_to_id.keys()						)

       if additional_special_tokens is not None:
            # Only add those special tokens if they are not already there.
            self._additional_special_tokens.extend(
                [t for t in additional_special_tokens if t not in self._additional_special_tokens]						)

       lowerCAmelCase_					:       str    =		src_lang if src_lang is not None else """eng_Latn"""
       lowerCAmelCase_					:       List[str]    =		self.lang_code_to_id[self._src_lang]
       lowerCAmelCase_					:       Union[str, Any]    =		tgt_lang
       self.set_src_lang_special_tokens(self._src_lang						)







  def __getstate__(	self						):
       lowerCAmelCase_					:       int    =		self.__dict__.copy()
       lowerCAmelCase_					:       List[Any]    =		None
       lowerCAmelCase_					:       Any    =		self.sp_model.serialized_model_proto()
       return state







  def __setstate__(	self    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		d

       # for backward compatibility
       if not hasattr(self    ,						"""sp_model_kwargs"""						):
            lowerCAmelCase_					:       Optional[Any]    =		{}

       lowerCAmelCase_					:       Union[str, Any]    =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)







  @property
  def 	UpperCAmelCase__		(	self						):
       return len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset + 1  # Plus 1 for the mask token







  @property
  def 	UpperCAmelCase__		(	self						):
       return self._src_lang







  @src_lang.setter
  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[Any]    =		new_src_lang
       self.set_src_lang_special_tokens(self._src_lang						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None    ,						_lowercase = False						):

       if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=_lowercase    ,						token_ids_a=_lowercase    ,						already_has_special_tokens=_lowercase						)

       lowerCAmelCase_					:       Optional[int]    =		[1] * len(self.prefix_tokens						)
       lowerCAmelCase_					:       str    =		[1] * len(self.suffix_tokens						)
       if token_ids_a is None:
            return prefix_ones + ([0] * len(_lowercase						)) + suffix_ones
       return prefix_ones + ([0] * len(_lowercase						)) + ([0] * len(_lowercase						)) + suffix_ones







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       if token_ids_a is None:
            return self.prefix_tokens + token_ids_a + self.suffix_tokens
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_					:       Dict    =		[self.sep_token_id]
       lowerCAmelCase_					:       Optional[Any]    =		[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    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						**_lowercase						):
       if src_lang is None or tgt_lang is None:
            raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""						)
       lowerCAmelCase_					:       Any    =		src_lang
       lowerCAmelCase_					:       str    =		self(_lowercase    ,						add_special_tokens=_lowercase    ,						return_tensors=_lowercase    ,						**_lowercase						)
       lowerCAmelCase_					:       str    =		self.convert_tokens_to_ids(_lowercase						)
       lowerCAmelCase_					:       Optional[Any]    =		tgt_lang_id
       return inputs







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       Union[str, Any]    =		{self.convert_ids_to_tokens(_lowercase						): i for i in range(self.vocab_size						)}
       vocab.update(self.added_tokens_encoder						)
       return vocab







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       return self.sp_model.encode(_lowercase    ,						out_type=_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       lowerCAmelCase_					:       Dict    =		self.sp_model.PieceToId(_lowercase						)

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_					:       List[Any]    =		"""""".join(_lowercase						).replace(_lowercase    ,						""" """						).strip()
       return out_string







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None						):
       if not os.path.isdir(_lowercase						):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
            return
       lowerCAmelCase_					:       List[str]    =		os.path.join(
           _lowercase    ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

       if os.path.abspath(self.vocab_file						) != os.path.abspath(_lowercase						) and os.path.isfile(self.vocab_file						):
            copyfile(self.vocab_file    ,						_lowercase						)
       elif not os.path.isfile(self.vocab_file						):
            with open(_lowercase    ,						"""wb"""						) as fi:
                 lowerCAmelCase_					:       int    =		self.sp_model.serialized_model_proto()
                 fi.write(_lowercase						)

       return (out_vocab_file,)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = "eng_Latn"    ,						_lowercase = None    ,						_lowercase = "fra_Latn"    ,						**_lowercase    ,						):
       lowerCAmelCase_					:       Optional[int]    =		src_lang
       lowerCAmelCase_					:       Dict    =		tgt_lang
       return super().prepare_seqaseq_batch(_lowercase    ,						_lowercase    ,						**_lowercase						)







  def 	UpperCAmelCase__		(	self						):
       return self.set_src_lang_special_tokens(self.src_lang						)







  def 	UpperCAmelCase__		(	self						):
       return self.set_tgt_lang_special_tokens(self.tgt_lang						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_					:       Dict    =		self.lang_code_to_id[src_lang]
       if self.legacy_behaviour:
            lowerCAmelCase_					:       Any    =		[]
            lowerCAmelCase_					:       Dict    =		[self.eos_token_id, self.cur_lang_code]
       else:
            lowerCAmelCase_					:       Any    =		[self.cur_lang_code]
            lowerCAmelCase_					:       List[str]    =		[self.eos_token_id]







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       lowerCAmelCase_					:       Any    =		self.lang_code_to_id[lang]
       if self.legacy_behaviour:
            lowerCAmelCase_					:       Union[str, Any]    =		[]
            lowerCAmelCase_					:       Dict    =		[self.eos_token_id, self.cur_lang_code]
       else:
            lowerCAmelCase_					:       List[str]    =		[self.cur_lang_code]
            lowerCAmelCase_					:       List[Any]    =		[self.eos_token_id]
