




import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
						raise Exception('''requires gluonnlp == 0.8.3''')

if version.parse(mx.__version__) != version.parse('''1.5.0'''):
						raise Exception('''requires mxnet == 1.5.0''')

logging.set_verbosity_info()
UpperCamelCase__ :   Optional[int]						=							logging.get_logger(__name__)

UpperCamelCase__ :   Optional[Any]						=							'''The Nymphenburg Palace is a beautiful palace in Munich!'''





def        __UpperCAmelCase						(							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     str  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : List[str]					  =      {
			    'attention_cell': 'multi_head',
			    'num_layers': 4,
			    'units': 10_24,
			    'hidden_size': 7_68,
			    'max_length': 5_12,
			    'num_heads': 8,
			    'scaled': True,
			    'dropout': 0.1,
			    'use_residual': True,
			    'embed_size': 10_24,
			    'embed_dropout': 0.1,
			    'word_embed': None,
			    'layer_norm_eps': 1E-5,
			    'token_type_vocab_size': 2,
			}

			SCREAMING_SNAKE_CASE_   : Dict					  =      bort_4_8_768_1024_hparams

			# Let's construct the original Bort model here
			# Taken from official BERT implementation, see:
			# https://github.com/alexa/bort/blob/master/bort/bort.py
			SCREAMING_SNAKE_CASE_   : int					  =      BERTEncoder(
			    attention_cell=predefined_args['attention_cell']  ,							num_layers=predefined_args['num_layers']  ,							units=predefined_args['units']  ,							hidden_size=predefined_args['hidden_size']  ,							max_length=predefined_args['max_length']  ,							num_heads=predefined_args['num_heads']  ,							scaled=predefined_args['scaled']  ,							dropout=predefined_args['dropout']  ,							output_attention=lowerCamelCase_  ,							output_all_encodings=lowerCamelCase_  ,							use_residual=predefined_args['use_residual']  ,							activation=predefined_args.get('activation'  ,							'gelu'  )  ,							layer_norm_eps=predefined_args.get('layer_norm_eps'  ,							lowerCamelCase_  )  ,							)

			# Vocab information needs to be fetched first
			# It's the same as RoBERTa, so RobertaTokenizer can be used later
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      'openwebtext_ccnews_stories_books_cased'

			# Specify download folder to Gluonnlp's vocab
			SCREAMING_SNAKE_CASE_   : List[str]					  =      os.path.join(get_home_dir()  ,							'models'  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      _load_vocab(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							cls=lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : List[str]					  =      nlp.model.BERTModel(
			    lowerCamelCase_  ,							len(lowerCamelCase_  )  ,							units=predefined_args['units']  ,							embed_size=predefined_args['embed_size']  ,							embed_dropout=predefined_args['embed_dropout']  ,							word_embed=predefined_args['word_embed']  ,							use_pooler=lowerCamelCase_  ,							use_token_type_embed=lowerCamelCase_  ,							token_type_vocab_size=predefined_args['token_type_vocab_size']  ,							use_classifier=lowerCamelCase_  ,							use_decoder=lowerCamelCase_  ,							)

			original_bort.load_parameters(lowerCamelCase_  ,							cast_dtype=lowerCamelCase_  ,							ignore_extra=lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      original_bort._collect_params_with_prefix()

			# Build our config 🤗
			SCREAMING_SNAKE_CASE_   : str					  =      {
			    'architectures': ['BertForMaskedLM'],
			    'attention_probs_dropout_prob': predefined_args['dropout'],
			    'hidden_act': 'gelu',
			    'hidden_dropout_prob': predefined_args['dropout'],
			    'hidden_size': predefined_args['embed_size'],
			    'initializer_range': 0.0_2,
			    'intermediate_size': predefined_args['hidden_size'],
			    'layer_norm_eps': predefined_args['layer_norm_eps'],
			    'max_position_embeddings': predefined_args['max_length'],
			    'model_type': 'bort',
			    'num_attention_heads': predefined_args['num_heads'],
			    'num_hidden_layers': predefined_args['num_layers'],
			    'pad_token_id': 1,  # 2 = BERT, 1 = RoBERTa
			    'type_vocab_size': 1,  # 2 = BERT, 1 = RoBERTa
			    'vocab_size': len(lowerCamelCase_  ),
			}

			SCREAMING_SNAKE_CASE_   : List[str]					  =      BertConfig.from_dict(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : int					  =      BertForMaskedLM(lowerCamelCase_  )
			hf_bort_model.eval()

			# Parameter mapping table (Gluonnlp to Transformers)
			# * denotes layer index
			#
			# | Gluon Parameter                                                | Transformers Parameter
			# | -------------------------------------------------------------- | ----------------------
			# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
			# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
			# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
			# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
			# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
			# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
			# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
			# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
			# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
			# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
			# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
			# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
			# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
			# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
			# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
			# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
			# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
			# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
			# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
			# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

			# Helper function to convert MXNET Arrays to PyTorch
			def to_torch(lowerCamelCase_ :     str  ) -> nn.Parameter:
						return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()  )  )

			# Check param shapes and map new HF param back
			def check_and_map_params(lowerCamelCase_ :     Optional[int]  ,							lowerCamelCase_ :     Optional[int]  ):
						SCREAMING_SNAKE_CASE_   : List[str]					  =      hf_param.shape

						SCREAMING_SNAKE_CASE_   : List[Any]					  =      to_torch(params[gluon_param]  )
						SCREAMING_SNAKE_CASE_   : int					  =      gluon_param.shape

						assert (
						    shape_hf == shape_gluon
						), F'The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'

						return gluon_param

			SCREAMING_SNAKE_CASE_   : Tuple					  =      check_and_map_params(
			    hf_bort_model.bert.embeddings.word_embeddings.weight  ,							'word_embed.0.weight'  )
			SCREAMING_SNAKE_CASE_   : Any					  =      check_and_map_params(
			    hf_bort_model.bert.embeddings.position_embeddings.weight  ,							'encoder.position_weight'  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.bias  ,							'encoder.layer_norm.beta'  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      check_and_map_params(
			    hf_bort_model.bert.embeddings.LayerNorm.weight  ,							'encoder.layer_norm.gamma'  )

			# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      torch.zeros_like(
			    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data  )

			for i in range(hf_bort_config.num_hidden_layers  ):
						SCREAMING_SNAKE_CASE_   : BertLayer					  =      hf_bort_model.bert.encoder.layer[i]

						# self attention
						SCREAMING_SNAKE_CASE_   : BertSelfAttention					  =      layer.attention.self

						SCREAMING_SNAKE_CASE_   : Any					  =      check_and_map_params(
						    self_attn.key.bias.data  ,							F'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'  )

						SCREAMING_SNAKE_CASE_   : Any					  =      check_and_map_params(
						    self_attn.key.weight.data  ,							F'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'  )
						SCREAMING_SNAKE_CASE_   : str					  =      check_and_map_params(
						    self_attn.query.bias.data  ,							F'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'  )
						SCREAMING_SNAKE_CASE_   : Any					  =      check_and_map_params(
						    self_attn.query.weight.data  ,							F'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'  )
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      check_and_map_params(
						    self_attn.value.bias.data  ,							F'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'  )
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      check_and_map_params(
						    self_attn.value.weight.data  ,							F'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'  )

						# self attention output
						SCREAMING_SNAKE_CASE_   : BertSelfOutput					  =      layer.attention.output

						SCREAMING_SNAKE_CASE_   : str					  =      check_and_map_params(
						    self_output.dense.bias  ,							F'encoder.transformer_cells.{i}.proj.bias'  )
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      check_and_map_params(
						    self_output.dense.weight  ,							F'encoder.transformer_cells.{i}.proj.weight'  )
						SCREAMING_SNAKE_CASE_   : int					  =      check_and_map_params(
						    self_output.LayerNorm.bias  ,							F'encoder.transformer_cells.{i}.layer_norm.beta'  )
						SCREAMING_SNAKE_CASE_   : Any					  =      check_and_map_params(
						    self_output.LayerNorm.weight  ,							F'encoder.transformer_cells.{i}.layer_norm.gamma'  )

						# intermediate
						SCREAMING_SNAKE_CASE_   : BertIntermediate					  =      layer.intermediate

						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      check_and_map_params(
						    intermediate.dense.bias  ,							F'encoder.transformer_cells.{i}.ffn.ffn_1.bias'  )
						SCREAMING_SNAKE_CASE_   : Dict					  =      check_and_map_params(
						    intermediate.dense.weight  ,							F'encoder.transformer_cells.{i}.ffn.ffn_1.weight'  )

						# output
						SCREAMING_SNAKE_CASE_   : BertOutput					  =      layer.output

						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      check_and_map_params(
						    bert_output.dense.bias  ,							F'encoder.transformer_cells.{i}.ffn.ffn_2.bias'  )
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      check_and_map_params(
						    bert_output.dense.weight  ,							F'encoder.transformer_cells.{i}.ffn.ffn_2.weight'  )
						SCREAMING_SNAKE_CASE_   : str					  =      check_and_map_params(
						    bert_output.LayerNorm.bias  ,							F'encoder.transformer_cells.{i}.ffn.layer_norm.beta'  )
						SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      check_and_map_params(
						    bert_output.LayerNorm.weight  ,							F'encoder.transformer_cells.{i}.ffn.layer_norm.gamma'  )

			# Save space and energy 🎄
			hf_bort_model.half()

			# Compare output of both models
			SCREAMING_SNAKE_CASE_   : Tuple					  =      RobertaTokenizer.from_pretrained('roberta-base'  )

			SCREAMING_SNAKE_CASE_   : int					  =      tokenizer.encode_plus(lowerCamelCase_  )['input_ids']

			# Get gluon output
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      mx.nd.array([input_ids]  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      original_bort(inputs=lowerCamelCase_  ,							token_types=[]  )

			# Get Transformer output (save and reload model again)
			hf_bort_model.save_pretrained(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : int					  =      BertModel.from_pretrained(lowerCamelCase_  )
			hf_bort_model.eval()

			SCREAMING_SNAKE_CASE_   : Any					  =      tokenizer.encode_plus(lowerCamelCase_  ,							return_tensors='pt'  )
			SCREAMING_SNAKE_CASE_   : str					  =      hf_bort_model(**lowerCamelCase_  )[0]

			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      output_gluon[0].asnumpy()
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      output_hf[0].detach().numpy()

			SCREAMING_SNAKE_CASE_   : Any					  =      np.max(np.abs(hf_layer - gluon_layer  )  ).item()
			SCREAMING_SNAKE_CASE_   : Tuple					  =      np.allclose(lowerCamelCase_  ,							lowerCamelCase_  ,							atol=1E-3  )

			if success:
						print('✔️ Both model do output the same tensors'  )
			else:
						print('❌ Both model do **NOT** output the same tensors'  )
						print('Absolute difference is:'  ,							lowerCamelCase_  )


if __name__ == "__main__":
						UpperCamelCase__ :   Union[str, Any]						=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						UpperCamelCase__ :   Optional[int]						=							parser.parse_args()
						convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)






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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_nllb import NllbTokenizer
else:
					_lowerCAmelCase  :      Any			 =None


_lowerCAmelCase  :      Union[str, Any]			 =logging.get_logger(__name__)


_lowerCAmelCase  :      Dict			 ={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase  :      Optional[int]			 ={
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
        ),
    },
}

_lowerCAmelCase  :      List[Any]			 ={
    """facebook/nllb-large-en-ro""": 10_24,
    """facebook/nllb-200-distilled-600M""": 10_24,
}

# fmt: off
_lowerCAmelCase  :      Union[str, Any]			 =["""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 __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   VOCAB_FILES_NAMES
					__magic_name__        =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__magic_name__        =   PRETRAINED_VOCAB_FILES_MAP
					__magic_name__        =   ["input_ids", "attention_mask"]
					__magic_name__        =   NllbTokenizer

					__magic_name__        =   []
					__magic_name__        =   []





					def __init__(				self				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__="<s>"				,      lowerCamelCase__="</s>"				,      lowerCamelCase__="</s>"				,      lowerCamelCase__="<s>"				,      lowerCamelCase__="<unk>"				,      lowerCamelCase__="<pad>"				,      lowerCamelCase__="<mask>"				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=None				,      lowerCamelCase__=False				,      **lowerCamelCase__				,      ):
						# Mask token behave like a normal word, i.e. include the space before it
						UpperCAmelCase__:	int									= AddedToken(lowerCamelCase__				,      lstrip=lowerCamelCase__				,      rstrip=lowerCamelCase__						) if isinstance(lowerCamelCase__				,      lowerCamelCase__						) else mask_token
						UpperCAmelCase__:	Tuple									= legacy_behaviour
						super().__init__(
						    vocab_file=lowerCamelCase__				,      tokenizer_file=lowerCamelCase__				,      bos_token=lowerCamelCase__				,      eos_token=lowerCamelCase__				,      sep_token=lowerCamelCase__				,      cls_token=lowerCamelCase__				,      unk_token=lowerCamelCase__				,      pad_token=lowerCamelCase__				,      mask_token=lowerCamelCase__				,      src_lang=lowerCamelCase__				,      tgt_lang=lowerCamelCase__				,      additional_special_tokens=lowerCamelCase__				,      legacy_behaviour=lowerCamelCase__				,      **lowerCamelCase__				,      )

						UpperCAmelCase__:	Optional[int]									= vocab_file
						UpperCAmelCase__:	Tuple									= False if not self.vocab_file else True

						UpperCAmelCase__:	str									= FAIRSEQ_LANGUAGE_CODES.copy()

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

						self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}						)
						UpperCAmelCase__:	Optional[int]									= {
						    lang_code: self.convert_tokens_to_ids(lowerCamelCase__						) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

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





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





					@src_lang.setter
					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Any									= new_src_lang
						self.set_src_lang_special_tokens(self._src_lang						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = 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				,      lowerCamelCase__				,      lowerCamelCase__ = None						):
						UpperCAmelCase__:	List[Any]									= [self.sep_token_id]
						UpperCAmelCase__:	Tuple									= [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__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						):
						if src_lang is None or tgt_lang is None:
							raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
						UpperCAmelCase__:	str									= src_lang
						UpperCAmelCase__:	Any									= self(lowerCamelCase__				,      add_special_tokens=lowerCamelCase__				,      return_tensors=lowerCamelCase__				,      **lowerCamelCase__						)
						UpperCAmelCase__:	List[Any]									= self.convert_tokens_to_ids(lowerCamelCase__						)
						UpperCAmelCase__:	int									= tgt_lang_id
						return inputs





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = "eng_Latn"				,      lowerCamelCase__ = None				,      lowerCamelCase__ = "fra_Latn"				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__:	Union[str, Any]									= src_lang
						UpperCAmelCase__:	Optional[int]									= tgt_lang
						return super().prepare_seqaseq_batch(lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						)





					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				,      lowerCamelCase__						):
						UpperCAmelCase__:	int									= self.convert_tokens_to_ids(lowerCamelCase__						)

						if self.legacy_behaviour:
							UpperCAmelCase__:	List[str]									= []
							UpperCAmelCase__:	List[Any]									= [self.eos_token_id, self.cur_lang_code]
						else:
							UpperCAmelCase__:	Optional[Any]									= [self.cur_lang_code]
							UpperCAmelCase__:	List[Any]									= [self.eos_token_id]

						UpperCAmelCase__:	Optional[Any]									= self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCAmelCase__:	Optional[Any]									= self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCAmelCase__:	Any									= processors.TemplateProcessing(
						    single=prefix_tokens_str + ["$A"] + suffix_tokens_str				,      pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str				,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,      self.prefix_tokens + self.suffix_tokens						)						)				,      )





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= self.convert_tokens_to_ids(lowerCamelCase__						)
						if self.legacy_behaviour:
							UpperCAmelCase__:	int									= []
							UpperCAmelCase__:	str									= [self.eos_token_id, self.cur_lang_code]
						else:
							UpperCAmelCase__:	Tuple									= [self.cur_lang_code]
							UpperCAmelCase__:	Tuple									= [self.eos_token_id]

						UpperCAmelCase__:	Union[str, Any]									= self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCAmelCase__:	Optional[int]									= self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCAmelCase__:	Optional[Any]									= processors.TemplateProcessing(
						    single=prefix_tokens_str + ["$A"] + suffix_tokens_str				,      pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str				,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,      self.prefix_tokens + self.suffix_tokens						)						)				,      )





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None						):
						if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."						)

						if not os.path.isdir(lowerCamelCase__						):
							logger.error(F"Vocabulary path ({save_directory}) should be a directory."						)
							return
						UpperCAmelCase__:	str									= os.path.join(
						    lowerCamelCase__				,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(lowerCamelCase__						):
							copyfile(self.vocab_file				,      lowerCamelCase__						)

						return (out_vocab_file,)