

"""simple docstring"""

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		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    """The Nymphenburg Palace is a beautiful palace in Munich!"""



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						str:
 lowerCAmelCase__   :				List[Any] 			=				{
     "attention_cell": "multi_head",
     "num_layers": 4,
     "units": 1_0_2_4,
     "hidden_size": 7_6_8,
     "max_length": 5_1_2,
     "num_heads": 8,
     "scaled": True,
     "dropout": 0.1,
     "use_residual": True,
     "embed_size": 1_0_2_4,
     "embed_dropout": 0.1,
     "word_embed": None,
     "layer_norm_eps": 1E-5,
     "token_type_vocab_size": 2,
 }

 lowerCAmelCase__   :				List[Any] 			=				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
 lowerCAmelCase__   :				Optional[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=lowercase__					,					output_all_encodings=lowercase__					,					use_residual=predefined_args["use_residual"]					,					activation=predefined_args.get("activation"					,					"gelu"   )					,					layer_norm_eps=predefined_args.get("layer_norm_eps"					,					lowercase__   )					,					)

 # Vocab information needs to be fetched first
 # It's the same as RoBERTa, so RobertaTokenizer can be used later
 lowerCAmelCase__   :				int 			=				"openwebtext_ccnews_stories_books_cased"

 # Specify download folder to Gluonnlp's vocab
 lowerCAmelCase__   :				Union[str, Any] 			=				os.path.join(get_home_dir()					,					"models"   )
 lowerCAmelCase__   :				Dict 			=				_load_vocab(lowercase__					,					lowercase__					,					lowercase__					,					cls=lowercase__   )

 lowerCAmelCase__   :				List[Any] 			=				nlp.model.BERTModel(
     lowercase__					,					len(lowercase__   )					,					units=predefined_args["units"]					,					embed_size=predefined_args["embed_size"]					,					embed_dropout=predefined_args["embed_dropout"]					,					word_embed=predefined_args["word_embed"]					,					use_pooler=lowercase__					,					use_token_type_embed=lowercase__					,					token_type_vocab_size=predefined_args["token_type_vocab_size"]					,					use_classifier=lowercase__					,					use_decoder=lowercase__					,					)

 original_bort.load_parameters(lowercase__					,					cast_dtype=lowercase__					,					ignore_extra=lowercase__   )
 lowerCAmelCase__   :				List[Any] 			=				original_bort._collect_params_with_prefix()

 # Build our config 🤗
 lowerCAmelCase__   :				int 			=				{
     "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": 0.02,
     "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(lowercase__   ),
 }

 lowerCAmelCase__   :				str 			=				BertConfig.from_dict(lowercase__   )
 lowerCAmelCase__   :				str 			=				BertForMaskedLM(lowercase__   )
 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(lowercase__   ) -> 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(lowercase__					,					lowercase__   ):
  lowerCAmelCase__   :				List[str] 			=				hf_param.shape

  lowerCAmelCase__   :				Union[str, Any] 			=				to_torch(params[gluon_param]   )
  lowerCAmelCase__   :				Optional[Any] 			=				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

 lowerCAmelCase__   :				List[Any] 			=				check_and_map_params(
     hf_bort_model.bert.embeddings.word_embeddings.weight					,					"word_embed.0.weight"   )
 lowerCAmelCase__   :				int 			=				check_and_map_params(
     hf_bort_model.bert.embeddings.position_embeddings.weight					,					"encoder.position_weight"   )
 lowerCAmelCase__   :				Union[str, Any] 			=				check_and_map_params(
     hf_bort_model.bert.embeddings.LayerNorm.bias					,					"encoder.layer_norm.beta"   )
 lowerCAmelCase__   :				List[str] 			=				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)
 lowerCAmelCase__   :				Any 			=				torch.zeros_like(
     hf_bort_model.bert.embeddings.token_type_embeddings.weight.data   )

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

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

  lowerCAmelCase__   :				Dict 			=				check_and_map_params(
      self_attn.key.bias.data					,					F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""   )

  lowerCAmelCase__   :				Dict 			=				check_and_map_params(
      self_attn.key.weight.data					,					F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""   )
  lowerCAmelCase__   :				str 			=				check_and_map_params(
      self_attn.query.bias.data					,					F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""   )
  lowerCAmelCase__   :				Dict 			=				check_and_map_params(
      self_attn.query.weight.data					,					F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""   )
  lowerCAmelCase__   :				Dict 			=				check_and_map_params(
      self_attn.value.bias.data					,					F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""   )
  lowerCAmelCase__   :				int 			=				check_and_map_params(
      self_attn.value.weight.data					,					F"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""   )

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

  lowerCAmelCase__   :				str 			=				check_and_map_params(
      self_output.dense.bias					,					F"""encoder.transformer_cells.{i}.proj.bias"""   )
  lowerCAmelCase__   :				List[str] 			=				check_and_map_params(
      self_output.dense.weight					,					F"""encoder.transformer_cells.{i}.proj.weight"""   )
  lowerCAmelCase__   :				Any 			=				check_and_map_params(
      self_output.LayerNorm.bias					,					F"""encoder.transformer_cells.{i}.layer_norm.beta"""   )
  lowerCAmelCase__   :				Optional[int] 			=				check_and_map_params(
      self_output.LayerNorm.weight					,					F"""encoder.transformer_cells.{i}.layer_norm.gamma"""   )

  # intermediate
  lowerCAmelCase__   :				BertIntermediate 			=				layer.intermediate

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

  # output
  lowerCAmelCase__   :				BertOutput 			=				layer.output

  lowerCAmelCase__   :				Dict 			=				check_and_map_params(
      bert_output.dense.bias					,					F"""encoder.transformer_cells.{i}.ffn.ffn_2.bias"""   )
  lowerCAmelCase__   :				Optional[int] 			=				check_and_map_params(
      bert_output.dense.weight					,					F"""encoder.transformer_cells.{i}.ffn.ffn_2.weight"""   )
  lowerCAmelCase__   :				Any 			=				check_and_map_params(
      bert_output.LayerNorm.bias					,					F"""encoder.transformer_cells.{i}.ffn.layer_norm.beta"""   )
  lowerCAmelCase__   :				Optional[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
 lowerCAmelCase__   :				Dict 			=				RobertaTokenizer.from_pretrained("roberta-base"   )

 lowerCAmelCase__   :				int 			=				tokenizer.encode_plus(lowercase__   )["input_ids"]

 # Get gluon output
 lowerCAmelCase__   :				List[Any] 			=				mx.nd.array([input_ids]   )
 lowerCAmelCase__   :				Tuple 			=				original_bort(inputs=lowercase__					,					token_types=[]   )

 # Get Transformer output (save and reload model again)
 hf_bort_model.save_pretrained(lowercase__   )
 lowerCAmelCase__   :				Tuple 			=				BertModel.from_pretrained(lowercase__   )
 hf_bort_model.eval()

 lowerCAmelCase__   :				List[str] 			=				tokenizer.encode_plus(lowercase__					,					return_tensors="pt"   )
 lowerCAmelCase__   :				Any 			=				hf_bort_model(**lowercase__   )[0]

 lowerCAmelCase__   :				Tuple 			=				output_gluon[0].asnumpy()
 lowerCAmelCase__   :				List[Any] 			=				output_hf[0].detach().numpy()

 lowerCAmelCase__   :				Any 			=				np.max(np.abs(hf_layer - gluon_layer   )   ).item()
 lowerCAmelCase__   :				str 			=				np.allclose(lowercase__					,					lowercase__					,					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:"					,					lowercase__   )


if __name__ == "__main__":
    _UpperCamelCase		     =    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		     =    parser.parse_args()
    convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)









"""simple docstring"""

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:
    _UpperCamelCase		     =    None


_UpperCamelCase		     =    logging.get_logger(__name__)


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

_UpperCamelCase		     =    {
    """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"""
        ),
    },
}

_UpperCamelCase		     =    {
    """facebook/nllb-large-en-ro""": 1_0_2_4,
    """facebook/nllb-200-distilled-600M""": 1_0_2_4,
}

# fmt: off
_UpperCamelCase		     =    ["""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 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      List[Any]    =						VOCAB_FILES_NAMES
       __UpperCamelCase					:      Dict    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase					:      Dict    =						PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase					:      int    =						['input_ids', 'attention_mask']
       __UpperCamelCase					:      Union[str, Any]    =						NllbTokenizer

       __UpperCamelCase					:      List[int]    =						[]
       __UpperCamelCase					:      List[int]    =						[]






       def __init__(   self			,					snake_case=None			,					snake_case=None			,					snake_case="<s>"			,					snake_case="</s>"			,					snake_case="</s>"			,					snake_case="<s>"			,					snake_case="<unk>"			,					snake_case="<pad>"			,					snake_case="<mask>"			,					snake_case=None			,					snake_case=None			,					snake_case=None			,					snake_case=False			,					**snake_case			,					):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				AddedToken(snake_case			,					lstrip=snake_case			,					rstrip=snake_case   ) if isinstance(snake_case			,					snake_case   ) else mask_token
        lowerCAmelCase__   :				Tuple 			=				legacy_behaviour
        super().__init__(
            vocab_file=snake_case			,					tokenizer_file=snake_case			,					bos_token=snake_case			,					eos_token=snake_case			,					sep_token=snake_case			,					cls_token=snake_case			,					unk_token=snake_case			,					pad_token=snake_case			,					mask_token=snake_case			,					src_lang=snake_case			,					tgt_lang=snake_case			,					additional_special_tokens=snake_case			,					legacy_behaviour=snake_case			,					**snake_case			,					)

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

        lowerCAmelCase__   :				Union[str, Any] 			=				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}   )
        lowerCAmelCase__   :				Tuple 			=				{
            lang_code: self.convert_tokens_to_ids(snake_case   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

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






       @property
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self._src_lang






       @src_lang.setter
       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				new_src_lang
        self.set_src_lang_special_tokens(self._src_lang   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				[self.sep_token_id]
        lowerCAmelCase__   :				Dict 			=				[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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					**snake_case   ):





        """simple docstring"""

        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__   :				Optional[Any] 			=				self(snake_case			,					add_special_tokens=snake_case			,					return_tensors=snake_case			,					**snake_case   )
        lowerCAmelCase__   :				str 			=				self.convert_tokens_to_ids(snake_case   )
        lowerCAmelCase__   :				Any 			=				tgt_lang_id
        return inputs






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = "eng_Latn"			,					snake_case = None			,					snake_case = "fra_Latn"			,					**snake_case			,					):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				src_lang
        lowerCAmelCase__   :				str 			=				tgt_lang
        return super().prepare_seqaseq_batch(snake_case			,					snake_case			,					**snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self.set_src_lang_special_tokens(self.src_lang   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        return self.set_tgt_lang_special_tokens(self.tgt_lang   )






       def 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				Tuple 			=				self.convert_tokens_to_ids(snake_case   )

        if self.legacy_behaviour:
         lowerCAmelCase__   :				Optional[int] 			=				[]
         lowerCAmelCase__   :				Optional[int] 			=				[self.eos_token_id, self.cur_lang_code]
        else:
         lowerCAmelCase__   :				int 			=				[self.cur_lang_code]
         lowerCAmelCase__   :				List[str] 			=				[self.eos_token_id]

        lowerCAmelCase__   :				Any 			=				self.convert_ids_to_tokens(self.prefix_tokens   )
        lowerCAmelCase__   :				str 			=				self.convert_ids_to_tokens(self.suffix_tokens   )

        lowerCAmelCase__   :				Tuple 			=				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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[Any] 			=				self.convert_tokens_to_ids(snake_case   )
        if self.legacy_behaviour:
         lowerCAmelCase__   :				Dict 			=				[]
         lowerCAmelCase__   :				Dict 			=				[self.eos_token_id, self.cur_lang_code]
        else:
         lowerCAmelCase__   :				str 			=				[self.cur_lang_code]
         lowerCAmelCase__   :				Tuple 			=				[self.eos_token_id]

        lowerCAmelCase__   :				Any 			=				self.convert_ids_to_tokens(self.prefix_tokens   )
        lowerCAmelCase__   :				List[Any] 			=				self.convert_ids_to_tokens(self.suffix_tokens   )

        lowerCAmelCase__   :				Optional[int] 			=				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 			SCREAMING_SNAKE_CASE_	(   self			,					snake_case			,					snake_case = None   ):





        """simple docstring"""

        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(snake_case   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""   )
         return
        lowerCAmelCase__   :				str 			=				os.path.join(
            snake_case			,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

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

        return (out_vocab_file,)






