





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__A     =    16
__A     =    32





def 					lowerCAmelCase_			(	__a     ,   __a = 16		)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:				int        =AutoTokenizer.from_pretrained("bert-base-cased"		)
  lowerCamelCase__:				Any        =load_dataset("glue"     ,   "mrpc"		)

  def tokenize_function(__a		):
    # max_length=None => use the model max length (it's actually the default)
    lowerCamelCase__:				int        =tokenizer(examples["sentence1"]     ,   examples["sentence2"]     ,   truncation=__a     ,   max_length=__a		)
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    lowerCamelCase__:				Tuple        =datasets.map(
        __a     ,   batched=__a     ,   remove_columns=["idx", "sentence1", "sentence2"]     ,   )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  lowerCamelCase__:				Optional[int]        =tokenized_datasets.rename_column("label"     ,   "labels"		)

  def collate_fn(__a		):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    lowerCamelCase__:				Dict        =128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      lowerCamelCase__:				str        =16
    elif accelerator.mixed_precision != "no":
      lowerCamelCase__:				str        =8
    else:
      lowerCamelCase__:				str        =None

    return tokenizer.pad(
        __a     ,   padding="longest"     ,   max_length=__a     ,   pad_to_multiple_of=__a     ,   return_tensors="pt"     ,   )

  # Instantiate dataloaders.
  lowerCamelCase__:				Union[str, Any]        =DataLoader(
      tokenized_datasets["train"]     ,   shuffle=__a     ,   collate_fn=__a     ,   batch_size=__a		)
  lowerCamelCase__:				Optional[int]        =DataLoader(
      tokenized_datasets["validation"]     ,   shuffle=__a     ,   collate_fn=__a     ,   batch_size=__a		)

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    __A     =    mocked_dataloaders  # noqa: F811





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		int:







  """simple docstring"""





  if os.environ.get("TESTING_MOCKED_DATALOADERS"     ,   __a		) == "1":
    lowerCamelCase__:				Union[str, Any]        =2
  # New Code #
  lowerCamelCase__:				List[Any]        =int(args.gradient_accumulation_steps		)
  # Initialize accelerator
  lowerCamelCase__:				Optional[int]        =Accelerator(
      cpu=args.cpu     ,   mixed_precision=args.mixed_precision     ,   gradient_accumulation_steps=__a		)
  if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
    raise NotImplementedError(
        "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"		)
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  lowerCamelCase__:				int        =config["lr"]
  lowerCamelCase__:				Tuple        =int(config["num_epochs"]		)
  lowerCamelCase__:				List[Any]        =int(config["seed"]		)
  lowerCamelCase__:				Tuple        =int(config["batch_size"]		)

  lowerCamelCase__:				List[Any]        =evaluate.load("glue"     ,   "mrpc"		)

  set_seed(__a		)
  lowerCamelCase__:				Any        =get_dataloaders(__a     ,   __a		)
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCamelCase__:				Dict        =AutoModelForSequenceClassification.from_pretrained("bert-base-cased"     ,   return_dict=__a		)

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  lowerCamelCase__:				Any        =model.to(accelerator.device		)

  # Instantiate optimizer
  lowerCamelCase__:				List[Any]        =AdamW(params=model.parameters()     ,   lr=__a		)

  # Instantiate scheduler
  lowerCamelCase__:				str        =get_linear_schedule_with_warmup(
      optimizer=__a     ,   num_warmup_steps=100     ,   num_training_steps=(len(__a		) * num_epochs)     ,   )

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  lowerCamelCase__:				Optional[int]        =accelerator.prepare(
      __a     ,   __a     ,   __a     ,   __a     ,   __a		)

  # Now we train the model
  for epoch in range(__a		):
    model.train()
    for step, batch in enumerate(__a		):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device		)
      # New code #
      # We use the new `accumulate` context manager to perform gradient accumulation
      # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
      with accelerator.accumulate(__a		):
        lowerCamelCase__:				List[str]        =model(**__a		)
        lowerCamelCase__:				Dict        =output.loss
        accelerator.backward(__a		)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    for step, batch in enumerate(__a		):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device		)
      with torch.no_grad():
        lowerCamelCase__:				int        =model(**__a		)
      lowerCamelCase__:				int        =outputs.logits.argmax(dim=-1		)
      lowerCamelCase__:				Optional[int]        =accelerator.gather_for_metrics((predictions, batch["labels"])		)
      metric.add_batch(
          predictions=__a     ,   references=__a     ,   )

    lowerCamelCase__:				List[Any]        =metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(F"""epoch {epoch}:"""     ,   __a		)





def 					lowerCAmelCase_			(	)					->		List[Any]:







  """simple docstring"""





  lowerCamelCase__:				List[Any]        =argparse.ArgumentParser(description="Simple example of training script."		)
  parser.add_argument(
      "--mixed_precision"     ,   type=__a     ,   default=__a     ,   choices=["no", "fp16", "bf16", "fp8"]     ,   help="Whether to use mixed precision. Choose"
      "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
      "and an Nvidia Ampere GPU."     ,   )
  # New Code #
  parser.add_argument(
      "--gradient_accumulation_steps"     ,   type=__a     ,   default=1     ,   help="The number of minibatches to be ran before gradients are accumulated."     ,   )
  parser.add_argument("--cpu"     ,   action="store_true"     ,   help="If passed, will train on the CPU."		)
  lowerCamelCase__:				Tuple        =parser.parse_args()
  lowerCamelCase__:				Any        ={"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
  training_function(__a     ,   __a		)


if __name__ == "__main__":
    main()








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


__A     =    logging.get_logger(__name__)

__A     =    "▁"

__A     =    {"vocab_file": "sentencepiece.bpe.model"}

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

__A     =    {
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
__A     =    ["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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      VOCAB_FILES_NAMES
 lowercase_		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_		=      PRETRAINED_VOCAB_FILES_MAP
 lowercase_		=      ["input_ids", "attention_mask"]

 lowercase_		=      []
 lowercase_		=      []

 def __init__(self    :       Tuple	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       Tuple="<s>"	,			UpperCAmelCase_    :       int="</s>"	,			UpperCAmelCase_    :       List[str]="</s>"	,			UpperCAmelCase_    :       List[str]="<s>"	,			UpperCAmelCase_    :       Tuple="<unk>"	,			UpperCAmelCase_    :       List[Any]="<pad>"	,			UpperCAmelCase_    :       Tuple="<mask>"	,			UpperCAmelCase_    :       Optional[int]=None	,			UpperCAmelCase_    :       Dict=None	,			UpperCAmelCase_    :       Optional[int]=None	,			UpperCAmelCase_    :       Optional[Dict[str, Any]] = None	,			UpperCAmelCase_    :       Any=None	,			UpperCAmelCase_    :       Dict=False	,			**UpperCAmelCase_    :       Tuple	,			)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =AddedToken(UpperCAmelCase_	,			lstrip=UpperCAmelCase_	,			rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_	,			UpperCAmelCase_) else mask_token

   lowerCamelCase__:				Any        ={} if sp_model_kwargs is None else sp_model_kwargs
   lowerCamelCase__:				List[str]        =legacy_behaviour

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

   lowerCamelCase__:				Optional[Any]        =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(str(UpperCAmelCase_))
   lowerCamelCase__:				str        =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__:				str        ={"<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__:				List[str]        =1

   lowerCamelCase__:				Any        =len(self.sp_model)
   lowerCamelCase__:				Any        ={
       code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCAmelCase_)
   }
   lowerCamelCase__:				Dict        ={v: k for k, v in self.lang_code_to_id.items()}
   lowerCamelCase__:				Optional[int]        =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__:				List[str]        ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
   lowerCamelCase__:				List[str]        =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__:				int        =src_lang if src_lang is not None else "eng_Latn"
   lowerCamelCase__:				List[str]        =self.lang_code_to_id[self._src_lang]
   lowerCamelCase__:				Optional[int]        =tgt_lang
   self.set_src_lang_special_tokens(self._src_lang)

 def __getstate__(self    :       List[str])	->int:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =self.__dict__.copy()
   lowerCamelCase__:				Any        =None
   lowerCamelCase__:				Optional[int]        =self.sp_model.serialized_model_proto()
   return state

 def __setstate__(self    :       Dict	,			UpperCAmelCase_    :       Union[str, Any])	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =d

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

   lowerCamelCase__:				Dict        =spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.LoadFromSerializedProto(self.sp_model_proto)

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->List[str]:



   '''simple docstring'''



   return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  # Plus 1 for the mask token

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->str:



   '''simple docstring'''



   return self._src_lang

 @src_lang.setter
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       str)	->None:



   '''simple docstring'''



   lowerCamelCase__:				Any        =new_src_lang
   self.set_src_lang_special_tokens(self._src_lang)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       List[int]	,			UpperCAmelCase_    :       Optional[List[int]] = None	,			UpperCAmelCase_    :       bool = False)	->List[int]:



   '''simple docstring'''



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

   lowerCamelCase__:				int        =[1] * len(self.prefix_tokens)
   lowerCamelCase__:				Any        =[1] * len(self.suffix_tokens)
   if token_ids_a is None:
     return prefix_ones + ([0] * len(UpperCAmelCase_)) + suffix_ones
   return prefix_ones + ([0] * len(UpperCAmelCase_)) + ([0] * len(UpperCAmelCase_)) + suffix_ones

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       List[int]	,			UpperCAmelCase_    :       Optional[List[int]] = None)	->List[int]:



   '''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    :       Union[str, Any]	,			UpperCAmelCase_    :       List[int]	,			UpperCAmelCase_    :       Optional[List[int]] = None)	->List[int]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =[self.sep_token_id]
   lowerCamelCase__:				List[str]        =[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    :       Optional[Any]	,			UpperCAmelCase_    :       List[Any]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Optional[str]	,			UpperCAmelCase_    :       Optional[str]	,			**UpperCAmelCase_    :       Optional[int])	->Dict:



   '''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__:				Optional[int]        =src_lang
   lowerCamelCase__:				Dict        =self(UpperCAmelCase_	,			add_special_tokens=UpperCAmelCase_	,			return_tensors=UpperCAmelCase_	,			**UpperCAmelCase_)
   lowerCamelCase__:				int        =self.convert_tokens_to_ids(UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =tgt_lang_id
   return inputs

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        ={self.convert_ids_to_tokens(UpperCAmelCase_): i for i in range(self.vocab_size)}
   vocab.update(self.added_tokens_encoder)
   return vocab

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       str)	->List[str]:



   '''simple docstring'''



   return self.sp_model.encode(UpperCAmelCase_	,			out_type=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       Union[str, Any])	->Union[str, Any]:



   '''simple docstring'''



   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   lowerCamelCase__:				Dict        =self.sp_model.PieceToId(UpperCAmelCase_)

   # 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 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       Optional[int])	->List[Any]:



   '''simple docstring'''



   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 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       List[Any])	->int:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        ="".join(UpperCAmelCase_).replace(UpperCAmelCase_	,			" ").strip()
   return out_string

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Optional[str] = None)	->Tuple[str]:



   '''simple docstring'''



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

   if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCAmelCase_) and os.path.isfile(self.vocab_file):
     copyfile(self.vocab_file	,			UpperCAmelCase_)
   elif not os.path.isfile(self.vocab_file):
     with open(UpperCAmelCase_	,			"wb") as fi:
       lowerCamelCase__:				Optional[Any]        =self.sp_model.serialized_model_proto()
       fi.write(UpperCAmelCase_)

   return (out_vocab_file,)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       str = "eng_Latn"	,			UpperCAmelCase_    :       Optional[List[str]] = None	,			UpperCAmelCase_    :       str = "fra_Latn"	,			**UpperCAmelCase_    :       Dict	,			)	->BatchEncoding:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =src_lang
   lowerCamelCase__:				Union[str, Any]        =tgt_lang
   return super().prepare_seqaseq_batch(UpperCAmelCase_	,			UpperCAmelCase_	,			**UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Optional[Any]:



   '''simple docstring'''



   return self.set_src_lang_special_tokens(self.src_lang)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->int:



   '''simple docstring'''



   return self.set_tgt_lang_special_tokens(self.tgt_lang)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       List[Any])	->None:



   '''simple docstring'''



   lowerCamelCase__:				int        =self.lang_code_to_id[src_lang]
   if self.legacy_behaviour:
     lowerCamelCase__:				Any        =[]
     lowerCamelCase__:				Tuple        =[self.eos_token_id, self.cur_lang_code]
   else:
     lowerCamelCase__:				Any        =[self.cur_lang_code]
     lowerCamelCase__:				Dict        =[self.eos_token_id]







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       str)	->None:



   '''simple docstring'''



   lowerCamelCase__:				Any        =self.lang_code_to_id[lang]
   if self.legacy_behaviour:
     lowerCamelCase__:				int        =[]
     lowerCamelCase__:				str        =[self.eos_token_id, self.cur_lang_code]
   else:
     lowerCamelCase__:				Union[str, Any]        =[self.cur_lang_code]
     lowerCamelCase__:				Optional[Any]        =[self.eos_token_id]

