





import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      42
 lowercase_		=      jnp.floataa
 lowercase_		=      True

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Dict:



   '''simple docstring'''



   super().setup()
   lowerCamelCase__:				Any        =nn.Dense(5	,			dtype=self.dtype)







 def __call__(self    :       Tuple	,			*UpperCAmelCase_    :       Dict	,			**UpperCAmelCase_    :       int)	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =super().__call__(*UpperCAmelCase_	,			**UpperCAmelCase_)
   lowerCamelCase__:				Optional[Any]        =self.cls(outputs[2])
   return outputs[:2] + (cls_out,)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      FlaxBigBirdForNaturalQuestionsModule





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a		)					->		int:







  """simple docstring"""





  def cross_entropy(__a     ,   __a     ,   __a=None		):
    lowerCamelCase__:				int        =logits.shape[-1]
    lowerCamelCase__:				int        =(labels[..., None] == jnp.arange(__a		)[None]).astype("f4"		)
    lowerCamelCase__:				Optional[Any]        =jax.nn.log_softmax(__a     ,   axis=-1		)
    lowerCamelCase__:				Tuple        =-jnp.sum(labels * logits     ,   axis=-1		)
    if reduction is not None:
      lowerCamelCase__:				Optional[int]        =reduction(__a		)
    return loss

  lowerCamelCase__:				str        =partial(__a     ,   reduction=jnp.mean		)
  lowerCamelCase__:				Optional[Any]        =cross_entropy(__a     ,   __a		)
  lowerCamelCase__:				List[str]        =cross_entropy(__a     ,   __a		)
  lowerCamelCase__:				str        =cross_entropy(__a     ,   __a		)
  return (start_loss + end_loss + pooled_loss) / 3



@dataclass
class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=      "google/bigbird-roberta-base"
 lowercase_		=      3000
 lowercase_		=      1_0500

 lowercase_		=      128
 lowercase_		=      3

 lowercase_		=      1
 lowercase_		=      5

 # tx_args
 lowercase_		=      3E-5
 lowercase_		=      0.0
 lowercase_		=      2_0000
 lowercase_		=      0.0095

 lowercase_		=      "bigbird-roberta-natural-questions"
 lowercase_		=      "training-expt"
 lowercase_		=      "data/nq-training.jsonl"
 lowercase_		=      "data/nq-validation.jsonl"

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->int:



   '''simple docstring'''



   os.makedirs(self.base_dir	,			exist_ok=UpperCAmelCase_)
   lowerCamelCase__:				List[Any]        =os.path.join(self.base_dir	,			self.save_dir)
   lowerCamelCase__:				Tuple        =self.batch_size_per_device * jax.device_count()



@dataclass
class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=      42
 lowercase_		=      4096  # no dynamic padding on TPUs

 def __call__(self    :       str	,			UpperCAmelCase_    :       List[str])	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =self.collate_fn(UpperCAmelCase_)
   lowerCamelCase__:				Optional[Any]        =jax.tree_util.tree_map(UpperCAmelCase_	,			UpperCAmelCase_)
   return batch

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



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				List[str]        =self.fetch_inputs(features["input_ids"])
   lowerCamelCase__:				Dict        ={
       "input_ids": jnp.array(UpperCAmelCase_	,			dtype=jnp.intaa),
       "attention_mask": jnp.array(UpperCAmelCase_	,			dtype=jnp.intaa),
       "start_labels": jnp.array(features["start_token"]	,			dtype=jnp.intaa),
       "end_labels": jnp.array(features["end_token"]	,			dtype=jnp.intaa),
       "pooled_labels": jnp.array(features["category"]	,			dtype=jnp.intaa),
   }
   return batch

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



   '''simple docstring'''



   lowerCamelCase__:				Dict        =[self._fetch_inputs(UpperCAmelCase_) for ids in input_ids]
   return zip(*UpperCAmelCase_)







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



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =[1 for _ in range(len(UpperCAmelCase_))]
   while len(UpperCAmelCase_) < self.max_length:
     input_ids.append(self.pad_id)
     attention_mask.append(0)
   return input_ids, attention_mask





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a=None		)					->		int:







  """simple docstring"""





  if seed is not None:
    lowerCamelCase__:				Dict        =dataset.shuffle(seed=__a		)
  for i in range(len(__a		) // batch_size		):
    lowerCamelCase__:				Optional[Any]        =dataset[i * batch_size : (i + 1) * batch_size]
    yield dict(__a		)





@partial(jax.pmap     ,   axis_name="batch"		)
def 					lowerCAmelCase_			(	__a     ,   __a     ,   **__a		)					->		Optional[Any]:







  """simple docstring"""





  def loss_fn(__a		):
    lowerCamelCase__:				Dict        =model_inputs.pop("start_labels"		)
    lowerCamelCase__:				str        =model_inputs.pop("end_labels"		)
    lowerCamelCase__:				Optional[int]        =model_inputs.pop("pooled_labels"		)

    lowerCamelCase__:				str        =state.apply_fn(**__a     ,   params=__a     ,   dropout_rng=__a     ,   train=__a		)
    lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Dict        =outputs

    return state.loss_fn(
        __a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a     ,   )

  lowerCamelCase__  , lowerCamelCase__:				str        =jax.random.split(__a		)
  lowerCamelCase__:				List[str]        =jax.value_and_grad(__a		)
  lowerCamelCase__  , lowerCamelCase__:				List[Any]        =grad_fn(state.params		)
  lowerCamelCase__:				Any        =jax.lax.pmean({"loss": loss}     ,   axis_name="batch"		)
  lowerCamelCase__:				Dict        =jax.lax.pmean(__a     ,   "batch"		)

  lowerCamelCase__:				Optional[Any]        =state.apply_gradients(grads=__a		)
  return state, metrics, new_drp_rng





@partial(jax.pmap     ,   axis_name="batch"		)
def 					lowerCAmelCase_			(	__a     ,   **__a		)					->		int:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =model_inputs.pop("start_labels"		)
  lowerCamelCase__:				Any        =model_inputs.pop("end_labels"		)
  lowerCamelCase__:				Union[str, Any]        =model_inputs.pop("pooled_labels"		)

  lowerCamelCase__:				Tuple        =state.apply_fn(**__a     ,   params=state.params     ,   train=__a		)
  lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				str        =outputs

  lowerCamelCase__:				str        =state.loss_fn(__a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a		)
  lowerCamelCase__:				Tuple        =jax.lax.pmean({"loss": loss}     ,   axis_name="batch"		)
  return metrics



class        _SCREAMING_SNAKE_CASE						(     train_state.TrainState   ):







 '''simple docstring'''
 lowercase_		=      struct.field(pytree_node=__SCREAMING_SNAKE_CASE   )



@dataclass
class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=      42
 lowercase_		=      42
 lowercase_		=      42
 lowercase_		=      42
 lowercase_		=      42
 lowercase_		=      42
 lowercase_		=      None

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



   '''simple docstring'''



   lowerCamelCase__:				str        =model.params
   lowerCamelCase__:				int        =TrainState.create(
       apply_fn=model.__call__	,			params=UpperCAmelCase_	,			tx=UpperCAmelCase_	,			loss_fn=UpperCAmelCase_	,			)
   if ckpt_dir is not None:
     lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				List[str]        =restore_checkpoint(UpperCAmelCase_	,			UpperCAmelCase_)
     lowerCamelCase__:				List[str]        ={
         "lr": args.lr,
         "init_lr": args.init_lr,
         "warmup_steps": args.warmup_steps,
         "num_train_steps": num_train_steps,
         "weight_decay": args.weight_decay,
     }
     lowerCamelCase__  , lowerCamelCase__:				List[str]        =build_tx(**UpperCAmelCase_)
     lowerCamelCase__:				List[Any]        =train_state.TrainState(
         step=UpperCAmelCase_	,			apply_fn=model.__call__	,			params=UpperCAmelCase_	,			tx=UpperCAmelCase_	,			opt_state=UpperCAmelCase_	,			)
     lowerCamelCase__:				Optional[Any]        =args
     lowerCamelCase__:				List[str]        =data_collator
     lowerCamelCase__:				List[Any]        =lr
     lowerCamelCase__:				Union[str, Any]        =params
   lowerCamelCase__:				str        =jax_utils.replicate(UpperCAmelCase_)
   return state

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



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        =self.args
   lowerCamelCase__:				Dict        =len(UpperCAmelCase_) // args.batch_size

   lowerCamelCase__:				Union[str, Any]        =jax.random.PRNGKey(0)
   lowerCamelCase__:				Optional[int]        =jax.random.split(UpperCAmelCase_	,			jax.device_count())
   for epoch in range(args.max_epochs):
     lowerCamelCase__:				List[str]        =jnp.array(0	,			dtype=jnp.floataa)
     lowerCamelCase__:				Any        =get_batched_dataset(UpperCAmelCase_	,			args.batch_size	,			seed=UpperCAmelCase_)
     lowerCamelCase__:				List[Any]        =0
     for batch in tqdm(UpperCAmelCase_	,			total=UpperCAmelCase_	,			desc=F"""Running EPOCH-{epoch}"""):
       lowerCamelCase__:				Dict        =self.data_collator(UpperCAmelCase_)
       lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Optional[int]        =self.train_step_fn(UpperCAmelCase_	,			UpperCAmelCase_	,			**UpperCAmelCase_)
       running_loss += jax_utils.unreplicate(metrics["loss"])
       i += 1
       if i % args.logging_steps == 0:
         lowerCamelCase__:				int        =jax_utils.unreplicate(state.step)
         lowerCamelCase__:				int        =running_loss.item() / i
         lowerCamelCase__:				Optional[int]        =self.scheduler_fn(state_step - 1)

         lowerCamelCase__:				List[Any]        =self.evaluate(UpperCAmelCase_	,			UpperCAmelCase_)
         lowerCamelCase__:				Dict        ={
             "step": state_step.item(),
             "eval_loss": eval_loss.item(),
             "tr_loss": tr_loss,
             "lr": lr.item(),
         }
         tqdm.write(str(UpperCAmelCase_))
         self.logger.log(UpperCAmelCase_	,			commit=UpperCAmelCase_)

       if i % args.save_steps == 0:
         self.save_checkpoint(args.save_dir + F"""-e{epoch}-s{i}"""	,			state=UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:				str        =get_batched_dataset(UpperCAmelCase_	,			self.args.batch_size)
   lowerCamelCase__:				Optional[Any]        =len(UpperCAmelCase_) // self.args.batch_size
   lowerCamelCase__:				Dict        =jnp.array(0	,			dtype=jnp.floataa)
   lowerCamelCase__:				str        =0
   for batch in tqdm(UpperCAmelCase_	,			total=UpperCAmelCase_	,			desc="Evaluating ... "):
     lowerCamelCase__:				Any        =self.data_collator(UpperCAmelCase_)
     lowerCamelCase__:				Any        =self.val_step_fn(UpperCAmelCase_	,			**UpperCAmelCase_)
     running_loss += jax_utils.unreplicate(metrics["loss"])
     i += 1
   return running_loss / i







 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       str)	->str:



   '''simple docstring'''



   lowerCamelCase__:				int        =jax_utils.unreplicate(UpperCAmelCase_)
   print(F"""SAVING CHECKPOINT IN {save_dir}"""	,			end=" ... ")
   self.model_save_fn(UpperCAmelCase_	,			params=state.params)
   with open(os.path.join(UpperCAmelCase_	,			"opt_state.msgpack")	,			"wb") as f:
     f.write(to_bytes(state.opt_state))
   joblib.dump(self.args	,			os.path.join(UpperCAmelCase_	,			"args.joblib"))
   joblib.dump(self.data_collator	,			os.path.join(UpperCAmelCase_	,			"data_collator.joblib"))
   with open(os.path.join(UpperCAmelCase_	,			"training_state.json")	,			"w") as f:
     json.dump({"step": state.step.item()}	,			UpperCAmelCase_)
   print("DONE")





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Any:







  """simple docstring"""





  print(F"""RESTORING CHECKPOINT FROM {save_dir}"""     ,   end=" ... "		)
  with open(os.path.join(__a     ,   "flax_model.msgpack"		)     ,   "rb"		) as f:
    lowerCamelCase__:				Dict        =from_bytes(state.params     ,   f.read()		)

  with open(os.path.join(__a     ,   "opt_state.msgpack"		)     ,   "rb"		) as f:
    lowerCamelCase__:				int        =from_bytes(state.opt_state     ,   f.read()		)

  lowerCamelCase__:				str        =joblib.load(os.path.join(__a     ,   "args.joblib"		)		)
  lowerCamelCase__:				Tuple        =joblib.load(os.path.join(__a     ,   "data_collator.joblib"		)		)

  with open(os.path.join(__a     ,   "training_state.json"		)     ,   "r"		) as f:
    lowerCamelCase__:				List[str]        =json.load(__a		)
  lowerCamelCase__:				List[Any]        =training_state["step"]

  print("DONE"		)
  return params, opt_state, step, args, data_collator





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a		)					->		str:







  """simple docstring"""





  lowerCamelCase__:				Tuple        =num_train_steps - warmup_steps
  lowerCamelCase__:				int        =optax.linear_schedule(init_value=__a     ,   end_value=__a     ,   transition_steps=__a		)
  lowerCamelCase__:				Union[str, Any]        =optax.linear_schedule(init_value=__a     ,   end_value=1e-7     ,   transition_steps=__a		)
  lowerCamelCase__:				Dict        =optax.join_schedules(schedules=[warmup_fn, decay_fn]     ,   boundaries=[warmup_steps]		)
  return lr






def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a     ,   __a		)					->		Optional[int]:







  """simple docstring"""





  def weight_decay_mask(__a		):
    lowerCamelCase__:				int        =traverse_util.flatten_dict(__a		)
    lowerCamelCase__:				Optional[int]        ={k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
    return traverse_util.unflatten_dict(__a		)

  lowerCamelCase__:				Optional[int]        =scheduler_fn(__a     ,   __a     ,   __a     ,   __a		)

  lowerCamelCase__:				str        =optax.adamw(learning_rate=__a     ,   weight_decay=__a     ,   mask=__a		)
  return tx, lr








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]

