


'''simple docstring'''



import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowercase    :    Tuple			     =  logging.getLogger(__name__)






@dataclass
class  UpperCamelCase__:
				__magic_name__       :   str							= field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
				__magic_name__       :   Optional[str]							= field(
				    default=lowerCAmelCase		,       metadata={"help": "Pretrained config name or path if not the same as model_name"} )
				__magic_name__       :   Optional[str]							= field(
				    default="NER"		,       metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"} )
				__magic_name__       :   Optional[str]							= field(
				    default=lowerCAmelCase		,       metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
				__magic_name__       :   bool							= field(default=lowerCAmelCase		,       metadata={"help": "Set this flag to use fast tokenization."} )
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				__magic_name__       :   Optional[str]							= field(
				    default=lowerCAmelCase		,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}		,       )







@dataclass
class  UpperCamelCase__:
				__magic_name__       :   str							= field(
				    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."} )
				__magic_name__       :   Optional[str]							= field(
				    default=lowerCAmelCase		,       metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}		,       )
				__magic_name__       :   int							= field(
				    default=128		,       metadata={
				        "help": (
				            "The maximum total input sequence length after tokenization. Sequences longer "
				            "than this will be truncated, sequences shorter will be padded."
				        )
				    }		,       )
				__magic_name__       :   bool							= field(
				    default=lowerCAmelCase		,       metadata={"help": "Overwrite the cached training and evaluation sets"} )







def    lowerCamelCase__   ( ):




						'''simple docstring'''






						UpperCAmelCase         =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
						if len(sys.argv  ) == 2 and sys.argv[1].endswith('''.json'''  ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
						else:
												UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir  )
						    and os.listdir(training_args.output_dir  )
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
												    ''' --overwrite_output_dir to overcome.'''  )

						UpperCAmelCase         =			import_module('''tasks'''  )
						try:
												UpperCAmelCase         =			getattr(A   ,     model_args.task_type  )
												UpperCAmelCase         =			token_classification_task_clazz()
						except AttributeError:
												raise ValueError(
												    f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
												    f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""  )

						# Setup logging
						logging.basicConfig(
						    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''   ,     datefmt='''%m/%d/%Y %H:%M:%S'''   ,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,     )
						logger.warning(
						    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''   ,     training_args.local_rank   ,     training_args.device   ,     training_args.n_gpu   ,     bool(training_args.local_rank != -1  )   ,     training_args.fpaa   ,     )
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank  ):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info('''Training/evaluation parameters %s'''   ,     A  )

						# Set seed
						set_seed(training_args.seed  )

						# Prepare CONLL-2003 task
						UpperCAmelCase         =			token_classification_task.get_labels(data_args.labels  )
						UpperCAmelCase         =			dict(enumerate(A  )  )
						UpperCAmelCase         =			len(A  )

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						UpperCAmelCase         =			AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,     num_labels=A   ,     idalabel=A   ,     labelaid={label: i for i, label in enumerate(A  )}   ,     cache_dir=model_args.cache_dir   ,     )
						UpperCAmelCase         =			AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,     cache_dir=model_args.cache_dir   ,     use_fast=model_args.use_fast   ,     )
						UpperCAmelCase         =			AutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path   ,     from_tf=bool('''.ckpt''' in model_args.model_name_or_path  )   ,     config=A   ,     cache_dir=model_args.cache_dir   ,     )

						# Get datasets
						UpperCAmelCase         =			(
						    TokenClassificationDataset(
						        token_classification_task=A   ,     data_dir=data_args.data_dir   ,     tokenizer=A   ,     labels=A   ,     model_type=config.model_type   ,     max_seq_length=data_args.max_seq_length   ,     overwrite_cache=data_args.overwrite_cache   ,     mode=Split.train   ,     )
						    if training_args.do_train
						    else None
						)
						UpperCAmelCase         =			(
						    TokenClassificationDataset(
						        token_classification_task=A   ,     data_dir=data_args.data_dir   ,     tokenizer=A   ,     labels=A   ,     model_type=config.model_type   ,     max_seq_length=data_args.max_seq_length   ,     overwrite_cache=data_args.overwrite_cache   ,     mode=Split.dev   ,     )
						    if training_args.do_eval
						    else None
						)

						def align_predictions(A     :    np.ndarray   ,     A     :    np.ndarray  ) -> Tuple[List[int], List[int]]:
												UpperCAmelCase         =			np.argmax(A   ,     axis=2  )

												UpperCAmelCase       ,       UpperCAmelCase         =			preds.shape

												UpperCAmelCase         =			[[] for _ in range(A  )]
												UpperCAmelCase         =			[[] for _ in range(A  )]

												for i in range(A  ):
																		for j in range(A  ):
																								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																														out_label_list[i].append(label_map[label_ids[i][j]]  )
																														preds_list[i].append(label_map[preds[i][j]]  )

												return preds_list, out_label_list

						def compute_metrics(A     :    EvalPrediction  ) -> Dict:
												UpperCAmelCase       ,       UpperCAmelCase         =			align_predictions(p.predictions   ,     p.label_ids  )
												return {
												    "accuracy_score": accuracy_score(A   ,     A  ),
												    "precision": precision_score(A   ,     A  ),
												    "recall": recall_score(A   ,     A  ),
												    "f1": fa_score(A   ,     A  ),
												}

						# Data collator
						UpperCAmelCase         =			DataCollatorWithPadding(A   ,     pad_to_multiple_of=8  ) if training_args.fpaa else None

						# Initialize our Trainer
						UpperCAmelCase         =			Trainer(
						    model=A   ,     args=A   ,     train_dataset=A   ,     eval_dataset=A   ,     compute_metrics=A   ,     data_collator=A   ,     )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_process_zero():
																		tokenizer.save_pretrained(training_args.output_dir  )

    # Evaluation
						UpperCAmelCase         =			{}
						if training_args.do_eval:
												logger.info('''*** Evaluate ***'''  )

												UpperCAmelCase         =			trainer.evaluate()

												UpperCAmelCase         =			os.path.join(training_args.output_dir   ,     '''eval_results.txt'''  )
												if trainer.is_world_process_zero():
																		with open(A   ,     '''w'''  ) as writer:
																								logger.info('''***** Eval results *****'''  )
																								for key, value in result.items():
																														logger.info('''  %s = %s'''   ,     A   ,     A  )
																														writer.write('''%s = %s\n''' % (key, value)  )

																		results.update(A  )

    # Predict
						if training_args.do_predict:
												UpperCAmelCase         =			TokenClassificationDataset(
												    token_classification_task=A   ,     data_dir=data_args.data_dir   ,     tokenizer=A   ,     labels=A   ,     model_type=config.model_type   ,     max_seq_length=data_args.max_seq_length   ,     overwrite_cache=data_args.overwrite_cache   ,     mode=Split.test   ,     )

												UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			trainer.predict(A  )
												UpperCAmelCase       ,       UpperCAmelCase         =			align_predictions(A   ,     A  )

												UpperCAmelCase         =			os.path.join(training_args.output_dir   ,     '''test_results.txt'''  )
												if trainer.is_world_process_zero():
																		with open(A   ,     '''w'''  ) as writer:
																								for key, value in metrics.items():
																														logger.info('''  %s = %s'''   ,     A   ,     A  )
																														writer.write('''%s = %s\n''' % (key, value)  )

        # Save predictions
												UpperCAmelCase         =			os.path.join(training_args.output_dir   ,     '''test_predictions.txt'''  )
												if trainer.is_world_process_zero():
																		with open(A   ,     '''w'''  ) as writer:
																								with open(os.path.join(data_args.data_dir   ,     '''test.txt'''  )   ,     '''r'''  ) as f:
																														token_classification_task.write_predictions_to_file(A   ,     A   ,     A  )

						return results


def    lowerCamelCase__   ( A     :    Tuple  ):




						'''simple docstring'''






						main()


if __name__ == "__main__":
	main()









'''simple docstring'''



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


_lowercase    :    str			     =  logging.get_logger(__name__)

_lowercase    :    List[Any]			     =  """▁"""

_lowercase    :    Optional[Any]			     =  {"""vocab_file""": """sentencepiece.bpe.model"""}

_lowercase    :    Any			     =  {
    """vocab_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

_lowercase    :    int			     =  {
    """facebook/mbart-large-50-one-to-many-mmt""": 1024,
}

# fmt: off
_lowercase    :    int			     =  ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN""", """af_ZA""", """az_AZ""", """bn_IN""", """fa_IR""", """he_IL""", """hr_HR""", """id_ID""", """ka_GE""", """km_KH""", """mk_MK""", """ml_IN""", """mn_MN""", """mr_IN""", """pl_PL""", """ps_AF""", """pt_XX""", """sv_SE""", """sw_KE""", """ta_IN""", """te_IN""", """th_TH""", """tl_XX""", """uk_UA""", """ur_PK""", """xh_ZA""", """gl_ES""", """sl_SI"""]






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Optional[int]							= VOCAB_FILES_NAMES
				__magic_name__       :   Union[str, Any]							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__       :   Optional[int]							= PRETRAINED_VOCAB_FILES_MAP
				__magic_name__       :   Union[str, Any]							= ["input_ids", "attention_mask"]

				__magic_name__       :   List[int]							= []
				__magic_name__       :   List[int]							= []




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

										"""simple docstring"""




										UpperCAmelCase         =			AddedToken(lowerCAmelCase   ,					lstrip=lowerCAmelCase   ,					rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase   ,					lowerCAmelCase   ) else mask_token

										UpperCAmelCase         =			{} if sp_model_kwargs is None else sp_model_kwargs

										UpperCAmelCase         =			kwargs.get('''additional_special_tokens'''   ,					[]   )
										kwargs["additional_special_tokens"] += [
										    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
										]

										super().__init__(
										    src_lang=lowerCAmelCase   ,					tgt_lang=lowerCAmelCase   ,					eos_token=lowerCAmelCase   ,					unk_token=lowerCAmelCase   ,					sep_token=lowerCAmelCase   ,					cls_token=lowerCAmelCase   ,					pad_token=lowerCAmelCase   ,					mask_token=lowerCAmelCase   ,					sp_model_kwargs=self.sp_model_kwargs   ,					**lowerCAmelCase   ,					)

										UpperCAmelCase         =			spm.SentencePieceProcessor(**self.sp_model_kwargs   )
										self.sp_model.Load(str(lowerCAmelCase   )   )
										UpperCAmelCase         =			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>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# Mimic fairseq token-to-id alignment for the first 4 token
										UpperCAmelCase         =			{'''<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
										UpperCAmelCase         =			1

										UpperCAmelCase         =			len(self.sp_model   )
										UpperCAmelCase         =			{
										    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCAmelCase   )
										}
										UpperCAmelCase         =			{v: k for k, v in self.lang_code_to_id.items()}
										UpperCAmelCase         =			len(self.sp_model   ) + len(self.lang_code_to_id   ) + self.fairseq_offset

										self.fairseq_tokens_to_ids.update(self.lang_code_to_id   )
										UpperCAmelCase         =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}

										UpperCAmelCase         =			src_lang if src_lang is not None else '''en_XX'''
										UpperCAmelCase         =			self.lang_code_to_id[self._src_lang]
										UpperCAmelCase         =			tgt_lang
										self.set_src_lang_special_tokens(self._src_lang   )




				@property
				def   a__(	self    :				Union[str, Any]   )->  int:

										"""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   a__(	self    :				str   )->  str:

										"""simple docstring"""




										return self._src_lang




				@src_lang.setter
				def   a__(	self    :				Any   ,					lowerCAmelCase    :				str   )->  None:

										"""simple docstring"""




										UpperCAmelCase         =			new_src_lang
										self.set_src_lang_special_tokens(self._src_lang   )




				def __getstate__(	self    :				Tuple   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			self.__dict__.copy()
										UpperCAmelCase         =			None
										return state




				def __setstate__(	self    :				Dict   ,					lowerCAmelCase    :				Dict   )->  None:

										"""simple docstring"""




										UpperCAmelCase         =			d

										# for backward compatibility
										if not hasattr(self   ,					'''sp_model_kwargs'''   ):
																UpperCAmelCase         =			{}

										UpperCAmelCase         =			spm.SentencePieceProcessor(**self.sp_model_kwargs   )
										self.sp_model.Load(self.vocab_file   )




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

										"""simple docstring"""




										UpperCAmelCase         =			{self.convert_ids_to_tokens(lowerCAmelCase   ): i for i in range(self.vocab_size   )}
										vocab.update(self.added_tokens_encoder   )
										return vocab




				def   a__(	self    :				str   ,					lowerCAmelCase    :				str   )->  List[str]:

										"""simple docstring"""




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




				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				str   )->  int:

										"""simple docstring"""




										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										UpperCAmelCase         =			self.sp_model.PieceToId(lowerCAmelCase   )

										# 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   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				int   )->  str:

										"""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   a__(	self    :				int   ,					lowerCAmelCase    :				List[Any]   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			[]
										UpperCAmelCase         =			''''''
										UpperCAmelCase         =			False
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						if not prev_is_special:
																												out_string += " "
																						out_string += self.sp_model.decode(lowerCAmelCase   ) + token
																						UpperCAmelCase         =			True
																						UpperCAmelCase         =			[]
																else:
																						current_sub_tokens.append(lowerCAmelCase   )
																						UpperCAmelCase         =			False
										out_string += self.sp_model.decode(lowerCAmelCase   )
										return out_string.strip()




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

										"""simple docstring"""




										if not os.path.isdir(lowerCAmelCase   ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
																return
										UpperCAmelCase         =			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   ) and os.path.isfile(self.vocab_file   ):
																copyfile(self.vocab_file   ,					lowerCAmelCase   )
										elif not os.path.isfile(self.vocab_file   ):
																with open(lowerCAmelCase   ,					'''wb'''   ) as fi:
																						UpperCAmelCase         =			self.sp_model.serialized_model_proto()
																						fi.write(lowerCAmelCase   )

										return (out_vocab_file,)




				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				List[int]   ,					lowerCAmelCase    :				Optional[List[int]] = None   ,					lowerCAmelCase    :				bool = False   )->  List[int]:

										"""simple docstring"""




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

										UpperCAmelCase         =			[1] * len(self.prefix_tokens   )
										UpperCAmelCase         =			[1] * len(self.suffix_tokens   )
										if token_ids_a is None:
																return prefix_ones + ([0] * len(lowerCAmelCase   )) + suffix_ones
										return prefix_ones + ([0] * len(lowerCAmelCase   )) + ([0] * len(lowerCAmelCase   )) + suffix_ones




				def   a__(	self    :				Tuple   ,					lowerCAmelCase    :				List[int]   ,					lowerCAmelCase    :				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   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Optional[str]   ,					lowerCAmelCase    :				Optional[str]   ,					**lowerCAmelCase    :				Optional[int]   )->  Optional[Any]:

										"""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'''   )
										UpperCAmelCase         =			src_lang
										UpperCAmelCase         =			self(lowerCAmelCase   ,					add_special_tokens=lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )
										UpperCAmelCase         =			self.convert_tokens_to_ids(lowerCAmelCase   )
										UpperCAmelCase         =			tgt_lang_id
										return inputs




				def   a__(	self    :				str   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				str = "en_XX"   ,					lowerCAmelCase    :				Optional[List[str]] = None   ,					lowerCAmelCase    :				str = "ro_RO"   ,					**lowerCAmelCase    :				List[str]   ,					)->  BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase         =			src_lang
										UpperCAmelCase         =			tgt_lang
										return super().prepare_seqaseq_batch(lowerCAmelCase   ,					lowerCAmelCase   ,					**lowerCAmelCase   )




				def   a__(	self    :				Optional[int]   )->  Union[str, Any]:

										"""simple docstring"""




										return self.set_src_lang_special_tokens(self.src_lang   )




				def   a__(	self    :				List[Any]   )->  int:

										"""simple docstring"""




										return self.set_tgt_lang_special_tokens(self.tgt_lang   )




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				str   )->  None:

										"""simple docstring"""




										UpperCAmelCase         =			self.lang_code_to_id[src_lang]
										UpperCAmelCase         =			[self.cur_lang_code_id]
										UpperCAmelCase         =			[self.eos_token_id]





				def   a__(	self    :				int   ,					lowerCAmelCase    :				str   )->  None:

										"""simple docstring"""




										UpperCAmelCase         =			self.lang_code_to_id[tgt_lang]
										UpperCAmelCase         =			[self.cur_lang_code_id]
										UpperCAmelCase         =			[self.eos_token_id]





