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

from tokenizers import processors

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


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


lowerCAmelCase__							=				logging.get_logger(__name__)


lowerCAmelCase__							=				{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase__							=				{
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase__							=				{
    '''facebook/nllb-large-en-ro''': 1024,
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
lowerCAmelCase__							=				['''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  snake_case     (							SCREAMING_SNAKE_CASE__   ):
							UpperCAmelCase__						     =      VOCAB_FILES_NAMES
							UpperCAmelCase__						     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__						     =      PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__						     =      ['''input_ids''', '''attention_mask''']
							UpperCAmelCase__						     =      NllbTokenizer

							UpperCAmelCase__						     =      []
							UpperCAmelCase__						     =      []

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

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = AddedToken(_lowercase       ,       lstrip=_lowercase       ,       rstrip=_lowercase    ) if isinstance(_lowercase       ,       _lowercase    ) else mask_token
									SCREAMING_SNAKE_CASE_						 = legacy_behaviour
									super().__init__(
									    vocab_file=_lowercase       ,       tokenizer_file=_lowercase       ,       bos_token=_lowercase       ,       eos_token=_lowercase       ,       sep_token=_lowercase       ,       cls_token=_lowercase       ,       unk_token=_lowercase       ,       pad_token=_lowercase       ,       mask_token=_lowercase       ,       src_lang=_lowercase       ,       tgt_lang=_lowercase       ,       additional_special_tokens=_lowercase       ,       legacy_behaviour=_lowercase       ,       **_lowercase       ,       )

									SCREAMING_SNAKE_CASE_						 = vocab_file
									SCREAMING_SNAKE_CASE_						 = False if not self.vocab_file else True

									SCREAMING_SNAKE_CASE_						 = 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}    )
									SCREAMING_SNAKE_CASE_						 = {
									    lang_code: self.convert_tokens_to_ids(_lowercase    ) for lang_code in FAIRSEQ_LANGUAGE_CODES
									}

									SCREAMING_SNAKE_CASE_						 = src_lang if src_lang is not None else """eng_Latn"""
									SCREAMING_SNAKE_CASE_						 = self.convert_tokens_to_ids(self._src_lang    )
									SCREAMING_SNAKE_CASE_						 = tgt_lang
									self.set_src_lang_special_tokens(self._src_lang    )

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self._src_lang

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

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = new_src_lang
									self.set_src_lang_special_tokens(self._src_lang    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_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 _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = None    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = [self.sep_token_id]
									SCREAMING_SNAKE_CASE_						 = [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 _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_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'''    )
									SCREAMING_SNAKE_CASE_						 = src_lang
									SCREAMING_SNAKE_CASE_						 = self(_lowercase       ,       add_special_tokens=_lowercase       ,       return_tensors=_lowercase       ,       **_lowercase    )
									SCREAMING_SNAKE_CASE_						 = self.convert_tokens_to_ids(_lowercase    )
									SCREAMING_SNAKE_CASE_						 = tgt_lang_id
									return inputs

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

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = src_lang
									SCREAMING_SNAKE_CASE_						 = tgt_lang
									return super().prepare_seqaseq_batch(_lowercase       ,       _lowercase       ,       **_lowercase    )

							def _lowercase					(self    ):

									"""simple docstring"""







									return self.set_src_lang_special_tokens(self.src_lang    )

							def _lowercase					(self    ):

									"""simple docstring"""







									return self.set_tgt_lang_special_tokens(self.tgt_lang    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.convert_tokens_to_ids(_lowercase    )

									if self.legacy_behaviour:
											SCREAMING_SNAKE_CASE_						 = []
											SCREAMING_SNAKE_CASE_						 = [self.eos_token_id, self.cur_lang_code]
									else:
											SCREAMING_SNAKE_CASE_						 = [self.cur_lang_code]
											SCREAMING_SNAKE_CASE_						 = [self.eos_token_id]

									SCREAMING_SNAKE_CASE_						 = self.convert_ids_to_tokens(self.prefix_tokens    )
									SCREAMING_SNAKE_CASE_						 = self.convert_ids_to_tokens(self.suffix_tokens    )

									SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.convert_tokens_to_ids(_lowercase    )
									if self.legacy_behaviour:
											SCREAMING_SNAKE_CASE_						 = []
											SCREAMING_SNAKE_CASE_						 = [self.eos_token_id, self.cur_lang_code]
									else:
											SCREAMING_SNAKE_CASE_						 = [self.cur_lang_code]
											SCREAMING_SNAKE_CASE_						 = [self.eos_token_id]

									SCREAMING_SNAKE_CASE_						 = self.convert_ids_to_tokens(self.prefix_tokens    )
									SCREAMING_SNAKE_CASE_						 = self.convert_ids_to_tokens(self.suffix_tokens    )

									SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_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(_lowercase    ):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory.'    )
											return
									SCREAMING_SNAKE_CASE_						 = os.path.join(
									    _lowercase       ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

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

									return (out_vocab_file,)

"""simple docstring"""




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

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


lowerCAmelCase__							=				logging.getLogger(__name__)



def        _lowerCamelCase ( __a,							__a    ):
		return (preds == labels).mean()





@dataclass
class  snake_case     :
							UpperCAmelCase__						     =      field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}   )
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}   )
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}   )
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,			)







@dataclass
class  snake_case     :
							UpperCAmelCase__						     =      field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()   )}   )
							UpperCAmelCase__						     =      field(metadata={'''help''': '''Should contain the data files for the task.'''}   )
							UpperCAmelCase__						     =      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.'''
							        )
							    }       ,			)
							UpperCAmelCase__						     =      field(
							    default=__lowercase       ,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )







def        _lowerCamelCase ( ):
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		SCREAMING_SNAKE_CASE_						 = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 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.'''    )

		# 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    )

		try:
				SCREAMING_SNAKE_CASE_						 = processors[data_args.task_name]()
				SCREAMING_SNAKE_CASE_						 = processor.get_labels()
				SCREAMING_SNAKE_CASE_						 = len(__a    )
		except KeyError:
				raise ValueError('''Task not found: %s''' % (data_args.task_name)    )

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

		SCREAMING_SNAKE_CASE_						 = AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path,							num_labels=__a,							finetuning_task=data_args.task_name,							cache_dir=model_args.cache_dir,							)
		SCREAMING_SNAKE_CASE_						 = 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,							)
		SCREAMING_SNAKE_CASE_						 = AutoModelForMultipleChoice.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
		SCREAMING_SNAKE_CASE_						 = (
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir,							tokenizer=__a,							task=data_args.task_name,							max_seq_length=data_args.max_seq_length,							overwrite_cache=data_args.overwrite_cache,							mode=Split.train,							)
		    if training_args.do_train
		    else None
		)
		SCREAMING_SNAKE_CASE_						 = (
		    MultipleChoiceDataset(
		        data_dir=data_args.data_dir,							tokenizer=__a,							task=data_args.task_name,							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 compute_metrics(__a    ) -> Dict:
				SCREAMING_SNAKE_CASE_						 = np.argmax(p.predictions,							axis=1    )
				return {"acc": simple_accuracy(__a,							p.label_ids    )}

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

		# Initialize our Trainer
		SCREAMING_SNAKE_CASE_						 = 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_master():
						tokenizer.save_pretrained(training_args.output_dir    )

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

				SCREAMING_SNAKE_CASE_						 = trainer.evaluate()

				SCREAMING_SNAKE_CASE_						 = os.path.join(training_args.output_dir,							'''eval_results.txt'''    )
				if trainer.is_world_master():
						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    )

		return results



def        _lowerCamelCase ( __a    ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
						main()