
"""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


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				'▁'

lowerCAmelCase__							=				{'vocab_file': 'sentencepiece.bpe.model'}

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

lowerCAmelCase__							=				{
    '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     (							__lowercase   ):
							UpperCAmelCase__						     =      VOCAB_FILES_NAMES
							UpperCAmelCase__						     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase__						     =      PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase__						     =      ['''input_ids''', '''attention_mask''']

							UpperCAmelCase__						     =      []
							UpperCAmelCase__						     =      []

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       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_ = None       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=False       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = AddedToken(SCREAMING_SNAKE_CASE_       ,       lstrip=SCREAMING_SNAKE_CASE_       ,       rstrip=SCREAMING_SNAKE_CASE_    ) if isinstance(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ) else mask_token

									SCREAMING_SNAKE_CASE_						 = {} if sp_model_kwargs is None else sp_model_kwargs
									SCREAMING_SNAKE_CASE_						 = legacy_behaviour

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

									SCREAMING_SNAKE_CASE_						 = spm.SentencePieceProcessor(**self.sp_model_kwargs    )
									self.sp_model.Load(str(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = 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
									SCREAMING_SNAKE_CASE_						 = {'''<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
									SCREAMING_SNAKE_CASE_						 = 1

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

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

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

							def __getstate__(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.__dict__.copy()
									SCREAMING_SNAKE_CASE_						 = None
									SCREAMING_SNAKE_CASE_						 = self.sp_model.serialized_model_proto()
									return state

							def __setstate__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = d

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

									SCREAMING_SNAKE_CASE_						 = spm.SentencePieceProcessor(**self.sp_model_kwargs    )
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )

							@property
							def _lowercase					(self    ):

									"""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 _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       ,       SCREAMING_SNAKE_CASE_ = False    ):

									"""simple docstring"""







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

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

							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(SCREAMING_SNAKE_CASE_       ,       add_special_tokens=SCREAMING_SNAKE_CASE_       ,       return_tensors=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = tgt_lang_id
									return inputs

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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

									# 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 _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""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 _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = ''''''.join(SCREAMING_SNAKE_CASE_    ).replace(SCREAMING_SNAKE_CASE_       ,       ''' '''    ).strip()
									return out_string

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

									"""simple docstring"""







									if not os.path.isdir(SCREAMING_SNAKE_CASE_    ):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
											return
									SCREAMING_SNAKE_CASE_						 = os.path.join(
									    SCREAMING_SNAKE_CASE_       ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

									if os.path.abspath(self.vocab_file    ) != os.path.abspath(SCREAMING_SNAKE_CASE_    ) and os.path.isfile(self.vocab_file    ):
											copyfile(self.vocab_file       ,       SCREAMING_SNAKE_CASE_    )
									elif not os.path.isfile(self.vocab_file    ):
											with open(SCREAMING_SNAKE_CASE_       ,       '''wb'''    ) as fi:
													SCREAMING_SNAKE_CASE_						 = self.sp_model.serialized_model_proto()
													fi.write(SCREAMING_SNAKE_CASE_    )

									return (out_vocab_file,)

							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(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )

							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.lang_code_to_id[src_lang]
									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]







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.lang_code_to_id[lang]
									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]

"""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()