



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:
							SCREAMING_SNAKE_CASE__       =      None


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)


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

SCREAMING_SNAKE_CASE__       =      {
    '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'
        ),
    },
}

SCREAMING_SNAKE_CASE__       =      {
    'facebook/nllb-large-en-ro': 1_0_2_4,
    'facebook/nllb-200-distilled-600M': 1_0_2_4,
}

# fmt: off
SCREAMING_SNAKE_CASE__       =      ['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      __lowerCAmelCase     (		snake_case__      ):
					A__				:		Tuple			       =    VOCAB_FILES_NAMES
					A__				:		Tuple			       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__				:		List[str]			       =    PRETRAINED_VOCAB_FILES_MAP
					A__				:		Optional[int]			       =    ["""input_ids""", """attention_mask"""]
					A__				:		List[Any]			       =    NllbTokenizer

					A__				:		List[int]			       =    []
					A__				:		List[int]			       =    []



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






								"""simple docstring"""


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

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

								A__       =    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}					)
								A__       =    {
								    lang_code: self.convert_tokens_to_ids(UpperCAmelCase_					) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

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



					@property
					def     _a							(     self					:       Any					):






								"""simple docstring"""


								return self._src_lang



					@src_lang.setter
					def     _a							(     self					:       Tuple				,					_snake_case					:       str					):






								"""simple docstring"""


								A__       =    new_src_lang
								self.set_src_lang_special_tokens(self._src_lang					)



					def     _a							(     self					:       Tuple				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = 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     _a							(     self					:       str				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None					):






								"""simple docstring"""


								A__       =    [self.sep_token_id]
								A__       =    [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     _a							(     self					:       Optional[int]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       str				,					_snake_case					:       Optional[str]				,					_snake_case					:       Optional[str]				,					**_snake_case					:       int					):






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



					def     _a							(     self					:       List[Any]				,					_snake_case					:       List[str]				,					_snake_case					:       str = "eng_Latn"				,					_snake_case					:       Optional[List[str]] = None				,					_snake_case					:       str = "fra_Latn"				,					**_snake_case					:       List[Any]				,					):






								"""simple docstring"""


								A__       =    src_lang
								A__       =    tgt_lang
								return super().prepare_seqaseq_batch(UpperCAmelCase_				,					UpperCAmelCase_				,					**UpperCAmelCase_					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								return self.set_src_lang_special_tokens(self.src_lang					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								return self.set_tgt_lang_special_tokens(self.tgt_lang					)



					def     _a							(     self					:       Optional[int]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								A__       =    self.convert_tokens_to_ids(UpperCAmelCase_					)

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

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

								A__       =    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     _a							(     self					:       Optional[Any]				,					_snake_case					:       str					):






								"""simple docstring"""


								A__       =    self.convert_tokens_to_ids(UpperCAmelCase_					)
								if self.legacy_behaviour:
											A__       =    []
											A__       =    [self.eos_token_id, self.cur_lang_code]
								else:
											A__       =    [self.cur_lang_code]
											A__       =    [self.eos_token_id]

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

								A__       =    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     _a							(     self					:       str				,					_snake_case					:       str				,					_snake_case					:       Optional[str] = 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(UpperCAmelCase_					):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''					)
											return
								A__       =    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_					):
											copyfile(self.vocab_file				,					UpperCAmelCase_					)

								return (out_vocab_file,)









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


SCREAMING_SNAKE_CASE__       =      logging.getLogger(__name__)


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      List[Any]:
			return (preds == labels).mean()


@dataclass
class      __lowerCAmelCase     :
					"""simple docstring"""






					A__				:		str			       =    field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
					A__				:		Optional[str]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
					A__				:		Optional[str]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}      )
					A__				:		Optional[str]			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}							,     )







@dataclass
class      __lowerCAmelCase     :
					"""simple docstring"""






					A__				:		str			       =    field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()      )}      )
					A__				:		str			       =    field(metadata={"help": "Should contain the data files for the task."}      )
					A__				:		int			       =    field(
					    default=1_28							,     metadata={
					        "help": (
					            "The maximum total input sequence length after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded."
					        )
					    }							,     )
					A__				:		bool			       =    field(
					    default=UpperCAmelCase_							,     metadata={"help": "Overwrite the cached training and evaluation sets"}      )







def A						(       )						->      Any:
			# 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.

			A__       =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
			A__     , A__     , A__       =    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' ,	__UpperCamelCase					)

			# Set seed
			set_seed(training_args.seed					)

			try:
						A__       =    processors[data_args.task_name]()
						A__       =    processor.get_labels()
						A__       =    len(__UpperCamelCase					)
			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.

			A__       =    AutoConfig.from_pretrained(
			    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,	num_labels=__UpperCamelCase ,	finetuning_task=data_args.task_name ,	cache_dir=model_args.cache_dir ,	)
			A__       =    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 ,	)
			A__       =    AutoModelForMultipleChoice.from_pretrained(
			    model_args.model_name_or_path ,	from_tf=bool('.ckpt' in model_args.model_name_or_path					) ,	config=__UpperCamelCase ,	cache_dir=model_args.cache_dir ,	)

			# Get datasets
			A__       =    (
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir ,	tokenizer=__UpperCamelCase ,	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
			)
			A__       =    (
			    MultipleChoiceDataset(
			        data_dir=data_args.data_dir ,	tokenizer=__UpperCamelCase ,	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(__UpperCamelCase					) -> Dict:
						A__       =    np.argmax(p.predictions ,	axis=1					)
						return {"acc": simple_accuracy(__UpperCamelCase ,	p.label_ids					)}

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

			# Initialize our Trainer
			A__       =    Trainer(
			    model=__UpperCamelCase ,	args=__UpperCamelCase ,	train_dataset=__UpperCamelCase ,	eval_dataset=__UpperCamelCase ,	compute_metrics=__UpperCamelCase ,	data_collator=__UpperCamelCase ,	)

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

						A__       =    trainer.evaluate()

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

												results.update(__UpperCamelCase					)

			return results


def A						(       __UpperCamelCase					)						->      List[Any]:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()




