


'''simple docstring'''


import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')

lowerCamelCase__					=   logging.getLogger(__name__)






@dataclass
class 		lowerCAmelCase__	:
			lowerCAmelCase       :		Optional[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."
			        )
			    }			,				)
			lowerCAmelCase       :		bool				      =   field(
			    default=UpperCAmelCase__			,				metadata={"help": "Overwrite the cached preprocessed datasets or not."}		)
			lowerCAmelCase       :		bool				      =   field(
			    default=UpperCAmelCase__			,				metadata={
			        "help": (
			            "Whether to pad all samples to `max_seq_length`. "
			            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
			        )
			    }			,				)
			lowerCAmelCase       :		Optional[int]				      =   field(
			    default=UpperCAmelCase__			,				metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of training examples to this "
			            "value if set."
			        )
			    }			,				)
			lowerCAmelCase       :		Optional[int]				      =   field(
			    default=UpperCAmelCase__			,				metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
			            "value if set."
			        )
			    }			,				)
			lowerCAmelCase       :		Optional[int]				      =   field(
			    default=UpperCAmelCase__			,				metadata={
			        "help": (
			            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
			            "value if set."
			        )
			    }			,				)






@dataclass
class 		lowerCAmelCase__	:
			lowerCAmelCase       :		str				      =   field(
			    default=UpperCAmelCase__			,				metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}		)
			lowerCAmelCase       :		str				      =   field(
			    default=UpperCAmelCase__			,				metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}		)
			lowerCAmelCase       :		Optional[str]				      =   field(
			    default=UpperCAmelCase__			,				metadata={"help": "Train language if it is different from the evaluation language."}		)
			lowerCAmelCase       :		Optional[str]				      =   field(
			    default=UpperCAmelCase__			,				metadata={"help": "Pretrained config name or path if not the same as model_name"}		)
			lowerCAmelCase       :		Optional[str]				      =   field(
			    default=UpperCAmelCase__			,				metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}		)
			lowerCAmelCase       :		Optional[str]				      =   field(
			    default=UpperCAmelCase__			,				metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,				)
			lowerCAmelCase       :		Optional[bool]				      =   field(
			    default=UpperCAmelCase__			,				metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}			,				)
			lowerCAmelCase       :		bool				      =   field(
			    default=UpperCAmelCase__			,				metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}			,				)
			lowerCAmelCase       :		str				      =   field(
			    default="main"			,				metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}			,				)
			lowerCAmelCase       :		bool				      =   field(
			    default=UpperCAmelCase__			,				metadata={
			        "help": (
			            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
			            "with private models)."
			        )
			    }			,				)
			lowerCAmelCase       :		bool				      =   field(
			    default=UpperCAmelCase__			,				metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}			,				)






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.

		_UpperCAmelCase						:				Union[str, Any]	    =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
		_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase						:				int	    =					parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry("run_xnli"					,       __lowerCAmelCase      )

		# Setup logging
		logging.basicConfig(
		    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,       datefmt="%m/%d/%Y %H:%M:%S"					,       handlers=[logging.StreamHandler(sys.stdout      )]					,       )

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		_UpperCAmelCase						:				int	    =					training_args.get_process_log_level()
		logger.setLevel(__lowerCAmelCase      )
		datasets.utils.logging.set_verbosity(__lowerCAmelCase      )
		transformers.utils.logging.set_verbosity(__lowerCAmelCase      )
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
		    + F"""distributed training: {bool(training_args.local_rank != -1      )}, 16-bits training: {training_args.fpaa}"""      )
		logger.info(F"""Training/evaluation parameters {training_args}"""      )

		# Detecting last checkpoint.
		_UpperCAmelCase						:				Union[str, Any]	    =					None
		if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
				_UpperCAmelCase						:				Dict	    =					get_last_checkpoint(training_args.output_dir      )
				if last_checkpoint is None and len(os.listdir(training_args.output_dir      )      ) > 0:
						raise ValueError(
						    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
						    "Use --overwrite_output_dir to overcome."      )
				elif last_checkpoint is not None:
						logger.info(
						    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
						    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."      )

    # Set seed before initializing model.
		set_seed(training_args.seed      )

		# In distributed training, the load_dataset function guarantees that only one local process can concurrently
		# download the dataset.
		# Downloading and loading xnli dataset from the hub.
		if training_args.do_train:
				if model_args.train_language is None:
						_UpperCAmelCase						:				List[Any]	    =					load_dataset(
						    "xnli"					,       model_args.language					,       split="train"					,       cache_dir=model_args.cache_dir					,       use_auth_token=True if model_args.use_auth_token else None					,       )
				else:
						_UpperCAmelCase						:				Union[str, Any]	    =					load_dataset(
						    "xnli"					,       model_args.train_language					,       split="train"					,       cache_dir=model_args.cache_dir					,       use_auth_token=True if model_args.use_auth_token else None					,       )
				_UpperCAmelCase						:				List[str]	    =					train_dataset.features["label"].names

		if training_args.do_eval:
				_UpperCAmelCase						:				Optional[Any]	    =					load_dataset(
				    "xnli"					,       model_args.language					,       split="validation"					,       cache_dir=model_args.cache_dir					,       use_auth_token=True if model_args.use_auth_token else None					,       )
				_UpperCAmelCase						:				Optional[Any]	    =					eval_dataset.features["label"].names

		if training_args.do_predict:
				_UpperCAmelCase						:				int	    =					load_dataset(
				    "xnli"					,       model_args.language					,       split="test"					,       cache_dir=model_args.cache_dir					,       use_auth_token=True if model_args.use_auth_token else None					,       )
				_UpperCAmelCase						:				Dict	    =					predict_dataset.features["label"].names

		# Labels
		_UpperCAmelCase						:				Optional[int]	    =					len(__lowerCAmelCase      )

		# Load pretrained model and tokenizer
		# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		_UpperCAmelCase						:				List[str]	    =					AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,       num_labels=__lowerCAmelCase					,       idalabel={str(__lowerCAmelCase      ): label for i, label in enumerate(__lowerCAmelCase      )}					,       labelaid={label: i for i, label in enumerate(__lowerCAmelCase      )}					,       finetuning_task="xnli"					,       cache_dir=model_args.cache_dir					,       revision=model_args.model_revision					,       use_auth_token=True if model_args.use_auth_token else None					,       )
		_UpperCAmelCase						:				Union[str, Any]	    =					AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,       do_lower_case=model_args.do_lower_case					,       cache_dir=model_args.cache_dir					,       use_fast=model_args.use_fast_tokenizer					,       revision=model_args.model_revision					,       use_auth_token=True if model_args.use_auth_token else None					,       )
		_UpperCAmelCase						:				str	    =					AutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path					,       from_tf=bool(".ckpt" in model_args.model_name_or_path      )					,       config=__lowerCAmelCase					,       cache_dir=model_args.cache_dir					,       revision=model_args.model_revision					,       use_auth_token=True if model_args.use_auth_token else None					,       ignore_mismatched_sizes=model_args.ignore_mismatched_sizes					,       )

		# Preprocessing the datasets
		# Padding strategy
		if data_args.pad_to_max_length:
				_UpperCAmelCase						:				Optional[Any]	    =					"max_length"
		else:
				# We will pad later, dynamically at batch creation, to the max sequence length in each batch
				_UpperCAmelCase						:				List[str]	    =					False

		def preprocess_function(__lowerCAmelCase      ):
				# Tokenize the texts
				return tokenizer(
				    examples["premise"]					,       examples["hypothesis"]					,       padding=__lowerCAmelCase					,       max_length=data_args.max_seq_length					,       truncation=__lowerCAmelCase					,       )

		if training_args.do_train:
				if data_args.max_train_samples is not None:
						_UpperCAmelCase						:				Any	    =					min(len(__lowerCAmelCase      )					,       data_args.max_train_samples      )
						_UpperCAmelCase						:				Union[str, Any]	    =					train_dataset.select(range(__lowerCAmelCase      )      )
				with training_args.main_process_first(desc="train dataset map pre-processing"      ):
						_UpperCAmelCase						:				Optional[Any]	    =					train_dataset.map(
						    __lowerCAmelCase					,       batched=__lowerCAmelCase					,       load_from_cache_file=not data_args.overwrite_cache					,       desc="Running tokenizer on train dataset"					,       )
				# Log a few random samples from the training set:
				for index in random.sample(range(len(__lowerCAmelCase      )      )					,       3      ):
						logger.info(F"""Sample {index} of the training set: {train_dataset[index]}."""      )

		if training_args.do_eval:
				if data_args.max_eval_samples is not None:
						_UpperCAmelCase						:				List[str]	    =					min(len(__lowerCAmelCase      )					,       data_args.max_eval_samples      )
						_UpperCAmelCase						:				Union[str, Any]	    =					eval_dataset.select(range(__lowerCAmelCase      )      )
				with training_args.main_process_first(desc="validation dataset map pre-processing"      ):
						_UpperCAmelCase						:				Tuple	    =					eval_dataset.map(
						    __lowerCAmelCase					,       batched=__lowerCAmelCase					,       load_from_cache_file=not data_args.overwrite_cache					,       desc="Running tokenizer on validation dataset"					,       )

		if training_args.do_predict:
				if data_args.max_predict_samples is not None:
						_UpperCAmelCase						:				int	    =					min(len(__lowerCAmelCase      )					,       data_args.max_predict_samples      )
						_UpperCAmelCase						:				Dict	    =					predict_dataset.select(range(__lowerCAmelCase      )      )
				with training_args.main_process_first(desc="prediction dataset map pre-processing"      ):
						_UpperCAmelCase						:				Tuple	    =					predict_dataset.map(
						    __lowerCAmelCase					,       batched=__lowerCAmelCase					,       load_from_cache_file=not data_args.overwrite_cache					,       desc="Running tokenizer on prediction dataset"					,       )

    # Get the metric function
		_UpperCAmelCase						:				Dict	    =					evaluate.load("xnli"      )

		# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
		# predictions and label_ids field) and has to return a dictionary string to float.
		def compute_metrics(__lowerCAmelCase      ):
				_UpperCAmelCase						:				Union[str, Any]	    =					p.predictions[0] if isinstance(p.predictions					,       __lowerCAmelCase      ) else p.predictions
				_UpperCAmelCase						:				Union[str, Any]	    =					np.argmax(__lowerCAmelCase					,       axis=1      )
				return metric.compute(predictions=__lowerCAmelCase					,       references=p.label_ids      )

		# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
		if data_args.pad_to_max_length:
				_UpperCAmelCase						:				Union[str, Any]	    =					default_data_collator
		elif training_args.fpaa:
				_UpperCAmelCase						:				List[Any]	    =					DataCollatorWithPadding(__lowerCAmelCase					,       pad_to_multiple_of=8      )
		else:
				_UpperCAmelCase						:				Tuple	    =					None

		# Initialize our Trainer
		_UpperCAmelCase						:				int	    =					Trainer(
		    model=__lowerCAmelCase					,       args=__lowerCAmelCase					,       train_dataset=train_dataset if training_args.do_train else None					,       eval_dataset=eval_dataset if training_args.do_eval else None					,       compute_metrics=__lowerCAmelCase					,       tokenizer=__lowerCAmelCase					,       data_collator=__lowerCAmelCase					,       )

		# Training
		if training_args.do_train:
				_UpperCAmelCase						:				Union[str, Any]	    =					None
				if training_args.resume_from_checkpoint is not None:
						_UpperCAmelCase						:				str	    =					training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						_UpperCAmelCase						:				int	    =					last_checkpoint
				_UpperCAmelCase						:				List[str]	    =					trainer.train(resume_from_checkpoint=__lowerCAmelCase      )
				_UpperCAmelCase						:				int	    =					train_result.metrics
				_UpperCAmelCase						:				Optional[int]	    =					(
				    data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase      )
				)
				_UpperCAmelCase						:				Any	    =					min(__lowerCAmelCase					,       len(__lowerCAmelCase      )      )

				trainer.save_model()  # Saves the tokenizer too for easy upload

				trainer.log_metrics("train"					,       __lowerCAmelCase      )
				trainer.save_metrics("train"					,       __lowerCAmelCase      )
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				logger.info("*** Evaluate ***"      )
				_UpperCAmelCase						:				Any	    =					trainer.evaluate(eval_dataset=__lowerCAmelCase      )

				_UpperCAmelCase						:				Tuple	    =					data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase      )
				_UpperCAmelCase						:				List[str]	    =					min(__lowerCAmelCase					,       len(__lowerCAmelCase      )      )

				trainer.log_metrics("eval"					,       __lowerCAmelCase      )
				trainer.save_metrics("eval"					,       __lowerCAmelCase      )

		# Prediction
		if training_args.do_predict:
				logger.info("*** Predict ***"      )
				_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase						:				int	    =					trainer.predict(__lowerCAmelCase					,       metric_key_prefix="predict"      )

				_UpperCAmelCase						:				List[Any]	    =					(
				    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase      )
				)
				_UpperCAmelCase						:				int	    =					min(__lowerCAmelCase					,       len(__lowerCAmelCase      )      )

				trainer.log_metrics("predict"					,       __lowerCAmelCase      )
				trainer.save_metrics("predict"					,       __lowerCAmelCase      )

				_UpperCAmelCase						:				Optional[int]	    =					np.argmax(__lowerCAmelCase					,       axis=1      )
				_UpperCAmelCase						:				Dict	    =					os.path.join(training_args.output_dir					,       "predictions.txt"      )
				if trainer.is_world_process_zero():
						with open(__lowerCAmelCase					,       "w"      ) as writer:
								writer.write("index\tprediction\n"      )
								for index, item in enumerate(__lowerCAmelCase      ):
										_UpperCAmelCase						:				Tuple	    =					label_list[item]
										writer.write(F"""{index}\t{item}\n"""      )


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, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__					=   logging.get_logger(__name__)

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

lowerCamelCase__					=   {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
}

lowerCamelCase__					=   {
    'moussaKam/mbarthez': 1_024,
    'moussaKam/barthez': 1_024,
    'moussaKam/barthez-orangesum-title': 1_024,
}

lowerCamelCase__					=   '▁'






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Any				      =   VOCAB_FILES_NAMES
			lowerCAmelCase       :		Any				      =   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase       :		Union[str, Any]				      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase       :		int				      =   ["input_ids", "attention_mask"]




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



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					AddedToken(lowerCamelCase__		,     lstrip=lowerCamelCase__		,     rstrip=lowerCamelCase__       ) if isinstance(lowerCamelCase__		,     lowerCamelCase__       ) else mask_token

					_UpperCAmelCase						:				Any	    =					{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=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						:				Union[str, Any]	    =					vocab_file
					_UpperCAmelCase						:				Dict	    =					spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.Load(str(lowerCamelCase__       )       )

					_UpperCAmelCase						:				Optional[int]	    =					{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

					_UpperCAmelCase						:				List[str]	    =					len(self.sp_model       ) - 1
					_UpperCAmelCase						:				List[str]	    =					{v: k for k, v in self.fairseq_tokens_to_ids.items()}




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					_UpperCAmelCase						:				Union[str, Any]	    =					[self.cls_token_id]
					_UpperCAmelCase						:				str	    =					[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep




			def 							lowerCAmelCase__			(						self     :   int		,     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__       )

					if token_ids_a is None:
							return [1] + ([0] * len(lowerCamelCase__       )) + [1]
					return [1] + ([0] * len(lowerCamelCase__       )) + [1, 1] + ([0] * len(lowerCamelCase__       )) + [1]




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					[self.sep_token_id]
					_UpperCAmelCase						:				Optional[int]	    =					[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]




			@property
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->List[str]:



					'''simple docstring'''







					return len(self.sp_model       )




			def 							lowerCAmelCase__			(						self     :   int       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				List[Any]	    =					{self.convert_ids_to_tokens(lowerCamelCase__       ): i for i in range(self.vocab_size       )}
					vocab.update(self.added_tokens_encoder       )
					return vocab




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   str       )      ->List[str]:



					'''simple docstring'''







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




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Union[str, Any]       )      ->List[Any]:



					'''simple docstring'''







					if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
					_UpperCAmelCase						:				Optional[Any]	    =					self.sp_model.PieceToId(lowerCamelCase__       )

					return spm_id if spm_id else self.unk_token_id




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   Any       )      ->Optional[Any]:



					'''simple docstring'''







					if index in self.fairseq_ids_to_tokens:
							return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Any		,     lowerCamelCase__     :   List[Any]       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Union[str, Any]	    =					[]
					_UpperCAmelCase						:				Optional[Any]	    =					""
					_UpperCAmelCase						:				Optional[int]	    =					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						:				Union[str, Any]	    =					True
									_UpperCAmelCase						:				Union[str, Any]	    =					[]
							else:
									current_sub_tokens.append(lowerCamelCase__       )
									_UpperCAmelCase						:				int	    =					False
					out_string += self.sp_model.decode(lowerCamelCase__       )
					return out_string.strip()




			def __getstate__(						self     :   Dict       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.__dict__.copy()
					_UpperCAmelCase						:				Optional[int]	    =					None
					return state




			def __setstate__(						self     :   Any		,     lowerCamelCase__     :   Dict       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					d

					# for backward compatibility
					if not hasattr(self		,     "sp_model_kwargs"       ):
							_UpperCAmelCase						:				List[Any]	    =					{}

					_UpperCAmelCase						:				Optional[int]	    =					spm.SentencePieceProcessor(**self.sp_model_kwargs       )
					self.sp_model.Load(self.vocab_file       )






			def 							lowerCAmelCase__			(						self     :   Optional[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						:				Union[str, Any]	    =					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						:				Dict	    =					self.sp_model.serialized_model_proto()
									fi.write(lowerCamelCase__       )

					return (out_vocab_file,)



