



'''simple docstring'''

import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
				from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
				UpperCAmelCase     		=   True
				from torch.cuda.amp import autocast

UpperCAmelCase     		=   logging.getLogger(__name__)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]=None							,		_SCREAMING_SNAKE_CASE					:						Optional[int]=None							)      ->						List[str]:







	"""simple docstring"""
	return field(default_factory=lambda: default							,		metadata=_SCREAMING_SNAKE_CASE							)

@dataclass
class __snake_case:


						'''simple docstring'''



						UpperCAmelCase							:   str									=		field(
						    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
						UpperCAmelCase							:   Optional[str]									=		field(
						    default=_lowerCAmelCase		,							metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}		,							)
						UpperCAmelCase							:   Optional[bool]									=		field(
						    default=_lowerCAmelCase		,							metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
						UpperCAmelCase							:   Optional[float]									=		field(
						    default=0.1		,							metadata={"help": "The dropout ratio for the attention probabilities."} )
						UpperCAmelCase							:   Optional[float]									=		field(
						    default=0.1		,							metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
						UpperCAmelCase							:   Optional[float]									=		field(
						    default=0.1		,							metadata={
						        "help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
						    }		,							)
						UpperCAmelCase							:   Optional[float]									=		field(
						    default=0.1		,							metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}		,							)
						UpperCAmelCase							:   Optional[float]									=		field(
						    default=0.05		,							metadata={
						        "help": (
						            "Propability of each feature vector along the time axis to be chosen as the start of the vector"
						            "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
						            "vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
						        )
						    }		,							)
						UpperCAmelCase							:   Optional[float]									=		field(default=0.0		,							metadata={"help": "The LayerDrop probability."} )







@dataclass
class __snake_case:


						'''simple docstring'''



						UpperCAmelCase							:   Optional[str]									=		field(
						    default=_lowerCAmelCase		,							metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
						UpperCAmelCase							:   Optional[str]									=		field(
						    default="train+validation"		,							metadata={
						        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
						    }		,							)
						UpperCAmelCase							:   bool									=		field(
						    default=_lowerCAmelCase		,							metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
						UpperCAmelCase							:   Optional[int]									=		field(
						    default=_lowerCAmelCase		,							metadata={"help": "The number of processes to use for the preprocessing."}		,							)
						UpperCAmelCase							:   Optional[int]									=		field(
						    default=_lowerCAmelCase		,							metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of training examples to this "
						            "value if set."
						        )
						    }		,							)
						UpperCAmelCase							:   Optional[int]									=		field(
						    default=_lowerCAmelCase		,							metadata={
						        "help": (
						            "For debugging purposes or quicker training, truncate the number of validation examples to this "
						            "value if set."
						        )
						    }		,							)
						UpperCAmelCase							:   List[str]									=		list_field(
						    default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"]		,							metadata={"help": "A list of characters to remove from the transcripts."}		,							)







@dataclass
class __snake_case:


						'''simple docstring'''



						UpperCAmelCase							:   WavaVecaProcessor
						UpperCAmelCase							:   Union[bool, str]									=		True
						UpperCAmelCase							:   Optional[int]									=		None
						UpperCAmelCase							:   Optional[int]									=		None
						UpperCAmelCase							:   Optional[int]									=		None
						UpperCAmelCase							:   Optional[int]									=		None


						def __call__( self     ,	A_				)     ->     Dict[str, torch.Tensor]:
							# split inputs and labels since they have to be of different lenghts and need
							# different padding methods
							lowerCAmelCase   =     [{"""input_values""": feature["""input_values"""]} for feature in features]
							lowerCAmelCase   =     [{"""input_ids""": feature["""labels"""]} for feature in features]

							lowerCAmelCase   =     self.processor.pad(
							    A_     ,	padding=self.padding     ,	max_length=self.max_length     ,	pad_to_multiple_of=self.pad_to_multiple_of     ,	return_tensors="""pt"""     ,	)
							lowerCAmelCase   =     self.processor.pad(
							    labels=A_     ,	padding=self.padding     ,	max_length=self.max_length_labels     ,	pad_to_multiple_of=self.pad_to_multiple_of_labels     ,	return_tensors="""pt"""     ,	)

							# replace padding with -100 to ignore loss correctly
							lowerCAmelCase   =     labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1				)     ,	-100				)

							lowerCAmelCase   =     labels

							return batch







class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     torch.Tensor:
							model.train()
							lowerCAmelCase   =     self._prepare_inputs(A_				)

							if self.use_amp:
								with autocast():
									lowerCAmelCase   =     self.compute_loss(A_     ,	A_				)
							else:
								lowerCAmelCase   =     self.compute_loss(A_     ,	A_				)

							if self.args.n_gpu > 1:
								if model.module.config.ctc_loss_reduction == "mean":
									lowerCAmelCase   =     loss.mean()
								elif model.module.config.ctc_loss_reduction == "sum":
									lowerCAmelCase   =     loss.sum() / (inputs["""labels"""] >= 0).sum()
								else:
									raise ValueError(f'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'				)

							if self.args.gradient_accumulation_steps > 1:
								lowerCAmelCase   =     loss / self.args.gradient_accumulation_steps

							if self.use_amp:
								self.scaler.scale(A_				).backward()
							elif self.use_apex:
								with amp.scale_loss(A_     ,	self.optimizer				) as scaled_loss:
									scaled_loss.backward()
							elif self.deepspeed:
								self.deepspeed.backward(A_				)
							else:
								loss.backward()

							return loss.detach()







def 		_snake_case		( )      ->						Any:







	"""simple docstring"""
	# 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.

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

	# Detecting last checkpoint.
	lowerCAmelCase   =     None
	if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
		lowerCAmelCase   =     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."""							)

    # 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							)]							,		)
	logger.setLevel(logging.INFO if is_main_process(training_args.local_rank							) else logging.WARN							)

	# 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}'							)
	# 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()
	logger.info("""Training/evaluation parameters %s"""							,		_SCREAMING_SNAKE_CASE							)

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

	# Get the datasets:
	lowerCAmelCase   =     datasets.load_dataset(
	    """common_voice"""							,		data_args.dataset_config_name							,		split=data_args.train_split_name							)
	lowerCAmelCase   =     datasets.load_dataset("""common_voice"""							,		data_args.dataset_config_name							,		split="""test"""							)

	# Create and save tokenizer
	lowerCAmelCase   =     f'[{"".join(data_args.chars_to_ignore							)}]'

	def remove_special_characters(_SCREAMING_SNAKE_CASE					:						Tuple							):
		lowerCAmelCase   =     re.sub(_SCREAMING_SNAKE_CASE							,		""""""							,		batch["""sentence"""]							).lower() + """ """
		return batch

	lowerCAmelCase   =     train_dataset.map(_SCREAMING_SNAKE_CASE							,		remove_columns=["""sentence"""]							)
	lowerCAmelCase   =     eval_dataset.map(_SCREAMING_SNAKE_CASE							,		remove_columns=["""sentence"""]							)

	def extract_all_chars(_SCREAMING_SNAKE_CASE					:						Optional[Any]							):
		lowerCAmelCase   =     """ """.join(batch["""text"""]							)
		lowerCAmelCase   =     list(set(_SCREAMING_SNAKE_CASE							)							)
		return {"vocab": [vocab], "all_text": [all_text]}

	lowerCAmelCase   =     train_dataset.map(
	    _SCREAMING_SNAKE_CASE							,		batched=_SCREAMING_SNAKE_CASE							,		batch_size=-1							,		keep_in_memory=_SCREAMING_SNAKE_CASE							,		remove_columns=train_dataset.column_names							,		)
	lowerCAmelCase   =     train_dataset.map(
	    _SCREAMING_SNAKE_CASE							,		batched=_SCREAMING_SNAKE_CASE							,		batch_size=-1							,		keep_in_memory=_SCREAMING_SNAKE_CASE							,		remove_columns=eval_dataset.column_names							,		)

	lowerCAmelCase   =     list(set(vocab_train["""vocab"""][0]							) | set(vocab_test["""vocab"""][0]							)							)
	lowerCAmelCase   =     {v: k for k, v in enumerate(_SCREAMING_SNAKE_CASE							)}
	lowerCAmelCase   =     vocab_dict[""" """]
	del vocab_dict[" "]
	lowerCAmelCase   =     len(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     len(_SCREAMING_SNAKE_CASE							)

	with open("""vocab.json"""							,		"""w"""							) as vocab_file:
		json.dump(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.
	lowerCAmelCase   =     WavaVecaCTCTokenizer(
	    """vocab.json"""							,		unk_token="""[UNK]"""							,		pad_token="""[PAD]"""							,		word_delimiter_token="""|"""							,		)
	lowerCAmelCase   =     WavaVecaFeatureExtractor(
	    feature_size=1							,		sampling_rate=16_000							,		padding_value=0.0							,		do_normalize=_SCREAMING_SNAKE_CASE							,		return_attention_mask=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE							,		tokenizer=_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     WavaVecaForCTC.from_pretrained(
	    model_args.model_name_or_path							,		cache_dir=model_args.cache_dir							,		activation_dropout=model_args.activation_dropout							,		attention_dropout=model_args.attention_dropout							,		hidden_dropout=model_args.hidden_dropout							,		feat_proj_dropout=model_args.feat_proj_dropout							,		mask_time_prob=model_args.mask_time_prob							,		gradient_checkpointing=training_args.gradient_checkpointing							,		layerdrop=model_args.layerdrop							,		ctc_loss_reduction="""mean"""							,		pad_token_id=processor.tokenizer.pad_token_id							,		vocab_size=len(processor.tokenizer							)							,		)

	if data_args.max_train_samples is not None:
		lowerCAmelCase   =     min(len(_SCREAMING_SNAKE_CASE							)							,		data_args.max_train_samples							)
		lowerCAmelCase   =     train_dataset.select(range(_SCREAMING_SNAKE_CASE							)							)

	if data_args.max_val_samples is not None:
		lowerCAmelCase   =     eval_dataset.select(range(data_args.max_val_samples							)							)

	lowerCAmelCase   =     torchaudio.transforms.Resample(48_000							,		16_000							)

	# Preprocessing the datasets.
	# We need to read the aduio files as arrays and tokenize the targets.
	def speech_file_to_array_fn(_SCREAMING_SNAKE_CASE					:						str							):
		lowerCAmelCase,       lowerCAmelCase   =     torchaudio.load(batch["""path"""]							)
		lowerCAmelCase   =     resampler(_SCREAMING_SNAKE_CASE							).squeeze().numpy()
		lowerCAmelCase   =     16_000
		lowerCAmelCase   =     batch["""text"""]
		return batch

	lowerCAmelCase   =     train_dataset.map(
	    _SCREAMING_SNAKE_CASE							,		remove_columns=train_dataset.column_names							,		num_proc=data_args.preprocessing_num_workers							,		)
	lowerCAmelCase   =     eval_dataset.map(
	    _SCREAMING_SNAKE_CASE							,		remove_columns=eval_dataset.column_names							,		num_proc=data_args.preprocessing_num_workers							,		)

	def prepare_dataset(_SCREAMING_SNAKE_CASE					:						str							):
		# check that all files have the correct sampling rate
		assert (
		    len(set(batch["""sampling_rate"""]							)							) == 1
		), f'Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'

		lowerCAmelCase   =     processor(
		    audio=batch["""speech"""]							,		text=batch["""target_text"""]							,		sampling_rate=batch["""sampling_rate"""][0]							)
		batch.update(_SCREAMING_SNAKE_CASE							)
		return batch

	lowerCAmelCase   =     train_dataset.map(
	    _SCREAMING_SNAKE_CASE							,		remove_columns=train_dataset.column_names							,		batch_size=training_args.per_device_train_batch_size							,		batched=_SCREAMING_SNAKE_CASE							,		num_proc=data_args.preprocessing_num_workers							,		)
	lowerCAmelCase   =     eval_dataset.map(
	    _SCREAMING_SNAKE_CASE							,		remove_columns=eval_dataset.column_names							,		batch_size=training_args.per_device_train_batch_size							,		batched=_SCREAMING_SNAKE_CASE							,		num_proc=data_args.preprocessing_num_workers							,		)

	# Metric
	lowerCAmelCase   =     datasets.load_metric("""wer"""							)

	def compute_metrics(_SCREAMING_SNAKE_CASE					:						Optional[int]							):
		lowerCAmelCase   =     pred.predictions
		lowerCAmelCase   =     np.argmax(_SCREAMING_SNAKE_CASE							,		axis=-1							)

		lowerCAmelCase   =     processor.tokenizer.pad_token_id

		lowerCAmelCase   =     processor.batch_decode(_SCREAMING_SNAKE_CASE							)
		# we do not want to group tokens when computing the metrics
		lowerCAmelCase   =     processor.batch_decode(pred.label_ids							,		group_tokens=_SCREAMING_SNAKE_CASE							)

		lowerCAmelCase   =     wer_metric.compute(predictions=_SCREAMING_SNAKE_CASE							,		references=_SCREAMING_SNAKE_CASE							)

		return {"wer": wer}

	if model_args.freeze_feature_extractor:
		model.freeze_feature_extractor()

	# Data collator
	lowerCAmelCase   =     DataCollatorCTCWithPadding(processor=_SCREAMING_SNAKE_CASE							,		padding=_SCREAMING_SNAKE_CASE							)

	# Initialize our Trainer
	lowerCAmelCase   =     CTCTrainer(
	    model=_SCREAMING_SNAKE_CASE							,		data_collator=_SCREAMING_SNAKE_CASE							,		args=_SCREAMING_SNAKE_CASE							,		compute_metrics=_SCREAMING_SNAKE_CASE							,		train_dataset=train_dataset if training_args.do_train else None							,		eval_dataset=eval_dataset if training_args.do_eval else None							,		tokenizer=processor.feature_extractor							,		)

	# Training
	if training_args.do_train:
		if last_checkpoint is not None:
			lowerCAmelCase   =     last_checkpoint
		elif os.path.isdir(model_args.model_name_or_path							):
			lowerCAmelCase   =     model_args.model_name_or_path
		else:
			lowerCAmelCase   =     None

		# Save the feature_extractor and the tokenizer
		if is_main_process(training_args.local_rank							):
			processor.save_pretrained(training_args.output_dir							)

		lowerCAmelCase   =     trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE							)
		trainer.save_model()

		lowerCAmelCase   =     train_result.metrics
		lowerCAmelCase   =     (
		    data_args.max_train_samples if data_args.max_train_samples is not None else len(_SCREAMING_SNAKE_CASE							)
		)
		lowerCAmelCase   =     min(_SCREAMING_SNAKE_CASE							,		len(_SCREAMING_SNAKE_CASE							)							)

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

	# Evaluation
	lowerCAmelCase   =     {}
	if training_args.do_eval:
		logger.info("""*** Evaluate ***"""							)
		lowerCAmelCase   =     trainer.evaluate()
		lowerCAmelCase   =     data_args.max_val_samples if data_args.max_val_samples is not None else len(_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase   =     min(_SCREAMING_SNAKE_CASE							,		len(_SCREAMING_SNAKE_CASE							)							)

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

	return results


if __name__ == "__main__":
				main()




'''simple docstring'''

import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase     		=   logging.get_logger(__name__)

UpperCAmelCase     		=   '▁'

UpperCAmelCase     		=   {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCAmelCase     		=   {
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

UpperCAmelCase     		=   {
    'facebook/m2m100_418M': 1024,
}

# fmt: off
UpperCAmelCase     		=   {
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Optional[int]									=		VOCAB_FILES_NAMES
						UpperCAmelCase							:   Optional[Any]									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase							:   Optional[int]									=		PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase							:   int									=		["input_ids", "attention_mask"]

						UpperCAmelCase							:   List[int]									=		[]
						UpperCAmelCase							:   List[int]									=		[]


						def __init__( self     ,	A_     ,	A_     ,	A_=None     ,	A_=None     ,	A_="<s>"     ,	A_="</s>"     ,	A_="</s>"     ,	A_="<pad>"     ,	A_="<unk>"     ,	A_="m2m100"     ,	A_ = None     ,	A_=8     ,	**A_     ,	)     ->     None:
							lowerCAmelCase   =     {} if sp_model_kwargs is None else sp_model_kwargs

							lowerCAmelCase   =     language_codes
							lowerCAmelCase   =     FAIRSEQ_LANGUAGE_CODES[language_codes]
							lowerCAmelCase   =     {lang_code: f'__{lang_code}__' for lang_code in fairseq_language_code}

							lowerCAmelCase   =     kwargs.get("""additional_special_tokens"""     ,	[]				)
							kwargs["additional_special_tokens"] += [
							    self.get_lang_token(A_				)
							    for lang_code in fairseq_language_code
							    if self.get_lang_token(A_				) not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    src_lang=A_     ,	tgt_lang=A_     ,	bos_token=A_     ,	eos_token=A_     ,	sep_token=A_     ,	unk_token=A_     ,	pad_token=A_     ,	language_codes=A_     ,	sp_model_kwargs=self.sp_model_kwargs     ,	num_madeup_words=A_     ,	**A_     ,	)

							lowerCAmelCase   =     vocab_file
							lowerCAmelCase   =     load_json(A_				)
							lowerCAmelCase   =     {v: k for k, v in self.encoder.items()}
							lowerCAmelCase   =     spm_file
							lowerCAmelCase   =     load_spm(A_     ,	self.sp_model_kwargs				)

							lowerCAmelCase   =     len(self.encoder				)

							lowerCAmelCase   =     {
							    self.get_lang_token(A_				): self.encoder_size + i for i, lang_code in enumerate(A_				)
							}
							lowerCAmelCase   =     {lang_code: self.encoder_size + i for i, lang_code in enumerate(A_				)}
							lowerCAmelCase   =     {v: k for k, v in self.lang_token_to_id.items()}

							lowerCAmelCase   =     src_lang if src_lang is not None else """en"""
							lowerCAmelCase   =     tgt_lang
							lowerCAmelCase   =     self.get_lang_id(self._src_lang				)
							self.set_src_lang_special_tokens(self._src_lang				)

							lowerCAmelCase   =     num_madeup_words


						@property
						def     __snake_case   ( self				)     ->     int:
							return len(self.encoder				) + len(self.lang_token_to_id				)


						@property
						def     __snake_case   ( self				)     ->     str:
							return self._src_lang


						@src_lang.setter
						def     __snake_case   ( self     ,	A_				)     ->     None:
							lowerCAmelCase   =     new_src_lang
							self.set_src_lang_special_tokens(self._src_lang				)


						def     __snake_case   ( self     ,	A_				)     ->     List[str]:
							return self.sp_model.encode(A_     ,	out_type=A_				)


						def     __snake_case   ( self     ,	A_				)     ->     Any:
							if token in self.lang_token_to_id:
								return self.lang_token_to_id[token]
							return self.encoder.get(A_     ,	self.encoder[self.unk_token]				)


						def     __snake_case   ( self     ,	A_				)     ->     str:
							if index in self.id_to_lang_token:
								return self.id_to_lang_token[index]
							return self.decoder.get(A_     ,	self.unk_token				)


						def     __snake_case   ( self     ,	A_				)     ->     List[str]:
							lowerCAmelCase   =     []
							lowerCAmelCase   =     """"""
							for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
									out_string += self.sp_model.decode(A_				) + token
									lowerCAmelCase   =     []
								else:
									current_sub_tokens.append(A_				)
							out_string += self.sp_model.decode(A_				)
							return out_string.strip()


						def     __snake_case   ( self     ,	A_     ,	A_ = None     ,	A_ = False				)     ->     List[int]:

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

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


						def     __snake_case   ( self     ,	A_     ,	A_ = None				)     ->     List[int]:
							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     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase   =     {self.convert_ids_to_tokens(A_				): i for i in range(self.vocab_size				)}
							vocab.update(self.added_tokens_encoder				)
							return vocab


						def __getstate__( self				)     ->     Dict:
							lowerCAmelCase   =     self.__dict__.copy()
							lowerCAmelCase   =     None
							return state


						def __setstate__( self     ,	A_				)     ->     None:
							lowerCAmelCase   =     d

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

							lowerCAmelCase   =     load_spm(self.spm_file     ,	self.sp_model_kwargs				)


						def     __snake_case   ( self     ,	A_     ,	A_ = None				)     ->     Tuple[str]:
							lowerCAmelCase   =     Path(A_				)
							if not save_dir.is_dir():
								raise OSError(f'{save_directory} should be a directory'				)
							lowerCAmelCase   =     save_dir / (
							    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
							)
							lowerCAmelCase   =     save_dir / (
							    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
							)

							save_json(self.encoder     ,	A_				)

							if os.path.abspath(self.spm_file				) != os.path.abspath(A_				) and os.path.isfile(self.spm_file				):
								copyfile(self.spm_file     ,	A_				)
							elif not os.path.isfile(self.spm_file				):
								with open(A_     ,	"""wb"""				) as fi:
									lowerCAmelCase   =     self.sp_model.serialized_model_proto()
									fi.write(A_				)

							return (str(A_				), str(A_				))


						def     __snake_case   ( self     ,	A_     ,	A_ = "en"     ,	A_ = None     ,	A_ = "ro"     ,	**A_     ,	)     ->     BatchEncoding:
							lowerCAmelCase   =     src_lang
							lowerCAmelCase   =     tgt_lang
							self.set_src_lang_special_tokens(self.src_lang				)
							return super().prepare_seqaseq_batch(A_     ,	A_     ,	**A_				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	**A_				)     ->     str:
							if src_lang is None or tgt_lang is None:
								raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""				)
							lowerCAmelCase   =     src_lang
							lowerCAmelCase   =     self(A_     ,	add_special_tokens=A_     ,	**A_				)
							lowerCAmelCase   =     self.get_lang_id(A_				)
							lowerCAmelCase   =     tgt_lang_id
							return inputs


						def     __snake_case   ( self				)     ->     Any:
							self.set_src_lang_special_tokens(self.src_lang				)


						def     __snake_case   ( self				)     ->     Optional[int]:
							self.set_tgt_lang_special_tokens(self.tgt_lang				)


						def     __snake_case   ( self     ,	A_				)     ->     None:
							lowerCAmelCase   =     self.get_lang_token(A_				)
							lowerCAmelCase   =     self.lang_token_to_id[lang_token]
							lowerCAmelCase   =     [self.cur_lang_id]
							lowerCAmelCase   =     [self.eos_token_id]


						def     __snake_case   ( self     ,	A_				)     ->     None:
							lowerCAmelCase   =     self.get_lang_token(A_				)
							lowerCAmelCase   =     self.lang_token_to_id[lang_token]
							lowerCAmelCase   =     [self.cur_lang_id]
							lowerCAmelCase   =     [self.eos_token_id]


						def     __snake_case   ( self     ,	A_				)     ->     str:
							return self.lang_code_to_token[lang]




						def     __snake_case   ( self     ,	A_				)     ->     int:
							lowerCAmelCase   =     self.get_lang_token(A_				)
							return self.lang_token_to_id[lang_token]







def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Dict[str, Any]							)      ->						sentencepiece.SentencePieceProcessor:







	"""simple docstring"""
	lowerCAmelCase   =     sentencepiece.SentencePieceProcessor(**_SCREAMING_SNAKE_CASE							)
	spm.Load(str(_SCREAMING_SNAKE_CASE							)							)
	return spm






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							)      ->						Union[Dict, List]:







	"""simple docstring"""
	with open(_SCREAMING_SNAKE_CASE							,		"""r"""							) as f:
		return json.load(_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						None:







	"""simple docstring"""
	with open(_SCREAMING_SNAKE_CASE							,		"""w"""							) as f:
		json.dump(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		indent=2							)