'''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"""):
  __a:			Dict	       =       True
  from torch.cuda.amp import autocast

__a:			str	       =       logging.getLogger(__name__)
def       __UpperCamelCase	(       UpperCAmelCase=None		,					UpperCAmelCase=None					):
 return field(default_factory=lambda: default		,					metadata=UpperCAmelCase					)



@dataclass
class       UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
       SCREAMING_SNAKE_CASE 		=   field(
           default=a__   ,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,						)
       SCREAMING_SNAKE_CASE 		=   field(
           default=a__   ,						metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
       SCREAMING_SNAKE_CASE 		=   field(
           default=0.1   ,						metadata={"help": "The dropout ratio for the attention probabilities."} )
       SCREAMING_SNAKE_CASE 		=   field(
           default=0.1   ,						metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
       SCREAMING_SNAKE_CASE 		=   field(
           default=0.1   ,						metadata={
               "help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
           }   ,						)
       SCREAMING_SNAKE_CASE 		=   field(
           default=0.1   ,						metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}   ,						)
       SCREAMING_SNAKE_CASE 		=   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``."
               )
           }   ,						)
       SCREAMING_SNAKE_CASE 		=   field(default=0.0   ,						metadata={"help": "The LayerDrop probability."} )



@dataclass
class       UpperCAmelCase  :





       '''simple docstring'''



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



@dataclass
class       UpperCAmelCase  :





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   42
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None
       SCREAMING_SNAKE_CASE 		=   None

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

        lowercase__    :   Tuple						= self.processor.pad(
            __lowerCAmelCase     ,       padding=self.padding     ,       max_length=self.max_length     ,       pad_to_multiple_of=self.pad_to_multiple_of     ,       return_tensors='''pt'''     ,       )
        lowercase__    :   List[str]						= self.processor.pad(
            labels=__lowerCAmelCase     ,       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
        lowercase__    :   Any						= labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1			)     ,       -100			)

        lowercase__    :   Dict						= labels

        return batch



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  torch.Tensor:
        model.train()
        lowercase__    :   Union[str, Any]						= self._prepare_inputs(__lowerCAmelCase			)

        if self.use_amp:
         with autocast():
          lowercase__    :   Optional[int]						= self.compute_loss(__lowerCAmelCase     ,       __lowerCAmelCase			)
        else:
         lowercase__    :   Optional[Any]						= self.compute_loss(__lowerCAmelCase     ,       __lowerCAmelCase			)

        if self.args.n_gpu > 1:
         if model.module.config.ctc_loss_reduction == "mean":
          lowercase__    :   Union[str, Any]						= loss.mean()
         elif model.module.config.ctc_loss_reduction == "sum":
          lowercase__    :   List[Any]						= 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:
         lowercase__    :   Union[str, Any]						= loss / self.args.gradient_accumulation_steps

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

        return loss.detach()


def       __UpperCamelCase	(       ):
 # 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.

 lowercase__    :   Union[str, Any]						= 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.
  lowercase__			,							lowercase__			,							lowercase__    :   Dict						= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
 else:
  lowercase__			,							lowercase__			,							lowercase__    :   Union[str, Any]						= parser.parse_args_into_dataclasses()

 # Detecting last checkpoint.
 lowercase__    :   Tuple						= None
 if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
  lowercase__    :   Optional[Any]						= 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'''		,					UpperCAmelCase					)

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

 # Get the datasets:
 lowercase__    :   int						= datasets.load_dataset(
     '''common_voice'''		,					data_args.dataset_config_name		,					split=data_args.train_split_name					)
 lowercase__    :   Optional[Any]						= datasets.load_dataset('''common_voice'''		,					data_args.dataset_config_name		,					split='''test'''					)

 # Create and save tokenizer
 lowercase__    :   Any						= F"""[{''.join(data_args.chars_to_ignore					)}]"""

 def remove_special_characters(UpperCAmelCase					):
  lowercase__    :   Union[str, Any]						= re.sub(UpperCAmelCase		,					''''''		,					batch['''sentence''']					).lower() + ''' '''
  return batch

 lowercase__    :   Optional[int]						= train_dataset.map(UpperCAmelCase		,					remove_columns=['''sentence''']					)
 lowercase__    :   Tuple						= eval_dataset.map(UpperCAmelCase		,					remove_columns=['''sentence''']					)

 def extract_all_chars(UpperCAmelCase					):
  lowercase__    :   str						= ''' '''.join(batch['''text''']					)
  lowercase__    :   Optional[Any]						= list(set(UpperCAmelCase					)					)
  return {"vocab": [vocab], "all_text": [all_text]}

 lowercase__    :   str						= train_dataset.map(
     UpperCAmelCase		,					batched=UpperCAmelCase		,					batch_size=-1		,					keep_in_memory=UpperCAmelCase		,					remove_columns=train_dataset.column_names		,					)
 lowercase__    :   Tuple						= train_dataset.map(
     UpperCAmelCase		,					batched=UpperCAmelCase		,					batch_size=-1		,					keep_in_memory=UpperCAmelCase		,					remove_columns=eval_dataset.column_names		,					)

 lowercase__    :   int						= list(set(vocab_train['''vocab'''][0]					) | set(vocab_test['''vocab'''][0]					)					)
 lowercase__    :   Any						= {v: k for k, v in enumerate(UpperCAmelCase					)}
 lowercase__    :   Union[str, Any]						= vocab_dict[''' ''']
 del vocab_dict[" "]
 lowercase__    :   Dict						= len(UpperCAmelCase					)
 lowercase__    :   str						= len(UpperCAmelCase					)

 with open('''vocab.json'''		,					'''w'''					) as vocab_file:
  json.dump(UpperCAmelCase		,					UpperCAmelCase					)

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 lowercase__    :   List[Any]						= WavaVecaCTCTokenizer(
     '''vocab.json'''		,					unk_token='''[UNK]'''		,					pad_token='''[PAD]'''		,					word_delimiter_token='''|'''		,					)
 lowercase__    :   List[Any]						= WavaVecaFeatureExtractor(
     feature_size=1		,					sampling_rate=1_6000		,					padding_value=0.0		,					do_normalize=UpperCAmelCase		,					return_attention_mask=UpperCAmelCase					)
 lowercase__    :   Union[str, Any]						= WavaVecaProcessor(feature_extractor=UpperCAmelCase		,					tokenizer=UpperCAmelCase					)
 lowercase__    :   Optional[Any]						= 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:
  lowercase__    :   str						= min(len(UpperCAmelCase					)		,					data_args.max_train_samples					)
  lowercase__    :   Optional[int]						= train_dataset.select(range(UpperCAmelCase					)					)

 if data_args.max_val_samples is not None:
  lowercase__    :   List[Any]						= eval_dataset.select(range(data_args.max_val_samples					)					)

 lowercase__    :   int						= torchaudio.transforms.Resample(4_8000		,					1_6000					)

 # Preprocessing the datasets.
 # We need to read the aduio files as arrays and tokenize the targets.
 def speech_file_to_array_fn(UpperCAmelCase					):
  lowercase__			,							lowercase__    :   Any						= torchaudio.load(batch['''path''']					)
  lowercase__    :   List[str]						= resampler(UpperCAmelCase					).squeeze().numpy()
  lowercase__    :   Optional[Any]						= 1_6000
  lowercase__    :   List[str]						= batch['''text''']
  return batch

 lowercase__    :   Any						= train_dataset.map(
     UpperCAmelCase		,					remove_columns=train_dataset.column_names		,					num_proc=data_args.preprocessing_num_workers		,					)
 lowercase__    :   List[Any]						= eval_dataset.map(
     UpperCAmelCase		,					remove_columns=eval_dataset.column_names		,					num_proc=data_args.preprocessing_num_workers		,					)

 def prepare_dataset(UpperCAmelCase					):
  # 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}."""

  lowercase__    :   Optional[Any]						= processor(
      audio=batch['''speech''']		,					text=batch['''target_text''']		,					sampling_rate=batch['''sampling_rate'''][0]					)
  batch.update(UpperCAmelCase					)
  return batch

 lowercase__    :   Tuple						= train_dataset.map(
     UpperCAmelCase		,					remove_columns=train_dataset.column_names		,					batch_size=training_args.per_device_train_batch_size		,					batched=UpperCAmelCase		,					num_proc=data_args.preprocessing_num_workers		,					)
 lowercase__    :   List[str]						= eval_dataset.map(
     UpperCAmelCase		,					remove_columns=eval_dataset.column_names		,					batch_size=training_args.per_device_train_batch_size		,					batched=UpperCAmelCase		,					num_proc=data_args.preprocessing_num_workers		,					)

 # Metric
 lowercase__    :   Tuple						= datasets.load_metric('''wer'''					)

 def compute_metrics(UpperCAmelCase					):
  lowercase__    :   Dict						= pred.predictions
  lowercase__    :   Any						= np.argmax(UpperCAmelCase		,					axis=-1					)

  lowercase__    :   Union[str, Any]						= processor.tokenizer.pad_token_id

  lowercase__    :   Optional[int]						= processor.batch_decode(UpperCAmelCase					)
  # we do not want to group tokens when computing the metrics
  lowercase__    :   List[str]						= processor.batch_decode(pred.label_ids		,					group_tokens=UpperCAmelCase					)

  lowercase__    :   Optional[int]						= wer_metric.compute(predictions=UpperCAmelCase		,					references=UpperCAmelCase					)

  return {"wer": wer}

 if model_args.freeze_feature_extractor:
  model.freeze_feature_extractor()

 # Data collator
 lowercase__    :   Union[str, Any]						= DataCollatorCTCWithPadding(processor=UpperCAmelCase		,					padding=UpperCAmelCase					)

 # Initialize our Trainer
 lowercase__    :   Optional[int]						= CTCTrainer(
     model=UpperCAmelCase		,					data_collator=UpperCAmelCase		,					args=UpperCAmelCase		,					compute_metrics=UpperCAmelCase		,					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:
   lowercase__    :   List[Any]						= last_checkpoint
  elif os.path.isdir(model_args.model_name_or_path					):
   lowercase__    :   Optional[Any]						= model_args.model_name_or_path
  else:
   lowercase__    :   Union[str, Any]						= None

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

  lowercase__    :   Tuple						= trainer.train(resume_from_checkpoint=UpperCAmelCase					)
  trainer.save_model()

  lowercase__    :   Union[str, Any]						= train_result.metrics
  lowercase__    :   Tuple						= (
      data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCAmelCase					)
  )
  lowercase__    :   List[str]						= min(UpperCAmelCase		,					len(UpperCAmelCase					)					)

  trainer.log_metrics('''train'''		,					UpperCAmelCase					)
  trainer.save_metrics('''train'''		,					UpperCAmelCase					)
  trainer.save_state()

 # Evaluation
 lowercase__    :   List[Any]						= {}
 if training_args.do_eval:
  logger.info('''*** Evaluate ***'''					)
  lowercase__    :   Optional[int]						= trainer.evaluate()
  lowercase__    :   Dict						= data_args.max_val_samples if data_args.max_val_samples is not None else len(UpperCAmelCase					)
  lowercase__    :   Optional[Any]						= min(UpperCAmelCase		,					len(UpperCAmelCase					)					)

  trainer.log_metrics('''eval'''		,					UpperCAmelCase					)
  trainer.save_metrics('''eval'''		,					UpperCAmelCase					)

 return results


if __name__ == "__main__":
  main()


'''simple docstring'''

import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a:			List[str]	       =       logging.get_logger(__name__)

__a:			int	       =       """▁"""

__a:			Optional[int]	       =       {"""vocab_file""": """prophetnet.tokenizer"""}

__a:			Optional[int]	       =       {
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

__a:			List[str]	       =       {
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

__a:			Tuple	       =       {
    """microsoft/xprophetnet-large-wiki100-cased""": 5_12,
}
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Dict						= collections.OrderedDict()
 with open(UpperCAmelCase		,					'''r'''		,					encoding='''utf-8'''					) as reader:
  lowercase__    :   List[Any]						= reader.readlines()
 for index, token in enumerate(UpperCAmelCase					):
  lowercase__    :   List[Any]						= token.rstrip('''\n'''					)
  lowercase__    :   Tuple						= index
 return vocab





class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE 		=   PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE 		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE 		=   ["input_ids", "attention_mask"]

       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[UNK]"     ,       __lowerCAmelCase="[PAD]"     ,       __lowerCAmelCase="[CLS]"     ,       __lowerCAmelCase="[MASK]"     ,       __lowerCAmelCase = None     ,       **__lowerCAmelCase     ,       )  ->  None:
        lowercase__    :   Dict						= {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__lowerCAmelCase     ,       eos_token=__lowerCAmelCase     ,       sep_token=__lowerCAmelCase     ,       unk_token=__lowerCAmelCase     ,       pad_token=__lowerCAmelCase     ,       cls_token=__lowerCAmelCase     ,       mask_token=__lowerCAmelCase     ,       sp_model_kwargs=self.sp_model_kwargs     ,       **__lowerCAmelCase     ,       )

        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
             ''' pip install sentencepiece'''			)
         raise

        lowercase__    :   str						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(str(__lowerCAmelCase			)			)
        lowercase__    :   Optional[int]						= 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>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # put special tokens and [unused] tokens into the vocab
        lowercase__    :   str						= {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

        for i in range(10			):
         lowercase__    :   Tuple						= F"""[unused{i}]"""
         lowercase__    :   List[Any]						= 5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        lowercase__    :   Optional[Any]						= 12
        lowercase__    :   Tuple						= {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
         self.unique_no_split_tokens.append(__lowerCAmelCase			)

       def __getstate__(						self			)  ->  Union[str, Any]:
        lowercase__    :   Dict						= self.__dict__.copy()
        lowercase__    :   Optional[Any]						= None
        return state

       def __setstate__(						self     ,       __lowerCAmelCase			)  ->  Dict:
        lowercase__    :   Any						= d
        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
             ''' pip install sentencepiece'''			)
         raise

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

        lowercase__    :   int						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(self.vocab_file			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = False			)  ->  List[int]:

        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 ([0] * len(__lowerCAmelCase			)) + [1]
        return ([0] * len(__lowerCAmelCase			)) + [1] + ([0] * len(__lowerCAmelCase			)) + [1]

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  List[int]:
        lowercase__    :   List[str]						= [self.sep_token_id]

        if token_ids_a is None:
         return len(token_ids_a + sep			) * [0]
        return len(token_ids_a + sep + sep + token_ids_a + sep			) * [0]

       @property
       def _lowerCAmelCase(						self			)  ->  List[Any]:
        return len(self.sp_model			) + self.fairseq_offset

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   str						= {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     ,       __lowerCAmelCase			)  ->  str:
        return self.sp_model.encode(__lowerCAmelCase     ,       out_type=__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
        lowercase__    :   Tuple						= self.sp_model.PieceToId(__lowerCAmelCase			)

        # 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 _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        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 _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= ''''''.join(__lowerCAmelCase			).replace(__lowerCAmelCase     ,       ''' '''			).strip()
        return out_string

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Tuple[str]:
        if not os.path.isdir(__lowerCAmelCase			):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
         return
        lowercase__    :   str						= 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:
          lowercase__    :   List[Any]						= self.sp_model.serialized_model_proto()
          fi.write(__lowerCAmelCase			)

        return (out_vocab_file,)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  List[int]:

        if token_ids_a is None:
         return token_ids_a + [self.sep_token_id]
        lowercase__    :   str						= [self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep

