'''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 __lowerCAmelCase			(							UpperCamelCase__=None    ,  UpperCamelCase__=None    )     ->   Any:
    return field(default_factory=lambda: default    ,  metadata=UpperCamelCase__    )




@dataclass
class    a__				:
     lowerCamelCase  :					str							=field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
     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": "Whether to freeze the feature extractor layers of the model."}			)
     lowerCamelCase  :					Optional[float]							=field(
         default=0.1							,       metadata={"help": "The dropout ratio for the attention probabilities."}			)
     lowerCamelCase  :					Optional[float]							=field(
         default=0.1							,       metadata={"help": "The dropout ratio for activations inside the fully connected layer."}			)
     lowerCamelCase  :					Optional[float]							=field(
         default=0.1							,       metadata={
             "help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
         }							,       )
     lowerCamelCase  :					Optional[float]							=field(
         default=0.1							,       metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}							,       )
     lowerCamelCase  :					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``."
             )
         }							,       )
     lowerCamelCase  :					Optional[float]							=field(default=0.0							,       metadata={"help": "The LayerDrop probability."}			)




@dataclass
class    a__				:
     lowerCamelCase  :					Optional[str]							=field(
         default=UpperCAmelCase__							,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}			)
     lowerCamelCase  :					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'"
         }							,       )
     lowerCamelCase  :					bool							=field(
         default=UpperCAmelCase__							,       metadata={"help": "Overwrite the cached preprocessed datasets or not."}			)
     lowerCamelCase  :					Optional[int]							=field(
         default=UpperCAmelCase__							,       metadata={"help": "The number of processes to use for the preprocessing."}							,       )
     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 validation examples to this "
                 "value if set."
             )
         }							,       )
     lowerCamelCase  :					List[str]							=list_field(
         default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"]							,       metadata={"help": "A list of characters to remove from the transcripts."}							,       )




@dataclass
class    a__				:
     lowerCamelCase  :					WavaVecaProcessor
     lowerCamelCase  :					Union[bool, str]							=True
     lowerCamelCase  :					Optional[int]							=None
     lowerCamelCase  :					Optional[int]							=None
     lowerCamelCase  :					Optional[int]							=None
     lowerCamelCase  :					Optional[int]							=None






     def __call__(			self		:		Any  ,    a		:		List[Dict[str, Union[List[int], torch.Tensor]]]   ):





         """simple docstring"""



         __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   )  ,    -1_00   )

         __lowerCamelCase          =  labels

         return batch




class    a__				(       UpperCAmelCase__			):






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		nn.Module  ,    a		:		Dict[str, Union[torch.Tensor, Any]]   ):





         """simple docstring"""



         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 __lowerCAmelCase			(							)     ->   str:
    # 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'''    ,  UpperCamelCase__    )

    # 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(UpperCamelCase__    ):
        __lowerCamelCase          =  re.sub(UpperCamelCase__    ,  ''''''    ,  batch['''sentence''']    ).lower() + ''' '''
        return batch

    __lowerCamelCase          =  train_dataset.map(UpperCamelCase__    ,  remove_columns=['''sentence''']    )
    __lowerCamelCase          =  eval_dataset.map(UpperCamelCase__    ,  remove_columns=['''sentence''']    )

    def extract_all_chars(UpperCamelCase__    ):
        __lowerCamelCase          =  ''' '''.join(batch['''text''']    )
        __lowerCamelCase          =  list(set(UpperCamelCase__    )    )
        return {"vocab": [vocab], "all_text": [all_text]}

    __lowerCamelCase          =  train_dataset.map(
        UpperCamelCase__    ,  batched=UpperCamelCase__    ,  batch_size=-1    ,  keep_in_memory=UpperCamelCase__    ,  remove_columns=train_dataset.column_names    ,  )
    __lowerCamelCase          =  train_dataset.map(
        UpperCamelCase__    ,  batched=UpperCamelCase__    ,  batch_size=-1    ,  keep_in_memory=UpperCamelCase__    ,  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(UpperCamelCase__    )}
    __lowerCamelCase          =  vocab_dict[''' ''']
    del vocab_dict[" "]
    __lowerCamelCase          =  len(UpperCamelCase__    )
    __lowerCamelCase          =  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.
    __lowerCamelCase          =  WavaVecaCTCTokenizer(
        '''vocab.json'''    ,  unk_token='''[UNK]'''    ,  pad_token='''[PAD]'''    ,  word_delimiter_token='''|'''    ,  )
    __lowerCamelCase          =  WavaVecaFeatureExtractor(
        feature_size=1    ,  sampling_rate=1_60_00    ,  padding_value=0.0    ,  do_normalize=UpperCamelCase__    ,  return_attention_mask=UpperCamelCase__    )
    __lowerCamelCase          =  WavaVecaProcessor(feature_extractor=UpperCamelCase__    ,  tokenizer=UpperCamelCase__    )
    __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(UpperCamelCase__    )    ,  data_args.max_train_samples    )
        __lowerCamelCase          =  train_dataset.select(range(UpperCamelCase__    )    )

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

    __lowerCamelCase          =  torchaudio.transforms.Resample(4_80_00    ,  1_60_00    )

    # Preprocessing the datasets.
    # We need to read the aduio files as arrays and tokenize the targets.
    def speech_file_to_array_fn(UpperCamelCase__    ):
        __lowerCamelCase     ,  __lowerCamelCase          =  torchaudio.load(batch['''path''']    )
        __lowerCamelCase          =  resampler(UpperCamelCase__    ).squeeze().numpy()
        __lowerCamelCase          =  1_60_00
        __lowerCamelCase          =  batch['''text''']
        return batch

    __lowerCamelCase          =  train_dataset.map(
        UpperCamelCase__    ,  remove_columns=train_dataset.column_names    ,  num_proc=data_args.preprocessing_num_workers    ,  )
    __lowerCamelCase          =  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}."""

        __lowerCamelCase          =  processor(
            audio=batch['''speech''']    ,  text=batch['''target_text''']    ,  sampling_rate=batch['''sampling_rate'''][0]    )
        batch.update(UpperCamelCase__    )
        return batch

    __lowerCamelCase          =  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    ,  )
    __lowerCamelCase          =  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
    __lowerCamelCase          =  datasets.load_metric('''wer'''    )

    def compute_metrics(UpperCamelCase__    ):
        __lowerCamelCase          =  pred.predictions
        __lowerCamelCase          =  np.argmax(UpperCamelCase__    ,  axis=-1    )

        __lowerCamelCase          =  processor.tokenizer.pad_token_id

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

        __lowerCamelCase          =  wer_metric.compute(predictions=UpperCamelCase__    ,  references=UpperCamelCase__    )

        return {"wer": wer}

    if model_args.freeze_feature_extractor:
        model.freeze_feature_extractor()

    # Data collator
    __lowerCamelCase          =  DataCollatorCTCWithPadding(processor=UpperCamelCase__    ,  padding=UpperCamelCase__    )

    # Initialize our Trainer
    __lowerCamelCase          =  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:
            __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=UpperCamelCase__    )
        trainer.save_model()

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

        trainer.log_metrics('''train'''    ,  UpperCamelCase__    )
        trainer.save_metrics('''train'''    ,  UpperCamelCase__    )
        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(UpperCamelCase__    )
        __lowerCamelCase          =  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


__UpperCAmelCase												=logging.get_logger(__name__)

__UpperCAmelCase												="▁"

__UpperCAmelCase												={"vocab_file": "prophetnet.tokenizer"}

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

__UpperCAmelCase												={
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

__UpperCAmelCase												={
    "microsoft/xprophetnet-large-wiki100-cased": 5_1_2,
}






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   List[str]:
    __lowerCamelCase          =  collections.OrderedDict()
    with open(UpperCamelCase__    ,  '''r'''    ,  encoding='''utf-8'''    ) as reader:
        __lowerCamelCase          =  reader.readlines()
    for index, token in enumerate(UpperCamelCase__    ):
        __lowerCamelCase          =  token.rstrip('''\n'''    )
        __lowerCamelCase          =  index
    return vocab




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Optional[Any]							=VOCAB_FILES_NAMES
     lowerCamelCase  :					Any							=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase  :					Optional[int]							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase  :					Union[str, Any]							=["input_ids", "attention_mask"]






     def __init__(			self		:		int  ,    a		:		List[str]  ,    a		:		Optional[int]="[SEP]"  ,    a		:		int="[SEP]"  ,    a		:		str="[SEP]"  ,    a		:		List[Any]="[UNK]"  ,    a		:		List[Any]="[PAD]"  ,    a		:		str="[CLS]"  ,    a		:		List[str]="[MASK]"  ,    a		:		Optional[Dict[str, Any]] = None  ,    **a		:		str  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  {} if sp_model_kwargs is None else sp_model_kwargs

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

         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

         __lowerCamelCase          =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(str(a   )   )
         __lowerCamelCase          =  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
         __lowerCamelCase          =  {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

         for i in range(10   ):
             __lowerCamelCase          =  f"""[unused{i}]"""
             __lowerCamelCase          =  5 + i

         # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
         __lowerCamelCase          =  12
         __lowerCamelCase          =  {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(a   )






     def __getstate__(			self		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.__dict__.copy()
         __lowerCamelCase          =  None
         return state






     def __setstate__(			self		:		int  ,    a		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  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'''   ):
             __lowerCamelCase          =  {}

         __lowerCamelCase          =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(self.vocab_file   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None  ,    a		:		bool = False   ):





         """simple docstring"""



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

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  [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        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         return len(self.sp_model   ) + self.fairseq_offset






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		str   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		int   ):





         """simple docstring"""



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

         # 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        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    a		:		Union[str, Any]   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]  ,    a		:		Tuple   ):





         """simple docstring"""



         __lowerCamelCase          =  ''''''.join(a   ).replace(a  ,    ''' '''   ).strip()
         return out_string






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		str  ,    a		:		Optional[str] = None   ):





         """simple docstring"""



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

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

         return (out_vocab_file,)






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None   ):





         """simple docstring"""



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