
"""simple docstring"""

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

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
       from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
       UpperCamelCase     :    str       =			True
       from torch.cuda.amp import autocast


UpperCamelCase     :    Union[str, Any]       =			logging.getLogger(__name__)

@dataclass
class __lowerCAmelCase		:
     lowercase						       =						field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
     lowercase						       =						field(
         default=__SCREAMING_SNAKE_CASE      ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}      ,  )
     lowercase						       =						field(
         default=__SCREAMING_SNAKE_CASE      ,  metadata={"help": "Whether to freeze the feature extractor layers of the model."}      )
     lowercase						       =						field(
         default=__SCREAMING_SNAKE_CASE      ,  metadata={"help": "Whether to log verbose messages or not."}      ,  )
     lowercase						       =						field(
         default=2.0      ,  metadata={"help": "Maximum temperature for gumbel softmax."}      )
     lowercase						       =						field(
         default=0.5      ,  metadata={"help": "Minimum temperature for gumbel softmax."}      )
     lowercase						       =						field(
         default=0.999_995      ,  metadata={"help": "Decay of gumbel temperature during training."}      )






def 							A						(    snake_case				:ModelArguments    , snake_case				:TrainingArguments		)				->     str:
 logging.basicConfig(
     format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'    , datefmt='%m/%d/%Y %H:%M:%S'    , handlers=[logging.StreamHandler(sys.stdout		)]    , )
 __UpperCamelCase      =	logging.WARNING
 if model_args.verbose_logging:
  __UpperCamelCase      =	logging.DEBUG
 elif trainer_utils.is_main_process(training_args.local_rank		):
  __UpperCamelCase      =	logging.INFO
 logger.setLevel(snake_case		)

@dataclass
class __lowerCAmelCase		:
     lowercase						       =						field(
         default=__SCREAMING_SNAKE_CASE      ,  metadata={"help": "The name of the dataset to use (via the datasets library)."}      )
     lowercase						       =						field(
         default=__SCREAMING_SNAKE_CASE      ,  metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}      )
     lowercase						       =						field(
         default="train"      ,  metadata={
             "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
         }      ,  )
     lowercase						       =						field(
         default="validation"      ,  metadata={
             "help": (
                 "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
             )
         }      ,  )
     lowercase						       =						field(
         default="file"      ,  metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"}      ,  )
     lowercase						       =						field(
         default=__SCREAMING_SNAKE_CASE      ,  metadata={"help": "Overwrite the cached preprocessed datasets or not."}      )
     lowercase						       =						field(
         default=1      ,  metadata={
             "help": "The percentage of the train set used as validation set in case there's no validation split"
         }      ,  )
     lowercase						       =						field(
         default=__SCREAMING_SNAKE_CASE      ,  metadata={"help": "The number of processes to use for the preprocessing."}      ,  )
     lowercase						       =						field(
         default=20.0      ,  metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"}      )


@dataclass
class __lowerCAmelCase		:
     lowercase						       =						42
     lowercase						       =						42
     lowercase						       =						"longest"
     lowercase						       =						None
     lowercase						       =						None





     def __call__(     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.feature_extractor.pad(
          __UpperCAmelCase     ,					max_length=self.max_length     ,					padding=self.padding     ,					pad_to_multiple_of=self.pad_to_multiple_of     ,					return_tensors='pt'     ,					)
      __UpperCamelCase      =	self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1]				)

      __UpperCamelCase      =	batch['input_values'].shape[0]

      # make sure that no loss is computed on padded inputs
      if batch["attention_mask"] is not None:
       # compute real output lengths according to convolution formula
       __UpperCamelCase      =	self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1				)				).to(
           torch.long				)

       __UpperCamelCase      =	torch.zeros(
           (batch_size, mask_indices_seq_length)     ,					dtype=torch.long     ,					device=batch['input_values'].device				)

       # these two operations makes sure that all values
       # before the output lengths indices are attended to
       __UpperCamelCase      =	1
       __UpperCamelCase      =	attention_mask.flip([-1]				).cumsum(-1				).flip([-1]				).bool()

      # sample randomly masked indices
      __UpperCamelCase      =	_compute_mask_indices(
          (batch_size, mask_indices_seq_length)     ,					self.model.config.mask_time_prob     ,					self.model.config.mask_time_length     ,					attention_mask=__UpperCAmelCase     ,					min_masks=2     ,					)

      return batch


class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):





     def __init__(     self     ,					*__UpperCAmelCase     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=1.0     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      super().__init__(*__UpperCAmelCase     ,					**__UpperCAmelCase				)
      __UpperCamelCase      =	0
      __UpperCamelCase      =	max_gumbel_temp
      __UpperCamelCase      =	min_gumbel_temp
      __UpperCamelCase      =	gumbel_temp_decay







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      model.train()
      __UpperCamelCase      =	self._prepare_inputs(__UpperCAmelCase				)

      if self.use_amp:
       with autocast():
        __UpperCamelCase      =	self.compute_loss(__UpperCAmelCase     ,					__UpperCAmelCase				)
      else:
       __UpperCamelCase      =	self.compute_loss(__UpperCAmelCase     ,					__UpperCAmelCase				)

      if self.args.n_gpu > 1 or self.deepspeed:
       if model.module.config.ctc_loss_reduction == "mean":
        __UpperCamelCase      =	loss.mean()
       elif model.module.config.ctc_loss_reduction == "sum":
        __UpperCamelCase      =	loss.sum() / (inputs['mask_time_indices']).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:
       __UpperCamelCase      =	loss / self.args.gradient_accumulation_steps

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

      self.num_update_step += 1
      # make sure gumbel softmax temperature is decayed
      if self.args.n_gpu > 1 or self.deepspeed:
       model.module.set_gumbel_temperature(
           max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step     ,					self.min_gumbel_temp				)				)
      else:
       model.set_gumbel_temperature(
           max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step     ,					self.min_gumbel_temp				)				)

      return loss.detach()






def 							A						(    )				->     int:
 # 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      =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)		)

 __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	parser.parse_args_into_dataclasses()
 configure_logger(snake_case    , snake_case		)

 # Downloading and loading a dataset from the hub.
 __UpperCamelCase      =	load_dataset(data_args.dataset_name    , data_args.dataset_config_name    , cache_dir=model_args.cache_dir		)

 if "validation" not in datasets.keys():
  # make sure only "validation" and "train" keys remain"
  __UpperCamelCase      =	DatasetDict()
  __UpperCamelCase      =	load_dataset(
      data_args.dataset_name    , data_args.dataset_config_name    , split=f'{data_args.train_split_name}[:{data_args.validation_split_percentage}%]'    , cache_dir=model_args.cache_dir    , )
  __UpperCamelCase      =	load_dataset(
      data_args.dataset_name    , data_args.dataset_config_name    , split=f'{data_args.train_split_name}[{data_args.validation_split_percentage}%:]'    , cache_dir=model_args.cache_dir    , )
 else:
  # make sure only "validation" and "train" keys remain"
  __UpperCamelCase      =	DatasetDict()
  __UpperCamelCase      =	load_dataset(
      data_args.dataset_name    , data_args.dataset_config_name    , split='validation'    , cache_dir=model_args.cache_dir    , )
  __UpperCamelCase      =	load_dataset(
      data_args.dataset_name    , data_args.dataset_config_name    , split=f'{data_args.train_split_name}'    , cache_dir=model_args.cache_dir    , )

 # only normalized-inputs-training is supported
 __UpperCamelCase      =	WavaVecaFeatureExtractor.from_pretrained(
     model_args.model_name_or_path    , cache_dir=model_args.cache_dir    , do_normalize=snake_case		)

 def prepare_dataset(snake_case				:str		):
  # check that all files have the correct sampling rate
  __UpperCamelCase  ,     __UpperCamelCase      =	librosa.load(batch[data_args.speech_file_column]    , sr=feature_extractor.sampling_rate		)
  return batch

 # load audio files into numpy arrays
 __UpperCamelCase      =	datasets.map(
     snake_case    , num_proc=data_args.preprocessing_num_workers    , remove_columns=datasets['train'].column_names		)

 # filter audio files that are too long
 __UpperCamelCase      =	vectorized_datasets.filter(
     lambda snake_case		: len(data['speech']		) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate		)		)

 def normalize(snake_case				:Union[str, Any]		):
  return feature_extractor(batch['speech']    , sampling_rate=feature_extractor.sampling_rate		)

 # normalize and transform to `BatchFeatures`
 __UpperCamelCase      =	vectorized_datasets.map(
     snake_case    , batched=snake_case    , num_proc=data_args.preprocessing_num_workers    , load_from_cache_file=not data_args.overwrite_cache    , remove_columns=vectorized_datasets['train'].column_names    , )

 # pretraining is only supported for "newer" stable layer norm architecture
 # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
 __UpperCamelCase      =	WavaVecaConfig.from_pretrained(
     model_args.model_name_or_path    , cache_dir=model_args.cache_dir    , gradient_checkpointing=training_args.gradient_checkpointing    , )

 if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
  raise ValueError(
      'PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'
      ' ``config.feat_extract_norm=\'layer\''		)

 __UpperCamelCase      =	WavaVecaForPreTraining(snake_case		)

 __UpperCamelCase      =	DataCollatorForWavaVecaPretraining(model=snake_case    , feature_extractor=snake_case		)

 __UpperCamelCase      =	WavaVecaPreTrainer(
     model=snake_case    , data_collator=snake_case    , args=snake_case    , train_dataset=vectorized_datasets['train']    , eval_dataset=vectorized_datasets['validation']    , tokenizer=snake_case    , max_gumbel_temp=model_args.max_gumbel_temperature    , min_gumbel_temp=model_args.min_gumbel_temperature    , gumbel_temp_decay=model_args.gumbel_temperature_decay    , )
 trainer.train()


if __name__ == "__main__":
       main()





"""simple docstring"""

import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


UpperCamelCase     :    Tuple       =			logging.get_logger(__name__)

UpperCamelCase     :    int       =			{
    "artists_file": "artists.json",
    "lyrics_file": "lyrics.json",
    "genres_file": "genres.json",
}

UpperCamelCase     :    Optional[Any]       =			{
    "artists_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json",
    },
    "genres_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json",
    },
    "lyrics_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json",
    },
}

UpperCamelCase     :    Any       =			{
    "jukebox": 5_1_2,
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						VOCAB_FILES_NAMES
     lowercase						       =						PRETRAINED_VOCAB_FILES_MAP
     lowercase						       =						PRETRAINED_LYRIC_TOKENS_SIZES
     lowercase						       =						["input_ids", "attention_mask"]





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=["v3", "v2", "v2"]     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=5     ,					__UpperCAmelCase="<|endoftext|>"     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	AddedToken(__UpperCAmelCase     ,					lstrip=__UpperCAmelCase     ,					rstrip=__UpperCAmelCase				) if isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				) else unk_token
      super().__init__(
          unk_token=__UpperCAmelCase     ,					n_genres=__UpperCAmelCase     ,					version=__UpperCAmelCase     ,					max_n_lyric_tokens=__UpperCAmelCase     ,					**__UpperCAmelCase     ,					)
      __UpperCamelCase      =	version
      __UpperCamelCase      =	max_n_lyric_tokens
      __UpperCamelCase      =	n_genres

      with open(__UpperCAmelCase     ,					encoding='utf-8'				) as vocab_handle:
       __UpperCamelCase      =	json.load(__UpperCAmelCase				)

      with open(__UpperCAmelCase     ,					encoding='utf-8'				) as vocab_handle:
       __UpperCamelCase      =	json.load(__UpperCAmelCase				)

      with open(__UpperCAmelCase     ,					encoding='utf-8'				) as vocab_handle:
       __UpperCamelCase      =	json.load(__UpperCAmelCase				)

      __UpperCamelCase      =	R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'
      # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
      if len(self.lyrics_encoder				) == 79:
       __UpperCamelCase      =	oov.replace(R'\-\''     ,					R'\-+\''				)

      __UpperCamelCase      =	regex.compile(__UpperCAmelCase				)
      __UpperCamelCase      =	{v: k for k, v in self.artists_encoder.items()}
      __UpperCamelCase      =	{v: k for k, v in self.genres_encoder.items()}
      __UpperCamelCase      =	{v: k for k, v in self.lyrics_encoder.items()}





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return len(self.artists_encoder				) + len(self.genres_encoder				) + len(self.lyrics_encoder				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return dict(self.artists_encoder     ,					self.genres_encoder     ,					self.lyrics_encoder				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	[self.artists_encoder.get(__UpperCAmelCase     ,					0				) for artist in list_artists]
      for genres in range(len(__UpperCAmelCase				)				):
       __UpperCamelCase      =	[self.genres_encoder.get(__UpperCAmelCase     ,					0				) for genre in list_genres[genres]]
       __UpperCamelCase      =	list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]				))

      __UpperCamelCase      =	[[self.lyrics_encoder.get(__UpperCAmelCase     ,					0				) for character in list_lyrics[0]], [], []]
      return artists_id, list_genres, lyric_ids





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      return list(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					**__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	self.prepare_for_tokenization(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)
      __UpperCamelCase      =	self._tokenize(__UpperCAmelCase				)
      return artist, genre, lyrics





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = False				):

      '''simple docstring'''




      for idx in range(len(self.version				)				):
       if self.version[idx] == "v3":
        __UpperCamelCase      =	artists[idx].lower()
        __UpperCamelCase      =	[genres[idx].lower()]
       else:
        __UpperCamelCase      =	self._normalize(artists[idx]				) + '.v2'
        __UpperCamelCase      =	[
            self._normalize(__UpperCAmelCase				) + '.v2' for genre in genres[idx].split('_'				)
        ]  # split is for the full dictionary with combined genres

      if self.version[0] == "v2":
       __UpperCamelCase      =	regex.compile(R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'				)
       __UpperCamelCase      =	'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'
       __UpperCamelCase      =	{vocab[index]: index + 1 for index in range(len(__UpperCAmelCase				)				)}
       __UpperCamelCase      =	0
       __UpperCamelCase      =	len(__UpperCAmelCase				) + 1
       __UpperCamelCase      =	self.vocab
       __UpperCamelCase      =	{v: k for k, v in self.vocab.items()}
       __UpperCamelCase      =	''
      else:
       __UpperCamelCase      =	regex.compile(R'[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+'				)

      __UpperCamelCase      =	self._run_strip_accents(__UpperCAmelCase				)
      __UpperCamelCase      =	lyrics.replace('\\'     ,					'\n'				)
      __UpperCamelCase      =	self.out_of_vocab.sub(''     ,					__UpperCAmelCase				), [], []
      return artists, genres, lyrics





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	unicodedata.normalize('NFD'     ,					__UpperCAmelCase				)
      __UpperCamelCase      =	[]
      for char in text:
       __UpperCamelCase      =	unicodedata.category(__UpperCAmelCase				)
       if cat == "Mn":
        continue
       output.append(__UpperCAmelCase				)
      return "".join(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	(
          [chr(__UpperCAmelCase				) for i in range(ord('a'				)     ,					ord('z'				) + 1				)]
          + [chr(__UpperCAmelCase				) for i in range(ord('A'				)     ,					ord('Z'				) + 1				)]
          + [chr(__UpperCAmelCase				) for i in range(ord('0'				)     ,					ord('9'				) + 1				)]
          + ['.']
      )
      __UpperCamelCase      =	frozenset(__UpperCAmelCase				)
      __UpperCamelCase      =	re.compile(R'_+'				)
      __UpperCamelCase      =	''.join([c if c in accepted else '_' for c in text.lower()]				)
      __UpperCamelCase      =	pattern.sub('_'     ,					__UpperCAmelCase				).strip('_'				)
      return text





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      return " ".join(__UpperCAmelCase				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = False				):

      '''simple docstring'''




      if not isinstance(__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	TensorType(__UpperCAmelCase				)

      # Get a function reference for the correct framework
      if tensor_type == TensorType.TENSORFLOW:
       if not is_tf_available():
        raise ImportError(
            'Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.'				)
       import tensorflow as tf

       __UpperCamelCase      =	tf.constant
       __UpperCamelCase      =	tf.is_tensor
      elif tensor_type == TensorType.PYTORCH:
       if not is_torch_available():
        raise ImportError('Unable to convert output to PyTorch tensors format, PyTorch is not installed.'				)
       import torch

       __UpperCamelCase      =	torch.tensor
       __UpperCamelCase      =	torch.is_tensor
      elif tensor_type == TensorType.JAX:
       if not is_flax_available():
        raise ImportError('Unable to convert output to JAX tensors format, JAX is not installed.'				)
       import jax.numpy as jnp  # noqa: F811

       __UpperCamelCase      =	jnp.array
       __UpperCamelCase      =	_is_jax
      else:
       __UpperCamelCase      =	np.asarray
       __UpperCamelCase      =	_is_numpy

      # Do the tensor conversion in batch

      try:
       if prepend_batch_axis:
        __UpperCamelCase      =	[inputs]

       if not is_tensor(__UpperCAmelCase				):
        __UpperCamelCase      =	as_tensor(__UpperCAmelCase				)
      except:  # noqa E722
       raise ValueError(
           'Unable to create tensor, you should probably activate truncation and/or padding '
           'with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.'				)

      return inputs





     def __call__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=""     ,					__UpperCAmelCase="pt"				):

      '''simple docstring'''




      __UpperCamelCase      =	[0, 0, 0]
      __UpperCamelCase      =	[artist] * len(self.version				)
      __UpperCamelCase      =	[genres] * len(self.version				)

      __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	self.tokenize(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)
      __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	self._convert_token_to_id(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)

      __UpperCamelCase      =	[-INFINITY] * len(full_tokens[-1]				)
      __UpperCamelCase      =	[
          self.convert_to_tensors(
              [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]     ,					tensor_type=__UpperCAmelCase				)
          for i in range(len(self.version				)				)
      ]
      return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks}				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				):

      '''simple docstring'''




      if not os.path.isdir(__UpperCAmelCase				):
       logger.error(F'Vocabulary path ({save_directory}) should be a directory'				)
       return

      __UpperCamelCase      =	os.path.join(
          __UpperCAmelCase     ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['artists_file']				)
      with open(__UpperCAmelCase     ,					'w'     ,					encoding='utf-8'				) as f:
       f.write(json.dumps(self.artists_encoder     ,					ensure_ascii=__UpperCAmelCase				)				)

      __UpperCamelCase      =	os.path.join(
          __UpperCAmelCase     ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['genres_file']				)
      with open(__UpperCAmelCase     ,					'w'     ,					encoding='utf-8'				) as f:
       f.write(json.dumps(self.genres_encoder     ,					ensure_ascii=__UpperCAmelCase				)				)

      __UpperCamelCase      =	os.path.join(
          __UpperCAmelCase     ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['lyrics_file']				)
      with open(__UpperCAmelCase     ,					'w'     ,					encoding='utf-8'				) as f:
       f.write(json.dumps(self.lyrics_encoder     ,					ensure_ascii=__UpperCAmelCase				)				)

      return (artists_file, genres_file, lyrics_file)







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.artists_decoder.get(__UpperCAmelCase				)
      __UpperCamelCase      =	[self.genres_decoder.get(__UpperCAmelCase				) for genre in genres_index]
      __UpperCamelCase      =	[self.lyrics_decoder.get(__UpperCAmelCase				) for character in lyric_index]
      return artist, genres, lyrics



