
"""simple docstring"""

import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


UpperCamelCase     :    str       =			logging.getLogger(__name__)

UpperCamelCase     :    List[str]       =			5_0  # max width of layer names
UpperCamelCase     :    Union[str, Any]       =			7_0  # max width of quantizer names






def 							A						(    snake_case				:Dict		)				->     Optional[Any]:
 __UpperCamelCase      =	parser.add_argument_group('quant_trainer arguments'		)
 group.add_argument('--wprec'    , type=snake_case    , default=8    , help='weight precision'		)
 group.add_argument('--aprec'    , type=snake_case    , default=8    , help='activation precision'		)
 group.add_argument('--quant-per-tensor'    , action='store_true'    , help='per tensor weight scaling'		)
 group.add_argument('--quant-disable'    , action='store_true'    , help='disable all quantizers'		)
 group.add_argument('--quant-disable-embeddings'    , action='store_true'    , help='disable all embeddings quantizers'		)
 group.add_argument('--quant-disable-keyword'    , type=snake_case    , nargs='+'    , help='disable quantizers by keyword'		)
 group.add_argument('--quant-disable-layer-module'    , type=snake_case    , help='disable quantizers by keyword under layer.'		)
 group.add_argument('--quant-enable-layer-module'    , type=snake_case    , help='enable quantizers by keyword under layer'		)
 group.add_argument('--calibrator'    , default='max'    , help='which quantization range calibrator to use'		)
 group.add_argument('--percentile'    , default=snake_case    , type=snake_case    , help='percentile for PercentileCalibrator'		)
 group.add_argument('--fuse-qkv'    , action='store_true'    , help='use the same scale factor for qkv'		)
 group.add_argument('--clip-gelu'    , metavar='N'    , type=snake_case    , help='clip gelu output maximum value to N'		)
 group.add_argument(
     '--recalibrate-weights'    , action='store_true'    , help=(
         'recalibrate weight amaxes by taking the max of the weights.'
         ' amaxes will be computed with the current quantization granularity (axis).'
     )    , )






def 							A						(    snake_case				:Any		)				->     Optional[int]:

 if args.calibrator == "max":
  __UpperCamelCase      =	'max'
 elif args.calibrator == "percentile":
  if args.percentile is None:
   raise ValueError('Specify --percentile when using percentile calibrator'		)
  __UpperCamelCase      =	'histogram'
 elif args.calibrator == "mse":
  __UpperCamelCase      =	'histogram'
 else:
  raise ValueError(f'Invalid calibrator {args.calibrator}'		)

 __UpperCamelCase      =	QuantDescriptor(num_bits=args.aprec    , calib_method=snake_case		)
 __UpperCamelCase      =	QuantDescriptor(num_bits=args.wprec    , axis=(None if args.quant_per_tensor else (0,))		)
 quant_nn.QuantLinear.set_default_quant_desc_input(snake_case		)
 quant_nn.QuantLinear.set_default_quant_desc_weight(snake_case		)






def 							A						(    snake_case				:str    , snake_case				:Tuple    , snake_case				:Optional[Any]=False    , snake_case				:List[str]=False		)				->     Optional[Any]:
 logger.info('Configuring Model for Quantization'		)
 logger.info(f'using quantization package {pytorch_quantization.__file__}'		)

 if not calib:
  if args.quant_disable_embeddings:
   set_quantizer_by_name(snake_case    , ['embeddings']    , which='weight'    , _disabled=snake_case		)

  if args.quant_disable:
   set_quantizer_by_name(snake_case    , ['']    , _disabled=snake_case		)

  if args.quant_disable_keyword:
   set_quantizer_by_name(snake_case    , args.quant_disable_keyword    , _disabled=snake_case		)

  if args.quant_disable_layer_module:
   set_quantizer_by_name(snake_case    , [r'layer.\d+.' + args.quant_disable_layer_module]    , _disabled=snake_case		)

  if args.quant_enable_layer_module:
   set_quantizer_by_name(snake_case    , [r'layer.\d+.' + args.quant_enable_layer_module]    , _disabled=snake_case		)

  if args.recalibrate_weights:
   recalibrate_weights(snake_case		)

  if args.fuse_qkv:
   fuse_qkv(snake_case    , snake_case		)

 if args.clip_gelu:
  clip_gelu(snake_case    , args.clip_gelu		)

 # if args.local_rank in [-1, 0] and not calib:
 print_quant_summary(snake_case		)






def 							A						(    snake_case				:List[str]		)				->     List[str]:
 logger.info('Enabling Calibration'		)
 for name, module in model.named_modules():
  if name.endswith('_quantizer'		):
   if module._calibrator is not None:
    module.disable_quant()
    module.enable_calib()
   else:
    module.disable()
   logger.info(f'{name:80}: {module}'		)






def 							A						(    snake_case				:int    , snake_case				:Union[str, Any]		)				->     Optional[int]:
 logger.info('Loading calibrated amax'		)
 for name, module in model.named_modules():
  if name.endswith('_quantizer'		):
   if module._calibrator is not None:
    if isinstance(module._calibrator    , calib.MaxCalibrator		):
     module.load_calib_amax()
    else:
     module.load_calib_amax('percentile'    , percentile=args.percentile		)
    module.enable_quant()
    module.disable_calib()
   else:
    module.enable()
 model.cuda()
 print_quant_summary(snake_case		)






def 							A						(    snake_case				:Union[str, Any]    , snake_case				:Any		)				->     Any:

 def fusea(snake_case				:Optional[int]    , snake_case				:Optional[Any]    , snake_case				:Dict		):
  for mod in [qq, qk, qv]:
   if not hasattr(snake_case    , '_amax'		):
    print('          WARNING: NO AMAX BUFFER'		)
    return
  __UpperCamelCase      =	qq._amax.detach().item()
  __UpperCamelCase      =	qk._amax.detach().item()
  __UpperCamelCase      =	qv._amax.detach().item()

  __UpperCamelCase      =	max(snake_case    , snake_case    , snake_case		)
  qq._amax.fill_(snake_case		)
  qk._amax.fill_(snake_case		)
  qv._amax.fill_(snake_case		)
  logger.info(f'          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}'		)

 for name, mod in model.named_modules():
  if name.endswith('.attention.self'		):
   logger.info(f'FUSE_QKV: {name:{name_width}}'		)
   fusea(mod.matmul_q_input_quantizer    , mod.matmul_k_input_quantizer    , mod.matmul_v_input_quantizer		)
   if args.quant_per_tensor:
    fusea(mod.query._weight_quantizer    , mod.key._weight_quantizer    , mod.value._weight_quantizer		)






def 							A						(    snake_case				:Optional[int]    , snake_case				:Optional[Any]		)				->     Optional[Any]:

 for name, mod in model.named_modules():
  if name.endswith('.output.dense'		) and not name.endswith('attention.output.dense'		):
   __UpperCamelCase      =	mod._input_quantizer._amax.data.detach().item()
   mod._input_quantizer._amax.data.detach().clamp_(max=snake_case		)
   __UpperCamelCase      =	mod._input_quantizer._amax.data.detach().item()
   logger.info(f'CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}'		)






def 							A						(    snake_case				:Union[str, Any]		)				->     List[str]:

 for name, mod in model.named_modules():
  if hasattr(snake_case    , '_weight_quantizer'		) and mod._weight_quantizer.axis is not None:
   __UpperCamelCase      =	mod.weight.shape[0]
   __UpperCamelCase      =	mod._weight_quantizer._amax.detach()
   __UpperCamelCase      =	torch.ones(snake_case    , dtype=amax.dtype    , device=amax.device		) * amax
   print(f'expanding {name} {amax} -> {mod._weight_quantizer._amax}'		)






def 							A						(    snake_case				:List[str]		)				->     int:

 for name, mod in model.named_modules():
  if hasattr(snake_case    , '_weight_quantizer'		):
   if not hasattr(mod.weight_quantizer    , '_amax'		):
    print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER'		)
    continue

   # determine which axes to reduce across
   # e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
   __UpperCamelCase      =	set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis		)
   __UpperCamelCase      =	set(range(len(mod.weight.size()		)		)		) - axis_set
   __UpperCamelCase      =	pytorch_quantization.utils.reduce_amax(mod.weight    , axis=snake_case    , keepdims=snake_case		).detach()
   logger.info(f'RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}'		)
   __UpperCamelCase      =	amax






def 							A						(    snake_case				:Any    , snake_case				:Dict=2_5    , snake_case				:str=1_8_0    , snake_case				:int=None		)				->     str:

 if ignore is None:
  __UpperCamelCase      =	[]
 elif not isinstance(snake_case    , snake_case		):
  __UpperCamelCase      =	[ignore]

 __UpperCamelCase      =	0
 for name, mod in model.named_modules():
  if not hasattr(snake_case    , 'weight'		):
   continue
  __UpperCamelCase      =	max(snake_case    , len(snake_case		)		)

 for name, mod in model.named_modules():
  __UpperCamelCase      =	getattr(snake_case    , '_input_quantizer'    , snake_case		)
  __UpperCamelCase      =	getattr(snake_case    , '_weight_quantizer'    , snake_case		)
  if not hasattr(snake_case    , 'weight'		):
   continue
  if type(snake_case		) in ignore:
   continue
  if [True for s in ignore if type(snake_case		) is str and s in name]:
   continue
  __UpperCamelCase      =	f'Act:{input_q.extra_repr()}'
  __UpperCamelCase      =	f'Wgt:{weight_q.extra_repr()}'
  __UpperCamelCase      =	f'{name:{name_width}} {act_str} {wgt_str}'
  if len(snake_case		) <= line_width:
   logger.info(snake_case		)
  else:
   logger.info(f'{name:{name_width}} {act_str}'		)
   logger.info(f'{"  ":{name_width}} {wgt_str}'		)






def 							A						(    snake_case				:Dict		)				->     Union[str, Any]:
 __UpperCamelCase      =	0
 for name, mod in model.named_modules():
  if isinstance(snake_case    , pytorch_quantization.nn.TensorQuantizer		):
   print(f'{name:80} {mod}'		)
   count += 1
 print(f'{count} TensorQuantizers found in model'		)






def 							A						(    snake_case				:int    , snake_case				:str    , snake_case				:str    , snake_case				:Union[str, Any]    , snake_case				:int		)				->     Union[str, Any]:
 __UpperCamelCase      =	getattr(snake_case    , snake_case    , snake_case		)
 if quantizer_mod is not None:
  assert hasattr(snake_case    , snake_case		)
  setattr(snake_case    , snake_case    , snake_case		)
 else:
  logger.warning(f'{name} has no {quantizer}'		)






def 							A						(    snake_case				:int    , snake_case				:str    , snake_case				:Tuple="both"    , **snake_case				:Optional[Any]		)				->     str:
 __UpperCamelCase      =	f'Warning: changing {which} quantizers of {name:{qname_width}}'
 for k, v in kwargs.items():
  s += f' {k}={v}'
  if which in ["input", "both"]:
   set_quantizer(snake_case    , snake_case    , '_input_quantizer'    , snake_case    , snake_case		)
  if which in ["weight", "both"]:
   set_quantizer(snake_case    , snake_case    , '_weight_quantizer'    , snake_case    , snake_case		)
 logger.info(snake_case		)






def 							A						(    snake_case				:Union[str, Any]    , snake_case				:int    , **snake_case				:Any		)				->     Optional[Any]:

 for name, mod in model.named_modules():
  if hasattr(snake_case    , '_input_quantizer'		) or hasattr(snake_case    , '_weight_quantizer'		):
   for n in names:
    if re.search(snake_case    , snake_case		):
     set_quantizers(snake_case    , snake_case    , **snake_case		)
  elif name.endswith('_quantizer'		):
   for n in names:
    if re.search(snake_case    , snake_case		):
     __UpperCamelCase      =	f'Warning: changing {name:{name_width}}'
     for k, v in kwargs.items():
      s += f' {k}={v}'
      setattr(snake_case    , snake_case    , snake_case		)
     logger.info(snake_case		)





"""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



