'''simple docstring'''




import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
						import torch

						from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class 							a__:


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       __lowerCAmelCase="resnet50"			,       __lowerCAmelCase=3			,       __lowerCAmelCase=32			,       __lowerCAmelCase=3			,       __lowerCAmelCase=True			,       __lowerCAmelCase=True			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    parent
					lowerCAmelCase     =    out_indices if out_indices is not None else [4]
					lowerCAmelCase     =    stage_names
					lowerCAmelCase     =    out_features
					lowerCAmelCase     =    backbone
					lowerCAmelCase     =    batch_size
					lowerCAmelCase     =    image_size
					lowerCAmelCase     =    num_channels
					lowerCAmelCase     =    use_pretrained_backbone
					lowerCAmelCase     =    is_training



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
					lowerCAmelCase     =    self.get_config()

					return config, pixel_values



			def 				a_			(  self):




					"""simple docstring"""


					return TimmBackboneConfig(
					    image_size=self.image_size			,       num_channels=self.num_channels			,       out_features=self.out_features			,       out_indices=self.out_indices			,       stage_names=self.stage_names			,       use_pretrained_backbone=self.use_pretrained_backbone			,       backbone=self.backbone			,       )



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    TimmBackbone(config=__lowerCAmelCase)
					model.to(__lowerCAmelCase)
					model.eval()
					with torch.no_grad():
							lowerCAmelCase     =    model(__lowerCAmelCase)
					self.parent.assertEqual(
					    result.feature_map[-1].shape			,       (self.batch_size, model.channels[-1], 14, 14)			,       )







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.prepare_config_and_inputs()
					lowerCAmelCase     =    config_and_inputs
					lowerCAmelCase     =    {"""pixel_values""": pixel_values}
					return config, inputs_dict







@require_torch
@require_timm
class 							a__(   __snake_case					,		__snake_case					,		__snake_case					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Optional[int]						=			(TimmBackbone,) if is_torch_available() else ()
			UpperCAmelCase_     :	str						=			{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
			UpperCAmelCase_     :	Optional[Any]						=			False
			UpperCAmelCase_     :	int						=			False
			UpperCAmelCase_     :	List[Any]						=			False
			UpperCAmelCase_     :	List[str]						=			False



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    TimmBackboneModelTester(self)
					lowerCAmelCase     =    ConfigTester(self			,       config_class=__lowerCAmelCase			,       has_text_modality=__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    """resnet18"""
					lowerCAmelCase     =    """microsoft/resnet-18"""

					lowerCAmelCase     =    AutoBackbone.from_pretrained(__lowerCAmelCase			,       use_timm_backbone=__lowerCAmelCase)
					lowerCAmelCase     =    AutoBackbone.from_pretrained(__lowerCAmelCase)

					self.assertEqual(len(timm_model.out_features)			,       len(transformers_model.out_features))
					self.assertEqual(len(timm_model.stage_names)			,       len(transformers_model.stage_names))
					self.assertEqual(timm_model.channels			,       transformers_model.channels)
					# Out indices are set to the last layer by default. For timm models, we don't know
					# the number of layers in advance, so we set it to (-1,), whereas for transformers
					# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
					self.assertEqual(timm_model.out_indices			,       (-1,))
					self.assertEqual(transformers_model.out_indices			,       [len(timm_model.stage_names) - 1])

					lowerCAmelCase     =    AutoBackbone.from_pretrained(__lowerCAmelCase			,       use_timm_backbone=__lowerCAmelCase			,       out_indices=[1, 2, 3])
					lowerCAmelCase     =    AutoBackbone.from_pretrained(__lowerCAmelCase			,       out_indices=[1, 2, 3])

					self.assertEqual(timm_model.out_indices			,       transformers_model.out_indices)
					self.assertEqual(len(timm_model.out_features)			,       len(transformers_model.out_features))
					self.assertEqual(timm_model.channels			,       transformers_model.channels)



			@unittest.skip("""TimmBackbone doesn't support feed forward chunking""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""TimmBackbone initialization is managed on the timm side""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""model weights aren't tied in TimmBackbone.""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""model weights aren't tied in TimmBackbone.""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration.""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""TimmBackbone doesn't support output_attentions.""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""Safetensors is not supported by timm.""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""")
			def 				a_			(  self):




					"""simple docstring"""


					pass



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							lowerCAmelCase     =    inspect.signature(model.forward)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							lowerCAmelCase     =    [*signature.parameters.keys()]

							lowerCAmelCase     =    ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]			,       __lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs_for_common()
					lowerCAmelCase     =    True
					lowerCAmelCase     =    self.has_attentions

					# no need to test all models as different heads yield the same functionality
					lowerCAmelCase     =    self.all_model_classes[0]
					lowerCAmelCase     =    model_class(__lowerCAmelCase)
					model.to(__lowerCAmelCase)

					lowerCAmelCase     =    self._prepare_for_class(__lowerCAmelCase			,       __lowerCAmelCase)
					lowerCAmelCase     =    model(**__lowerCAmelCase)
					lowerCAmelCase     =    outputs[0][-1]

					# Encoder-/Decoder-only models
					lowerCAmelCase     =    outputs.hidden_states[0]
					hidden_states.retain_grad()

					if self.has_attentions:
							lowerCAmelCase     =    outputs.attentions[0]
							attentions.retain_grad()

					output.flatten()[0].backward(retain_graph=__lowerCAmelCase)

					self.assertIsNotNone(hidden_states.grad)

					if self.has_attentions:
							self.assertIsNotNone(attentions.grad)







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.eval()
							lowerCAmelCase     =    model(**__lowerCAmelCase)

							self.assertEqual(len(result.feature_maps)			,       len(config.out_indices))
							self.assertEqual(len(model.channels)			,       len(config.out_indices))

							# Check output of last stage is taken if out_features=None, out_indices=None
							lowerCAmelCase     =    copy.deepcopy(__lowerCAmelCase)
							lowerCAmelCase     =    None
							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.eval()
							lowerCAmelCase     =    model(**__lowerCAmelCase)

							self.assertEqual(len(result.feature_maps)			,       1)
							self.assertEqual(len(model.channels)			,       1)

							# Check backbone can be initialized with fresh weights
							lowerCAmelCase     =    copy.deepcopy(__lowerCAmelCase)
							lowerCAmelCase     =    False
							lowerCAmelCase     =    model_class(__lowerCAmelCase)
							model.to(__lowerCAmelCase)
							model.eval()
							lowerCAmelCase     =    model(**__lowerCAmelCase)












"""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


_lowerCAmelCase            =	logging.get_logger(__name__)

_lowerCAmelCase            =	{
    'artists_file': 'artists.json',
    'lyrics_file': 'lyrics.json',
    'genres_file': 'genres.json',
}

_lowerCAmelCase            =	{
    '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',
    },
}

_lowerCAmelCase            =	{
    'jukebox': 5_12,
}


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE  : Tuple												=		PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE  : int												=		PRETRAINED_LYRIC_TOKENS_SIZES
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		["""input_ids""", """attention_mask"""]






						def __init__(		self  :Tuple			, __magic_name__  :Dict			, __magic_name__  :int			, __magic_name__  :str			, __magic_name__  :List[Any]=["v3", "v2", "v2"]			, __magic_name__  :List[Any]=512			, __magic_name__  :Optional[Any]=5			, __magic_name__  :Tuple="<|endoftext|>"			, **__magic_name__  :Tuple			, )      ->Union[str, Any]:
										lowercase    :      Any       = AddedToken(__magic_name__			, lstrip=__magic_name__			, rstrip=__magic_name__				) if isinstance(__magic_name__			, __magic_name__				) else unk_token
										super().__init__(
										    unk_token=__magic_name__			, n_genres=__magic_name__			, version=__magic_name__			, max_n_lyric_tokens=__magic_name__			, **__magic_name__			, )
										lowercase    :      Optional[int]       = version
										lowercase    :      Optional[int]       = max_n_lyric_tokens
										lowercase    :      Optional[int]       = n_genres

										with open(__magic_name__			, encoding="""utf-8"""				) as vocab_handle:
														lowercase    :      Optional[int]       = json.load(__magic_name__				)

										with open(__magic_name__			, encoding="""utf-8"""				) as vocab_handle:
														lowercase    :      Any       = json.load(__magic_name__				)

										with open(__magic_name__			, encoding="""utf-8"""				) as vocab_handle:
														lowercase    :      List[str]       = json.load(__magic_name__				)

										lowercase    :      str       = 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:
														lowercase    :      Dict       = oov.replace(r"""\-'"""			, r"""\-+'"""				)

										lowercase    :      Union[str, Any]       = regex.compile(__magic_name__				)
										lowercase    :      Tuple       = {v: k for k, v in self.artists_encoder.items()}
										lowercase    :      Tuple       = {v: k for k, v in self.genres_encoder.items()}
										lowercase    :      Dict       = {v: k for k, v in self.lyrics_encoder.items()}






						@property
						def      __snake_case      (		self  :str				)      ->Optional[Any]:
										return len(self.artists_encoder				) + len(self.genres_encoder				) + len(self.lyrics_encoder				)






						def      __snake_case      (		self  :int				)      ->Optional[int]:
										return dict(self.artists_encoder			, self.genres_encoder			, self.lyrics_encoder				)






						def      __snake_case      (		self  :str			, __magic_name__  :Optional[int]			, __magic_name__  :Dict			, __magic_name__  :Optional[Any]				)      ->List[str]:
										lowercase    :      Optional[int]       = [self.artists_encoder.get(__magic_name__			, 0				) for artist in list_artists]
										for genres in range(len(__magic_name__				)				):
														lowercase    :      int       = [self.genres_encoder.get(__magic_name__			, 0				) for genre in list_genres[genres]]
														lowercase    :      Optional[Any]       = list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]				))

										lowercase    :      str       = [[self.lyrics_encoder.get(__magic_name__			, 0				) for character in list_lyrics[0]], [], []]
										return artists_id, list_genres, lyric_ids






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :Optional[Any]				)      ->List[str]:
										return list(__magic_name__				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :Tuple			, __magic_name__  :List[str]			, __magic_name__  :str			, **__magic_name__  :Union[str, Any]				)      ->Tuple:
										lowercase      ,      lowercase      ,      lowercase    :      List[str]       = self.prepare_for_tokenization(__magic_name__			, __magic_name__			, __magic_name__				)
										lowercase    :      List[str]       = self._tokenize(__magic_name__				)
										return artist, genre, lyrics






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :str			, __magic_name__  :str			, __magic_name__  :str			, __magic_name__  :bool = False				)      ->Tuple[str, str, str, Dict[str, Any]]:
										for idx in range(len(self.version				)				):
														if self.version[idx] == "v3":
																		lowercase    :      Tuple       = artists[idx].lower()
																		lowercase    :      Dict       = [genres[idx].lower()]
														else:
																		lowercase    :      str       = self._normalize(artists[idx]				) + """.v2"""
																		lowercase    :      str       = [
																		    self._normalize(__magic_name__				) + """.v2""" for genre in genres[idx].split("""_"""				)
																		]  # split is for the full dictionary with combined genres

										if self.version[0] == "v2":
														lowercase    :      Dict       = regex.compile(r"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"""				)
														lowercase    :      Dict       = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"""
														lowercase    :      Optional[Any]       = {vocab[index]: index + 1 for index in range(len(__magic_name__				)				)}
														lowercase    :      List[Any]       = 0
														lowercase    :      str       = len(__magic_name__				) + 1
														lowercase    :      List[Any]       = self.vocab
														lowercase    :      Optional[Any]       = {v: k for k, v in self.vocab.items()}
														lowercase    :      Tuple       = """"""
										else:
														lowercase    :      Optional[int]       = regex.compile(r"""[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+"""				)

										lowercase    :      str       = self._run_strip_accents(__magic_name__				)
										lowercase    :      str       = lyrics.replace("""\\"""			, """\n"""				)
										lowercase    :      int       = self.out_of_vocab.sub(""""""			, __magic_name__				), [], []
										return artists, genres, lyrics






						def      __snake_case      (		self  :List[Any]			, __magic_name__  :Optional[Any]				)      ->List[str]:
										lowercase    :      Any       = unicodedata.normalize("""NFD"""			, __magic_name__				)
										lowercase    :      Optional[Any]       = []
										for char in text:
														lowercase    :      int       = unicodedata.category(__magic_name__				)
														if cat == "Mn":
																		continue
														output.append(__magic_name__				)
										return "".join(__magic_name__				)






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :str				)      ->str:
										lowercase    :      Tuple       = (
										    [chr(__magic_name__				) for i in range(ord("""a"""				)			, ord("""z"""				) + 1				)]
										    + [chr(__magic_name__				) for i in range(ord("""A"""				)			, ord("""Z"""				) + 1				)]
										    + [chr(__magic_name__				) for i in range(ord("""0"""				)			, ord("""9"""				) + 1				)]
										    + ["""."""]
										)
										lowercase    :      Dict       = frozenset(__magic_name__				)
										lowercase    :      Optional[int]       = re.compile(r"""_+"""				)
										lowercase    :      Optional[int]       = """""".join([c if c in accepted else """_""" for c in text.lower()]				)
										lowercase    :      Union[str, Any]       = pattern.sub("""_"""			, __magic_name__				).strip("""_"""				)
										return text






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :List[str]				)      ->str:
										return " ".join(__magic_name__				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :Optional[int]			, __magic_name__  :Optional[Union[str, TensorType]] = None			, __magic_name__  :bool = False				)      ->Tuple:
										# Convert to TensorType
										if not isinstance(__magic_name__			, __magic_name__				):
														lowercase    :      Optional[Any]       = TensorType(__magic_name__				)

										# 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

														lowercase    :      int       = tf.constant
														lowercase    :      List[str]       = 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

														lowercase    :      List[Any]       = torch.tensor
														lowercase    :      List[str]       = 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

														lowercase    :      int       = jnp.array
														lowercase    :      Dict       = _is_jax
										else:
														lowercase    :      List[Any]       = np.asarray
														lowercase    :      Tuple       = _is_numpy

										# Do the tensor conversion in batch

										try:
														if prepend_batch_axis:
																		lowercase    :      Dict       = [inputs]

														if not is_tensor(__magic_name__				):
																		lowercase    :      str       = as_tensor(__magic_name__				)
										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  :Optional[int]			, __magic_name__  :Union[str, Any]			, __magic_name__  :str			, __magic_name__  :Optional[Any]=""			, __magic_name__  :Optional[int]="pt"				)      ->BatchEncoding:
										lowercase    :      Union[str, Any]       = [0, 0, 0]
										lowercase    :      Optional[int]       = [artist] * len(self.version				)
										lowercase    :      Dict       = [genres] * len(self.version				)

										lowercase      ,      lowercase      ,      lowercase    :      Dict       = self.tokenize(__magic_name__			, __magic_name__			, __magic_name__				)
										lowercase      ,      lowercase      ,      lowercase    :      Dict       = self._convert_token_to_id(__magic_name__			, __magic_name__			, __magic_name__				)

										lowercase    :      Tuple       = [-INFINITY] * len(full_tokens[-1]				)
										lowercase    :      Union[str, Any]       = [
										    self.convert_to_tensors(
										        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]			, tensor_type=__magic_name__				)
										    for i in range(len(self.version				)				)
										]
										return BatchEncoding({"""input_ids""": input_ids, """attention_masks""": attention_masks}				)






						def      __snake_case      (		self  :int			, __magic_name__  :str			, __magic_name__  :Optional[str] = None				)      ->Tuple[str]:
										if not os.path.isdir(__magic_name__				):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
														return

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

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

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

										return (artists_file, genres_file, lyrics_file)






						def      __snake_case      (		self  :int			, __magic_name__  :int			, __magic_name__  :Dict			, __magic_name__  :Union[str, Any]				)      ->List[str]:
										lowercase    :      Union[str, Any]       = self.artists_decoder.get(__magic_name__				)
										lowercase    :      int       = [self.genres_decoder.get(__magic_name__				) for genre in genres_index]
										lowercase    :      Union[str, Any]       = [self.lyrics_decoder.get(__magic_name__				) for character in lyric_index]
										return artist, genres, lyrics
