'''simple docstring'''




import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


__lowercase        =    logging.get_logger(__name__)

__lowercase        =    '''▁'''

__lowercase        =    {'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}

__lowercase        =    {
    '''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
    '''vocab_file''': '''vocab.txt''',
}

__lowercase        =    {
    '''vocab_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
    },
    '''sentencepiece_model_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
    },
}

__lowercase        =    {
    '''ernie-m-base''': 5_1_4,
    '''ernie-m-large''': 5_1_4,
}

__lowercase        =    {
    '''ernie-m-base''': {'''do_lower_case''': False},
    '''ernie-m-large''': {'''do_lower_case''': False},
}






class 							a__(   __snake_case      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[str]						=			["input_ids"]

			UpperCAmelCase_     :	Tuple						=			VOCAB_FILES_NAMES
			UpperCAmelCase_     :	Union[str, Any]						=			PRETRAINED_INIT_CONFIGURATION
			UpperCAmelCase_     :	Tuple						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_     :	Optional[Any]						=			PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_     :	int						=			RESOURCE_FILES_NAMES



			def __init__(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=None			,       __lowerCAmelCase=False			,       __lowerCAmelCase="utf8"			,       __lowerCAmelCase="[UNK]"			,       __lowerCAmelCase="[SEP]"			,       __lowerCAmelCase="[PAD]"			,       __lowerCAmelCase="[CLS]"			,       __lowerCAmelCase="[MASK]"			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    {} if sp_model_kwargs is None else sp_model_kwargs
					super().__init__(
					    do_lower_case=__lowerCAmelCase			,       unk_token=__lowerCAmelCase			,       sep_token=__lowerCAmelCase			,       pad_token=__lowerCAmelCase			,       cls_token=__lowerCAmelCase			,       mask_token=__lowerCAmelCase			,       vocab_file=__lowerCAmelCase			,       encoding=__lowerCAmelCase			,       sp_model_kwargs=self.sp_model_kwargs			,       **__lowerCAmelCase			,       )
					lowerCAmelCase     =    do_lower_case
					lowerCAmelCase     =    sentencepiece_model_ckpt
					lowerCAmelCase     =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(__lowerCAmelCase)

					# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
					if vocab_file is not None:
							lowerCAmelCase     =    self.load_vocab(filepath=__lowerCAmelCase)
					else:
							lowerCAmelCase     =    {self.sp_model.id_to_piece(__lowerCAmelCase): id for id in range(self.sp_model.get_piece_size())}
					lowerCAmelCase     =    {v: k for k, v in self.vocab.items()}



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if text is None:
							return None

					lowerCAmelCase     =    self.tokenize(__lowerCAmelCase)
					lowerCAmelCase     =    """""", []

					for i, ch in enumerate(__lowerCAmelCase):
							if ch in self.SP_CHAR_MAPPING:
									lowerCAmelCase     =    self.SP_CHAR_MAPPING.get(__lowerCAmelCase)
							else:
									lowerCAmelCase     =    unicodedata.normalize("""NFKC"""			,       __lowerCAmelCase)
							if self.is_whitespace(__lowerCAmelCase):
									continue
							normalized_text += ch
							char_mapping.extend([i] * len(__lowerCAmelCase))

					lowerCAmelCase     =    normalized_text, [], 0

					if self.do_lower_case:
							lowerCAmelCase     =    text.lower()

					for token in split_tokens:
							if token[:1] == "▁":
									lowerCAmelCase     =    token[1:]
							lowerCAmelCase     =    text[offset:].index(__lowerCAmelCase) + offset
							lowerCAmelCase     =    start + len(__lowerCAmelCase)

							token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1))
							lowerCAmelCase     =    end
					return token_mapping



			@property
			def 				a_			(  self):




					"""simple docstring"""


					return len(self.vocab)



			def 				a_			(  self):




					"""simple docstring"""


					return dict(self.vocab			,       **self.added_tokens_encoder)



			def __getstate__(  self):




					"""simple docstring"""


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



			def __setstate__(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    d

					# for backward compatibility
					if not hasattr(self			,       """sp_model_kwargs"""):
							lowerCAmelCase     =    {}

					lowerCAmelCase     =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(self.sentencepiece_model_ckpt)



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					return "".join((self.SP_CHAR_MAPPING.get(__lowerCAmelCase			,       __lowerCAmelCase) for c in text))



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=False			,       __lowerCAmelCase=64			,       __lowerCAmelCase=0.1):




					"""simple docstring"""


					if self.sp_model_kwargs.get("""enable_sampling""") is True:
							lowerCAmelCase     =    True
					if self.sp_model_kwargs.get("""alpha""") is not None:
							lowerCAmelCase     =    self.sp_model_kwargs.get("""alpha""")
					if self.sp_model_kwargs.get("""nbest_size""") is not None:
							lowerCAmelCase     =    self.sp_model_kwargs.get("""nbest_size""")

					if not enable_sampling:
							lowerCAmelCase     =    self.sp_model.EncodeAsPieces(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    self.sp_model.SampleEncodeAsPieces(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase)
					lowerCAmelCase     =    []
					for pi, piece in enumerate(__lowerCAmelCase):
							if piece == SPIECE_UNDERLINE:
									if not pieces[pi + 1].startswith(__lowerCAmelCase) and pi != 0:
											new_pieces.append(__lowerCAmelCase)
											continue
									else:
											continue
							lowerCAmelCase     =    0
							for i, chunk in enumerate(__lowerCAmelCase):
									if chunk == SPIECE_UNDERLINE:
											continue
									if self.is_ch_char(__lowerCAmelCase) or self.is_punct(__lowerCAmelCase):
											if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
													new_pieces.append(piece[lst_i:i])
											new_pieces.append(__lowerCAmelCase)
											lowerCAmelCase     =    i + 1
									elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
											if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
													new_pieces.append(piece[lst_i:i])
											lowerCAmelCase     =    i
									elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
											if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
													new_pieces.append(piece[lst_i:i])
											lowerCAmelCase     =    i
							if len(__lowerCAmelCase) > lst_i:
									new_pieces.append(piece[lst_i:])
					return new_pieces



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    """""".join(__lowerCAmelCase).replace(__lowerCAmelCase			,       """ """).strip()
					return out_string



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    self.convert_ids_to_tokens(__lowerCAmelCase)
					lowerCAmelCase     =    """""".join(__lowerCAmelCase).replace(__lowerCAmelCase			,       """ """).strip()
					return out_string



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					return self.vocab.get(__lowerCAmelCase			,       self.vocab.get(self.unk_token))



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					return self.reverse_vocab.get(__lowerCAmelCase			,       self.unk_token)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=None):




					"""simple docstring"""


					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					lowerCAmelCase     =    [self.cls_token_id]
					lowerCAmelCase     =    [self.sep_token_id]
					return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=None):




					"""simple docstring"""


					if offset_mapping_a is None:
							return [(0, 0)] + offset_mapping_a + [(0, 0)]

					return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=None			,       __lowerCAmelCase=False):




					"""simple docstring"""


					if already_has_special_tokens:
							if token_ids_a is not None:
									raise ValueError(
									    """You should not supply a second sequence if the provided sequence of """
									    """ids is already formatted with special tokens for the model.""")
							return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

					if token_ids_a is not None:
							return [1] + ([0] * len(__lowerCAmelCase)) + [1, 1] + ([0] * len(__lowerCAmelCase)) + [1]
					return [1] + ([0] * len(__lowerCAmelCase)) + [1]



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None):




					"""simple docstring"""


					if token_ids_a is None:
							# [CLS] X [SEP]
							return (len(__lowerCAmelCase) + 2) * [0]

					# [CLS] A [SEP] [SEP] B [SEP]
					return [0] * (len(__lowerCAmelCase) + 1) + [1] * (len(__lowerCAmelCase) + 3)



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if "\u4e00" <= char <= "\u9fff":
							return True
					return False



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if ("a" <= char <= "z") or ("A" <= char <= "Z"):
							return True
					return False



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if char in ",;:.?!~，；：。？！《》【】":
							return True
					return False



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					if char == " " or char == "\t" or char == "\n" or char == "\r":
							return True
					if len(__lowerCAmelCase) == 1:
							lowerCAmelCase     =    unicodedata.category(__lowerCAmelCase)
							if cat == "Zs":
									return True
					return False



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    {}
					with io.open(__lowerCAmelCase			,       """r"""			,       encoding="""utf-8""") as f:
							for index, line in enumerate(__lowerCAmelCase):
									lowerCAmelCase     =    line.rstrip("""\n""")
									lowerCAmelCase     =    int(__lowerCAmelCase)

					return token_to_idx







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase     =    0
					if os.path.isdir(__lowerCAmelCase):
							lowerCAmelCase     =    os.path.join(
							    __lowerCAmelCase			,       (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
					else:
							lowerCAmelCase     =    (filename_prefix + """-""" if filename_prefix else """""") + save_directory
					with open(__lowerCAmelCase			,       """w"""			,       encoding="""utf-8""") as writer:
							for token, token_index in sorted(self.vocab.items()			,       key=lambda __lowerCAmelCase: kv[1]):
									if index != token_index:
											logger.warning(
											    f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
											    """ Please check that the vocabulary is not corrupted!""")
											lowerCAmelCase     =    token_index
									writer.write(token + """\n""")
									index += 1

					lowerCAmelCase     =    os.path.join(__lowerCAmelCase			,       """sentencepiece.bpe.model""")
					with open(__lowerCAmelCase			,       """wb""") as fi:
							lowerCAmelCase     =    self.sp_model.serialized_model_proto()
							fi.write(__lowerCAmelCase)

					return (vocab_file,)












"""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
