'''simple docstring'''




import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map






def 							snake_case__				(     _A:   Union[str, Any]					)       ->      List[Tuple[int, ...]]:

		'''simple docstring'''


		lowerCAmelCase     =    []
		if isinstance(_A ,  _A					):
				for v in tree.values():
						shapes.extend(_fetch_dims(_A					)					)
		elif isinstance(_A ,  (list, tuple)					):
				for t in tree:
						shapes.extend(_fetch_dims(_A					)					)
		elif isinstance(_A ,  torch.Tensor					):
				shapes.append(tree.shape					)
		else:
				raise ValueError("""Not supported"""					)

		return shapes






@torch.jit.ignore
def 							snake_case__				(     _A:   str ,  _A:   Any					)       ->      Tuple[int, ...]:

		'''simple docstring'''


		lowerCAmelCase     =    []
		for d in reversed(_A					):
				idx.append(flat_idx % d					)
				lowerCAmelCase     =    flat_idx // d

		return tuple(reversed(_A					)					)






@torch.jit.ignore
def 							snake_case__				(     _A:   Optional[int] ,  _A:   Union[str, Any] ,  _A:   str ,  _A:   str = None ,  _A:   Dict = None ,  )       ->      List[Tuple[slice, ...]]:

		'''simple docstring'''


		def reduce_edge_list(_A:   Any					) -> None:
				lowerCAmelCase     =    True
				for i in range(len(_A					)					):
						lowerCAmelCase     =    -1 * (i + 1)
						l[reversed_idx] &= tally
						lowerCAmelCase     =    l[reversed_idx]

		if start_edges is None:
				lowerCAmelCase     =    [s == 0 for s in start]
				reduce_edge_list(_A					)
		if end_edges is None:
				lowerCAmelCase     =    [e == (d - 1) for e, d in zip(_A ,  _A					)]
				reduce_edge_list(_A					)

		# Base cases. Either start/end are empty and we're done, or the final,
		# one-dimensional tensor can be simply sliced
		if len(_A					) == 0:
				return [()]
		elif len(_A					) == 1:
				return [(slice(start[0] ,  end[0] + 1					),)]

		lowerCAmelCase     =    []
		lowerCAmelCase     =    []

		# Dimensions common to start and end can be selected directly
		for s, e in zip(_A ,  _A					):
				if s == e:
						path_list.append(slice(_A ,  s + 1					)					)
				else:
						break

		lowerCAmelCase     =    tuple(_A					)
		lowerCAmelCase     =    len(_A					)

		# start == end, and we're done
		if divergence_idx == len(_A					):
				return [path]

		def upper() -> Tuple[Tuple[slice, ...], ...]:
				assert start_edges is not None
				assert end_edges is not None

				lowerCAmelCase     =    start[divergence_idx]
				return tuple(
				    path + (slice(_A ,  sdi + 1					),) + s
				    for s in _get_minimal_slice_set(
				        start[divergence_idx + 1 :] ,  [d - 1 for d in dims[divergence_idx + 1 :]] ,  dims[divergence_idx + 1 :] ,  start_edges=start_edges[divergence_idx + 1 :] ,  end_edges=[True for _ in end_edges[divergence_idx + 1 :]] ,  )					)

		def lower() -> Tuple[Tuple[slice, ...], ...]:
				assert start_edges is not None
				assert end_edges is not None

				lowerCAmelCase     =    end[divergence_idx]
				return tuple(
				    path + (slice(_A ,  edi + 1					),) + s
				    for s in _get_minimal_slice_set(
				        [0 for _ in start[divergence_idx + 1 :]] ,  end[divergence_idx + 1 :] ,  dims[divergence_idx + 1 :] ,  start_edges=[True for _ in start_edges[divergence_idx + 1 :]] ,  end_edges=end_edges[divergence_idx + 1 :] ,  )					)

		# If both start and end are at the edges of the subtree rooted at
		# divergence_idx, we can just select the whole subtree at once
		if start_edges[divergence_idx] and end_edges[divergence_idx]:
				slices.append(path + (slice(start[divergence_idx] ,  end[divergence_idx] + 1					),)					)
		# If just start is at the edge, we can grab almost all of the subtree,
		# treating only the ragged bottom edge as an edge case
		elif start_edges[divergence_idx]:
				slices.append(path + (slice(start[divergence_idx] ,  end[divergence_idx]					),)					)
				slices.extend(lower()					)
		# Analogous to the previous case, but the top is ragged this time
		elif end_edges[divergence_idx]:
				slices.extend(upper()					)
				slices.append(path + (slice(start[divergence_idx] + 1 ,  end[divergence_idx] + 1					),)					)
		# If both sides of the range are ragged, we need to handle both sides
		# separately. If there's contiguous meat in between them, we can index it
		# in one big chunk
		else:
				slices.extend(upper()					)
				lowerCAmelCase     =    end[divergence_idx] - start[divergence_idx]
				if middle_ground > 1:
						slices.append(path + (slice(start[divergence_idx] + 1 ,  end[divergence_idx]					),)					)
				slices.extend(lower()					)

		return slices






@torch.jit.ignore
def 							snake_case__				(     _A:   List[str] ,  _A:   List[Any] ,  _A:   Dict ,  _A:   Optional[Any]					)       ->      torch.Tensor:

		'''simple docstring'''


		lowerCAmelCase     =    t.shape[:no_batch_dims]
		lowerCAmelCase     =    list(_flat_idx_to_idx(_A ,  _A					)					)
		# _get_minimal_slice_set is inclusive
		lowerCAmelCase     =    list(_flat_idx_to_idx(flat_end - 1 ,  _A					)					)

		# Get an ordered list of slices to perform
		lowerCAmelCase     =    _get_minimal_slice_set(
		    _A ,  _A ,  _A ,  )

		lowerCAmelCase     =    [t[s] for s in slices]

		return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]					) for s in sliced_tensors]					)






def 							snake_case__				(     _A:   Optional[int] ,  _A:   List[Any] ,  _A:   List[str] ,  _A:   Optional[int] ,  _A:   str = False ,  _A:   int = None ,  _A:   Optional[Any] = False ,  )       ->      Any:

		'''simple docstring'''


		if not (len(_A					) > 0):
				raise ValueError("""Must provide at least one input"""					)

		lowerCAmelCase     =    [shape[:no_batch_dims] for shape in _fetch_dims(_A					)]
		lowerCAmelCase     =    tuple([max(_A					) for s in zip(*_A					)]					)

		def _prep_inputs(_A:   int					) -> torch.Tensor:
				if not low_mem:
						if not sum(t.shape[:no_batch_dims]					) == no_batch_dims:
								lowerCAmelCase     =    t.expand(orig_batch_dims + t.shape[no_batch_dims:]					)
						lowerCAmelCase     =    t.reshape(-1 ,  *t.shape[no_batch_dims:]					)
				else:
						lowerCAmelCase     =    t.expand(orig_batch_dims + t.shape[no_batch_dims:]					)
				return t

		lowerCAmelCase     =    tensor_tree_map(_prep_inputs ,  _A					)
		lowerCAmelCase     =    None
		if _out is not None:
				lowerCAmelCase     =    tensor_tree_map(lambda _A					: t.view([-1] + list(t.shape[no_batch_dims:]					)					) ,  _out					)

		lowerCAmelCase     =    1
		for d in orig_batch_dims:
				flat_batch_dim *= d

		lowerCAmelCase     =    flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

		def _select_chunk(_A:   Dict					) -> torch.Tensor:
				return t[i : i + chunk_size] if t.shape[0] != 1 else t

		lowerCAmelCase     =    0
		lowerCAmelCase     =    prepped_outputs
		for _ in range(_A					):
				# Chunk the input
				if not low_mem:
						lowerCAmelCase     =    _select_chunk
				else:
						lowerCAmelCase     =    partial(
						    _chunk_slice ,  flat_start=_A ,  flat_end=min(_A ,  i + chunk_size					) ,  no_batch_dims=len(_A					) ,  )

				lowerCAmelCase     =    tensor_tree_map(_A ,  _A					)

				# Run the layer on the chunk
				lowerCAmelCase     =    layer(**_A					)

				# Allocate space for the output
				if out is None:
						lowerCAmelCase     =    tensor_tree_map(lambda _A					: t.new_zeros((flat_batch_dim,) + t.shape[1:]					) ,  _A					)

				# Put the chunk in its pre-allocated space
				if isinstance(_A ,  _A					):

						def assign(_A:   List[str] ,  _A:   str					) -> None:
								for k, v in da.items():
										if isinstance(_A ,  _A					):
												assign(_A ,  da[k]					)
										else:
												if _add_into_out:
														v[i : i + chunk_size] += da[k]
												else:
														lowerCAmelCase     =    da[k]

						assign(_A ,  _A					)
				elif isinstance(_A ,  _A					):
						for xa, xa in zip(_A ,  _A					):
								if _add_into_out:
										xa[i : i + chunk_size] += xa
								else:
										lowerCAmelCase     =    xa
				elif isinstance(_A ,  torch.Tensor					):
						if _add_into_out:
								out[i : i + chunk_size] += output_chunk
						else:
								lowerCAmelCase     =    output_chunk
				else:
						raise ValueError("""Not supported"""					)

				i += chunk_size

		lowerCAmelCase     =    tensor_tree_map(lambda _A					: t.view(orig_batch_dims + t.shape[1:]					) ,  _A					)

		return out






class 							a__:


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase = 512			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    max_chunk_size
					lowerCAmelCase     =    None
					lowerCAmelCase     =    None



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					logging.info("""Tuning chunk size...""")

					if min_chunk_size >= self.max_chunk_size:
							return min_chunk_size

					lowerCAmelCase     =    [2**l for l in range(int(math.log(self.max_chunk_size			,       2)) + 1)]
					lowerCAmelCase     =    [c for c in candidates if c > min_chunk_size]
					lowerCAmelCase     =    [min_chunk_size] + candidates
					candidates[-1] += 4

					def test_chunk_size(__lowerCAmelCase) -> bool:
							try:
									with torch.no_grad():
											fn(*__lowerCAmelCase			,       chunk_size=__lowerCAmelCase)
									return True
							except RuntimeError:
									return False

					lowerCAmelCase     =    0
					lowerCAmelCase     =    len(__lowerCAmelCase) - 1
					while i > min_viable_chunk_size_index:
							lowerCAmelCase     =    test_chunk_size(candidates[i])
							if not viable:
									lowerCAmelCase     =    (min_viable_chunk_size_index + i) // 2
							else:
									lowerCAmelCase     =    i
									lowerCAmelCase     =    (i + len(__lowerCAmelCase) - 1) // 2

					return candidates[min_viable_chunk_size_index]



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    True
					for aa, aa in zip(__lowerCAmelCase			,       __lowerCAmelCase):
							assert type(__lowerCAmelCase) == type(__lowerCAmelCase)
							if isinstance(__lowerCAmelCase			,       (list, tuple)):
									consistent &= self._compare_arg_caches(__lowerCAmelCase			,       __lowerCAmelCase)
							elif isinstance(__lowerCAmelCase			,       __lowerCAmelCase):
									lowerCAmelCase     =    [v for _, v in sorted(aa.items()			,       key=lambda __lowerCAmelCase: x[0])]
									lowerCAmelCase     =    [v for _, v in sorted(aa.items()			,       key=lambda __lowerCAmelCase: x[0])]
									consistent &= self._compare_arg_caches(__lowerCAmelCase			,       __lowerCAmelCase)
							else:
									consistent &= aa == aa

					return consistent







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    True
					lowerCAmelCase     =    tree_map(lambda __lowerCAmelCase: a.shape if isinstance(__lowerCAmelCase			,       torch.Tensor) else a			,       __lowerCAmelCase			,       __lowerCAmelCase)
					if self.cached_arg_data is not None:
							# If args have changed shape/value, we need to re-tune
							assert len(self.cached_arg_data) == len(__lowerCAmelCase)
							lowerCAmelCase     =    self._compare_arg_caches(self.cached_arg_data			,       __lowerCAmelCase)
					else:
							# Otherwise, we can reuse the precomputed value
							lowerCAmelCase     =    False

					if not consistent:
							lowerCAmelCase     =    self._determine_favorable_chunk_size(
							    __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase			,       )
							lowerCAmelCase     =    arg_data

					assert self.cached_chunk_size is not None

					return self.cached_chunk_size












"""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
