import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


a  :      Tuple            =    re.compile(r"""\b(a|an|the)\b""", re.UNICODE)

a  :      Any            =    None
def 		snake_case__	(		):
   lowerCAmelCase_:							Optional[int]       =	argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0."					)
   parser.add_argument("data_file"							,				metavar="data.json"							,				help="Input data JSON file."					)
   parser.add_argument("pred_file"							,				metavar="pred.json"							,				help="Model predictions."					)
   parser.add_argument(
       "--out-file"							,				"-o"							,				metavar="eval.json"							,				help="Write accuracy metrics to file (default is stdout)."					)
   parser.add_argument(
       "--na-prob-file"							,				"-n"							,				metavar="na_prob.json"							,				help="Model estimates of probability of no answer."					)
   parser.add_argument(
       "--na-prob-thresh"							,				"-t"							,				type=lowercase							,				default=1.0							,				help="Predict \"\" if no-answer probability exceeds this (default = 1.0)."							,				)
   parser.add_argument(
       "--out-image-dir"							,				"-p"							,				metavar="out_images"							,				default=lowercase							,				help="Save precision-recall curves to directory."					)
   parser.add_argument("--verbose"							,				"-v"							,				action="store_true"					)
   if len(sys.argv					) == 1:
      parser.print_help()
      sys.exit(1					)
   return parser.parse_args()
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							Optional[Any]       =	{}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            lowerCAmelCase_:							Tuple       =	bool(qa["answers"]["text"]					)
   return qid_to_has_ans
def 		snake_case__	(		lowercase					):

   def remove_articles(lowercase					):
      return ARTICLES_REGEX.sub(" "							,				lowercase					)

   def white_space_fix(lowercase					):
      return " ".join(text.split()					)

   def remove_punc(lowercase					):
      lowerCAmelCase_:							Optional[int]       =	set(string.punctuation					)
      return "".join(ch for ch in text if ch not in exclude					)

   def lower(lowercase					):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(lowercase					)					)					)					)
def 		snake_case__	(		lowercase					):
   if not s:
      return []
   return normalize_answer(lowercase					).split()
def 		snake_case__	(		lowercase							,				lowercase					):
   return int(normalize_answer(lowercase					) == normalize_answer(lowercase					)					)
def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							Union[str, Any]       =	get_tokens(lowercase					)
   lowerCAmelCase_:							List[str]       =	get_tokens(lowercase					)
   lowerCAmelCase_:							Optional[Any]       =	collections.Counter(lowercase					) & collections.Counter(lowercase					)
   lowerCAmelCase_:							Optional[Any]       =	sum(common.values()					)
   if len(lowercase					) == 0 or len(lowercase					) == 0:
      # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
      return int(gold_toks == pred_toks					)
   if num_same == 0:
      return 0
   lowerCAmelCase_:							str       =	1.0 * num_same / len(lowercase					)
   lowerCAmelCase_:							Optional[int]       =	1.0 * num_same / len(lowercase					)
   lowerCAmelCase_:							Union[str, Any]       =	(2 * precision * recall) / (precision + recall)
   return fa
def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							List[str]       =	{}
   lowerCAmelCase_:							str       =	{}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            lowerCAmelCase_:							Tuple       =	qa["id"]
            lowerCAmelCase_:							Optional[int]       =	[t for t in qa["answers"]["text"] if normalize_answer(lowercase					)]
            if not gold_answers:
               # For unanswerable questions, only correct answer is empty string
               lowerCAmelCase_:							int       =	[""]
            if qid not in preds:
               print(F'''Missing prediction for {qid}'''					)
               continue
            lowerCAmelCase_:							int       =	preds[qid]
            # Take max over all gold answers
            lowerCAmelCase_:							Optional[int]       =	max(compute_exact(lowercase							,				lowercase					) for a in gold_answers					)
            lowerCAmelCase_:							Optional[Any]       =	max(compute_fa(lowercase							,				lowercase					) for a in gold_answers					)
   return exact_scores, fa_scores
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Optional[int]       =	{}
   for qid, s in scores.items():
      lowerCAmelCase_:							List[str]       =	na_probs[qid] > na_prob_thresh
      if pred_na:
         lowerCAmelCase_:							Dict       =	float(not qid_to_has_ans[qid]					)
      else:
         lowerCAmelCase_:							Optional[int]       =	s
   return new_scores
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase=None					):
   if not qid_list:
      lowerCAmelCase_:							Optional[Any]       =	len(lowercase					)
      return collections.OrderedDict(
          [
              ("exact", 100.0 * sum(exact_scores.values()					) / total),
              ("f1", 100.0 * sum(fa_scores.values()					) / total),
              ("total", total),
          ]					)
   else:
      lowerCAmelCase_:							Optional[int]       =	len(lowercase					)
      return collections.OrderedDict(
          [
              ("exact", 100.0 * sum(exact_scores[k] for k in qid_list					) / total),
              ("f1", 100.0 * sum(fa_scores[k] for k in qid_list					) / total),
              ("total", total),
          ]					)
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   for k in new_eval:
      lowerCAmelCase_:							List[str]       =	new_eval[k]
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   plt.step(lowercase							,				lowercase							,				color="b"							,				alpha=0.2							,				where="post"					)
   plt.fill_between(lowercase							,				lowercase							,				step="post"							,				alpha=0.2							,				color="b"					)
   plt.xlabel("Recall"					)
   plt.ylabel("Precision"					)
   plt.xlim([0.0, 1.05]					)
   plt.ylim([0.0, 1.05]					)
   plt.title(lowercase					)
   plt.savefig(lowercase					)
   plt.clf()
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase=None							,				lowercase=None					):
   lowerCAmelCase_:							Optional[Any]       =	sorted(lowercase							,				key=lambda lowercase					: na_probs[k]					)
   lowerCAmelCase_:							Optional[int]       =	0.0
   lowerCAmelCase_:							Any       =	1.0
   lowerCAmelCase_:							List[str]       =	0.0
   lowerCAmelCase_:							Any       =	[1.0]
   lowerCAmelCase_:							Dict       =	[0.0]
   lowerCAmelCase_:							Dict       =	0.0
   for i, qid in enumerate(lowercase					):
      if qid_to_has_ans[qid]:
         true_pos += scores[qid]
      lowerCAmelCase_:							Tuple       =	true_pos / float(i + 1					)
      lowerCAmelCase_:							Dict       =	true_pos / float(lowercase					)
      if i == len(lowercase					) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
         # i.e., if we can put a threshold after this point
         avg_prec += cur_p * (cur_r - recalls[-1])
         precisions.append(lowercase					)
         recalls.append(lowercase					)
   if out_image:
      plot_pr_curve(lowercase							,				lowercase							,				lowercase							,				lowercase					)
   return {"ap": 100.0 * avg_prec}
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase					):
   if out_image_dir and not os.path.exists(lowercase					):
      os.makedirs(lowercase					)
   lowerCAmelCase_:							List[str]       =	sum(1 for v in qid_to_has_ans.values() if v					)
   if num_true_pos == 0:
      return
   lowerCAmelCase_:							Optional[Any]       =	make_precision_recall_eval(
       lowercase							,				lowercase							,				lowercase							,				lowercase							,				out_image=os.path.join(lowercase							,				"pr_exact.png"					)							,				title="Precision-Recall curve for Exact Match score"							,				)
   lowerCAmelCase_:							List[Any]       =	make_precision_recall_eval(
       lowercase							,				lowercase							,				lowercase							,				lowercase							,				out_image=os.path.join(lowercase							,				"pr_f1.png"					)							,				title="Precision-Recall curve for F1 score"							,				)
   lowerCAmelCase_:							Tuple       =	{k: float(lowercase					) for k, v in qid_to_has_ans.items()}
   lowerCAmelCase_:							Optional[int]       =	make_precision_recall_eval(
       lowercase							,				lowercase							,				lowercase							,				lowercase							,				out_image=os.path.join(lowercase							,				"pr_oracle.png"					)							,				title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)"							,				)
   merge_eval(lowercase							,				lowercase							,				"pr_exact"					)
   merge_eval(lowercase							,				lowercase							,				"pr_f1"					)
   merge_eval(lowercase							,				lowercase							,				"pr_oracle"					)
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   if not qid_list:
      return
   lowerCAmelCase_:							Dict       =	[na_probs[k] for k in qid_list]
   lowerCAmelCase_:							Union[str, Any]       =	np.ones_like(lowercase					) / float(len(lowercase					)					)
   plt.hist(lowercase							,				weights=lowercase							,				bins=20							,				range=(0.0, 1.0)					)
   plt.xlabel("Model probability of no-answer"					)
   plt.ylabel("Proportion of dataset"					)
   plt.title(F'''Histogram of no-answer probability: {name}'''					)
   plt.savefig(os.path.join(lowercase							,				F'''na_prob_hist_{name}.png'''					)					)
   plt.clf()
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							int       =	sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]					)
   lowerCAmelCase_:							Optional[Any]       =	num_no_ans
   lowerCAmelCase_:							str       =	cur_score
   lowerCAmelCase_:							str       =	0.0
   lowerCAmelCase_:							Optional[int]       =	sorted(lowercase							,				key=lambda lowercase					: na_probs[k]					)
   for i, qid in enumerate(lowercase					):
      if qid not in scores:
         continue
      if qid_to_has_ans[qid]:
         lowerCAmelCase_:							Dict       =	scores[qid]
      else:
         if preds[qid]:
            lowerCAmelCase_:							List[str]       =	-1
         else:
            lowerCAmelCase_:							Tuple       =	0
      cur_score += diff
      if cur_score > best_score:
         lowerCAmelCase_:							List[str]       =	cur_score
         lowerCAmelCase_:							int       =	na_probs[qid]
   return 100.0 * best_score / len(lowercase					), best_thresh
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	find_best_thresh(lowercase							,				lowercase							,				lowercase							,				lowercase					)
   lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	find_best_thresh(lowercase							,				lowercase							,				lowercase							,				lowercase					)
   lowerCAmelCase_:							Optional[int]       =	best_exact
   lowerCAmelCase_:							Dict       =	exact_thresh
   lowerCAmelCase_:							str       =	best_fa
   lowerCAmelCase_:							str       =	fa_thresh
def 		snake_case__	(		):
   with open(OPTS.data_file					) as f:
      lowerCAmelCase_:							Optional[Any]       =	json.load(lowercase					)
      lowerCAmelCase_:							Dict       =	dataset_json["data"]
   with open(OPTS.pred_file					) as f:
      lowerCAmelCase_:							Union[str, Any]       =	json.load(lowercase					)
   if OPTS.na_prob_file:
      with open(OPTS.na_prob_file					) as f:
         lowerCAmelCase_:							Tuple       =	json.load(lowercase					)
   else:
      lowerCAmelCase_:							Union[str, Any]       =	{k: 0.0 for k in preds}
   lowerCAmelCase_:							List[Any]       =	make_qid_to_has_ans(lowercase					)  # maps qid to True/False
   lowerCAmelCase_:							str       =	[k for k, v in qid_to_has_ans.items() if v]
   lowerCAmelCase_:							Tuple       =	[k for k, v in qid_to_has_ans.items() if not v]
   lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	get_raw_scores(lowercase							,				lowercase					)
   lowerCAmelCase_:							List[Any]       =	apply_no_ans_threshold(lowercase							,				lowercase							,				lowercase							,				OPTS.na_prob_thresh					)
   lowerCAmelCase_:							Dict       =	apply_no_ans_threshold(lowercase							,				lowercase							,				lowercase							,				OPTS.na_prob_thresh					)
   lowerCAmelCase_:							Optional[int]       =	make_eval_dict(lowercase							,				lowercase					)
   if has_ans_qids:
      lowerCAmelCase_:							Union[str, Any]       =	make_eval_dict(lowercase							,				lowercase							,				qid_list=lowercase					)
      merge_eval(lowercase							,				lowercase							,				"HasAns"					)
   if no_ans_qids:
      lowerCAmelCase_:							List[Any]       =	make_eval_dict(lowercase							,				lowercase							,				qid_list=lowercase					)
      merge_eval(lowercase							,				lowercase							,				"NoAns"					)
   if OPTS.na_prob_file:
      find_all_best_thresh(lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase					)
   if OPTS.na_prob_file and OPTS.out_image_dir:
      run_precision_recall_analysis(lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase							,				OPTS.out_image_dir					)
      histogram_na_prob(lowercase							,				lowercase							,				OPTS.out_image_dir							,				"hasAns"					)
      histogram_na_prob(lowercase							,				lowercase							,				OPTS.out_image_dir							,				"noAns"					)
   if OPTS.out_file:
      with open(OPTS.out_file							,				"w"					) as f:
         json.dump(lowercase							,				lowercase					)
   else:
      print(json.dumps(lowercase							,				indent=2					)					)


if __name__ == "__main__":
 a  :      int            =    parse_args()
 if OPTS.out_image_dir:
  import matplotlib

  matplotlib.use("""Agg""")
  import matplotlib.pyplot as plt
 main()
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


a  :      List[Any]            =    logging.get_logger(__name__)

a  :      Optional[Any]            =    {
    """artists_file""": """artists.json""",
    """lyrics_file""": """lyrics.json""",
    """genres_file""": """genres.json""",
}

a  :      str            =    {
    """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""",
    },
}

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

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[int]          =    VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE:				List[Any]          =    PRETRAINED_LYRIC_TOKENS_SIZES
    SCREAMING_SNAKE_CASE:				Dict          =    ['input_ids', 'attention_mask']




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

       with open(lowerCamelCase__       ,						encoding="utf-8" ) as vocab_handle:
          lowerCAmelCase_:							List[Any]       =	json.load(lowerCamelCase__ )

       with open(lowerCamelCase__       ,						encoding="utf-8" ) as vocab_handle:
          lowerCAmelCase_:							Tuple       =	json.load(lowerCamelCase__ )

       with open(lowerCamelCase__       ,						encoding="utf-8" ) as vocab_handle:
          lowerCAmelCase_:							Tuple       =	json.load(lowerCamelCase__ )

       lowerCAmelCase_:							List[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:
          lowerCAmelCase_:							List[Any]       =	oov.replace(R"\-'"       ,						R"\-+'" )

       lowerCAmelCase_:							List[Any]       =	regex.compile(lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	{v: k for k, v in self.artists_encoder.items()}
       lowerCAmelCase_:							List[str]       =	{v: k for k, v in self.genres_encoder.items()}
       lowerCAmelCase_:							int       =	{v: k for k, v in self.lyrics_encoder.items()}




    @property
    def      _a    (    self ):
       return len(self.artists_encoder ) + len(self.genres_encoder ) + len(self.lyrics_encoder )




    def      _a    (    self ):
       return dict(self.artists_encoder       ,						self.genres_encoder       ,						self.lyrics_encoder )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Union[str, Any]       =	[self.artists_encoder.get(lowerCamelCase__       ,						0 ) for artist in list_artists]
       for genres in range(len(lowerCamelCase__ ) ):
          lowerCAmelCase_:							Tuple       =	[self.genres_encoder.get(lowerCamelCase__       ,						0 ) for genre in list_genres[genres]]
          lowerCAmelCase_:							Any       =	list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres] ))

       lowerCAmelCase_:							Optional[Any]       =	[[self.lyrics_encoder.get(lowerCamelCase__       ,						0 ) for character in list_lyrics[0]], [], []]
       return artists_id, list_genres, lyric_ids




    def      _a    (    self       ,						lowerCamelCase__ ):
       return list(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						**lowerCamelCase__ ):
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Union[str, Any]       =	self.prepare_for_tokenization(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	self._tokenize(lowerCamelCase__ )
       return artist, genre, lyrics




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = False ):
       for idx in range(len(self.version ) ):
          if self.version[idx] == "v3":
             lowerCAmelCase_:							List[Any]       =	artists[idx].lower()
             lowerCAmelCase_:							Tuple       =	[genres[idx].lower()]
          else:
             lowerCAmelCase_:							Optional[Any]       =	self._normalize(artists[idx] ) + ".v2"
             lowerCAmelCase_:							int       =	[
                 self._normalize(lowerCamelCase__ ) + ".v2" for genre in genres[idx].split("_" )
             ]  # split is for the full dictionary with combined genres

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

       lowerCAmelCase_:							int       =	self._run_strip_accents(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[Any]       =	lyrics.replace("\\"       ,						"\n" )
       lowerCAmelCase_:							List[str]       =	self.out_of_vocab.sub(""       ,						lowerCamelCase__ ), [], []
       return artists, genres, lyrics




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							int       =	unicodedata.normalize("NFD"       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	[]
       for char in text:
          lowerCAmelCase_:							Optional[Any]       =	unicodedata.category(lowerCamelCase__ )
          if cat == "Mn":
             continue
          output.append(lowerCamelCase__ )
       return "".join(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	(
           [chr(lowerCamelCase__ ) for i in range(ord("a" )       ,						ord("z" ) + 1 )]
           + [chr(lowerCamelCase__ ) for i in range(ord("A" )       ,						ord("Z" ) + 1 )]
           + [chr(lowerCamelCase__ ) for i in range(ord("0" )       ,						ord("9" ) + 1 )]
           + ["."]
       )
       lowerCAmelCase_:							List[str]       =	frozenset(lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	re.compile(R"_+" )
       lowerCAmelCase_:							List[Any]       =	"".join([c if c in accepted else "_" for c in text.lower()] )
       lowerCAmelCase_:							Union[str, Any]       =	pattern.sub("_"       ,						lowerCamelCase__ ).strip("_" )
       return text




    def      _a    (    self       ,						lowerCamelCase__ ):
       return " ".join(lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = False ):
       # Convert to TensorType
       if not isinstance(lowerCamelCase__       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							str       =	TensorType(lowerCamelCase__ )

       # 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

          lowerCAmelCase_:							Optional[int]       =	tf.constant
          lowerCAmelCase_:							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

          lowerCAmelCase_:							Optional[Any]       =	torch.tensor
          lowerCAmelCase_:							Optional[Any]       =	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

          lowerCAmelCase_:							Optional[Any]       =	jnp.array
          lowerCAmelCase_:							List[str]       =	_is_jax
       else:
          lowerCAmelCase_:							Optional[int]       =	np.asarray
          lowerCAmelCase_:							int       =	_is_numpy

       # Do the tensor conversion in batch

       try:
          if prepend_batch_axis:
             lowerCAmelCase_:							Any       =	[inputs]

          if not is_tensor(lowerCamelCase__ ):
             lowerCAmelCase_:							Optional[int]       =	as_tensor(lowerCamelCase__ )
       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       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__=""       ,						lowerCamelCase__="pt" ):
       lowerCAmelCase_:							Any       =	[0, 0, 0]
       lowerCAmelCase_:							Dict       =	[artist] * len(self.version )
       lowerCAmelCase_:							int       =	[genres] * len(self.version )

       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[str]       =	self.tokenize(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							str       =	self._convert_token_to_id(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )

       lowerCAmelCase_:							List[Any]       =	[-INFINITY] * len(full_tokens[-1] )
       lowerCAmelCase_:							Optional[Any]       =	[
           self.convert_to_tensors(
               [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]       ,						tensor_type=lowerCamelCase__ )
           for i in range(len(self.version ) )
       ]
       return BatchEncoding({"input_ids": input_ids, "attention_masks": attention_masks} )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ = None ):
       if not os.path.isdir(lowerCamelCase__ ):
          logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
          return

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

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

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

       return (artists_file, genres_file, lyrics_file)




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							str       =	self.artists_decoder.get(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	[self.genres_decoder.get(lowerCamelCase__ ) for genre in genres_index]
       lowerCAmelCase_:							Optional[int]       =	[self.lyrics_decoder.get(lowerCamelCase__ ) for character in lyric_index]
       return artist, genres, lyrics