






"""simple docstring"""







from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFTransfoXLForSequenceClassification,
     TFTransfoXLLMHeadModel,
     TFTransfoXLModel,
 )





class   UpperCamelCase_ :



  def __init__(      self ,		snake_case__ ,		)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			parent
         UpperCAmelCase	    =			13
         UpperCAmelCase	    =			7
         UpperCAmelCase	    =			30
         UpperCAmelCase	    =			self.seq_length + self.mem_len
         UpperCAmelCase	    =			15
         UpperCAmelCase	    =			True
         UpperCAmelCase	    =			True
         UpperCAmelCase	    =			99
         UpperCAmelCase	    =			[10, 50, 80]
         UpperCAmelCase	    =			32
         UpperCAmelCase	    =			32
         UpperCAmelCase	    =			4
         UpperCAmelCase	    =			8
         UpperCAmelCase	    =			1_28
         UpperCAmelCase	    =			2
         UpperCAmelCase	    =			2
         UpperCAmelCase	    =			None
         UpperCAmelCase	    =			1
         UpperCAmelCase	    =			0
         UpperCAmelCase	    =			3
         UpperCAmelCase	    =			self.vocab_size - 1
         UpperCAmelCase	    =			0.01



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size				)
         UpperCAmelCase	    =			ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size				)

         UpperCAmelCase	    =			None
         if self.use_labels:
                UpperCAmelCase	    =			ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size				)

         UpperCAmelCase	    =			TransfoXLConfig(
             vocab_size=self.vocab_size ,		mem_len=self.mem_len ,		clamp_len=self.clamp_len ,		cutoffs=self.cutoffs ,		d_model=self.hidden_size ,		d_embed=self.d_embed ,		n_head=self.num_attention_heads ,		d_head=self.d_head ,		d_inner=self.d_inner ,		div_val=self.div_val ,		n_layer=self.num_hidden_layers ,		eos_token_id=self.eos_token_id ,		pad_token_id=self.vocab_size - 1 ,		init_range=self.init_range ,		num_labels=self.num_labels ,		)

         return (config, input_ids_a, input_ids_a, lm_labels)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         random.seed(self.seed				)
         tf.random.set_seed(self.seed				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			TFTransfoXLModel(snake_case__				)

         UpperCAmelCase    ,       UpperCAmelCase	    =			model(snake_case__				).to_tuple()

         UpperCAmelCase	    =			{"""input_ids""": input_ids_a, """mems""": mems_a}

         UpperCAmelCase    ,       UpperCAmelCase	    =			model(snake_case__				).to_tuple()

         self.parent.assertEqual(hidden_states_a.shape ,		(self.batch_size, self.seq_length, self.hidden_size)				)
         self.parent.assertEqual(hidden_states_a.shape ,		(self.batch_size, self.seq_length, self.hidden_size)				)
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a] ,		[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,		)
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a] ,		[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,		)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			TFTransfoXLLMHeadModel(snake_case__				)

         UpperCAmelCase    ,       UpperCAmelCase	    =			model(snake_case__				).to_tuple()

         UpperCAmelCase	    =			{"""input_ids""": input_ids_a, """labels""": lm_labels}
         UpperCAmelCase    ,       UpperCAmelCase	    =			model(snake_case__				).to_tuple()

         UpperCAmelCase    ,       UpperCAmelCase	    =			model([input_ids_a, mems_a]				).to_tuple()

         UpperCAmelCase	    =			{"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

         UpperCAmelCase    ,       UpperCAmelCase	    =			model(snake_case__				).to_tuple()

         self.parent.assertEqual(lm_logits_a.shape ,		(self.batch_size, self.seq_length, self.vocab_size)				)
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a] ,		[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,		)

         self.parent.assertEqual(lm_logits_a.shape ,		(self.batch_size, self.seq_length, self.vocab_size)				)
         self.parent.assertListEqual(
             [mem.shape for mem in mems_a] ,		[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,		)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase	    =			TFTransfoXLForSequenceClassification(snake_case__				)
         UpperCAmelCase	    =			model(snake_case__				)
         self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.num_labels)				)



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			self.prepare_config_and_inputs()
         ((UpperCAmelCase)    ,       (UpperCAmelCase)    ,       (UpperCAmelCase)    ,       (UpperCAmelCase))	    =			config_and_inputs
         UpperCAmelCase	    =			{"""input_ids""": input_ids_a}
         return config, inputs_dict





@require_tf
class   UpperCamelCase_ (       a_   ,					a_   ,					unittest.TestCase  ):
  _A       :					Optional[Any]				=	(
      (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
  )
  _A       :					int				=	() if is_tf_available() else ()
  _A       :					Tuple				=	(
      {
          'feature-extraction': TFTransfoXLModel,
          'text-classification': TFTransfoXLForSequenceClassification,
          'text-generation': TFTransfoXLLMHeadModel,
          'zero-shot': TFTransfoXLForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
  _A       :					Union[str, Any]				=	False
  _A       :					str				=	False
  _A       :					Dict				=	False
  _A       :					Any				=	False



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			List[str]:



         """simple docstring"""





         if pipeline_test_casse_name == "TextGenerationPipelineTests":
                # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
                # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
                # tokenizer.
                return True

         return False



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			TFTransfoXLModelTester(self				)
         UpperCAmelCase	    =			ConfigTester(self ,		config_class=snake_case__ ,		d_embed=37				)



  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         self.config_tester.run_common_tests()



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         self.model_tester.set_seed()
         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_model(*snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Optional[int]:



         """simple docstring"""





         self.model_tester.set_seed()
         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_lm_head(*snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*snake_case__				)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase	    =			self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase	    =			[TFTransfoXLForSequenceClassification]

         for model_class in self.all_model_classes:
                UpperCAmelCase	    =			model_class(snake_case__				)
                assert isinstance(model.get_input_embeddings() ,		tf.keras.layers.Layer				)
                if model_class in list_other_models_with_output_ebd:
                       UpperCAmelCase	    =			model.get_output_embeddings()
                       assert isinstance(snake_case__ ,		tf.keras.layers.Layer				)
                       UpperCAmelCase	    =			model.get_bias()
                       assert name is None
                else:
                       UpperCAmelCase	    =			model.get_output_embeddings()
                       assert x is None
                       UpperCAmelCase	    =			model.get_bias()
                       assert name is None



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         pass



  @slow
  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase	    =			TFTransfoXLModel.from_pretrained(snake_case__				)
                self.assertIsNotNone(snake_case__				)



  @unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss."""				)
  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         pass





@require_tf
class   UpperCamelCase_ (       unittest.TestCase  ):



  @unittest.skip("""Skip test until #12651 is resolved."""				)
  @slow
  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""				)
         # fmt: off
         UpperCAmelCase	    =			tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]] ,		dtype=tf.intaa				)  # noqa: E231
         # fmt: on
         #  In 1991 , the remains of Russian Tsar Nicholas II and his family
         #  ( except for Alexei and Maria ) are discovered .
         #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
         #  remainder of the story . 1883 Western Siberia ,
         #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
         #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
         #  father initially slaps him for making such an accusation , Rasputin watches as the
         #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
         #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
         #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

         # fmt: off
         UpperCAmelCase	    =			[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # noqa: E231
         # fmt: on
         #  In 1991, the remains of Russian Tsar Nicholas II and his family (
         #  except for Alexei and Maria ) are discovered. The voice of young son,
         #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
         #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
         #  and a group of men to perform magic. Rasputin has a vision and
         #  denounces one of the men as a horse thief. Although his father initially
         #  slaps him for making such an accusation, Rasputin watches as the man
         #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
         #  of the Virgin Mary, prompting him to become a priest.
         #  Rasputin quickly becomes famous, with people, even a bishop, begging for
         #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
         # Nicholas II and his family were discovered. The voice of <unk> young son,
         # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

         UpperCAmelCase	    =			model.generate(snake_case__ ,		max_length=2_00 ,		do_sample=snake_case__				)
         self.assertListEqual(output_ids[0].numpy().tolist() ,		snake_case__				)








"""simple docstring"""







import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


lowerCAmelCase_	:    Any            =      logging.get_logger(__name__)

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

lowerCAmelCase_	:    List[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''',
    },
}

lowerCAmelCase_	:    Union[str, Any]            =      {
    '''jukebox''': 5_1_2,
}





class   UpperCamelCase_ (       a_  ):
  _A       :					int				=	VOCAB_FILES_NAMES
  _A       :					Dict				=	PRETRAINED_VOCAB_FILES_MAP
  _A       :					List[Any]				=	PRETRAINED_LYRIC_TOKENS_SIZES
  _A       :					Optional[Any]				=	['input_ids', 'attention_mask']



  def __init__(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__=["v3", "v2", "v2"] ,		snake_case__=5_12 ,		snake_case__=5 ,		snake_case__="<|endoftext|>" ,		**snake_case__ ,		)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			AddedToken(snake_case__ ,		lstrip=snake_case__ ,		rstrip=snake_case__				) if isinstance(snake_case__ ,		snake_case__				) else unk_token
         super().__init__(
             unk_token=snake_case__ ,		n_genres=snake_case__ ,		version=snake_case__ ,		max_n_lyric_tokens=snake_case__ ,		**snake_case__ ,		)
         UpperCAmelCase	    =			version
         UpperCAmelCase	    =			max_n_lyric_tokens
         UpperCAmelCase	    =			n_genres

         with open(snake_case__ ,		encoding="""utf-8"""				) as vocab_handle:
                UpperCAmelCase	    =			json.load(snake_case__				)

         with open(snake_case__ ,		encoding="""utf-8"""				) as vocab_handle:
                UpperCAmelCase	    =			json.load(snake_case__				)

         with open(snake_case__ ,		encoding="""utf-8"""				) as vocab_handle:
                UpperCAmelCase	    =			json.load(snake_case__				)

         UpperCAmelCase	    =			R"""[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"""
         # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
         if len(self.lyrics_encoder				) == 79:
                UpperCAmelCase	    =			oov.replace(R"""\-'""" ,		R"""\-+'"""				)

         UpperCAmelCase	    =			regex.compile(snake_case__				)
         UpperCAmelCase	    =			{v: k for k, v in self.artists_encoder.items()}
         UpperCAmelCase	    =			{v: k for k, v in self.genres_encoder.items()}
         UpperCAmelCase	    =			{v: k for k, v in self.lyrics_encoder.items()}



  @property
  def 		UpperCamelCase_				(      self				)			->			Optional[Any]:



         """simple docstring"""





         return len(self.artists_encoder				) + len(self.genres_encoder				) + len(self.lyrics_encoder				)



  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         return dict(self.artists_encoder ,		self.genres_encoder ,		self.lyrics_encoder				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			Any:



         """simple docstring"""





         UpperCAmelCase	    =			[self.artists_encoder.get(snake_case__ ,		0				) for artist in list_artists]
         for genres in range(len(snake_case__				)				):
                UpperCAmelCase	    =			[self.genres_encoder.get(snake_case__ ,		0				) for genre in list_genres[genres]]
                UpperCAmelCase	    =			list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]				))

         UpperCAmelCase	    =			[[self.lyrics_encoder.get(snake_case__ ,		0				) for character in list_lyrics[0]], [], []]
         return artists_id, list_genres, lyric_ids



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			List[Any]:



         """simple docstring"""





         return list(snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		**snake_case__				)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			self.prepare_for_tokenization(snake_case__ ,		snake_case__ ,		snake_case__				)
         UpperCAmelCase	    =			self._tokenize(snake_case__				)
         return artist, genre, lyrics



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ = False				)			->			Tuple[str, str, str, Dict[str, Any]]:



         """simple docstring"""





         for idx in range(len(self.version				)				):
                if self.version[idx] == "v3":
                       UpperCAmelCase	    =			artists[idx].lower()
                       UpperCAmelCase	    =			[genres[idx].lower()]
                else:
                       UpperCAmelCase	    =			self._normalize(artists[idx]				) + """.v2"""
                       UpperCAmelCase	    =			[
                           self._normalize(snake_case__				) + """.v2""" for genre in genres[idx].split("""_"""				)
                       ]  # split is for the full dictionary with combined genres

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

         UpperCAmelCase	    =			self._run_strip_accents(snake_case__				)
         UpperCAmelCase	    =			lyrics.replace("""\\""" ,		"""\n"""				)
         UpperCAmelCase	    =			self.out_of_vocab.sub("""""" ,		snake_case__				), [], []
         return artists, genres, lyrics



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			unicodedata.normalize("""NFD""" ,		snake_case__				)
         UpperCAmelCase	    =			[]
         for char in text:
                UpperCAmelCase	    =			unicodedata.category(snake_case__				)
                if cat == "Mn":
                       continue
                output.append(snake_case__				)
         return "".join(snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			(
             [chr(snake_case__				) for i in range(ord("""a"""				) ,		ord("""z"""				) + 1				)]
             + [chr(snake_case__				) for i in range(ord("""A"""				) ,		ord("""Z"""				) + 1				)]
             + [chr(snake_case__				) for i in range(ord("""0"""				) ,		ord("""9"""				) + 1				)]
             + ["""."""]
         )
         UpperCAmelCase	    =			frozenset(snake_case__				)
         UpperCAmelCase	    =			re.compile(R"""_+"""				)
         UpperCAmelCase	    =			"""""".join([c if c in accepted else """_""" for c in text.lower()]				)
         UpperCAmelCase	    =			pattern.sub("""_""" ,		snake_case__				).strip("""_"""				)
         return text



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			str:



         """simple docstring"""





         return " ".join(snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None ,		snake_case__ = False				)			->			List[str]:



         """simple docstring"""





         if not isinstance(snake_case__ ,		snake_case__				):
                UpperCAmelCase	    =			TensorType(snake_case__				)

         # Get a function reference for the correct framework
         if tensor_type == TensorType.TENSORFLOW:
                if not is_tf_available():
                       raise ImportError(
                           """Unable to convert output to TensorFlow tensors format, TensorFlow is not installed."""				)
                import tensorflow as tf

                UpperCAmelCase	    =			tf.constant
                UpperCAmelCase	    =			tf.is_tensor
         elif tensor_type == TensorType.PYTORCH:
                if not is_torch_available():
                       raise ImportError("""Unable to convert output to PyTorch tensors format, PyTorch is not installed."""				)
                import torch

                UpperCAmelCase	    =			torch.tensor
                UpperCAmelCase	    =			torch.is_tensor
         elif tensor_type == TensorType.JAX:
                if not is_flax_available():
                       raise ImportError("""Unable to convert output to JAX tensors format, JAX is not installed."""				)
                import jax.numpy as jnp  # noqa: F811

                UpperCAmelCase	    =			jnp.array
                UpperCAmelCase	    =			_is_jax
         else:
                UpperCAmelCase	    =			np.asarray
                UpperCAmelCase	    =			_is_numpy

         # Do the tensor conversion in batch

         try:
                if prepend_batch_axis:
                       UpperCAmelCase	    =			[inputs]

                if not is_tensor(snake_case__				):
                       UpperCAmelCase	    =			as_tensor(snake_case__				)
         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 ,		snake_case__ ,		snake_case__ ,		snake_case__="" ,		snake_case__="pt"				)			->			BatchEncoding:



         """simple docstring"""





         UpperCAmelCase	    =			[0, 0, 0]
         UpperCAmelCase	    =			[artist] * len(self.version				)
         UpperCAmelCase	    =			[genres] * len(self.version				)

         UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			self.tokenize(snake_case__ ,		snake_case__ ,		snake_case__				)
         UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			self._convert_token_to_id(snake_case__ ,		snake_case__ ,		snake_case__				)

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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None				)			->			Tuple[str]:



         """simple docstring"""





         if not os.path.isdir(snake_case__				):
                logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
                return

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

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

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

         return (artists_file, genres_file, lyrics_file)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			Optional[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.artists_decoder.get(snake_case__				)
         UpperCAmelCase	    =			[self.genres_decoder.get(snake_case__				) for genre in genres_index]
         UpperCAmelCase	    =			[self.lyrics_decoder.get(snake_case__				) for character in lyric_index]
         return artist, genres, lyrics
