






"""simple docstring"""







import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_	:    Optional[int]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Dict            =      {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowerCAmelCase_	:    Dict            =      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       for attribute in key.split("""."""		):
              UpperCAmelCase	    =			getattr(lowerCAmelCase	,      lowerCAmelCase		)

       if weight_type is not None:
              UpperCAmelCase	    =			getattr(lowerCAmelCase	,      lowerCAmelCase		).shape
       else:
              UpperCAmelCase	    =			hf_pointer.shape

       assert hf_shape == value.shape, (
           F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
           F''' {value.shape} for {full_name}'''
       )

       if weight_type == "weight":
              UpperCAmelCase	    =			value
       elif weight_type == "weight_g":
              UpperCAmelCase	    =			value
       elif weight_type == "weight_v":
              UpperCAmelCase	    =			value
       elif weight_type == "bias":
              UpperCAmelCase	    =			value
       else:
              UpperCAmelCase	    =			value

       logger.info(F'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''		)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[]
       UpperCAmelCase	    =			fairseq_model.state_dict()

       UpperCAmelCase	    =			hf_model.feature_extractor
       UpperCAmelCase	    =			hf_model.adapter

       for name, value in fairseq_dict.items():
              UpperCAmelCase	    =			False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      hf_model.config.feat_extract_norm == """group"""	,      )
                     UpperCAmelCase	    =			True
              elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]		):
                     load_adapter(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
                     UpperCAmelCase	    =			True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
                                   UpperCAmelCase	    =			True
                                   if "*" in mapped_key:
                                          UpperCAmelCase	    =			name.split(lowerCAmelCase		)[0].split("""."""		)[-2]
                                          UpperCAmelCase	    =			mapped_key.replace("""*"""	,      lowerCAmelCase		)
                                   if "weight_g" in name:
                                          UpperCAmelCase	    =			"""weight_g"""
                                   elif "weight_v" in name:
                                          UpperCAmelCase	    =			"""weight_v"""
                                   elif "bias" in name:
                                          UpperCAmelCase	    =			"""bias"""
                                   elif "weight" in name:
                                          UpperCAmelCase	    =			"""weight"""
                                   else:
                                          UpperCAmelCase	    =			None
                                   set_recursively(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
                            continue
              if not is_used:
                     unused_weights.append(lowerCAmelCase		)

       logger.warning(F'''Unused weights: {unused_weights}'''		)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			full_name.split("""conv_layers."""		)[-1]
       UpperCAmelCase	    =			name.split("""."""		)
       UpperCAmelCase	    =			int(items[0]		)
       UpperCAmelCase	    =			int(items[1]		)

       if type_id == 0:
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         F'''{full_name} has size {value.shape}, but'''
                         F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
                     )
                     UpperCAmelCase	    =			value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         F'''{full_name} has size {value.shape}, but'''
                         F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
                     )
                     UpperCAmelCase	    =			value
                     logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''		)
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                         " found."
                     )
                     UpperCAmelCase	    =			value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         F'''{full_name} has size {value.shape}, but'''
                         F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
                     )
                     UpperCAmelCase	    =			value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
       else:
              unused_weights.append(lowerCAmelCase		)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			full_name.split("""adaptor."""		)[-1]
       UpperCAmelCase	    =			name.split("""."""		)

       if items[1].isdigit():
              UpperCAmelCase	    =			int(items[1]		)
       else:
              UpperCAmelCase	    =			None

       if "adaptor" not in full_name:
              if "proj_ln" in full_name:
                     # has to be layer norm
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.bias.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
                            UpperCAmelCase	    =			value
                            logger.info(F'''Adapter proj layer norm bias was initialized from {full_name}.'''		)
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.weight.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
                            UpperCAmelCase	    =			value
              else:
                     # has to be projection layer
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj.bias.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
                            UpperCAmelCase	    =			value
                            logger.info(F'''Adapter proj layer bias was initialized from {full_name}.'''		)
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj.weight.data.shape
                            ), F'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
                            UpperCAmelCase	    =			value
                            logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''		)
       elif isinstance(lowerCAmelCase	,      lowerCAmelCase		):
              if "bias" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.bias.data.shape
                     ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
                     UpperCAmelCase	    =			value
                     logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''		)
              elif "weight" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.weight.data.shape
                     ), F'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
                     UpperCAmelCase	    =			value
                     logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''		)
       else:
              unused_weights.append(lowerCAmelCase		)






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase    ,       UpperCAmelCase	    =			emb.weight.shape
       UpperCAmelCase	    =			nn.Linear(lowerCAmelCase	,      lowerCAmelCase	,      bias=lowerCAmelCase		)
       UpperCAmelCase	    =			emb.weight.data
       return lin_layer






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      ):







       '''simple docstring'''
       UpperCAmelCase	    =			WavaVecaConfig.from_pretrained(
           lowerCAmelCase	,      add_adapter=lowerCAmelCase	,      adapter_stride=lowerCAmelCase	,      adapter_kernel_size=lowerCAmelCase	,      use_auth_token=lowerCAmelCase	,      output_hidden_size=lowerCAmelCase	,      )
       UpperCAmelCase	    =			MBartConfig.from_pretrained(lowerCAmelCase		)

       # load model
       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]	,      arg_overrides={
               """config_yaml""": config_yaml_path,
               """data""": """/""".join(dict_path.split("""/"""		)[:-1]		),
               """w2v_path""": checkpoint_path,
               """load_pretrained_decoder_from""": None,
           }	,      )
       UpperCAmelCase	    =			model[0].eval()

       # load feature extractor
       UpperCAmelCase	    =			WavaVecaFeatureExtractor.from_pretrained(lowerCAmelCase	,      use_auth_token=lowerCAmelCase		)

       # set weights for wav2vec2 encoder
       UpperCAmelCase	    =			WavaVecaModel(lowerCAmelCase		)

       recursively_load_weights_wavaveca(model.encoder	,      lowerCAmelCase		)

       # load decoder weights
       UpperCAmelCase	    =			MBartForCausalLM(lowerCAmelCase		)
       UpperCAmelCase    ,       UpperCAmelCase	    =			hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,      strict=lowerCAmelCase		)
       logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}'''		)
       logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''		)

       UpperCAmelCase	    =			SpeechEncoderDecoderModel(encoder=lowerCAmelCase	,      decoder=lowerCAmelCase		)
       UpperCAmelCase	    =			False

       UpperCAmelCase	    =			MBartaaTokenizer(lowerCAmelCase		)
       tokenizer.save_pretrained(lowerCAmelCase		)

       UpperCAmelCase	    =			hf_wavavec.config.to_dict()
       UpperCAmelCase	    =			tokenizer.pad_token_id
       UpperCAmelCase	    =			tokenizer.bos_token_id
       UpperCAmelCase	    =			tokenizer.eos_token_id
       UpperCAmelCase	    =			"""mbart50"""
       UpperCAmelCase	    =			"""wav2vec2"""

       UpperCAmelCase	    =			tokenizer.eos_token_id
       UpperCAmelCase	    =			250004
       UpperCAmelCase	    =			tokenizer.eos_token_id

       UpperCAmelCase	    =			SpeechEncoderDecoderConfig.from_dict(lowerCAmelCase		)

       hf_wavavec.save_pretrained(lowerCAmelCase		)
       feature_extractor.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
   lowerCAmelCase_	:    List[str]            =      argparse.ArgumentParser()
   parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
   parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
   parser.add_argument('''--config_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
   parser.add_argument(
       '''--encoder_config_path''',
       default='''facebook/wav2vec2-xls-r-1b''',
       type=str,
       help='''Path to hf encoder wav2vec2 checkpoint config''',
   )
   parser.add_argument(
       '''--decoder_config_path''',
       default='''facebook/mbart-large-50-one-to-many-mmt''',
       type=str,
       help='''Path to hf decoder checkpoint config''',
   )
   parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
   parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
   parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
   parser.add_argument('''--encoder_output_dim''', default=1_0_2_4, type=int, help='''encoder output dim''')
   parser.add_argument('''--start_token_id''', default=2_5_0_0_0_4, type=int, help='''`decoder_start_token_id` of model config''')

   lowerCAmelCase_	:    Optional[int]            =      parser.parse_args()
   convert_wavaveca_checkpoint(
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.dict_path,
       args.config_yaml_path,
       encoder_config_path=args.encoder_config_path,
       decoder_config_path=args.decoder_config_path,
       add_adapter=args.add_adapter,
       adapter_kernel_size=args.adapter_kernel_size,
       adapter_stride=args.adapter_stride,
       decoder_start_token_id=args.start_token_id,
       encoder_output_dim=args.encoder_output_dim,
   )








"""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
