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_    					=       logging.get_logger(__name__)

a_    					=       {
    'artists_file': 'artists.json',
    'lyrics_file': 'lyrics.json',
    'genres_file': 'genres.json',
}

a_    					=       {
    '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_    					=       {
    'jukebox': 512,
}





class 		_lowercase						(					_lowerCAmelCase       ):
 lowercase				     =   VOCAB_FILES_NAMES
 lowercase				     =   PRETRAINED_VOCAB_FILES_MAP
 lowercase				     =   PRETRAINED_LYRIC_TOKENS_SIZES
 lowercase				     =   ["input_ids", "attention_mask"]

 def __init__( self	:							Optional[int]   ,      snake_case	:							Optional[int]   ,      snake_case	:							str   ,      snake_case	:							List[str]   ,      snake_case	:							int=["v3", "v2", "v2"]   ,      snake_case	:							Optional[int]=5_1_2   ,      snake_case	:							Tuple=5   ,      snake_case	:							Tuple="<|endoftext|>"   ,      **snake_case	:							Union[str, Any]   ,      )   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						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   ,      )
  UpperCamelCase_					:						Dict =					version
  UpperCamelCase_					:						Optional[Any] =					max_n_lyric_tokens
  UpperCamelCase_					:						Tuple =					n_genres

  with open(_lowerCamelCase   ,      encoding='utf-8'			) as vocab_handle:
   UpperCamelCase_					:						str =					json.load(_lowerCamelCase			)

  with open(_lowerCamelCase   ,      encoding='utf-8'			) as vocab_handle:
   UpperCamelCase_					:						Union[str, Any] =					json.load(_lowerCamelCase			)

  with open(_lowerCamelCase   ,      encoding='utf-8'			) as vocab_handle:
   UpperCamelCase_					:						Tuple =					json.load(_lowerCamelCase			)

  UpperCamelCase_					:						Dict =					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			) == 7_9:
   UpperCamelCase_					:						List[Any] =					oov.replace(R'\-\''   ,      R'\-+\''			)

  UpperCamelCase_					:						str =					regex.compile(_lowerCamelCase			)
  UpperCamelCase_					:						Union[str, Any] =					{v: k for k, v in self.artists_encoder.items()}
  UpperCamelCase_					:						Dict =					{v: k for k, v in self.genres_encoder.items()}
  UpperCamelCase_					:						Union[str, Any] =					{v: k for k, v in self.lyrics_encoder.items()}

 @property
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Any:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						List[Any]:

  """simple docstring"""





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							Optional[int]   ,      snake_case	:							Optional[int]   ,      snake_case	:							Optional[int]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					[self.artists_encoder.get(_lowerCamelCase   ,      0			) for artist in list_artists]
  for genres in range(len(_lowerCamelCase			)			):
   UpperCamelCase_					:						List[Any] =					[self.genres_encoder.get(_lowerCamelCase   ,      0			) for genre in list_genres[genres]]
   UpperCamelCase_					:						Union[str, Any] =					list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]			))

  UpperCamelCase_					:						Tuple =					[[self.lyrics_encoder.get(_lowerCamelCase   ,      0			) for character in list_lyrics[0]], [], []]
  return artists_id, list_genres, lyric_ids

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Any			)   ->						Tuple:

  """simple docstring"""





  return list(_lowerCamelCase			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							List[str]   ,      snake_case	:							Any   ,      snake_case	:							Optional[int]   ,      **snake_case	:							Union[str, Any]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_, UpperCamelCase_					:						Tuple =					self.prepare_for_tokenization(_lowerCamelCase   ,      _lowerCamelCase   ,      _lowerCamelCase			)
  UpperCamelCase_					:						Optional[int] =					self._tokenize(_lowerCamelCase			)
  return artist, genre, lyrics

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							str   ,      snake_case	:							str   ,      snake_case	:							str   ,      snake_case	:							bool = False			)   ->						Dict:

  """simple docstring"""





  for idx in range(len(self.version			)			):
   if self.version[idx] == "v3":
    UpperCamelCase_					:						Optional[int] =					artists[idx].lower()
    UpperCamelCase_					:						Tuple =					[genres[idx].lower()]
   else:
    UpperCamelCase_					:						str =					self._normalize(artists[idx]			) + '.v2'
    UpperCamelCase_					:						Any =					[
        self._normalize(_lowerCamelCase			) + '.v2' for genre in genres[idx].split('_'			)
    ]  # split is for the full dictionary with combined genres

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

  UpperCamelCase_					:						Optional[int] =					self._run_strip_accents(_lowerCamelCase			)
  UpperCamelCase_					:						List[str] =					lyrics.replace('\\'   ,      '\n'			)
  UpperCamelCase_					:						str =					self.out_of_vocab.sub(''   ,      _lowerCamelCase			), [], []
  return artists, genres, lyrics

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							Optional[Any]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					unicodedata.normalize('NFD'   ,      _lowerCamelCase			)
  UpperCamelCase_					:						List[str] =					[]
  for char in text:
   UpperCamelCase_					:						List[Any] =					unicodedata.category(_lowerCamelCase			)
   if cat == "Mn":
    continue
   output.append(_lowerCamelCase			)
  return "".join(_lowerCamelCase			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							str			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					(
      [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			)]
      + ['.']
  )
  UpperCamelCase_					:						Dict =					frozenset(_lowerCamelCase			)
  UpperCamelCase_					:						Dict =					re.compile(R'_+'			)
  UpperCamelCase_					:						Dict =					''.join([c if c in accepted else '_' for c in text.lower()]			)
  UpperCamelCase_					:						List[Any] =					pattern.sub('_'   ,      _lowerCamelCase			).strip('_'			)
  return text

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							List[str]			)   ->						Union[str, Any]:

  """simple docstring"""





  return " ".join(_lowerCamelCase			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Optional[int]   ,      snake_case	:							Optional[Union[str, TensorType]] = None   ,      snake_case	:							bool = False			)   ->						Any:

  """simple docstring"""





  if not isinstance(_lowerCamelCase   ,      _lowerCamelCase			):
   UpperCamelCase_					:						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

   UpperCamelCase_					:						Union[str, Any] =					tf.constant
   UpperCamelCase_					:						List[Any] =					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_					:						Tuple =					torch.tensor
   UpperCamelCase_					:						Optional[int] =					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_					:						Union[str, Any] =					jnp.array
   UpperCamelCase_					:						Optional[Any] =					_is_jax
  else:
   UpperCamelCase_					:						Dict =					np.asarray
   UpperCamelCase_					:						int =					_is_numpy

  # Do the tensor conversion in batch

  try:
   if prepend_batch_axis:
    UpperCamelCase_					:						str =					[inputs]

   if not is_tensor(_lowerCamelCase			):
    UpperCamelCase_					:						List[Any] =					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	:							int   ,      snake_case	:							List[Any]   ,      snake_case	:							Optional[Any]   ,      snake_case	:							str=""   ,      snake_case	:							Optional[Any]="pt"			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					[0, 0, 0]
  UpperCamelCase_					:						int =					[artist] * len(self.version			)
  UpperCamelCase_					:						Dict =					[genres] * len(self.version			)

  UpperCamelCase_, UpperCamelCase_, UpperCamelCase_					:						Dict =					self.tokenize(_lowerCamelCase   ,      _lowerCamelCase   ,      _lowerCamelCase			)
  UpperCamelCase_, UpperCamelCase_, UpperCamelCase_					:						Any =					self._convert_token_to_id(_lowerCamelCase   ,      _lowerCamelCase   ,      _lowerCamelCase			)

  UpperCamelCase_					:						int =					[-INFINITY] * len(full_tokens[-1]			)
  UpperCamelCase_					:						int =					[
      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 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							str   ,      snake_case	:							Optional[str] = None			)   ->						List[Any]:

  """simple docstring"""





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

  UpperCamelCase_					:						Any =					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			)			)

  UpperCamelCase_					:						Dict =					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			)			)

  UpperCamelCase_					:						Optional[Any] =					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 						SCREAMING_SNAKE_CASE__   ( self	:							Dict   ,      snake_case	:							List[Any]   ,      snake_case	:							Optional[int]   ,      snake_case	:							Tuple			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					self.artists_decoder.get(_lowerCamelCase			)
  UpperCamelCase_					:						int =					[self.genres_decoder.get(_lowerCamelCase			) for genre in genres_index]
  UpperCamelCase_					:						str =					[self.lyrics_decoder.get(_lowerCamelCase			) for character in lyric_index]
  return artist, genres, lyrics











"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowercase_				     =     logging.get_logger(__name__)


class 					snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:	int						 = ["input_features", "attention_mask"]


   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Union[str, Any]=80,       _lowerCamelCase :     int=1_60_00,       _lowerCamelCase :     Any=80,       _lowerCamelCase :     List[str]=0.0,       _lowerCamelCase :     int=True,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     Optional[int]=True,       **_lowerCamelCase :     List[str],       ):

     '''simple docstring'''
     super().__init__(feature_size=_lowerCamelCase,       sampling_rate=_lowerCamelCase,       padding_value=_lowerCamelCase,       **_lowerCamelCase  )
     __A									= num_mel_bins
     __A									= do_ceptral_normalize
     __A									= normalize_means
     __A									= normalize_vars
     __A									= True


   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict,       _lowerCamelCase :     np.ndarray,       ):

     '''simple docstring'''
     __A									= waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
     __A									= torch.from_numpy(_lowerCamelCase  ).unsqueeze(0  )
     __A									= ta_kaldi.fbank(_lowerCamelCase,       num_mel_bins=self.num_mel_bins,       sample_frequency=self.sampling_rate  )
     return features.numpy()


   @staticmethod
   def 				_SCREAMING_SNAKE_CASE				(	_lowerCamelCase :     np.ndarray,       _lowerCamelCase :     int,       _lowerCamelCase :     Optional[bool] = True,       _lowerCamelCase :     Optional[bool] = True,       _lowerCamelCase :     float = 0.0,       ):

     '''simple docstring'''
     # make sure we normalize float32 arrays
     if normalize_means:
       __A									= x[:input_length].mean(axis=0  )
       __A									= np.subtract(_lowerCamelCase,       _lowerCamelCase  )
     if normalize_vars:
       __A									= x[:input_length].std(axis=0  )
       __A									= np.divide(_lowerCamelCase,       _lowerCamelCase  )

     if input_length < x.shape[0]:
       __A									= padding_value

     # make sure array is in float32
     __A									= x.astype(np.floataa  )

     return x


   def 				_SCREAMING_SNAKE_CASE				(	self :     str,       _lowerCamelCase :     List[np.ndarray],       _lowerCamelCase :     Optional[np.ndarray] = None  ):

     '''simple docstring'''
     __A									= attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
     return [
         self.utterance_cmvn(_lowerCamelCase,       _lowerCamelCase,       self.normalize_means,       self.normalize_vars,       self.padding_value  )
         for x, n in zip(_lowerCamelCase,       _lowerCamelCase  )
     ]






   def __call__(	self :     Optional[Any],       _lowerCamelCase :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],       _lowerCamelCase :     Union[bool, str, PaddingStrategy] = False,       _lowerCamelCase :     Optional[int] = None,       _lowerCamelCase :     bool = False,       _lowerCamelCase :     Optional[int] = None,       _lowerCamelCase :     Optional[Union[str, TensorType]] = None,       _lowerCamelCase :     Optional[int] = None,       _lowerCamelCase :     Optional[bool] = None,       **_lowerCamelCase :     Optional[Any],       ):

     '''simple docstring'''

     if sampling_rate is not None:
       if sampling_rate != self.sampling_rate:
         raise ValueError(
             f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
             f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
             f' {self.sampling_rate} and not {sampling_rate}.'  )
     else:
       logger.warning(
           '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
           '''Failing to do so can result in silent errors that might be hard to debug.'''  )

     __A									= isinstance(_lowerCamelCase,       np.ndarray  ) and len(raw_speech.shape  ) > 1
     if is_batched_numpy and len(raw_speech.shape  ) > 2:
       raise ValueError(f'Only mono-channel audio is supported for input to {self}'  )
     __A									= is_batched_numpy or (
         isinstance(_lowerCamelCase,       (list, tuple)  ) and (isinstance(raw_speech[0],       (np.ndarray, tuple, list)  ))
     )

     if is_batched:
       __A									= [np.asarray(_lowerCamelCase,       dtype=np.floataa  ) for speech in raw_speech]
     elif not is_batched and not isinstance(_lowerCamelCase,       np.ndarray  ):
       __A									= np.asarray(_lowerCamelCase,       dtype=np.floataa  )
     elif isinstance(_lowerCamelCase,       np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
       __A									= raw_speech.astype(np.floataa  )

     # always return batch
     if not is_batched:
       __A									= [raw_speech]

     # extract fbank features
     __A									= [self._extract_fbank_features(_lowerCamelCase  ) for waveform in raw_speech]

     # convert into correct format for padding
     __A									= BatchFeature({'''input_features''': features}  )

     __A									= self.pad(
         _lowerCamelCase,       padding=_lowerCamelCase,       max_length=_lowerCamelCase,       truncation=_lowerCamelCase,       pad_to_multiple_of=_lowerCamelCase,       return_attention_mask=_lowerCamelCase,       **_lowerCamelCase,       )

     # make sure list is in array format
     __A									= padded_inputs.get('''input_features'''  )
     if isinstance(input_features[0],       _lowerCamelCase  ):
       __A									= [np.asarray(_lowerCamelCase,       dtype=np.floataa  ) for feature in input_features]

     __A									= padded_inputs.get('''attention_mask'''  )
     if attention_mask is not None:
       __A									= [np.asarray(_lowerCamelCase,       dtype=np.intaa  ) for array in attention_mask]

     # Utterance-level cepstral mean and variance normalization
     if self.do_ceptral_normalize:
       __A									= (
           np.array(_lowerCamelCase,       dtype=np.intaa  )
           if self._get_padding_strategies(_lowerCamelCase,       max_length=_lowerCamelCase  ) is not PaddingStrategy.DO_NOT_PAD
           else None
       )
       __A									= self.normalize(
           padded_inputs['''input_features'''],       attention_mask=_lowerCamelCase  )

     if return_tensors is not None:
       __A									= padded_inputs.convert_to_tensors(_lowerCamelCase  )

     return padded_inputs




