
"""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


_lowercase							=				logging.get_logger(__name__)

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

_lowercase							=				{
    '''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''',
    },
}

_lowercase							=				{
    '''jukebox''': 512,
}

class 					__a     ( snake_case_       ):



							'''simple docstring'''







							_lowerCamelCase					:			Dict          =   VOCAB_FILES_NAMES
							_lowerCamelCase					:			str          =   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase					:			Union[str, Any]          =   PRETRAINED_LYRIC_TOKENS_SIZES
							_lowerCamelCase					:			Union[str, Any]          =   ["""input_ids""", """attention_mask"""]
							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase=["v3", "v2", "v2"]  ,    _lowerCamelCase=512  ,    _lowerCamelCase=5  ,    _lowerCamelCase="<|endoftext|>"  ,    **_lowerCamelCase  ,    )			->							Any:




										'''simple docstring'''
										__lowercase       =       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  ,    )
										__lowercase       =       version
										__lowercase       =       max_n_lyric_tokens
										__lowercase       =       n_genres

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

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

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

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

										__lowercase       =       regex.compile(_lowerCamelCase       )
										__lowercase       =       {v: k for k, v in self.artists_encoder.items()}
										__lowercase       =       {v: k for k, v in self.genres_encoder.items()}
										__lowercase       =       {v: k for k, v in self.lyrics_encoder.items()}
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							str:




										'''simple docstring'''
										return len(self.artists_encoder       ) + len(self.genres_encoder       ) + len(self.lyrics_encoder       )
							def   SCREAMING_SNAKE_CASE						(     self       )			->							List[Any]:




										'''simple docstring'''
										return dict(self.artists_encoder  ,    self.genres_encoder  ,    self.lyrics_encoder       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Optional[int]:




										'''simple docstring'''
										__lowercase       =       [self.artists_encoder.get(_lowerCamelCase  ,    0       ) for artist in list_artists]
										for genres in range(len(_lowerCamelCase       )       ):
													__lowercase       =       [self.genres_encoder.get(_lowerCamelCase  ,    0       ) for genre in list_genres[genres]]
													__lowercase       =       list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]       ))

										__lowercase       =       [[self.lyrics_encoder.get(_lowerCamelCase  ,    0       ) for character in list_lyrics[0]], [], []]
										return artists_id, list_genres, lyric_ids
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							List[str]:




										'''simple docstring'''
										return list(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    **_lowerCamelCase       )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase   ,					__lowercase   ,					__lowercase       =       self.prepare_for_tokenization(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       )
										__lowercase       =       self._tokenize(_lowerCamelCase       )
										return artist, genre, lyrics
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase = False       )			->							Tuple[str, str, str, Dict[str, Any]]:




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

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

										__lowercase       =       self._run_strip_accents(_lowerCamelCase       )
										__lowercase       =       lyrics.replace("\\"  ,    "\n"       )
										__lowercase       =       self.out_of_vocab.sub(""  ,    _lowerCamelCase       ), [], []
										return artists, genres, lyrics
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							Tuple:




										'''simple docstring'''
										__lowercase       =       unicodedata.normalize("NFD"  ,    _lowerCamelCase       )
										__lowercase       =       []
										for char in text:
													__lowercase       =       unicodedata.category(_lowerCamelCase       )
													if cat == "Mn":
																continue
													output.append(_lowerCamelCase       )
										return "".join(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										__lowercase       =       (
										    [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       )]
										    + ["."]
										)
										__lowercase       =       frozenset(_lowerCamelCase       )
										__lowercase       =       re.compile(R"_+"       )
										__lowercase       =       "".join([c if c in accepted else "_" for c in text.lower()]       )
										__lowercase       =       pattern.sub("_"  ,    _lowerCamelCase       ).strip("_"       )
										return text
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase       )			->							str:




										'''simple docstring'''
										return " ".join(_lowerCamelCase       )
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False       )			->							Optional[int]:




										'''simple docstring'''
										# Convert to TensorType
										if not isinstance(_lowerCamelCase  ,    _lowerCamelCase       ):
													__lowercase       =       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

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

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

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

										# Do the tensor conversion in batch

										try:
													if prepend_batch_axis:
																__lowercase       =       [inputs]

													if not is_tensor(_lowerCamelCase       ):
																__lowercase       =       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"       )			->							BatchEncoding:




										'''simple docstring'''
										__lowercase       =       [0, 0, 0]
										__lowercase       =       [artist] * len(self.version       )
										__lowercase       =       [genres] * len(self.version       )

										__lowercase   ,					__lowercase   ,					__lowercase       =       self.tokenize(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       )
										__lowercase   ,					__lowercase   ,					__lowercase       =       self._convert_token_to_id(_lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       )

										__lowercase       =       [-INFINITY] * len(full_tokens[-1]       )
										__lowercase       =       [
										    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  ,    _lowerCamelCase  ,    _lowerCamelCase = None       )			->							Tuple[str]:




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

										__lowercase       =       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       )       )

										__lowercase       =       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       )       )

										__lowercase       =       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  ,    _lowerCamelCase  ,    _lowerCamelCase  ,    _lowerCamelCase       )			->							Optional[Any]:




										'''simple docstring'''
										__lowercase       =       self.artists_decoder.get(_lowerCamelCase       )
										__lowercase       =       [self.genres_decoder.get(_lowerCamelCase       ) for genre in genres_index]
										__lowercase       =       [self.lyrics_decoder.get(_lowerCamelCase       ) for character in lyric_index]
										return artist, genres, lyrics







import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__A							=logging.get_logger(__name__)




class        _SCREAMING_SNAKE_CASE	( snake_case_		):
	lowerCAmelCase__   =					['input_values', 'attention_mask']



	def __init__(       self		,    lowercase = 1		,    lowercase = 16000		,    lowercase = 0.0		,    lowercase = False		,    lowercase = 80		,    lowercase = 16		,    lowercase = 64		,    lowercase = "hann_window"		,    lowercase = 1.0		,    lowercase = 80		,    lowercase = 7600		,    lowercase = 1e-10		,    lowercase = 2		,    lowercase = True		,    **lowercase		,    )							->			int:
					super().__init__(feature_size=lowercase		,    sampling_rate=lowercase		,    padding_value=lowercase		,    **lowercase					)
					lowerCamelCase_         =     do_normalize
					lowerCamelCase_         =     return_attention_mask

					lowerCamelCase_         =     num_mel_bins
					lowerCamelCase_         =     hop_length
					lowerCamelCase_         =     win_length
					lowerCamelCase_         =     win_function
					lowerCamelCase_         =     frame_signal_scale
					lowerCamelCase_         =     fmin
					lowerCamelCase_         =     fmax
					lowerCamelCase_         =     mel_floor
					lowerCamelCase_         =     reduction_factor

					lowerCamelCase_         =     win_length * sampling_rate // 1000
					lowerCamelCase_         =     hop_length * sampling_rate // 1000
					lowerCamelCase_         =     optimal_fft_length(self.sample_size					)
					lowerCamelCase_         =     (self.n_fft // 2) + 1

					lowerCamelCase_         =     window_function(window_length=self.sample_size		,    name=self.win_function		,    periodic=lowercase					)

					lowerCamelCase_         =     mel_filter_bank(
					    num_frequency_bins=self.n_freqs		,    num_mel_filters=self.num_mel_bins		,    min_frequency=self.fmin		,    max_frequency=self.fmax		,    sampling_rate=self.sampling_rate		,    norm="slaney"		,    mel_scale="slaney"		,    )

					if frame_signal_scale != 1.0:
									warnings.warn(
									    "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"		,    lowercase		,    )
					if reduction_factor != 2.0:
									warnings.warn(
									    "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"		,    lowercase		,    )



	@staticmethod
	# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
	def        SCREAMING_SNAKE_CASE_(       lowercase		,    lowercase		,    lowercase = 0.0					)							->			List[np.ndarray]:
					if attention_mask is not None:
									lowerCamelCase_         =     np.array(lowercase		,    np.intaa					)
									lowerCamelCase_         =     []

									for vector, length in zip(lowercase		,    attention_mask.sum(-1					)					):
													lowerCamelCase_         =     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7					)
													if length < normed_slice.shape[0]:
																	lowerCamelCase_         =     padding_value

													normed_input_values.append(lowercase					)
					else:
									lowerCamelCase_         =     [(x - x.mean()) / np.sqrt(x.var() + 1e-7					) for x in input_values]

					return normed_input_values



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    )							->			np.ndarray:
					lowerCamelCase_         =     spectrogram(
					    lowercase		,    window=self.window		,    frame_length=self.sample_size		,    hop_length=self.sample_stride		,    fft_length=self.n_fft		,    mel_filters=self.mel_filters		,    mel_floor=self.mel_floor		,    log_mel="log10"		,    )
					return log_mel_spec.T



	def __call__(       self		,    lowercase = None		,    lowercase = None		,    lowercase = False		,    lowercase = None		,    lowercase = False		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    **lowercase		,    )							->			BatchFeature:
					if audio is None and audio_target is None:
									raise ValueError("You must provide either `audio` or `audio_target` values."					)

					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 audio 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."					)

					if audio is not None:
									lowerCamelCase_         =     self._process_audio(
									    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    **lowercase		,    )
					else:
									lowerCamelCase_         =     None

					if audio_target is not None:
									lowerCamelCase_         =     self._process_audio(
									    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    lowercase		,    **lowercase		,    )

									if inputs is None:
													return inputs_target
									else:
													lowerCamelCase_         =     inputs_target["input_values"]
													lowerCamelCase_         =     inputs_target.get("attention_mask"					)
													if decoder_attention_mask is not None:
																	lowerCamelCase_         =     decoder_attention_mask

					return inputs



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase = False		,    lowercase = False		,    lowercase = None		,    lowercase = False		,    lowercase = None		,    lowercase = None		,    lowercase = None		,    **lowercase		,    )							->			BatchFeature:
					lowerCamelCase_         =     isinstance(lowercase		,    np.ndarray					) and len(speech.shape					) > 1
					if is_batched_numpy and len(speech.shape					) > 2:
									raise ValueError(f'Only mono-channel audio is supported for input to {self}'					)
					lowerCamelCase_         =     is_batched_numpy or (
					    isinstance(lowercase		,    (list, tuple)					) and (isinstance(speech[0]		,    (np.ndarray, tuple, list)					))
					)

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

					# always return batch
					if not is_batched:
									lowerCamelCase_         =     [speech]

					# needed to make pad() work on spectrogram inputs
					lowerCamelCase_         =     self.feature_size

					# convert into correct format for padding
					if is_target:
									lowerCamelCase_         =     [self._extract_mel_features(lowercase					) for waveform in speech]
									lowerCamelCase_         =     BatchFeature({"input_values": features}					)
									lowerCamelCase_         =     self.num_mel_bins
					else:
									lowerCamelCase_         =     BatchFeature({"input_values": speech}					)

					lowerCamelCase_         =     self.pad(
					    lowercase		,    padding=lowercase		,    max_length=lowercase		,    truncation=lowercase		,    pad_to_multiple_of=lowercase		,    return_attention_mask=lowercase		,    **lowercase		,    )

					lowerCamelCase_         =     feature_size_hack

					# convert input values to correct format
					lowerCamelCase_         =     padded_inputs["input_values"]
					if not isinstance(input_values[0]		,    np.ndarray					):
									lowerCamelCase_         =     [np.asarray(lowercase		,    dtype=np.floataa					) for array in input_values]
					elif (
					    not isinstance(lowercase		,    np.ndarray					)
					    and isinstance(input_values[0]		,    np.ndarray					)
					    and input_values[0].dtype is np.dtype(np.floataa					)
					):
									lowerCamelCase_         =     [array.astype(np.floataa					) for array in input_values]
					elif isinstance(lowercase		,    np.ndarray					) and input_values.dtype is np.dtype(np.floataa					):
									lowerCamelCase_         =     input_values.astype(np.floataa					)

					# convert attention_mask to correct format
					lowerCamelCase_         =     padded_inputs.get("attention_mask"					)
					if attention_mask is not None:
									lowerCamelCase_         =     [np.asarray(lowercase		,    dtype=np.intaa					) for array in attention_mask]

					# zero-mean and unit-variance normalization
					if not is_target and self.do_normalize:
									lowerCamelCase_         =     (
									    attention_mask
									    if self._get_padding_strategies(lowercase		,    max_length=lowercase					) is not PaddingStrategy.DO_NOT_PAD
									    else None
									)
									lowerCamelCase_         =     self.zero_mean_unit_var_norm(
									    padded_inputs["input_values"]		,    attention_mask=lowercase		,    padding_value=self.padding_value					)

					if return_tensors is not None:
									lowerCamelCase_         =     padded_inputs.convert_to_tensors(lowercase					)

					return padded_inputs



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict[str, Any]:
					lowerCamelCase_         =     super().to_dict()

					# Don't serialize these as they are derived from the other properties.
					lowerCamelCase_         =     ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
					for name in names:
									if name in output:
													del output[name]

					return output


