





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

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

lowercase      :       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',
    },
}

lowercase      :       Optional[Any]									=  {
    'jukebox': 5_1_2,
}





class  A__    (							_lowerCamelCase	):




   """simple docstring"""



   __A   :				str         =  VOCAB_FILES_NAMES
   __A   :				List[str]         =  PRETRAINED_VOCAB_FILES_MAP
   __A   :				Tuple         =  PRETRAINED_LYRIC_TOKENS_SIZES
   __A   :				List[str]         =  ['''input_ids''', '''attention_mask''']

   def __init__(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase=["v3", "v2", "v2"]					,  lowercase=512					,  lowercase=5					,  lowercase="<|endoftext|>"					,  **lowercase					,  )			->						List[Any]:







          '''simple docstring'''

          a__						:	Any =				AddedToken(_UpperCamelCase					,  lstrip=_UpperCamelCase					,  rstrip=_UpperCamelCase) if isinstance(_UpperCamelCase					,  _UpperCamelCase) else unk_token
          super().__init__(
              unk_token=_UpperCamelCase					,  n_genres=_UpperCamelCase					,  version=_UpperCamelCase					,  max_n_lyric_tokens=_UpperCamelCase					,  **_UpperCamelCase					,  )
          a__						:	Optional[Any] =				version
          a__						:	List[str] =				max_n_lyric_tokens
          a__						:	Optional[int] =				n_genres

          with open(_UpperCamelCase					,  encoding='utf-8') as vocab_handle:
                 a__						:	Optional[int] =				json.load(_UpperCamelCase)

          with open(_UpperCamelCase					,  encoding='utf-8') as vocab_handle:
                 a__						:	List[str] =				json.load(_UpperCamelCase)

          with open(_UpperCamelCase					,  encoding='utf-8') as vocab_handle:
                 a__						:	Optional[int] =				json.load(_UpperCamelCase)

          a__						:	Any =				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:
                 a__						:	List[str] =				oov.replace(r'\-\''					,  r'\-+\'')

          a__						:	List[Any] =				regex.compile(_UpperCamelCase)
          a__						:	Any =				{v: k for k, v in self.artists_encoder.items()}
          a__						:	List[str] =				{v: k for k, v in self.genres_encoder.items()}
          a__						:	Optional[Any] =				{v: k for k, v in self.lyrics_encoder.items()}

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







          '''simple docstring'''

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

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







          '''simple docstring'''

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

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Any:







          '''simple docstring'''

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

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

   def 							__lowercase		(					self					,  lowercase)			->						List[str]:







          '''simple docstring'''

          return list(_UpperCamelCase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  **lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__ ,							a__ ,							a__						:	List[str] =				self.prepare_for_tokenization(_UpperCamelCase					,  _UpperCamelCase					,  _UpperCamelCase)
          a__						:	Union[str, Any] =				self._tokenize(_UpperCamelCase)
          return artist, genre, lyrics

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







          '''simple docstring'''

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

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

          a__						:	Dict =				self._run_strip_accents(_UpperCamelCase)
          a__						:	Union[str, Any] =				lyrics.replace('\\'					,  '\n')
          a__						:	Optional[Any] =				self.out_of_vocab.sub(''					,  _UpperCamelCase), [], []
          return artists, genres, lyrics

   def 							__lowercase		(					self					,  lowercase)			->						int:







          '''simple docstring'''

          a__						:	List[str] =				unicodedata.normalize('NFD'					,  _UpperCamelCase)
          a__						:	int =				[]
          for char in text:
                 a__						:	Dict =				unicodedata.category(_UpperCamelCase)
                 if cat == "Mn":
                        continue
                 output.append(_UpperCamelCase)
          return "".join(_UpperCamelCase)

   def 							__lowercase		(					self					,  lowercase)			->						str:







          '''simple docstring'''

          a__						:	int =				(
              [chr(_UpperCamelCase) for i in range(ord('a')					,  ord('z') + 1)]
              + [chr(_UpperCamelCase) for i in range(ord('A')					,  ord('Z') + 1)]
              + [chr(_UpperCamelCase) for i in range(ord('0')					,  ord('9') + 1)]
              + ['.']
          )
          a__						:	List[Any] =				frozenset(_UpperCamelCase)
          a__						:	List[str] =				re.compile(r'_+')
          a__						:	Union[str, Any] =				''.join([c if c in accepted else '_' for c in text.lower()])
          a__						:	Optional[int] =				pattern.sub('_'					,  _UpperCamelCase).strip('_')
          return text

   def 							__lowercase		(					self					,  lowercase)			->						str:







          '''simple docstring'''

          return " ".join(_UpperCamelCase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase = None					,  lowercase = False)			->						Dict:







          '''simple docstring'''

          if not isinstance(_UpperCamelCase					,  _UpperCamelCase):
                 a__						:	List[str] =				TensorType(_UpperCamelCase)

          # 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

                 a__						:	Optional[Any] =				tf.constant
                 a__						:	str =				tf.is_tensor
          elif tensor_type == TensorType.PYTORCH:
                 if not is_torch_available():
                        raise ImportError('Unable to convert output to PyTorch tensors format, PyTorch is not installed.')
                 import torch

                 a__						:	int =				torch.tensor
                 a__						:	Optional[Any] =				torch.is_tensor
          elif tensor_type == TensorType.JAX:
                 if not is_flax_available():
                        raise ImportError('Unable to convert output to JAX tensors format, JAX is not installed.')
                 import jax.numpy as jnp  # noqa: F811

                 a__						:	Union[str, Any] =				jnp.array
                 a__						:	Tuple =				_is_jax
          else:
                 a__						:	Tuple =				np.asarray
                 a__						:	List[Any] =				_is_numpy

          # Do the tensor conversion in batch

          try:
                 if prepend_batch_axis:
                        a__						:	int =				[inputs]

                 if not is_tensor(_UpperCamelCase):
                        a__						:	Any =				as_tensor(_UpperCamelCase)
          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					,  lowercase					,  lowercase					,  lowercase=""					,  lowercase="pt")			->						BatchEncoding:







          '''simple docstring'''

          a__						:	Tuple =				[0, 0, 0]
          a__						:	List[Any] =				[artist] * len(self.version)
          a__						:	Optional[Any] =				[genres] * len(self.version)

          a__ ,							a__ ,							a__						:	int =				self.tokenize(_UpperCamelCase					,  _UpperCamelCase					,  _UpperCamelCase)
          a__ ,							a__ ,							a__						:	Any =				self._convert_token_to_id(_UpperCamelCase					,  _UpperCamelCase					,  _UpperCamelCase)

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

   def 							__lowercase		(					self					,  lowercase					,  lowercase = None)			->						Tuple[str]:







          '''simple docstring'''

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

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

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

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

          return (artists_file, genres_file, lyrics_file)

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Dict:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.artists_decoder.get(_UpperCamelCase)
          a__						:	Union[str, Any] =				[self.genres_decoder.get(_UpperCamelCase) for genre in genres_index]
          a__						:	str =				[self.lyrics_decoder.get(_UpperCamelCase) for character in lyric_index]
          return artist, genres, lyrics









import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
snake_case__					:							Dict													=     logging.get_logger(__name__)

snake_case__					:							Optional[Any]													=     {
    '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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    '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',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
snake_case__					:							Optional[int]													=     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]



def _a				(	lowerCamelCase:   List[Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   int )	->						List[str]:







      '''simple docstring'''






      for attribute in key.split('''.''' ):
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase )

      if weight_type is not None:
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase ).shape
      else:
            __A          =				hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                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":
            __A          =				value
      elif weight_type == "weight_g":
            __A          =				value
      elif weight_type == "weight_v":
            __A          =				value
      elif weight_type == "bias":
            __A          =				value
      else:
            __A          =				value

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



def _a				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Optional[int] )	->						Tuple:







      '''simple docstring'''






      __A          =				[]
      __A          =				fairseq_model.state_dict()

      __A          =				hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            __A          =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				hf_model.config.feat_extract_norm == '''group'''					,				)
                  __A          =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        __A          =				'''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                        if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
                              if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
                                    # special case since naming is very similar
                                    continue
                              __A          =				True
                              if "*" in mapped_key:
                                    __A          =				name.split(lowerCamelCase )[0].split('''.''' )[-2]
                                    __A          =				mapped_key.replace('''*'''					,				lowerCamelCase )
                              if "weight_g" in name:
                                    __A          =				'''weight_g'''
                              elif "weight_v" in name:
                                    __A          =				'''weight_v'''
                              elif "bias" in name:
                                    __A          =				'''bias'''
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    __A          =				'''weight'''
                              else:
                                    __A          =				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 _a				(	lowerCamelCase:   int					,				lowerCamelCase:   Any					,				lowerCamelCase:   int					,				lowerCamelCase:   int					,				lowerCamelCase:   List[str] )	->						Union[str, Any]:







      '''simple docstring'''






      __A          =				full_name.split('''conv_layers.''' )[-1]
      __A          =				name.split('''.''' )
      __A          =				int(items[0] )
      __A          =				int(items[1] )

      if type_id == 0:
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            F"""{full_name} has size {value.shape}, but"""
                            F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
                  __A          =				value
                  logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            F"""{full_name} has size {value.shape}, but"""
                            F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
                  __A          =				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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            F"""{full_name} has size {value.shape}, but"""
                            F""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
                  __A          =				value
                  logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            F"""{full_name} has size {value.shape}, but"""
                            F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
                  __A          =				value
                  logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
      else:
            unused_weights.append(lowerCamelCase )



@torch.no_grad()
def _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   int					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[int]=True )	->						List[Any]:







      '''simple docstring'''






      if config_path is not None:
            __A          =				UniSpeechSatConfig.from_pretrained(lowerCamelCase )
      else:
            __A          =				UniSpeechSatConfig()

      __A          =				''''''

      if is_finetuned:
            __A          =				UniSpeechSatForCTC(lowerCamelCase )
      else:
            __A          =				UniSpeechSatForPreTraining(lowerCamelCase )

      __A   ,						__A   ,						__A          =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]					,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
      __A          =				model[0].eval()

      recursively_load_weights(lowerCamelCase					,				lowerCamelCase )

      hf_wavavec.save_pretrained(lowerCamelCase )


if __name__ == "__main__":
       snake_case__					:							Tuple													=     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_path', default=None, type=str, help='Path to hf config.json of model to convert')
       parser.add_argument(
           '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
       )
       snake_case__					:							Any													=     parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )





