






'''simple docstring'''

import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__											=     logging.get_logger('transformers.models.encodec')

SCREAMING_SNAKE_CASE__											=     {
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
SCREAMING_SNAKE_CASE__											=     {
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
SCREAMING_SNAKE_CASE__											=     {
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
SCREAMING_SNAKE_CASE__											=     {
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
SCREAMING_SNAKE_CASE__											=     {
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
SCREAMING_SNAKE_CASE__											=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
SCREAMING_SNAKE_CASE__											=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
SCREAMING_SNAKE_CASE__											=     []
SCREAMING_SNAKE_CASE__											=     []







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Dict:
      for attribute in key.split("""."""					):
            UpperCamelCase       						=      getattr(__UpperCamelCase	,		__UpperCamelCase					)

      if weight_type is not None:
            UpperCamelCase       						=      getattr(__UpperCamelCase	,		__UpperCamelCase					).shape
      else:
            UpperCamelCase       						=      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":
            UpperCamelCase       						=      value
      elif weight_type == "weight_g":
            UpperCamelCase       						=      value
      elif weight_type == "weight_v":
            UpperCamelCase       						=      value
      elif weight_type == "bias":
            UpperCamelCase       						=      value
      elif weight_type == "running_mean":
            UpperCamelCase       						=      value
      elif weight_type == "running_var":
            UpperCamelCase       						=      value
      elif weight_type == "num_batches_tracked":
            UpperCamelCase       						=      value
      elif weight_type == "weight_ih_l0":
            UpperCamelCase       						=      value
      elif weight_type == "weight_hh_l0":
            UpperCamelCase       						=      value
      elif weight_type == "bias_ih_l0":
            UpperCamelCase       						=      value
      elif weight_type == "bias_hh_l0":
            UpperCamelCase       						=      value
      elif weight_type == "weight_ih_l1":
            UpperCamelCase       						=      value
      elif weight_type == "weight_hh_l1":
            UpperCamelCase       						=      value
      elif weight_type == "bias_ih_l1":
            UpperCamelCase       						=      value
      elif weight_type == "bias_hh_l1":
            UpperCamelCase       						=      value
      else:
            UpperCamelCase       						=      value

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







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Optional[Any]:
      for key in ignore_keys:
            if key.endswith(""".*"""					):
                  if name.startswith(key[:-1]					):
                        return True
            elif ".*." in key:
                  UpperCamelCase       						=      key.split(""".*."""					)
                  if prefix in name and suffix in name:
                        return True
            elif key in name:
                  return True
      return False







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Tuple:
      UpperCamelCase       						=      []

      if model_name == "encodec_24khz" or "encodec_32khz":
            UpperCamelCase       						=      MAPPING_24K
      elif model_name == "encodec_48khz":
            UpperCamelCase       						=      MAPPING_48K
      else:
            raise ValueError(F"Unsupported model: {model_name}"					)

      for name, value in orig_dict.items():
            if should_ignore(__UpperCamelCase	,		__UpperCamelCase					):
                  logger.info(F"{name} was ignored"					)
                  continue

            UpperCamelCase       						=      False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        UpperCamelCase       						=      key.split(""".*."""					)
                        if prefix in name and suffix in name:
                              UpperCamelCase       						=      suffix

                  if key in name:
                        # HACK otherwise .embed gets initialized with .embed_avg too
                        if key.endswith("""embed"""					) and name.endswith("""embed_avg"""					):
                              continue

                        UpperCamelCase       						=      True
                        if "*" in mapped_key:
                              UpperCamelCase       						=      name.split(__UpperCamelCase					)[0].split("""."""					)[-2]
                              UpperCamelCase       						=      mapped_key.replace("""*"""	,		__UpperCamelCase					)
                        if "weight_g" in name:
                              UpperCamelCase       						=      'weight_g'
                        elif "weight_v" in name:
                              UpperCamelCase       						=      'weight_v'
                        elif "weight_ih_l0" in name:
                              UpperCamelCase       						=      'weight_ih_l0'
                        elif "weight_hh_l0" in name:
                              UpperCamelCase       						=      'weight_hh_l0'
                        elif "bias_ih_l0" in name:
                              UpperCamelCase       						=      'bias_ih_l0'
                        elif "bias_hh_l0" in name:
                              UpperCamelCase       						=      'bias_hh_l0'
                        elif "weight_ih_l1" in name:
                              UpperCamelCase       						=      'weight_ih_l1'
                        elif "weight_hh_l1" in name:
                              UpperCamelCase       						=      'weight_hh_l1'
                        elif "bias_ih_l1" in name:
                              UpperCamelCase       						=      'bias_ih_l1'
                        elif "bias_hh_l1" in name:
                              UpperCamelCase       						=      'bias_hh_l1'
                        elif "bias" in name:
                              UpperCamelCase       						=      'bias'
                        elif "weight" in name:
                              UpperCamelCase       						=      'weight'
                        elif "running_mean" in name:
                              UpperCamelCase       						=      'running_mean'
                        elif "running_var" in name:
                              UpperCamelCase       						=      'running_var'
                        elif "num_batches_tracked" in name:
                              UpperCamelCase       						=      'num_batches_tracked'
                        else:
                              UpperCamelCase       						=      None
                        set_recursively(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)
                  continue
            if not is_used:
                  unused_weights.append(__UpperCamelCase					)

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







@torch.no_grad()
def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None	,		)->				List[Any]:
      if config_path is not None:
            UpperCamelCase       						=      EncodecConfig.from_pretrained(__UpperCamelCase					)
      else:
            UpperCamelCase       						=      EncodecConfig()

      if model_name == "encodec_24khz":
            pass  # config is already correct
      elif model_name == "encodec_32khz":
            UpperCamelCase       						=      [8, 5, 4, 4]
            UpperCamelCase       						=      [2.2]
            UpperCamelCase       						=      64
            UpperCamelCase       						=      32000
            UpperCamelCase       						=      2048
            UpperCamelCase       						=      False
            UpperCamelCase       						=      False
            UpperCamelCase       						=      False
      elif model_name == "encodec_48khz":
            UpperCamelCase       						=      [8, 5, 4, 2]
            UpperCamelCase       						=      [3.0, 6.0, 12.0, 24.0]
            UpperCamelCase       						=      48000
            UpperCamelCase       						=      2
            UpperCamelCase       						=      False
            UpperCamelCase       						=      'time_group_norm'
            UpperCamelCase       						=      True
            UpperCamelCase       						=      1.0
            UpperCamelCase       						=      0.01
      else:
            raise ValueError(F"Unknown model name: {model_name}"					)

      UpperCamelCase       						=      EncodecModel(__UpperCamelCase					)

      UpperCamelCase       						=      EncodecFeatureExtractor(
          feature_size=config.audio_channels	,		sampling_rate=config.sampling_rate	,		chunk_length_s=config.chunk_length_s	,		overlap=config.overlap	,		)
      feature_extractor.save_pretrained(__UpperCamelCase					)

      UpperCamelCase       						=      torch.load(__UpperCamelCase					)
      if "best_state" in original_checkpoint:
            # we might have a training state saved, in which case discard the yaml results and just retain the weights
            UpperCamelCase       						=      original_checkpoint['best_state']
      recursively_load_weights(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)
      model.save_pretrained(__UpperCamelCase					)

      if repo_id:
            print("""Pushing to the hub..."""					)
            feature_extractor.push_to_hub(__UpperCamelCase					)
            model.push_to_hub(__UpperCamelCase					)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()
     parser.add_argument(
         '--model',
         default='encodec_24khz',
         type=str,
         help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
     )
     parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     parser.add_argument(
         '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
     )
     parser.add_argument(
         '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
     )

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()
     convert_checkpoint(
         args.model,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.push_to_hub,
     )








'''simple docstring'''

def 					lowercase__				(   __UpperCamelCase					)->				str:
      if not all(char in """01""" for char in bin_string					):
            raise ValueError("""Non-binary value was passed to the function"""					)
      if not bin_string:
            raise ValueError("""Empty string was passed to the function"""					)
      UpperCamelCase       						=      """"""
      while len(__UpperCamelCase					) % 3 != 0:
            UpperCamelCase       						=      """0""" + bin_string
      UpperCamelCase       						=      [
          bin_string[index : index + 3]
          for index in range(len(__UpperCamelCase					)					)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            UpperCamelCase       						=      0
            for index, val in enumerate(__UpperCamelCase					):
                  oct_val += int(2 ** (2 - index) * int(__UpperCamelCase					)					)
            oct_string += str(__UpperCamelCase					)
      return oct_string


if __name__ == "__main__":
     from doctest import testmod

     testmod()
