



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()
a_			=					logging.get_logger("""transformers.models.encodec""")

a_			=					{
    """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""",
}
a_			=					{
    """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""",
}
a_			=					{
    """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""",
}
a_			=					{
    """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""",
}
a_			=					{
    """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""",
}
a_			=					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a_			=					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a_			=					[]
a_			=					[]





def 		a__			(		_UpperCamelCase			: Any ,_UpperCamelCase			: List[str] ,_UpperCamelCase			: Dict ,_UpperCamelCase			: str ,_UpperCamelCase			: Union[str, Any]							):
   for attribute in key.split('''.'''							):
      __lowerCamelCase													=    getattr(_UpperCamelCase ,_UpperCamelCase							)

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

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





def 		a__			(		_UpperCamelCase			: Dict ,_UpperCamelCase			: Any							):
   for key in ignore_keys:
      if key.endswith('''.*'''							):
         if name.startswith(key[:-1]							):
            return True
      elif ".*." in key:
         __lowerCamelCase							,__lowerCamelCase													=    key.split('''.*.'''							)
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False





def 		a__			(		_UpperCamelCase			: List[str] ,_UpperCamelCase			: List[str] ,_UpperCamelCase			: Optional[Any]							):
   __lowerCamelCase													=    []

   if model_name == "encodec_24khz" or "encodec_32khz":
      __lowerCamelCase													=    MAPPING_24K
   elif model_name == "encodec_48khz":
      __lowerCamelCase													=    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

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

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

            __lowerCamelCase													=    True
            if "*" in mapped_key:
               __lowerCamelCase													=    name.split(_UpperCamelCase							)[0].split('''.'''							)[-2]
               __lowerCamelCase													=    mapped_key.replace('''*''' ,_UpperCamelCase							)
            if "weight_g" in name:
               __lowerCamelCase													=    '''weight_g'''
            elif "weight_v" in name:
               __lowerCamelCase													=    '''weight_v'''
            elif "weight_ih_l0" in name:
               __lowerCamelCase													=    '''weight_ih_l0'''
            elif "weight_hh_l0" in name:
               __lowerCamelCase													=    '''weight_hh_l0'''
            elif "bias_ih_l0" in name:
               __lowerCamelCase													=    '''bias_ih_l0'''
            elif "bias_hh_l0" in name:
               __lowerCamelCase													=    '''bias_hh_l0'''
            elif "weight_ih_l1" in name:
               __lowerCamelCase													=    '''weight_ih_l1'''
            elif "weight_hh_l1" in name:
               __lowerCamelCase													=    '''weight_hh_l1'''
            elif "bias_ih_l1" in name:
               __lowerCamelCase													=    '''bias_ih_l1'''
            elif "bias_hh_l1" in name:
               __lowerCamelCase													=    '''bias_hh_l1'''
            elif "bias" in name:
               __lowerCamelCase													=    '''bias'''
            elif "weight" in name:
               __lowerCamelCase													=    '''weight'''
            elif "running_mean" in name:
               __lowerCamelCase													=    '''running_mean'''
            elif "running_var" in name:
               __lowerCamelCase													=    '''running_var'''
            elif "num_batches_tracked" in name:
               __lowerCamelCase													=    '''num_batches_tracked'''
            else:
               __lowerCamelCase													=    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 		a__			(		_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: List[Any] ,_UpperCamelCase			: Tuple ,_UpperCamelCase			: Optional[int]=None ,_UpperCamelCase			: Tuple=None ,):
   if config_path is not None:
      __lowerCamelCase													=    EncodecConfig.from_pretrained(_UpperCamelCase							)
   else:
      __lowerCamelCase													=    EncodecConfig()

   if model_name == "encodec_24khz":
      pass  # config is already correct
   elif model_name == "encodec_32khz":
      __lowerCamelCase													=    [8, 5, 4, 4]
      __lowerCamelCase													=    [2.2]
      __lowerCamelCase													=    64
      __lowerCamelCase													=    3_20_00
      __lowerCamelCase													=    20_48
      __lowerCamelCase													=    False
      __lowerCamelCase													=    False
      __lowerCamelCase													=    False
   elif model_name == "encodec_48khz":
      __lowerCamelCase													=    [8, 5, 4, 2]
      __lowerCamelCase													=    [3.0, 6.0, 12.0, 24.0]
      __lowerCamelCase													=    4_80_00
      __lowerCamelCase													=    2
      __lowerCamelCase													=    False
      __lowerCamelCase													=    '''time_group_norm'''
      __lowerCamelCase													=    True
      __lowerCamelCase													=    1.0
      __lowerCamelCase													=    0.01
   else:
      raise ValueError(F"""Unknown model name: {model_name}"""							)

   __lowerCamelCase													=    EncodecModel(_UpperCamelCase							)

   __lowerCamelCase													=    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							)

   __lowerCamelCase													=    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
      __lowerCamelCase													=    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__":
  a_			=					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."""
  )

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











def 		a__			(		_UpperCamelCase			: str ,_UpperCamelCase			: str = " "							):
   __lowerCamelCase													=    []

   __lowerCamelCase													=    0
   for index, char in enumerate(_UpperCamelCase							):
      if char == separator:
         split_words.append(string[last_index:index]							)
         __lowerCamelCase													=    index + 1
      elif index + 1 == len(_UpperCamelCase							):
         split_words.append(string[last_index : index + 1]							)
   return split_words


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

  testmod()






