





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()
_UpperCAmelCase             =							logging.get_logger('transformers.models.encodec')

_UpperCAmelCase             =							{
    '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',
}
_UpperCAmelCase             =							{
    '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',
}
_UpperCAmelCase             =							{
    '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',
}
_UpperCAmelCase             =							{
    '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',
}
_UpperCAmelCase             =							{
    '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',
}
_UpperCAmelCase             =							{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_UpperCAmelCase             =							{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_UpperCAmelCase             =							[]
_UpperCAmelCase             =							[]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[str]   ,   SCREAMING_SNAKE_CASE		:List[str]   ,   SCREAMING_SNAKE_CASE		:Any   ,   SCREAMING_SNAKE_CASE		:List[str]   ,   SCREAMING_SNAKE_CASE		:Union[str, Any]   )      ->   List[str]:
 for attribute in key.split("""."""   ):
  __lowerCAmelCase					:	Optional[int]      =       getattr(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 if weight_type is not None:
  __lowerCAmelCase					:	List[str]      =       getattr(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ).shape
 else:
  __lowerCAmelCase					:	List[str]      =       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					:	List[Any]      =       value
 elif weight_type == "weight_g":
  __lowerCAmelCase					:	Optional[Any]      =       value
 elif weight_type == "weight_v":
  __lowerCAmelCase					:	Optional[Any]      =       value
 elif weight_type == "bias":
  __lowerCAmelCase					:	int      =       value
 elif weight_type == "running_mean":
  __lowerCAmelCase					:	Dict      =       value
 elif weight_type == "running_var":
  __lowerCAmelCase					:	Union[str, Any]      =       value
 elif weight_type == "num_batches_tracked":
  __lowerCAmelCase					:	str      =       value
 elif weight_type == "weight_ih_l0":
  __lowerCAmelCase					:	Optional[int]      =       value
 elif weight_type == "weight_hh_l0":
  __lowerCAmelCase					:	int      =       value
 elif weight_type == "bias_ih_l0":
  __lowerCAmelCase					:	List[Any]      =       value
 elif weight_type == "bias_hh_l0":
  __lowerCAmelCase					:	Optional[Any]      =       value
 elif weight_type == "weight_ih_l1":
  __lowerCAmelCase					:	List[Any]      =       value
 elif weight_type == "weight_hh_l1":
  __lowerCAmelCase					:	Any      =       value
 elif weight_type == "bias_ih_l1":
  __lowerCAmelCase					:	Tuple      =       value
 elif weight_type == "bias_hh_l1":
  __lowerCAmelCase					:	List[Any]      =       value
 else:
  __lowerCAmelCase					:	Union[str, Any]      =       value

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






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:int   )      ->   str:
 for key in ignore_keys:
  if key.endswith(""".*"""   ):
   if name.startswith(key[:-1]   ):
    return True
  elif ".*." in key:
   __lowerCAmelCase					:	Optional[Any]      =       key.split(""".*."""   )
   if prefix in name and suffix in name:
    return True
  elif key in name:
   return True
 return False






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:str   ,   SCREAMING_SNAKE_CASE		:Any   ,   SCREAMING_SNAKE_CASE		:List[Any]   )      ->   Union[str, Any]:
 __lowerCAmelCase					:	Dict      =       []

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

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

  __lowerCAmelCase					:	str      =       False
  for key, mapped_key in MAPPING.items():
   if "*" in key:
    __lowerCAmelCase					:	List[Any]      =       key.split(""".*."""   )
    if prefix in name and suffix in name:
     __lowerCAmelCase					:	Dict      =       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					:	Union[str, Any]      =       True
    if "*" in mapped_key:
     __lowerCAmelCase					:	Tuple      =       name.split(SCREAMING_SNAKE_CASE   )[0].split("""."""   )[-2]
     __lowerCAmelCase					:	str      =       mapped_key.replace("""*"""   ,   SCREAMING_SNAKE_CASE   )
    if "weight_g" in name:
     __lowerCAmelCase					:	str      =       """weight_g"""
    elif "weight_v" in name:
     __lowerCAmelCase					:	str      =       """weight_v"""
    elif "weight_ih_l0" in name:
     __lowerCAmelCase					:	Any      =       """weight_ih_l0"""
    elif "weight_hh_l0" in name:
     __lowerCAmelCase					:	List[Any]      =       """weight_hh_l0"""
    elif "bias_ih_l0" in name:
     __lowerCAmelCase					:	List[Any]      =       """bias_ih_l0"""
    elif "bias_hh_l0" in name:
     __lowerCAmelCase					:	Union[str, Any]      =       """bias_hh_l0"""
    elif "weight_ih_l1" in name:
     __lowerCAmelCase					:	Optional[Any]      =       """weight_ih_l1"""
    elif "weight_hh_l1" in name:
     __lowerCAmelCase					:	int      =       """weight_hh_l1"""
    elif "bias_ih_l1" in name:
     __lowerCAmelCase					:	List[str]      =       """bias_ih_l1"""
    elif "bias_hh_l1" in name:
     __lowerCAmelCase					:	List[str]      =       """bias_hh_l1"""
    elif "bias" in name:
     __lowerCAmelCase					:	Union[str, Any]      =       """bias"""
    elif "weight" in name:
     __lowerCAmelCase					:	Any      =       """weight"""
    elif "running_mean" in name:
     __lowerCAmelCase					:	int      =       """running_mean"""
    elif "running_var" in name:
     __lowerCAmelCase					:	List[str]      =       """running_var"""
    elif "num_batches_tracked" in name:
     __lowerCAmelCase					:	Any      =       """num_batches_tracked"""
    else:
     __lowerCAmelCase					:	int      =       None
    set_recursively(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
   continue
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE   )

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






@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[Any]   ,   SCREAMING_SNAKE_CASE		:str   ,   SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:Tuple=None   ,   SCREAMING_SNAKE_CASE		:Union[str, Any]=None   ,   )      ->   List[str]:
 if config_path is not None:
  __lowerCAmelCase					:	Tuple      =       EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE   )
 else:
  __lowerCAmelCase					:	Any      =       EncodecConfig()

 if model_name == "encodec_24khz":
  pass  # config is already correct
 elif model_name == "encodec_32khz":
  __lowerCAmelCase					:	List[Any]      =       [8, 5, 4, 4]
  __lowerCAmelCase					:	Union[str, Any]      =       [2.2]
  __lowerCAmelCase					:	Optional[int]      =       64
  __lowerCAmelCase					:	List[str]      =       32_000
  __lowerCAmelCase					:	Optional[int]      =       2_048
  __lowerCAmelCase					:	Any      =       False
  __lowerCAmelCase					:	Dict      =       False
  __lowerCAmelCase					:	Optional[Any]      =       False
 elif model_name == "encodec_48khz":
  __lowerCAmelCase					:	Optional[int]      =       [8, 5, 4, 2]
  __lowerCAmelCase					:	Dict      =       [3.0, 6.0, 12.0, 24.0]
  __lowerCAmelCase					:	int      =       48_000
  __lowerCAmelCase					:	Optional[Any]      =       2
  __lowerCAmelCase					:	Tuple      =       False
  __lowerCAmelCase					:	Union[str, Any]      =       """time_group_norm"""
  __lowerCAmelCase					:	Union[str, Any]      =       True
  __lowerCAmelCase					:	str      =       1.0
  __lowerCAmelCase					:	str      =       0.01
 else:
  raise ValueError(F'''Unknown model name: {model_name}'''   )

 __lowerCAmelCase					:	int      =       EncodecModel(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Union[str, Any]      =       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(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Union[str, Any]      =       torch.load(SCREAMING_SNAKE_CASE   )
 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					:	int      =       original_checkpoint["""best_state"""]
 recursively_load_weights(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 model.save_pretrained(SCREAMING_SNAKE_CASE   )

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


if __name__ == "__main__":
 _UpperCAmelCase             =							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.'
 )

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






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list[list[int | float]]   )      ->   int:
 __lowerCAmelCase					:	str      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(matrix[0]   )
 __lowerCAmelCase					:	List[Any]      =       min(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 for row in range(SCREAMING_SNAKE_CASE   ):
  # Check if diagonal element is not zero
  if matrix[row][row] != 0:
   # Eliminate all the elements below the diagonal
   for col in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Union[str, Any]      =       matrix[col][row] / matrix[row][row]
    for i in range(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   __lowerCAmelCase					:	Optional[int]      =       True
   for i in range(row + 1   ,   SCREAMING_SNAKE_CASE   ):
    if matrix[i][row] != 0:
     __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       matrix[i], matrix[row]
     __lowerCAmelCase					:	Union[str, Any]      =       False
     break
   if reduce:
    rank -= 1
    for i in range(SCREAMING_SNAKE_CASE   ):
     __lowerCAmelCase					:	int      =       matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
   row -= 1

 return rank


if __name__ == "__main__":
      import doctest

      doctest.testmod()