



'''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()
lowerCAmelCase: int							=						logging.get_logger('transformers.models.encodec')

lowerCAmelCase: str							=						{
    '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',
}
lowerCAmelCase: Union[str, Any]							=						{
    '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',
}
lowerCAmelCase: Optional[int]							=						{
    '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',
}
lowerCAmelCase: int							=						{
    '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',
}
lowerCAmelCase: str							=						{
    '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',
}
lowerCAmelCase: Optional[Any]							=						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowerCAmelCase: Tuple							=						{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowerCAmelCase: Optional[Any]							=						[]
lowerCAmelCase: str							=						[]
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A			,      _A    ):
  for attribute in key.split('.'    ):
    a						: Optional[int]					       =					getattr(lowercase__			,      lowercase__    )

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

  logger.info(f"""{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}."""    )
def 				lowerCamelCase__	(						_A			,      _A    ):
  for key in ignore_keys:
    if key.endswith('.*'    ):
      if name.startswith(key[:-1]    ):
        return True
    elif ".*." in key:
      a       ,     a						: Dict					       =					key.split('.*.'    )
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  a						: str					       =					[]

  if model_name == "encodec_24khz" or "encodec_32khz":
    a						: int					       =					MAPPING_24K
  elif model_name == "encodec_48khz":
    a						: Optional[int]					       =					MAPPING_48K
  else:
    raise ValueError(f"""Unsupported model: {model_name}"""    )

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

    a						: Any					       =					False
    for key, mapped_key in MAPPING.items():
      if "*" in key:
        a       ,     a						: List[str]					       =					key.split('.*.'    )
        if prefix in name and suffix in name:
          a						: str					       =					suffix

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

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

  logger.warning(f"""Unused weights: {unused_weights}"""    )
@torch.no_grad()
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A=None			,      _A=None			,      ):
  if config_path is not None:
    a						: Tuple					       =					EncodecConfig.from_pretrained(lowercase__    )
  else:
    a						: List[str]					       =					EncodecConfig()

  if model_name == "encodec_24khz":
    pass  # config is already correct
  elif model_name == "encodec_32khz":
    a						: List[Any]					       =					[8, 5, 4, 4]
    a						: Optional[Any]					       =					[2.2]
    a						: List[Any]					       =					64
    a						: Any					       =					3_2000
    a						: Tuple					       =					2048
    a						: str					       =					False
    a						: List[str]					       =					False
    a						: int					       =					False
  elif model_name == "encodec_48khz":
    a						: Union[str, Any]					       =					[8, 5, 4, 2]
    a						: List[str]					       =					[3.0, 6.0, 12.0, 24.0]
    a						: Dict					       =					4_8000
    a						: List[str]					       =					2
    a						: Optional[Any]					       =					False
    a						: Union[str, Any]					       =					'time_group_norm'
    a						: int					       =					True
    a						: List[str]					       =					1.0
    a						: List[Any]					       =					0.01
  else:
    raise ValueError(f"""Unknown model name: {model_name}"""    )

  a						: Union[str, Any]					       =					EncodecModel(lowercase__    )

  a						: Dict					       =					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(lowercase__    )

  a						: Optional[Any]					       =					torch.load(lowercase__    )
  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
    a						: Tuple					       =					original_checkpoint['best_state']
  recursively_load_weights(lowercase__			,      lowercase__			,      lowercase__    )
  model.save_pretrained(lowercase__    )

  if repo_id:
    print('Pushing to the hub...'    )
    feature_extractor.push_to_hub(lowercase__    )
    model.push_to_hub(lowercase__    )


if __name__ == "__main__":
     lowerCAmelCase: Dict							=						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.'
     )

     lowerCAmelCase: Tuple							=						parser.parse_args()
     convert_checkpoint(
         args.model,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.push_to_hub,
     )




'''simple docstring'''






lowerCAmelCase: Union[str, Any]							=						{
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
lowerCAmelCase: Optional[Any]							=						{
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 1_2,
    'Pm': 1_5,
    'Em': 1_8,
    'Zm': 2_1,
    'Ym': 2_4,
}
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  a						: Optional[int]					       =					from_type.lower().strip('s'    )
  a						: Optional[Any]					       =					to_type.lower().strip('s'    )

  a						: Dict					       =					UNIT_SYMBOL.get(_A			,      _A    )
  a						: Optional[Any]					       =					UNIT_SYMBOL.get(_A			,      _A    )

  if from_sanitized not in METRIC_CONVERSION:
    a						: Optional[Any]					       =					(
        f"""Invalid 'from_type' value: {from_type!r}.\n"""
        f"""Conversion abbreviations are: {", ".join(_A    )}"""
    )
    raise ValueError(_A    )
  if to_sanitized not in METRIC_CONVERSION:
    a						: Union[str, Any]					       =					(
        f"""Invalid 'to_type' value: {to_type!r}.\n"""
        f"""Conversion abbreviations are: {", ".join(_A    )}"""
    )
    raise ValueError(_A    )
  a						: List[Any]					       =					METRIC_CONVERSION[from_sanitized]
  a						: int					       =					METRIC_CONVERSION[to_sanitized]
  a						: Tuple					       =					1

  if from_exponent > to_exponent:
    a						: Optional[int]					       =					from_exponent - to_exponent
  else:
    a						: Optional[Any]					       =					-(to_exponent - from_exponent)

  return value * pow(10			,      _A    )


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

     testmod()