

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_     :    List[str]     		=   logging.get_logger("transformers.models.encodec")

a_     :    List[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",
}
a_     :    Dict     		=   {
    "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_     :    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",
}
a_     :    Dict     		=   {
    "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_     :    int     		=   {
    "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_     :    List[Any]     		=   {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a_     :    List[Any]     		=   {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a_     :    List[str]     		=   []
a_     :    Any     		=   []


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Any		,				_UpperCamelCase		:	List[str]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	List[str]						)		->				Dict:
 '''simple docstring'''


 for attribute in key.split('.'						):
  SCREAMING_SNAKE_CASE		    =      getattr(_UpperCamelCase		,				_UpperCamelCase						)

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

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	Union[str, Any]						)		->				List[Any]:
 '''simple docstring'''


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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	List[Any]						)		->				List[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []

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

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

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

    SCREAMING_SNAKE_CASE		    =      True
    if "*" in mapped_key:
     SCREAMING_SNAKE_CASE		    =      name.split(_UpperCamelCase						)[0].split('.'						)[-2]
     SCREAMING_SNAKE_CASE		    =      mapped_key.replace('*'		,				_UpperCamelCase						)
    if "weight_g" in name:
     SCREAMING_SNAKE_CASE		    =      'weight_g'
    elif "weight_v" in name:
     SCREAMING_SNAKE_CASE		    =      'weight_v'
    elif "weight_ih_l0" in name:
     SCREAMING_SNAKE_CASE		    =      'weight_ih_l0'
    elif "weight_hh_l0" in name:
     SCREAMING_SNAKE_CASE		    =      'weight_hh_l0'
    elif "bias_ih_l0" in name:
     SCREAMING_SNAKE_CASE		    =      'bias_ih_l0'
    elif "bias_hh_l0" in name:
     SCREAMING_SNAKE_CASE		    =      'bias_hh_l0'
    elif "weight_ih_l1" in name:
     SCREAMING_SNAKE_CASE		    =      'weight_ih_l1'
    elif "weight_hh_l1" in name:
     SCREAMING_SNAKE_CASE		    =      'weight_hh_l1'
    elif "bias_ih_l1" in name:
     SCREAMING_SNAKE_CASE		    =      'bias_ih_l1'
    elif "bias_hh_l1" in name:
     SCREAMING_SNAKE_CASE		    =      'bias_hh_l1'
    elif "bias" in name:
     SCREAMING_SNAKE_CASE		    =      'bias'
    elif "weight" in name:
     SCREAMING_SNAKE_CASE		    =      'weight'
    elif "running_mean" in name:
     SCREAMING_SNAKE_CASE		    =      'running_mean'
    elif "running_var" in name:
     SCREAMING_SNAKE_CASE		    =      'running_var'
    elif "num_batches_tracked" in name:
     SCREAMING_SNAKE_CASE		    =      'num_batches_tracked'
    else:
     SCREAMING_SNAKE_CASE		    =      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 							__lowerCAmelCase     (							_UpperCamelCase		:	int		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	List[str]=None		,				_UpperCamelCase		:	int=None		,				)		->				List[Any]:
 '''simple docstring'''


 if config_path is not None:
  SCREAMING_SNAKE_CASE		    =      EncodecConfig.from_pretrained(_UpperCamelCase						)
 else:
  SCREAMING_SNAKE_CASE		    =      EncodecConfig()

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

 SCREAMING_SNAKE_CASE		    =      EncodecModel(_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      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						)

 SCREAMING_SNAKE_CASE		    =      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
  SCREAMING_SNAKE_CASE		    =      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_     :    List[Any]     		=   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_     :    Any     		=   parser.parse_args()
      convert_checkpoint(
          args.model,
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.push_to_hub,
      )




import baseaa


def 							__lowerCAmelCase     (							_UpperCamelCase		:	str						)		->				bytes:
 '''simple docstring'''


 return baseaa.aaaencode(string.encode('utf-8'						)						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	bytes						)		->				str:
 '''simple docstring'''


 return baseaa.aaadecode(_UpperCamelCase						).decode('utf-8'						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

