






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_		:		Union[str, Any]		     =			{
    '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_		:		List[str]		     =			{
    '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_		:		Any		     =			{
    '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_		:		Tuple		     =			{
    '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_		:		str		     =			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
A_		:		Any		     =			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
A_		:		int		     =			[]
A_		:		Optional[int]		     =			[]

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[Any]:
  for attribute in key.split('.'  ):
    UpperCamelCase_: str   				=    getattr(UpperCAmelCase__     ,   UpperCAmelCase__  )

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

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

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    str:
  UpperCamelCase_: Dict   				=    []

  if model_name == "encodec_24khz" or "encodec_32khz":
    UpperCamelCase_: Dict   				=    MAPPING_24K
  elif model_name == "encodec_48khz":
    UpperCamelCase_: Tuple   				=    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_: Any   				=    False
    for key, mapped_key in MAPPING.items():
      if "*" in key:
        UpperCamelCase_				,UpperCamelCase_: int   				=    key.split('.*.'  )
        if prefix in name and suffix in name:
          UpperCamelCase_: Tuple   				=    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_: List[str]   				=    True
        if "*" in mapped_key:
          UpperCamelCase_: List[Any]   				=    name.split(UpperCAmelCase__  )[0].split('.'  )[-2]
          UpperCamelCase_: Union[str, Any]   				=    mapped_key.replace('*'     ,   UpperCAmelCase__  )
        if "weight_g" in name:
          UpperCamelCase_: Optional[Any]   				=    'weight_g'
        elif "weight_v" in name:
          UpperCamelCase_: Dict   				=    'weight_v'
        elif "weight_ih_l0" in name:
          UpperCamelCase_: List[str]   				=    'weight_ih_l0'
        elif "weight_hh_l0" in name:
          UpperCamelCase_: int   				=    'weight_hh_l0'
        elif "bias_ih_l0" in name:
          UpperCamelCase_: List[str]   				=    'bias_ih_l0'
        elif "bias_hh_l0" in name:
          UpperCamelCase_: int   				=    'bias_hh_l0'
        elif "weight_ih_l1" in name:
          UpperCamelCase_: Any   				=    'weight_ih_l1'
        elif "weight_hh_l1" in name:
          UpperCamelCase_: Dict   				=    'weight_hh_l1'
        elif "bias_ih_l1" in name:
          UpperCamelCase_: List[Any]   				=    'bias_ih_l1'
        elif "bias_hh_l1" in name:
          UpperCamelCase_: Tuple   				=    'bias_hh_l1'
        elif "bias" in name:
          UpperCamelCase_: str   				=    'bias'
        elif "weight" in name:
          UpperCamelCase_: Optional[int]   				=    'weight'
        elif "running_mean" in name:
          UpperCamelCase_: int   				=    'running_mean'
        elif "running_var" in name:
          UpperCamelCase_: Any   				=    'running_var'
        elif "num_batches_tracked" in name:
          UpperCamelCase_: Dict   				=    'num_batches_tracked'
        else:
          UpperCamelCase_: List[str]   				=    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   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__=None     ,   UpperCAmelCase__=None     ,   )	->    Optional[int]:
  if config_path is not None:
    UpperCamelCase_: Any   				=    EncodecConfig.from_pretrained(UpperCAmelCase__  )
  else:
    UpperCamelCase_: Optional[int]   				=    EncodecConfig()

  if model_name == "encodec_24khz":
    pass  # config is already correct
  elif model_name == "encodec_32khz":
    UpperCamelCase_: List[str]   				=    [8, 5, 4, 4]
    UpperCamelCase_: int   				=    [2.2]
    UpperCamelCase_: List[Any]   				=    6_4
    UpperCamelCase_: List[Any]   				=    3_2_0_0_0
    UpperCamelCase_: Optional[Any]   				=    2_0_4_8
    UpperCamelCase_: List[str]   				=    False
    UpperCamelCase_: Optional[Any]   				=    False
    UpperCamelCase_: Tuple   				=    False
  elif model_name == "encodec_48khz":
    UpperCamelCase_: Dict   				=    [8, 5, 4, 2]
    UpperCamelCase_: List[str]   				=    [3.0, 6.0, 12.0, 24.0]
    UpperCamelCase_: Optional[Any]   				=    4_8_0_0_0
    UpperCamelCase_: Tuple   				=    2
    UpperCamelCase_: Optional[Any]   				=    False
    UpperCamelCase_: Union[str, Any]   				=    'time_group_norm'
    UpperCamelCase_: str   				=    True
    UpperCamelCase_: str   				=    1.0
    UpperCamelCase_: Tuple   				=    0.01
  else:
    raise ValueError(F'''Unknown model name: {model_name}'''  )

  UpperCamelCase_: Union[str, Any]   				=    EncodecModel(UpperCAmelCase__  )

  UpperCamelCase_: 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(UpperCAmelCase__  )

  UpperCamelCase_: Any   				=    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_: int   				=    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_		:		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.'
   )

   A_		:		Optional[Any]		     =			parser.parse_args()
   convert_checkpoint(
       args.model,
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )







from abc import ABC, abstractmethod
from argparse import ArgumentParser




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       @staticmethod
       @abstractmethod
       def       _a   ( _lowerCamelCase							):
         raise NotImplementedError()





       @abstractmethod
       def       _a   ( self							):
         raise NotImplementedError()