



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

_lowerCAmelCase							:   Tuple				=				{
    """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							:   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""",
}
_lowerCAmelCase							:   Union[str, 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""",
}
_lowerCAmelCase							:   Optional[Any]				=				{
    """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							:   Optional[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""",
}
_lowerCAmelCase							:   List[Any]				=				{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_lowerCAmelCase							:   int				=				{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_lowerCAmelCase							:   Dict				=				[]
_lowerCAmelCase							:   int				=				[]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Dict		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		List[str]							)    ->  List[Any]:





     '''simple docstring'''

     for attribute in key.split("."							):
          _UpperCAmelCase     :      List[Any]														=       getattr(SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_							)

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

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[str]		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  int:





     '''simple docstring'''

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		str							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       []

     if model_name == "encodec_24khz" or "encodec_32khz":
          _UpperCAmelCase     :      Optional[Any]														=       MAPPING_24K
     elif model_name == "encodec_48khz":
          _UpperCAmelCase     :      List[Any]														=       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

          _UpperCAmelCase     :      Optional[Any]														=       False
          for key, mapped_key in MAPPING.items():
               if "*" in key:
                    _UpperCAmelCase							,			_UpperCAmelCase     :      Any														=       key.split(".*."							)
                    if prefix in name and suffix in name:
                         _UpperCAmelCase     :      Union[str, Any]														=       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     :      Dict														=       True
                    if "*" in mapped_key:
                         _UpperCAmelCase     :      str														=       name.split(SCREAMING_SNAKE_CASE_							)[0].split("."							)[-2]
                         _UpperCAmelCase     :      List[Any]														=       mapped_key.replace("*"		,    SCREAMING_SNAKE_CASE_							)
                    if "weight_g" in name:
                         _UpperCAmelCase     :      str														=       "weight_g"
                    elif "weight_v" in name:
                         _UpperCAmelCase     :      List[Any]														=       "weight_v"
                    elif "weight_ih_l0" in name:
                         _UpperCAmelCase     :      Optional[Any]														=       "weight_ih_l0"
                    elif "weight_hh_l0" in name:
                         _UpperCAmelCase     :      Any														=       "weight_hh_l0"
                    elif "bias_ih_l0" in name:
                         _UpperCAmelCase     :      List[Any]														=       "bias_ih_l0"
                    elif "bias_hh_l0" in name:
                         _UpperCAmelCase     :      Union[str, Any]														=       "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     :      Optional[Any]														=       "bias_ih_l1"
                    elif "bias_hh_l1" in name:
                         _UpperCAmelCase     :      Dict														=       "bias_hh_l1"
                    elif "bias" in name:
                         _UpperCAmelCase     :      Tuple														=       "bias"
                    elif "weight" in name:
                         _UpperCAmelCase     :      List[Any]														=       "weight"
                    elif "running_mean" in name:
                         _UpperCAmelCase     :      List[str]														=       "running_mean"
                    elif "running_var" in name:
                         _UpperCAmelCase     :      Tuple														=       "running_var"
                    elif "num_batches_tracked" in name:
                         _UpperCAmelCase     :      Any														=       "num_batches_tracked"
                    else:
                         _UpperCAmelCase     :      Optional[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 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[str]		,    SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Optional[int]=None		,    SCREAMING_SNAKE_CASE__      :		List[str]=None		,    )    ->  Union[str, Any]:





     '''simple docstring'''

     if config_path is not None:
          _UpperCAmelCase     :      List[Any]														=       EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE_							)
     else:
          _UpperCAmelCase     :      Dict														=       EncodecConfig()

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

     _UpperCAmelCase     :      List[str]														=       EncodecModel(SCREAMING_SNAKE_CASE_							)

     _UpperCAmelCase     :      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_							)

     _UpperCAmelCase     :      Optional[int]														=       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
          _UpperCAmelCase     :      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__":
 _lowerCAmelCase							:   Tuple				=				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							:   Dict				=				parser.parse_args()
 convert_checkpoint(
     args.model,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )



def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						list:



    """simple docstring"""






    if bit_count < 0:
        raise ValueError("The given input must be positive"			)

    # get the generated string sequence
    UpperCamelCase_			  =						gray_code_sequence_string(SCREAMING_SNAKE_CASE_			)
    #
    # convert them to integers
    for i in range(len(SCREAMING_SNAKE_CASE_			)			):
        UpperCamelCase_			  =						int(sequence[i]			,						2			)

    return sequence




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						list:



    """simple docstring"""






    if bit_count == 0:
        return ["0"]

    if bit_count == 1:
        return ["0", "1"]

    UpperCamelCase_			  =						1 << bit_count  # defines the length of the sequence
    # 1<< n is equivalent to 2^n

    # recursive answer will generate answer for n-1 bits
    UpperCamelCase_			  =						gray_code_sequence_string(bit_count - 1			)

    UpperCamelCase_			  =						[]

    # append 0 to first half of the smaller sequence generated
    for i in range(seq_len // 2			):
        UpperCamelCase_			  =						"0" + smaller_sequence[i]
        sequence.append(SCREAMING_SNAKE_CASE_			)

    # append 1 to second half ... start from the end of the list
    for i in reversed(range(seq_len // 2			)			):
        UpperCamelCase_			  =						"1" + smaller_sequence[i]
        sequence.append(SCREAMING_SNAKE_CASE_			)

    return sequence


if __name__ == "__main__":
 import doctest

 doctest.testmod()
