






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

A__					: 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__					: int=	{
    """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__					: Optional[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""",
}
A__					: List[Any]=	{
    """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__					: Dict=	[]
A__					: Any=	[]







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Union[str, Any]:
    """simple docstring"""

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

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

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Any:
    """simple docstring"""

    UpperCamelCase__         =    []

    if model_name == "encodec_24khz" or "encodec_32khz":
        UpperCamelCase__         =    MAPPING_24K
    elif model_name == "encodec_48khz":
        UpperCamelCase__         =    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__         =    False
        for key, mapped_key in MAPPING.items():
            if "*" in key:
                UpperCamelCase__       ,						UpperCamelCase__         =    key.split('.*.'					)
                if prefix in name and suffix in name:
                    UpperCamelCase__         =    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__         =    True
                if "*" in mapped_key:
                    UpperCamelCase__         =    name.split(SCREAMING_SNAKE_CASE					)[0].split('.'					)[-2]
                    UpperCamelCase__         =    mapped_key.replace('*' ,	SCREAMING_SNAKE_CASE					)
                if "weight_g" in name:
                    UpperCamelCase__         =    'weight_g'
                elif "weight_v" in name:
                    UpperCamelCase__         =    'weight_v'
                elif "weight_ih_l0" in name:
                    UpperCamelCase__         =    'weight_ih_l0'
                elif "weight_hh_l0" in name:
                    UpperCamelCase__         =    'weight_hh_l0'
                elif "bias_ih_l0" in name:
                    UpperCamelCase__         =    'bias_ih_l0'
                elif "bias_hh_l0" in name:
                    UpperCamelCase__         =    'bias_hh_l0'
                elif "weight_ih_l1" in name:
                    UpperCamelCase__         =    'weight_ih_l1'
                elif "weight_hh_l1" in name:
                    UpperCamelCase__         =    'weight_hh_l1'
                elif "bias_ih_l1" in name:
                    UpperCamelCase__         =    'bias_ih_l1'
                elif "bias_hh_l1" in name:
                    UpperCamelCase__         =    'bias_hh_l1'
                elif "bias" in name:
                    UpperCamelCase__         =    'bias'
                elif "weight" in name:
                    UpperCamelCase__         =    'weight'
                elif "running_mean" in name:
                    UpperCamelCase__         =    'running_mean'
                elif "running_var" in name:
                    UpperCamelCase__         =    'running_var'
                elif "num_batches_tracked" in name:
                    UpperCamelCase__         =    'num_batches_tracked'
                else:
                    UpperCamelCase__         =    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   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=None ,	)					->	str:
    """simple docstring"""

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

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

    UpperCamelCase__         =    EncodecModel(SCREAMING_SNAKE_CASE					)

    UpperCamelCase__         =    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__         =    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__         =    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__":
  A__					: str=	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__					: List[str]=	parser.parse_args()
  convert_checkpoint(
      args.model,
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )












"""simple docstring"""





def   lowerCAmelCase_( SCREAMING_SNAKE_CASE = 50_00_00_00					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    set()
    UpperCamelCase__         =    int((limit - 24) ** (1 / 2)					)

    UpperCamelCase__         =    set(range(3 ,	prime_square_limit + 1 ,	2					)					)
    primes.add(2					)
    for p in range(3 ,	prime_square_limit + 1 ,	2					):
        if p not in primes:
            continue
        primes.difference_update(set(range(p * p ,	prime_square_limit + 1 ,	SCREAMING_SNAKE_CASE					)					)					)

    for primea in primes:
        UpperCamelCase__         =    primea * primea
        for primea in primes:
            UpperCamelCase__         =    primea * primea * primea
            if square + cube >= limit - 16:
                break
            for primea in primes:
                UpperCamelCase__         =    primea * primea * primea * primea
                UpperCamelCase__         =    square + cube + tetr
                if total >= limit:
                    break
                ret.add(SCREAMING_SNAKE_CASE					)

    return len(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  print(F"""{solution() = }""")




