






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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      Any      ,					__lowercase :      List[Any]      ,					__lowercase :      int      ,					__lowercase :      Any     )    ->   int:





    """simple docstring"""

    for attribute in key.split("""."""     ):
        __A       =						getattr(__lowercase      ,					__lowercase     )

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

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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Optional[Any]      ,					__lowercase :      Optional[int]     )    ->   Union[str, Any]:





    """simple docstring"""

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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]      ,					__lowercase :      List[str]      ,					__lowercase :      List[str]     )    ->   str:





    """simple docstring"""

    __A       =						[]

    if model_name == "encodec_24khz" or "encodec_32khz":
        __A       =						MAPPING_24K
    elif model_name == "encodec_48khz":
        __A       =						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       =						False
        for key, mapped_key in MAPPING.items():
            if "*" in key:
                __A     ,						__A       =						key.split(""".*."""     )
                if prefix in name and suffix in name:
                    __A       =						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       =						True
                if "*" in mapped_key:
                    __A       =						name.split(__lowercase     )[0].split("""."""     )[-2]
                    __A       =						mapped_key.replace("""*"""      ,					__lowercase     )
                if "weight_g" in name:
                    __A       =						"""weight_g"""
                elif "weight_v" in name:
                    __A       =						"""weight_v"""
                elif "weight_ih_l0" in name:
                    __A       =						"""weight_ih_l0"""
                elif "weight_hh_l0" in name:
                    __A       =						"""weight_hh_l0"""
                elif "bias_ih_l0" in name:
                    __A       =						"""bias_ih_l0"""
                elif "bias_hh_l0" in name:
                    __A       =						"""bias_hh_l0"""
                elif "weight_ih_l1" in name:
                    __A       =						"""weight_ih_l1"""
                elif "weight_hh_l1" in name:
                    __A       =						"""weight_hh_l1"""
                elif "bias_ih_l1" in name:
                    __A       =						"""bias_ih_l1"""
                elif "bias_hh_l1" in name:
                    __A       =						"""bias_hh_l1"""
                elif "bias" in name:
                    __A       =						"""bias"""
                elif "weight" in name:
                    __A       =						"""weight"""
                elif "running_mean" in name:
                    __A       =						"""running_mean"""
                elif "running_var" in name:
                    __A       =						"""running_var"""
                elif "num_batches_tracked" in name:
                    __A       =						"""num_batches_tracked"""
                else:
                    __A       =						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       _SCREAMING_SNAKE_CASE    (			__lowercase :      Any      ,					__lowercase :      Tuple      ,					__lowercase :      int      ,					__lowercase :      int=None      ,					__lowercase :      Optional[Any]=None      ,					)    ->   Dict:





    """simple docstring"""

    if config_path is not None:
        __A       =						EncodecConfig.from_pretrained(__lowercase     )
    else:
        __A       =						EncodecConfig()

    if model_name == "encodec_24khz":
        pass  # config is already correct
    elif model_name == "encodec_32khz":
        __A       =						[8, 5, 4, 4]
        __A       =						[2.2]
        __A       =						6_4
        __A       =						3_2_0_0_0
        __A       =						2_0_4_8
        __A       =						False
        __A       =						False
        __A       =						False
    elif model_name == "encodec_48khz":
        __A       =						[8, 5, 4, 2]
        __A       =						[3.0, 6.0, 12.0, 24.0]
        __A       =						4_8_0_0_0
        __A       =						2
        __A       =						False
        __A       =						"""time_group_norm"""
        __A       =						True
        __A       =						1.0
        __A       =						0.01
    else:
        raise ValueError(f"Unknown model name: {model_name}"     )

    __A       =						EncodecModel(__lowercase     )

    __A       =						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       =						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       =						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__":
  __a   :							Optional[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,
  )












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a   :							Any						= {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  __a   :							str						= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




