
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							=       logging.get_logger("""transformers.models.encodec""")

__lowerCamelCase							=       {
    """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							=       {
    """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							=       {
    """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							=       {
    """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							=       {
    """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							=       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__lowerCamelCase							=       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__lowerCamelCase							=       []
__lowerCamelCase							=       []


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : Tuple				,					__lowerCamelCase     : Optional[Any]				,					__lowerCamelCase     : Optional[int]    ):




       for attribute in key.split("."    ):
              snake_case       :   Optional[Any]							=      getattr(__lowerCamelCase				,					__lowerCamelCase    )

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

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


def 			UpperCamelCase					(    __lowerCamelCase     : Any				,					__lowerCamelCase     : Tuple    ):




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


def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : int				,					__lowerCamelCase     : int    ):
       snake_case       :   Union[str, Any]							=      []

       if model_name == "encodec_24khz" or "encodec_32khz":
              snake_case       :   Any							=      MAPPING_24K
       elif model_name == "encodec_48khz":
              snake_case       :   Any							=      MAPPING_48K
       else:
              raise ValueError(f"""Unsupported model: {model_name}"""    )

       for name, value in orig_dict.items():
              if should_ignore(__lowerCamelCase				,					__lowerCamelCase    ):
                     logger.info(f"""{name} was ignored"""    )
                     continue

              snake_case       :   List[Any]							=      False
              for key, mapped_key in MAPPING.items():
                     if "*" in key:
                            snake_case				,					snake_case       :   Any							=      key.split(".*."    )
                            if prefix in name and suffix in name:
                                   snake_case       :   Optional[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

                            snake_case       :   List[Any]							=      True
                            if "*" in mapped_key:
                                   snake_case       :   Optional[int]							=      name.split(__lowerCamelCase    )[0].split("."    )[-2]
                                   snake_case       :   Optional[Any]							=      mapped_key.replace("*"				,					__lowerCamelCase    )
                            if "weight_g" in name:
                                   snake_case       :   List[str]							=      "weight_g"
                            elif "weight_v" in name:
                                   snake_case       :   int							=      "weight_v"
                            elif "weight_ih_l0" in name:
                                   snake_case       :   Dict							=      "weight_ih_l0"
                            elif "weight_hh_l0" in name:
                                   snake_case       :   Optional[Any]							=      "weight_hh_l0"
                            elif "bias_ih_l0" in name:
                                   snake_case       :   Optional[Any]							=      "bias_ih_l0"
                            elif "bias_hh_l0" in name:
                                   snake_case       :   int							=      "bias_hh_l0"
                            elif "weight_ih_l1" in name:
                                   snake_case       :   Union[str, Any]							=      "weight_ih_l1"
                            elif "weight_hh_l1" in name:
                                   snake_case       :   List[str]							=      "weight_hh_l1"
                            elif "bias_ih_l1" in name:
                                   snake_case       :   Optional[int]							=      "bias_ih_l1"
                            elif "bias_hh_l1" in name:
                                   snake_case       :   Tuple							=      "bias_hh_l1"
                            elif "bias" in name:
                                   snake_case       :   Tuple							=      "bias"
                            elif "weight" in name:
                                   snake_case       :   List[str]							=      "weight"
                            elif "running_mean" in name:
                                   snake_case       :   str							=      "running_mean"
                            elif "running_var" in name:
                                   snake_case       :   Optional[int]							=      "running_var"
                            elif "num_batches_tracked" in name:
                                   snake_case       :   int							=      "num_batches_tracked"
                            else:
                                   snake_case       :   List[Any]							=      None
                            set_recursively(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )
                     continue
              if not is_used:
                     unused_weights.append(__lowerCamelCase    )

       logger.warning(f"""Unused weights: {unused_weights}"""    )


@torch.no_grad()
def 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : Tuple				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : Any=None				,					__lowerCamelCase     : List[Any]=None				,					):




       if config_path is not None:
              snake_case       :   Optional[Any]							=      EncodecConfig.from_pretrained(__lowerCamelCase    )
       else:
              snake_case       :   Optional[Any]							=      EncodecConfig()

       if model_name == "encodec_24khz":
              pass  # config is already correct
       elif model_name == "encodec_32khz":
              snake_case       :   List[Any]							=      [8, 5, 4, 4]
              snake_case       :   Optional[int]							=      [2.2]
              snake_case       :   Optional[int]							=      64
              snake_case       :   Any							=      32000
              snake_case       :   Dict							=      2048
              snake_case       :   Union[str, Any]							=      False
              snake_case       :   Union[str, Any]							=      False
              snake_case       :   Union[str, Any]							=      False
       elif model_name == "encodec_48khz":
              snake_case       :   List[str]							=      [8, 5, 4, 2]
              snake_case       :   Optional[Any]							=      [3.0, 6.0, 12.0, 24.0]
              snake_case       :   List[str]							=      48000
              snake_case       :   Optional[Any]							=      2
              snake_case       :   Optional[int]							=      False
              snake_case       :   Dict							=      "time_group_norm"
              snake_case       :   Union[str, Any]							=      True
              snake_case       :   Union[str, Any]							=      1.0
              snake_case       :   List[str]							=      0.01
       else:
              raise ValueError(f"""Unknown model name: {model_name}"""    )

       snake_case       :   List[Any]							=      EncodecModel(__lowerCamelCase    )

       snake_case       :   Optional[int]							=      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(__lowerCamelCase    )

       snake_case       :   Tuple							=      torch.load(__lowerCamelCase    )
       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
              snake_case       :   Any							=      original_checkpoint["best_state"]
       recursively_load_weights(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    )
       model.save_pretrained(__lowerCamelCase    )

       if repo_id:
              print("Pushing to the hub..."    )
              feature_extractor.push_to_hub(__lowerCamelCase    )
              model.push_to_hub(__lowerCamelCase    )


if __name__ == "__main__":
   __lowerCamelCase							=       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							=       parser.parse_args()
   convert_checkpoint(
       args.model,
       args.checkpoint_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )












"""simple docstring"""





from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate

@dataclass(frozen=_UpperCAmelCase					)
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      field(default='language-modeling'     ,  metadata={'include_in_asdict_even_if_is_default': True}					)

      _SCREAMING_SNAKE_CASE					=      Features({'text': Value('string'					)}					)
      _SCREAMING_SNAKE_CASE					=      Features({}					)
      _SCREAMING_SNAKE_CASE					=      "text"





      @property
      def     _snake_case			(       self )		->     Dict[str, str]:
            return {self.text_column: "text"}






