





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

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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						Union[str, Any]							,						SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						Optional[Any]							,						SCREAMING_SNAKE_CASE     :						Optional[int]			):







      '''simple docstring'''




      for attribute in key.split("""."""			):
            lowerCAmelCase  						=       getattr(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)

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

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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Any							,						SCREAMING_SNAKE_CASE     :						Tuple			):







      '''simple docstring'''




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


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						int							,						SCREAMING_SNAKE_CASE     :						int			):







      '''simple docstring'''




      lowerCAmelCase  						=       []

      if model_name == "encodec_24khz" or "encodec_32khz":
            lowerCAmelCase  						=       MAPPING_24K
      elif model_name == "encodec_48khz":
            lowerCAmelCase  						=       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

            lowerCAmelCase  						=       False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        lowerCAmelCase						,		lowerCAmelCase  						=       key.split(""".*."""			)
                        if prefix in name and suffix in name:
                              lowerCAmelCase  						=       suffix

                  if key in name:
                        # HACK otherwise .embed gets initialized with .embed_avg too
                        if key.endswith("""embed"""			) and name.endswith("""embed_avg"""			):
                              continue

                        lowerCAmelCase  						=       True
                        if "*" in mapped_key:
                              lowerCAmelCase  						=       name.split(SCREAMING_SNAKE_CASE			)[0].split("""."""			)[-2]
                              lowerCAmelCase  						=       mapped_key.replace("""*"""							,						SCREAMING_SNAKE_CASE			)
                        if "weight_g" in name:
                              lowerCAmelCase  						=       """weight_g"""
                        elif "weight_v" in name:
                              lowerCAmelCase  						=       """weight_v"""
                        elif "weight_ih_l0" in name:
                              lowerCAmelCase  						=       """weight_ih_l0"""
                        elif "weight_hh_l0" in name:
                              lowerCAmelCase  						=       """weight_hh_l0"""
                        elif "bias_ih_l0" in name:
                              lowerCAmelCase  						=       """bias_ih_l0"""
                        elif "bias_hh_l0" in name:
                              lowerCAmelCase  						=       """bias_hh_l0"""
                        elif "weight_ih_l1" in name:
                              lowerCAmelCase  						=       """weight_ih_l1"""
                        elif "weight_hh_l1" in name:
                              lowerCAmelCase  						=       """weight_hh_l1"""
                        elif "bias_ih_l1" in name:
                              lowerCAmelCase  						=       """bias_ih_l1"""
                        elif "bias_hh_l1" in name:
                              lowerCAmelCase  						=       """bias_hh_l1"""
                        elif "bias" in name:
                              lowerCAmelCase  						=       """bias"""
                        elif "weight" in name:
                              lowerCAmelCase  						=       """weight"""
                        elif "running_mean" in name:
                              lowerCAmelCase  						=       """running_mean"""
                        elif "running_var" in name:
                              lowerCAmelCase  						=       """running_var"""
                        elif "num_batches_tracked" in name:
                              lowerCAmelCase  						=       """num_batches_tracked"""
                        else:
                              lowerCAmelCase  						=       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 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str							,						SCREAMING_SNAKE_CASE     :						Tuple							,						SCREAMING_SNAKE_CASE     :						Any							,						SCREAMING_SNAKE_CASE     :						Any=None							,						SCREAMING_SNAKE_CASE     :						List[Any]=None							,						):







      '''simple docstring'''




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

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

      lowerCAmelCase  						=       EncodecModel(SCREAMING_SNAKE_CASE			)

      lowerCAmelCase  						=       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			)

      lowerCAmelCase  						=       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
            lowerCAmelCase  						=       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__":
       SCREAMING_SNAKE_CASE__					=   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."
       )

       SCREAMING_SNAKE_CASE__					=   parser.parse_args()
       convert_checkpoint(
           args.model,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )













"""simple docstring"""





# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


SCREAMING_SNAKE_CASE__					=   open  # noqa: we just need to have a builtin inside this module to test it properly






