





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        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Optional[int]:


      '''simple docstring'''


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

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

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



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


      '''simple docstring'''


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



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=   []

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

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

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

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


      '''simple docstring'''


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

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

      SCREAMING_SNAKE_CASE		=   EncodecModel(_SCREAMING_SNAKE_CASE     )

      SCREAMING_SNAKE_CASE		=   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     )

      SCREAMING_SNAKE_CASE		=   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
            SCREAMING_SNAKE_CASE		=   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,
      )









from __future__ import annotations



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       list[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						len(_SCREAMING_SNAKE_CASE     ) - 1

      while i < j:
            if nums[i] + nums[j] == target:
                  return [i, j]
            elif nums[i] + nums[j] < target:
                  SCREAMING_SNAKE_CASE		=						i + 1
            else:
                  SCREAMING_SNAKE_CASE		=						j - 1

      return []


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(F'''{two_pointer([2, 7, 1_1, 1_5], 9) = }''')


