


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()
UpperCAmelCase_ :					Any       =			logging.get_logger('transformers.models.encodec')

UpperCAmelCase_ :					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',
}
UpperCAmelCase_ :					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',
}
UpperCAmelCase_ :					Tuple       =			{
    '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',
}
UpperCAmelCase_ :					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',
}
UpperCAmelCase_ :					Optional[int]       =			{
    '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',
}
UpperCAmelCase_ :					Tuple       =			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
UpperCAmelCase_ :					List[Any]       =			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
UpperCAmelCase_ :					Optional[int]       =			[]
UpperCAmelCase_ :					Any       =			[]







def 			SCREAMING_SNAKE_CASE_		(     __A					:     Tuple    ,       __A					:     Optional[Any]    ,       __A					:     List[Any]    ,       __A					:     int    ,       __A					:     Dict							)      ->						List[Any]:

       """simple docstring"""

       for attribute in key.split('.'							):
              a_   :						Tuple				=    getattr(__UpperCamelCase    ,       __UpperCamelCase							)

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

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







def 			SCREAMING_SNAKE_CASE_		(     __A					:     Optional[Any]    ,       __A					:     str							)      ->						Dict:

       """simple docstring"""

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







def 			SCREAMING_SNAKE_CASE_		(     __A					:     Dict    ,       __A					:     Optional[Any]    ,       __A					:     int							)      ->						List[Any]:

       """simple docstring"""

       a_   :						Dict				=    []

       if model_name == "encodec_24khz" or "encodec_32khz":
              a_   :						str				=    MAPPING_24K
       elif model_name == "encodec_48khz":
              a_   :						List[Any]				=    MAPPING_48K
       else:
              raise ValueError(F"""Unsupported model: {model_name}"""							)

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

              a_   :						Optional[int]				=    False
              for key, mapped_key in MAPPING.items():
                     if "*" in key:
                            a_				,						a_   :						Union[str, Any]				=    key.split('.*.'							)
                            if prefix in name and suffix in name:
                                   a_   :						Optional[int]				=    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_   :						Union[str, Any]				=    True
                            if "*" in mapped_key:
                                   a_   :						int				=    name.split(__UpperCamelCase							)[0].split('.'							)[-2]
                                   a_   :						List[str]				=    mapped_key.replace('*'    ,       __UpperCamelCase							)
                            if "weight_g" in name:
                                   a_   :						Optional[int]				=    'weight_g'
                            elif "weight_v" in name:
                                   a_   :						Optional[int]				=    'weight_v'
                            elif "weight_ih_l0" in name:
                                   a_   :						Union[str, Any]				=    'weight_ih_l0'
                            elif "weight_hh_l0" in name:
                                   a_   :						Tuple				=    'weight_hh_l0'
                            elif "bias_ih_l0" in name:
                                   a_   :						Optional[int]				=    'bias_ih_l0'
                            elif "bias_hh_l0" in name:
                                   a_   :						int				=    'bias_hh_l0'
                            elif "weight_ih_l1" in name:
                                   a_   :						List[Any]				=    'weight_ih_l1'
                            elif "weight_hh_l1" in name:
                                   a_   :						Dict				=    'weight_hh_l1'
                            elif "bias_ih_l1" in name:
                                   a_   :						Any				=    'bias_ih_l1'
                            elif "bias_hh_l1" in name:
                                   a_   :						List[Any]				=    'bias_hh_l1'
                            elif "bias" in name:
                                   a_   :						List[str]				=    'bias'
                            elif "weight" in name:
                                   a_   :						str				=    'weight'
                            elif "running_mean" in name:
                                   a_   :						Dict				=    'running_mean'
                            elif "running_var" in name:
                                   a_   :						List[str]				=    'running_var'
                            elif "num_batches_tracked" in name:
                                   a_   :						List[str]				=    'num_batches_tracked'
                            else:
                                   a_   :						Union[str, Any]				=    None
                            set_recursively(__UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase							)
                     continue
              if not is_used:
                     unused_weights.append(__UpperCamelCase							)

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







@torch.no_grad()
def 			SCREAMING_SNAKE_CASE_		(     __A					:     str    ,       __A					:     Optional[Any]    ,       __A					:     Any    ,       __A					:     List[str]=None    ,       __A					:     List[str]=None    ,       )      ->						List[str]:

       """simple docstring"""

       if config_path is not None:
              a_   :						str				=    EncodecConfig.from_pretrained(__UpperCamelCase							)
       else:
              a_   :						Any				=    EncodecConfig()

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

       a_   :						int				=    EncodecModel(__UpperCamelCase							)

       a_   :						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(__UpperCamelCase							)

       a_   :						List[Any]				=    torch.load(__UpperCamelCase							)
       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_   :						List[str]				=    original_checkpoint['best_state']
       recursively_load_weights(__UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase							)
       model.save_pretrained(__UpperCamelCase							)

       if repo_id:
              print('Pushing to the hub...'							)
              feature_extractor.push_to_hub(__UpperCamelCase							)
              model.push_to_hub(__UpperCamelCase							)


if __name__ == "__main__":
   UpperCAmelCase_ :					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.'
   )

   UpperCAmelCase_ :					List[Any]       =			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 __future__ import annotations

def 					lowerCamelCase_ ( __UpperCamelCase					:       list							,    __UpperCamelCase					:       int | None = None							,    __UpperCamelCase					:       int | None = None		)					->	None:







     """simple docstring"""
     if start is None:
          _A				=							0

     if end is None:
          _A				=							len(__UpperCamelCase		) - 1

     if start >= end:
          return

     _A				=							(start + end) // 2

     slowsort(__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase		)
     slowsort(__UpperCamelCase							,    mid + 1							,    __UpperCamelCase		)

     if sequence[end] < sequence[mid]:
          _A		,				_A				=							sequence[mid], sequence[end]

     slowsort(__UpperCamelCase							,    __UpperCamelCase							,    end - 1		)


if __name__ == "__main__":
    from doctest import testmod

    testmod()





