





'''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()
lowercase__       : Tuple         =       logging.get_logger('''transformers.models.encodec''')

lowercase__       : 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''',
}
lowercase__       : Dict         =       {
    '''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''',
}
lowercase__       : Dict         =       {
    '''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''',
}
lowercase__       : Optional[Any]         =       {
    '''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''',
}
lowercase__       : Any         =       {
    '''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''',
}
lowercase__       : Tuple         =       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowercase__       : List[str]         =       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowercase__       : Tuple         =       []
lowercase__       : Dict         =       []






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					Any					,				__snake_case    :					int					,				__snake_case    :					Tuple					,				__snake_case    :					List[str]     )	->	Union[str, Any]:
     for attribute in key.split('.'     ):
          __A		:					str						=			getattr(__snake_case					,				__snake_case     )

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

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






def 			_lowerCAmelCase     (    __snake_case    :					Dict					,				__snake_case    :					Tuple     )	->	List[Any]:
     for key in ignore_keys:
          if key.endswith('.*'     ):
               if name.startswith(key[:-1]     ):
                    return True
          elif ".*." in key:
               __A					,__A		:					str						=			key.split('.*.'     )
               if prefix in name and suffix in name:
                    return True
          elif key in name:
               return True
     return False






def 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					Tuple					,				__snake_case    :					Any     )	->	str:
     __A		:					Optional[Any]						=			[]

     if model_name == "encodec_24khz" or "encodec_32khz":
          __A		:					Tuple						=			MAPPING_24K
     elif model_name == "encodec_48khz":
          __A		:					str						=			MAPPING_48K
     else:
          raise ValueError(f'Unsupported model: {model_name}'     )

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

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

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

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






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					Any					,				__snake_case    :					Union[str, Any]					,				__snake_case    :					List[Any]					,				__snake_case    :					str=None					,				__snake_case    :					str=None					,				)	->	Optional[Any]:
     if config_path is not None:
          __A		:					Dict						=			EncodecConfig.from_pretrained(__snake_case     )
     else:
          __A		:					List[Any]						=			EncodecConfig()

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

     __A		:					Union[str, Any]						=			EncodecModel(__snake_case     )

     __A		:					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(__snake_case     )

     __A		:					List[Any]						=			torch.load(__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
          __A		:					Dict						=			original_checkpoint['best_state']
     recursively_load_weights(__snake_case					,				__snake_case					,				__snake_case     )
     model.save_pretrained(__snake_case     )

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


if __name__ == "__main__":
  lowercase__       : str         =       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.'''
  )

  lowercase__       : str         =       parser.parse_args()
  convert_checkpoint(
      args.model,
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )






'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					int     )	->	bool:
     return sum(i for i in range(1					,				number // 2 + 1     ) if number % i == 0     ) == number


if __name__ == "__main__":
  print('''Program to check whether a number is a Perfect number or not...''')
  lowercase__       : int         =       int(input('''Enter number: ''').strip())
  print(f"""{number} is {"" if perfect(number) else "not "}a Perfect Number.""")