





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	:					Optional[Any]			  =					logging.get_logger("""transformers.models.encodec""")

__lowerCamelCase	:					Union[str, Any]			  =					{
    """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	:					str			  =					{
    """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	:					List[str]			  =					{
    """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	:					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""",
}
__lowerCamelCase	:					str			  =					{
    """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	:					Optional[Any]			  =					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__lowerCamelCase	:					List[str]			  =					{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__lowerCamelCase	:					List[str]			  =					[]
__lowerCamelCase	:					Tuple			  =					[]




def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			int:
 for attribute in key.split("."					):
  UpperCamelCase     : Optional[int]	     =       getattr(_lowerCAmelCase							,       _lowerCAmelCase					)

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

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




def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Optional[int]:
 for key in ignore_keys:
  if key.endswith(".*"					):
   if name.startswith(key[:-1]					):
    return True
  elif ".*." in key:
   UpperCamelCase     : Any	     =       key.split(".*."					)
   if prefix in name and suffix in name:
    return True
  elif key in name:
   return True
 return False




def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			List[Any]:
 UpperCamelCase     : Any	     =       []

 if model_name == "encodec_24khz" or "encodec_32khz":
  UpperCamelCase     : List[Any]	     =       MAPPING_24K
 elif model_name == "encodec_48khz":
  UpperCamelCase     : List[str]	     =       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

  UpperCamelCase     : Union[str, Any]	     =       False
  for key, mapped_key in MAPPING.items():
   if "*" in key:
    UpperCamelCase     : Tuple	     =       key.split(".*."					)
    if prefix in name and suffix in name:
     UpperCamelCase     : Dict	     =       suffix

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

    UpperCamelCase     : Union[str, Any]	     =       True
    if "*" in mapped_key:
     UpperCamelCase     : Union[str, Any]	     =       name.split(_lowerCAmelCase					)[0].split("."					)[-2]
     UpperCamelCase     : Optional[Any]	     =       mapped_key.replace("*"							,       _lowerCAmelCase					)
    if "weight_g" in name:
     UpperCamelCase     : Tuple	     =       "weight_g"
    elif "weight_v" in name:
     UpperCamelCase     : List[Any]	     =       "weight_v"
    elif "weight_ih_l0" in name:
     UpperCamelCase     : Any	     =       "weight_ih_l0"
    elif "weight_hh_l0" in name:
     UpperCamelCase     : Any	     =       "weight_hh_l0"
    elif "bias_ih_l0" in name:
     UpperCamelCase     : int	     =       "bias_ih_l0"
    elif "bias_hh_l0" in name:
     UpperCamelCase     : Tuple	     =       "bias_hh_l0"
    elif "weight_ih_l1" in name:
     UpperCamelCase     : Union[str, Any]	     =       "weight_ih_l1"
    elif "weight_hh_l1" in name:
     UpperCamelCase     : int	     =       "weight_hh_l1"
    elif "bias_ih_l1" in name:
     UpperCamelCase     : str	     =       "bias_ih_l1"
    elif "bias_hh_l1" in name:
     UpperCamelCase     : List[str]	     =       "bias_hh_l1"
    elif "bias" in name:
     UpperCamelCase     : Union[str, Any]	     =       "bias"
    elif "weight" in name:
     UpperCamelCase     : Optional[Any]	     =       "weight"
    elif "running_mean" in name:
     UpperCamelCase     : int	     =       "running_mean"
    elif "running_var" in name:
     UpperCamelCase     : int	     =       "running_var"
    elif "num_batches_tracked" in name:
     UpperCamelCase     : int	     =       "num_batches_tracked"
    else:
     UpperCamelCase     : Tuple	     =       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 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       )						->			List[Any]:
 if config_path is not None:
  UpperCamelCase     : Tuple	     =       EncodecConfig.from_pretrained(_lowerCAmelCase					)
 else:
  UpperCamelCase     : Optional[int]	     =       EncodecConfig()

 if model_name == "encodec_24khz":
  pass  # config is already correct
 elif model_name == "encodec_32khz":
  UpperCamelCase     : Any	     =       [8, 5, 4, 4]
  UpperCamelCase     : Dict	     =       [2.2]
  UpperCamelCase     : List[Any]	     =       64
  UpperCamelCase     : Optional[int]	     =       3_2000
  UpperCamelCase     : Tuple	     =       2048
  UpperCamelCase     : Optional[Any]	     =       False
  UpperCamelCase     : Union[str, Any]	     =       False
  UpperCamelCase     : List[str]	     =       False
 elif model_name == "encodec_48khz":
  UpperCamelCase     : List[str]	     =       [8, 5, 4, 2]
  UpperCamelCase     : Union[str, Any]	     =       [3.0, 6.0, 12.0, 24.0]
  UpperCamelCase     : str	     =       4_8000
  UpperCamelCase     : Any	     =       2
  UpperCamelCase     : Optional[Any]	     =       False
  UpperCamelCase     : Optional[int]	     =       "time_group_norm"
  UpperCamelCase     : List[Any]	     =       True
  UpperCamelCase     : Optional[Any]	     =       1.0
  UpperCamelCase     : Union[str, Any]	     =       0.01
 else:
  raise ValueError(F"""Unknown model name: {model_name}"""					)

 UpperCamelCase     : List[Any]	     =       EncodecModel(_lowerCAmelCase					)

 UpperCamelCase     : Any	     =       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					)

 UpperCamelCase     : Dict	     =       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
  UpperCamelCase     : int	     =       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	:					Tuple			  =					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	:					List[Any]			  =					parser.parse_args()
    convert_checkpoint(
        args.model,
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )







def 		A_    (					_lowerCAmelCase					)						->			int:
 if n == 1 or not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  return 0
 elif n == 2:
  return 1
 else:
  UpperCamelCase     : Optional[Any]	     =       [0, 1]
  for i in range(2							,       n + 1					):
   sequence.append(sequence[i - 1] + sequence[i - 2]					)

  return sequence[n]



def 		A_    (					_lowerCAmelCase					)						->			int:
 UpperCamelCase     : Any	     =       0
 UpperCamelCase     : List[Any]	     =       2

 while digits < n:
  index += 1
  UpperCamelCase     : Union[str, Any]	     =       len(str(fibonacci(_lowerCAmelCase					)					)					)

 return index



def 		A_    (					_lowerCAmelCase = 1000					)						->			int:
 return fibonacci_digits_index(_lowerCAmelCase					)


if __name__ == "__main__":
    print(solution(int(str(input()).strip())))
