


'''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()
a_  :				List[str]						= logging.get_logger("""transformers.models.encodec""")

a_  :				List[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""",
}
a_  :				int						= {
    """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""",
}
a_  :				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""",
}
a_  :				Dict						= {
    """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""",
}
a_  :				Union[str, 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""",
}
a_  :				Any						= {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a_  :				str						= {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a_  :				int						= []
a_  :				str						= []





def __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Optional[int]		):
 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 __snake_case					(      UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Union[str, Any]		):
 for key in ignore_keys:
  if key.endswith(".*"		):
   if name.startswith(key[:-1]		):
    return True
  elif ".*." in key:
   lowerCamelCase_      =  key.split(".*."		)
   if prefix in name and suffix in name:
    return True
  elif key in name:
   return True
 return False





def __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						List[Any]		):
 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_      =  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 __snake_case					(      UpperCAmelCase_						:						Dict					,				UpperCAmelCase_						:						Tuple					,				UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						str=None					,				UpperCAmelCase_						:						int=None					,				):
 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_      =  32000
  lowerCamelCase_      =  2048
  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_      =  48000
  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__":
     a_  :				List[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."""
     )

     a_  :				Optional[int]						= 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 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  if height >= 1:
    move_tower(height - 1		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    move_disk(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    move_tower(height - 1		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  print('moving disk from'		,     _SCREAMING_SNAKE_CASE		,     'to'		,     _SCREAMING_SNAKE_CASE   )







def 						__a   (	)	->List[str]:
  a__:     Dict										=	int(input('Height of hanoi: '   ).strip()   )
  move_tower(_SCREAMING_SNAKE_CASE		,     'A'		,     'B'		,     'C'   )


if __name__ == "__main__":
 main()


