


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

A     :   Tuple			    =			{
    "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     :   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",
}
A     :   int			    =			{
    "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     :   Optional[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",
}
A     :   Tuple			    =			{
    "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     :   Dict			    =			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
A     :   List[Any]			    =			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
A     :   Any			    =			[]
A     :   List[str]			    =			[]


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			for attribute in key.split("."      ):
						__lowerCAmelCase				    =			getattr(_UpperCamelCase    , _UpperCamelCase      )

			if weight_type is not None:
						__lowerCAmelCase				    =			getattr(_UpperCamelCase    , _UpperCamelCase      ).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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			__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(_UpperCamelCase    , _UpperCamelCase      ):
									logger.info(f"{name} was ignored"      )
									continue

						__lowerCAmelCase				    =			False
						for key, mapped_key in MAPPING.items():
									if "*" in key:
												__lowerCAmelCase    ,	__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(_UpperCamelCase      )[0].split("."      )[-2]
															__lowerCAmelCase				    =			mapped_key.replace("*"    , _UpperCamelCase      )
												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(_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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase=None    , _UpperCamelCase=None    , ):

			'''simple docstring'''




			if config_path is not None:
						__lowerCAmelCase				    =			EncodecConfig.from_pretrained(_UpperCamelCase      )
			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				    =			3_2000
						__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				    =			4_8000
						__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(_UpperCamelCase      )

			__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(_UpperCamelCase      )

			__lowerCAmelCase				    =			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
						__lowerCAmelCase				    =			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__":
	A     :   Optional[int]			    =			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     :   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	(		_UpperCamelCase = 6008_5147_5143      ):

			'''simple docstring'''




			try:
						__lowerCAmelCase				    =			int(_UpperCamelCase      )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."      )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."      )
			__lowerCAmelCase				    =			2
			__lowerCAmelCase				    =			0
			if n == 2:
						return 2
			while n > 2:
						while n % i != 0:
									i += 1
						__lowerCAmelCase				    =			i
						while n % i == 0:
									__lowerCAmelCase				    =			n // i
						i += 1
			return int(_UpperCamelCase      )


if __name__ == "__main__":
	print(f'''{solution() = }''')




