





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						=   logging.get_logger("transformers.models.encodec")

_a						=   {
    "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						=   {
    "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						=   {
    "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						=   {
    "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						=   {
    "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						=   {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_a						=   {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_a						=   []
_a						=   []





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Any:







	'''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__(__snake_case ,__snake_case							)       ->   Union[str, Any]:







	'''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__(__snake_case ,__snake_case ,__snake_case							)       ->   Any:







	'''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__(__snake_case ,__snake_case ,__snake_case ,__snake_case=None ,__snake_case=None ,)       ->   int:







	'''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__              =	32000
		lowerCamelCase__              =	2048
		lowerCamelCase__              =	False
		lowerCamelCase__              =	False
		lowerCamelCase__              =	False
	elif model_name == "encodec_48khz":
		lowerCamelCase__              =	[8, 5, 4, 2]
		lowerCamelCase__              =	[3.0, 6.0, 1_2.0, 2_4.0]
		lowerCamelCase__              =	48000
		lowerCamelCase__              =	2
		lowerCamelCase__              =	False
		lowerCamelCase__              =	'''time_group_norm'''
		lowerCamelCase__              =	True
		lowerCamelCase__              =	1.0
		lowerCamelCase__              =	0.0_1
	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						=   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						=   parser.parse_args()
			convert_checkpoint(
			    args.model,
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.config_path,
			    args.push_to_hub,
			)










from __future__ import annotations





def lowerCAmelCase__(__snake_case ,__snake_case = None ,__snake_case = None							)       ->   None:







	'''simple docstring'''







	if start is None:
		lowerCamelCase__              =	0

	if end is None:
		lowerCamelCase__              =	len(__snake_case							) - 1

	if start >= end:
		return

	lowerCamelCase__              =	(start + end) // 2

	slowsort(__snake_case ,__snake_case ,__snake_case							)
	slowsort(__snake_case ,mid + 1 ,__snake_case							)

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

	slowsort(__snake_case ,__snake_case ,end - 1							)


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

			testmod()



