




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()
__UpperCamelCase					:       int    					=      logging.get_logger("transformers.models.encodec")

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





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	for attribute in key.split("""."""    ):
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )

	if weight_type is not None:
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    ).shape
	else:
		a										=							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										=							value
	elif weight_type == "weight_g":
		a										=							value
	elif weight_type == "weight_v":
		a										=							value
	elif weight_type == "bias":
		a										=							value
	elif weight_type == "running_mean":
		a										=							value
	elif weight_type == "running_var":
		a										=							value
	elif weight_type == "num_batches_tracked":
		a										=							value
	elif weight_type == "weight_ih_l0":
		a										=							value
	elif weight_type == "weight_hh_l0":
		a										=							value
	elif weight_type == "bias_ih_l0":
		a										=							value
	elif weight_type == "bias_hh_l0":
		a										=							value
	elif weight_type == "weight_ih_l1":
		a										=							value
	elif weight_type == "weight_hh_l1":
		a										=							value
	elif weight_type == "bias_ih_l1":
		a										=							value
	elif weight_type == "bias_hh_l1":
		a										=							value
	else:
		a										=							value

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





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       int:
	for key in ignore_keys:
		if key.endswith(""".*"""    ):
			if name.startswith(key[:-1]    ):
				return True
		elif ".*." in key:
			a				, a										=							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    )      ->       Dict:
	a										=							[]

	if model_name == "encodec_24khz" or "encodec_32khz":
		a										=							MAPPING_24K
	elif model_name == "encodec_48khz":
		a										=							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

		a										=							False
		for key, mapped_key in MAPPING.items():
			if "*" in key:
				a				, a										=							key.split(""".*."""    )
				if prefix in name and suffix in name:
					a										=							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										=							True
				if "*" in mapped_key:
					a										=							name.split(__lowerCamelCase    )[0].split("""."""    )[-2]
					a										=							mapped_key.replace("""*"""							,			__lowerCamelCase    )
				if "weight_g" in name:
					a										=							"""weight_g"""
				elif "weight_v" in name:
					a										=							"""weight_v"""
				elif "weight_ih_l0" in name:
					a										=							"""weight_ih_l0"""
				elif "weight_hh_l0" in name:
					a										=							"""weight_hh_l0"""
				elif "bias_ih_l0" in name:
					a										=							"""bias_ih_l0"""
				elif "bias_hh_l0" in name:
					a										=							"""bias_hh_l0"""
				elif "weight_ih_l1" in name:
					a										=							"""weight_ih_l1"""
				elif "weight_hh_l1" in name:
					a										=							"""weight_hh_l1"""
				elif "bias_ih_l1" in name:
					a										=							"""bias_ih_l1"""
				elif "bias_hh_l1" in name:
					a										=							"""bias_hh_l1"""
				elif "bias" in name:
					a										=							"""bias"""
				elif "weight" in name:
					a										=							"""weight"""
				elif "running_mean" in name:
					a										=							"""running_mean"""
				elif "running_var" in name:
					a										=							"""running_var"""
				elif "num_batches_tracked" in name:
					a										=							"""num_batches_tracked"""
				else:
					a										=							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[str]:
	if config_path is not None:
		a										=							EncodecConfig.from_pretrained(__lowerCamelCase    )
	else:
		a										=							EncodecConfig()

	if model_name == "encodec_24khz":
		pass  # config is already correct
	elif model_name == "encodec_32khz":
		a										=							[8, 5, 4, 4]
		a										=							[2.2]
		a										=							64
		a										=							3_2000
		a										=							2048
		a										=							False
		a										=							False
		a										=							False
	elif model_name == "encodec_48khz":
		a										=							[8, 5, 4, 2]
		a										=							[3.0, 6.0, 12.0, 24.0]
		a										=							4_8000
		a										=							2
		a										=							False
		a										=							"""time_group_norm"""
		a										=							True
		a										=							1.0
		a										=							0.01
	else:
		raise ValueError(f'Unknown model name: {model_name}'    )

	a										=							EncodecModel(__lowerCamelCase    )

	a										=							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    )

	a										=							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
		a										=							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__":
		__UpperCamelCase					:       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."
		)

		__UpperCamelCase					:       List[str]    					=      parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)







from math import pi, sqrt





def 		__A		(	__lowerCamelCase    )      ->       float:
	if num <= 0:
		raise ValueError("""math domain error"""    )
	if num > 171.5:
		raise OverflowError("""math range error"""    )
	elif num - int(__lowerCamelCase    ) not in (0, 0.5):
		raise NotImplementedError("""num must be an integer or a half-integer"""    )
	elif num == 0.5:
		return sqrt(__lowerCamelCase    )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1    )





def 		__A		(	)      ->       None:
	assert gamma(0.5    ) == sqrt(__lowerCamelCase    )
	assert gamma(1    ) == 1.0
	assert gamma(2    ) == 1.0


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

		testmod()
		__UpperCamelCase					:       str    					=      1.0
		while num:
				__UpperCamelCase					:       Dict    					=      float(input("Gamma of: "))
				print(F'gamma({num}) = {gamma(num)}')
				print("\nEnter 0 to exit...")

