



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

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






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Dict							,	__lowerCamelCase       :					Optional[int]							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					str			):
			"""simple docstring"""
			for attribute in key.split('''.'''			):
						lowerCamelCase__	:	Tuple				=getattr(UpperCamelCase__							,	UpperCamelCase__			)

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

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






def   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Tuple			):
			"""simple docstring"""
			for key in ignore_keys:
						if key.endswith('''.*'''			):
									if name.startswith(key[:-1]			):
												return True
						elif ".*." in key:
									lowerCamelCase__	:	List[str]				=key.split('''.*.'''			)
									if prefix in name and suffix in name:
												return True
						elif key in name:
									return True
			return False






def   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					List[Any]			):
			"""simple docstring"""
			lowerCamelCase__	:	Dict				=[]

			if model_name == "encodec_24khz" or "encodec_32khz":
						lowerCamelCase__	:	int				=MAPPING_24K
			elif model_name == "encodec_48khz":
						lowerCamelCase__	:	Union[str, Any]				=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__	:	Union[str, Any]				=False
						for key, mapped_key in MAPPING.items():
									if "*" in key:
												lowerCamelCase__	:	str				=key.split('''.*.'''			)
												if prefix in name and suffix in name:
															lowerCamelCase__	:	List[str]				=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__	:	List[str]				=True
												if "*" in mapped_key:
															lowerCamelCase__	:	str				=name.split(UpperCamelCase__			)[0].split('''.'''			)[-2]
															lowerCamelCase__	:	Union[str, Any]				=mapped_key.replace('''*'''							,	UpperCamelCase__			)
												if "weight_g" in name:
															lowerCamelCase__	:	Dict				="""weight_g"""
												elif "weight_v" in name:
															lowerCamelCase__	:	Tuple				="""weight_v"""
												elif "weight_ih_l0" in name:
															lowerCamelCase__	:	Union[str, Any]				="""weight_ih_l0"""
												elif "weight_hh_l0" in name:
															lowerCamelCase__	:	Tuple				="""weight_hh_l0"""
												elif "bias_ih_l0" in name:
															lowerCamelCase__	:	List[Any]				="""bias_ih_l0"""
												elif "bias_hh_l0" in name:
															lowerCamelCase__	:	str				="""bias_hh_l0"""
												elif "weight_ih_l1" in name:
															lowerCamelCase__	:	Any				="""weight_ih_l1"""
												elif "weight_hh_l1" in name:
															lowerCamelCase__	:	List[str]				="""weight_hh_l1"""
												elif "bias_ih_l1" in name:
															lowerCamelCase__	:	Dict				="""bias_ih_l1"""
												elif "bias_hh_l1" in name:
															lowerCamelCase__	:	Dict				="""bias_hh_l1"""
												elif "bias" in name:
															lowerCamelCase__	:	Optional[int]				="""bias"""
												elif "weight" in name:
															lowerCamelCase__	:	str				="""weight"""
												elif "running_mean" in name:
															lowerCamelCase__	:	List[str]				="""running_mean"""
												elif "running_var" in name:
															lowerCamelCase__	:	Any				="""running_var"""
												elif "num_batches_tracked" in name:
															lowerCamelCase__	:	Tuple				="""num_batches_tracked"""
												else:
															lowerCamelCase__	:	List[str]				=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   snake_case__     (	__lowerCamelCase       :					Optional[int]							,	__lowerCamelCase       :					Any							,	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					Optional[int]=None							,	__lowerCamelCase       :					int=None							,	):
			"""simple docstring"""
			if config_path is not None:
						lowerCamelCase__	:	Any				=EncodecConfig.from_pretrained(UpperCamelCase__			)
			else:
						lowerCamelCase__	:	List[str]				=EncodecConfig()

			if model_name == "encodec_24khz":
						pass  # config is already correct
			elif model_name == "encodec_32khz":
						lowerCamelCase__	:	Optional[int]				=[8, 5, 4, 4]
						lowerCamelCase__	:	List[Any]				=[2.2]
						lowerCamelCase__	:	List[str]				=64
						lowerCamelCase__	:	Optional[Any]				=32000
						lowerCamelCase__	:	List[str]				=2048
						lowerCamelCase__	:	Dict				=False
						lowerCamelCase__	:	List[Any]				=False
						lowerCamelCase__	:	Any				=False
			elif model_name == "encodec_48khz":
						lowerCamelCase__	:	Dict				=[8, 5, 4, 2]
						lowerCamelCase__	:	Optional[Any]				=[3.0, 6.0, 12.0, 24.0]
						lowerCamelCase__	:	Optional[int]				=48000
						lowerCamelCase__	:	str				=2
						lowerCamelCase__	:	Tuple				=False
						lowerCamelCase__	:	Union[str, Any]				="""time_group_norm"""
						lowerCamelCase__	:	Optional[Any]				=True
						lowerCamelCase__	:	Dict				=1.0
						lowerCamelCase__	:	str				=0.01
			else:
						raise ValueError(f'''Unknown model name: {model_name}'''			)

			lowerCamelCase__	:	Optional[Any]				=EncodecModel(UpperCamelCase__			)

			lowerCamelCase__	:	Optional[int]				=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__	:	Any				=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__	:	Optional[Any]				=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__":
		_lowercase				:				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."
		)

		_lowercase				:				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   snake_case__     (	__lowerCamelCase       :					str			):
			"""simple docstring"""
			return " ".join(
			    ''''''.join(word[::-1]			) if len(__lowerCamelCase			) > 4 else word for word in sentence.split()			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(reverse_long_words("Hey wollef sroirraw"))




