
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  :					str													=       logging.get_logger('transformers.models.encodec')

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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			List[Any]						,  _lowerCamelCase :			Optional[Any]						,  _lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[Any])					->    int:






							'''simple docstring'''





							for attribute in key.split("."):
														__UpperCamelCase	:   Union[str, Any]			   =					getattr(_lowerCamelCase						,  _lowerCamelCase)

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

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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Dict						,  _lowerCamelCase :			Dict)					->    List[str]:






							'''simple docstring'''





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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			Tuple						,  _lowerCamelCase :			List[Any])					->    str:






							'''simple docstring'''





							__UpperCamelCase	:   str			   =					[]

							if model_name == "encodec_24khz" or "encodec_32khz":
														__UpperCamelCase	:   Optional[Any]			   =					MAPPING_24K
							elif model_name == "encodec_48khz":
														__UpperCamelCase	:   Optional[Any]			   =					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

														__UpperCamelCase	:   List[Any]			   =					False
														for key, mapped_key in MAPPING.items():
																					if "*" in key:
																												__UpperCamelCase   ,						__UpperCamelCase	:   Dict			   =					key.split(".*.")
																												if prefix in name and suffix in name:
																																			__UpperCamelCase	:   Optional[int]			   =					suffix

																					if key in name:
																												# HACK otherwise .embed gets initialized with .embed_avg too
																												if key.endswith("embed") and name.endswith("embed_avg"):
																																			continue

																												__UpperCamelCase	:   Optional[int]			   =					True
																												if "*" in mapped_key:
																																			__UpperCamelCase	:   List[str]			   =					name.split(_lowerCamelCase)[0].split(".")[-2]
																																			__UpperCamelCase	:   List[Any]			   =					mapped_key.replace("*"						,  _lowerCamelCase)
																												if "weight_g" in name:
																																			__UpperCamelCase	:   str			   =					"weight_g"
																												elif "weight_v" in name:
																																			__UpperCamelCase	:   int			   =					"weight_v"
																												elif "weight_ih_l0" in name:
																																			__UpperCamelCase	:   Union[str, Any]			   =					"weight_ih_l0"
																												elif "weight_hh_l0" in name:
																																			__UpperCamelCase	:   List[Any]			   =					"weight_hh_l0"
																												elif "bias_ih_l0" in name:
																																			__UpperCamelCase	:   int			   =					"bias_ih_l0"
																												elif "bias_hh_l0" in name:
																																			__UpperCamelCase	:   Optional[int]			   =					"bias_hh_l0"
																												elif "weight_ih_l1" in name:
																																			__UpperCamelCase	:   Tuple			   =					"weight_ih_l1"
																												elif "weight_hh_l1" in name:
																																			__UpperCamelCase	:   Tuple			   =					"weight_hh_l1"
																												elif "bias_ih_l1" in name:
																																			__UpperCamelCase	:   Optional[Any]			   =					"bias_ih_l1"
																												elif "bias_hh_l1" in name:
																																			__UpperCamelCase	:   int			   =					"bias_hh_l1"
																												elif "bias" in name:
																																			__UpperCamelCase	:   List[str]			   =					"bias"
																												elif "weight" in name:
																																			__UpperCamelCase	:   List[str]			   =					"weight"
																												elif "running_mean" in name:
																																			__UpperCamelCase	:   Union[str, Any]			   =					"running_mean"
																												elif "running_var" in name:
																																			__UpperCamelCase	:   List[str]			   =					"running_var"
																												elif "num_batches_tracked" in name:
																																			__UpperCamelCase	:   str			   =					"num_batches_tracked"
																												else:
																																			__UpperCamelCase	:   Optional[int]			   =					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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Any						,  _lowerCamelCase :			List[Any]						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Dict=None						,  _lowerCamelCase :			List[Any]=None						,  )					->    List[str]:






							'''simple docstring'''





							if config_path is not None:
														__UpperCamelCase	:   Optional[Any]			   =					EncodecConfig.from_pretrained(_lowerCamelCase)
							else:
														__UpperCamelCase	:   Any			   =					EncodecConfig()

							if model_name == "encodec_24khz":
														pass  # config is already correct
							elif model_name == "encodec_32khz":
														__UpperCamelCase	:   Any			   =					[8, 5, 4, 4]
														__UpperCamelCase	:   Dict			   =					[2.2]
														__UpperCamelCase	:   List[Any]			   =					64
														__UpperCamelCase	:   int			   =					32_000
														__UpperCamelCase	:   List[Any]			   =					2_048
														__UpperCamelCase	:   Dict			   =					False
														__UpperCamelCase	:   List[Any]			   =					False
														__UpperCamelCase	:   Optional[Any]			   =					False
							elif model_name == "encodec_48khz":
														__UpperCamelCase	:   Union[str, Any]			   =					[8, 5, 4, 2]
														__UpperCamelCase	:   List[str]			   =					[3.0, 6.0, 1_2.0, 2_4.0]
														__UpperCamelCase	:   Dict			   =					48_000
														__UpperCamelCase	:   Union[str, Any]			   =					2
														__UpperCamelCase	:   Optional[Any]			   =					False
														__UpperCamelCase	:   Dict			   =					"time_group_norm"
														__UpperCamelCase	:   Optional[int]			   =					True
														__UpperCamelCase	:   Any			   =					1.0
														__UpperCamelCase	:   Dict			   =					0.0_1
							else:
														raise ValueError(F'Unknown model name: {model_name}')

							__UpperCamelCase	:   str			   =					EncodecModel(_lowerCamelCase)

							__UpperCamelCase	:   str			   =					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)

							__UpperCamelCase	:   Tuple			   =					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
														__UpperCamelCase	:   Tuple			   =					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__":
		lowercase  :					Tuple													=       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  :					Dict													=       parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)

def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			int						,  _lowerCamelCase :			int)					->    float:






							'''simple docstring'''





							return base * power(_lowerCamelCase						,  (exponent - 1)) if exponent else 1


if __name__ == "__main__":
		print('Raise base to the power of exponent using recursion...')
		lowercase  :					Optional[int]													=       int(input('Enter the base: ').strip())
		lowercase  :					Tuple													=       int(input('Enter the exponent: ').strip())
		lowercase  :					str													=       power(base, abs(exponent))
		if exponent < 0:  # power() does not properly deal w/ negative exponents
				lowercase  :					Tuple													=       1 / result
		print(f"{base} to the power of {exponent} is {result}")