






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()
lowerCamelCase : Union[str, Any]						   =       logging.get_logger('''transformers.models.encodec''')

lowerCamelCase : 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''',
}
lowerCamelCase : Optional[int]						   =       {
    '''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''',
}
lowerCamelCase : 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''',
}
lowerCamelCase : 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''',
}
lowerCamelCase : List[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''',
}
lowerCamelCase : Optional[Any]						   =       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowerCamelCase : Dict						   =       {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowerCamelCase : Any						   =       []
lowerCamelCase : Any						   =       []
def       snake_case_		(     lowerCAmelCase_       :						Union[str, Any]       ,       lowerCAmelCase_       :						Optional[int]       ,       lowerCAmelCase_       :						Union[str, Any]       ,       lowerCAmelCase_       :						Optional[int]       ,       lowerCAmelCase_       :						Optional[int]      ):

							for attribute in key.split("""."""      ):
														__lowercase      :							Dict				=     getattr(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_      )

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

							logger.info(F"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."      )
def       snake_case_		(     lowerCAmelCase_       :						Dict       ,       lowerCAmelCase_       :						Dict      ):

							for key in ignore_keys:
														if key.endswith(""".*"""      ):
																					if name.startswith(key[:-1]      ):
																												return True
														elif ".*." in key:
																					__lowercase      :							Union[str, Any]				=     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_       :						Optional[Any]       ,       lowerCAmelCase_       :						Optional[Any]      ):
							__lowercase      :							Tuple				=     []

							if model_name == "encodec_24khz" or "encodec_32khz":
														__lowercase      :							List[Any]				=     MAPPING_24K
							elif model_name == "encodec_48khz":
														__lowercase      :							int				=     MAPPING_48K
							else:
														raise ValueError(F"Unsupported model: {model_name}"      )

							for name, value in orig_dict.items():
														if should_ignore(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_      ):
																					logger.info(F"{name} was ignored"      )
																					continue

														__lowercase      :							int				=     False
														for key, mapped_key in MAPPING.items():
																					if "*" in key:
																												__lowercase      :							int				=     key.split(""".*."""      )
																												if prefix in name and suffix in name:
																																			__lowercase      :							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

																												__lowercase      :							Any				=     True
																												if "*" in mapped_key:
																																			__lowercase      :							Optional[Any]				=     name.split(SCREAMING_SNAKE_CASE_      )[0].split("""."""      )[-2]
																																			__lowercase      :							Optional[Any]				=     mapped_key.replace("""*"""       ,       SCREAMING_SNAKE_CASE_      )
																												if "weight_g" in name:
																																			__lowercase      :							Any				=     "weight_g"
																												elif "weight_v" in name:
																																			__lowercase      :							Tuple				=     "weight_v"
																												elif "weight_ih_l0" in name:
																																			__lowercase      :							str				=     "weight_ih_l0"
																												elif "weight_hh_l0" in name:
																																			__lowercase      :							Any				=     "weight_hh_l0"
																												elif "bias_ih_l0" in name:
																																			__lowercase      :							Tuple				=     "bias_ih_l0"
																												elif "bias_hh_l0" in name:
																																			__lowercase      :							int				=     "bias_hh_l0"
																												elif "weight_ih_l1" in name:
																																			__lowercase      :							List[Any]				=     "weight_ih_l1"
																												elif "weight_hh_l1" in name:
																																			__lowercase      :							Optional[Any]				=     "weight_hh_l1"
																												elif "bias_ih_l1" in name:
																																			__lowercase      :							Union[str, Any]				=     "bias_ih_l1"
																												elif "bias_hh_l1" in name:
																																			__lowercase      :							Optional[Any]				=     "bias_hh_l1"
																												elif "bias" in name:
																																			__lowercase      :							Any				=     "bias"
																												elif "weight" in name:
																																			__lowercase      :							Dict				=     "weight"
																												elif "running_mean" in name:
																																			__lowercase      :							Tuple				=     "running_mean"
																												elif "running_var" in name:
																																			__lowercase      :							int				=     "running_var"
																												elif "num_batches_tracked" in name:
																																			__lowercase      :							List[str]				=     "num_batches_tracked"
																												else:
																																			__lowercase      :							Dict				=     None
																												set_recursively(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_      )
																					continue
														if not is_used:
																					unused_weights.append(SCREAMING_SNAKE_CASE_      )

							logger.warning(F"Unused weights: {unused_weights}"      )
@torch.no_grad()
def       snake_case_		(     lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						int=None       ,       lowerCAmelCase_       :						Optional[Any]=None       ,       ):

							if config_path is not None:
														__lowercase      :							int				=     EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE_      )
							else:
														__lowercase      :							Tuple				=     EncodecConfig()

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

							__lowercase      :							str				=     EncodecModel(SCREAMING_SNAKE_CASE_      )

							__lowercase      :							List[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(SCREAMING_SNAKE_CASE_      )

							__lowercase      :							int				=     torch.load(SCREAMING_SNAKE_CASE_      )
							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
														__lowercase      :							Optional[int]				=     original_checkpoint["best_state"]
							recursively_load_weights(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_      )
							model.save_pretrained(SCREAMING_SNAKE_CASE_      )

							if repo_id:
														print("""Pushing to the hub..."""      )
														feature_extractor.push_to_hub(SCREAMING_SNAKE_CASE_      )
														model.push_to_hub(SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
	lowerCamelCase : Union[str, 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.'''
	)

	lowerCamelCase : Union[str, Any]						   =       parser.parse_args()
	convert_checkpoint(
	    args.model,
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.push_to_hub,
	)







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin

class 		lowerCAmelCase     ( __a      ,       __a			):

					'''simple docstring'''

					_A						:			str									=      1



					@register_to_config
					def __init__(  self				:	Optional[int]		,     __a				:	Tuple=2000		,     __a				:	List[str]=0.1		,     __a				:	str=20		,     __a				:	Optional[int]=1E-3							)			->  int:


												"""simple docstring"""

												__lowercase      :							Tuple				=     None
												__lowercase      :							Union[str, Any]				=     None
												__lowercase      :							int				=     None



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	Any		,     __a				:	Union[str, torch.device] = None							)			->  str:


												"""simple docstring"""

												__lowercase      :							List[str]				=     torch.linspace(1		,     self.config.sampling_eps		,     __a		,     device=__a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	List[Any]		,     __a				:	Tuple		,     __a				:	int		,     __a				:	Optional[int]=None							)			->  str:


												"""simple docstring"""

												if self.timesteps is None:
																			raise ValueError(
																			    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""							)

												# TODO(Patrick) better comments + non-PyTorch
												# postprocess model score
												__lowercase      :							Dict				=     (
												    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
												)
												__lowercase      :							int				=     torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
												__lowercase      :							Union[str, Any]				=     std.flatten()
												while len(std.shape							) < len(score.shape							):
																			__lowercase      :							Optional[Any]				=     std.unsqueeze(-1							)
												__lowercase      :							List[Any]				=     -score / std

												# compute
												__lowercase      :							Dict				=     -1.0 / len(self.timesteps							)

												__lowercase      :							int				=     self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
												__lowercase      :							List[Any]				=     beta_t.flatten()
												while len(beta_t.shape							) < len(x.shape							):
																			__lowercase      :							Union[str, Any]				=     beta_t.unsqueeze(-1							)
												__lowercase      :							List[str]				=     -0.5 * beta_t * x

												__lowercase      :							int				=     torch.sqrt(__a							)
												__lowercase      :							Union[str, Any]				=     drift - diffusion**2 * score
												__lowercase      :							Optional[Any]				=     x + drift * dt

												# add noise
												__lowercase      :							List[str]				=     randn_tensor(x.shape		,     layout=x.layout		,     generator=__a		,     device=x.device		,     dtype=x.dtype							)
												__lowercase      :							str				=     x_mean + diffusion * math.sqrt(-dt							) * noise

												return x, x_mean



					def __len__(  self				:	Tuple							)			->  Optional[int]:


												"""simple docstring"""

												return self.config.num_train_timesteps