



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

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






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__						,       lowercase__   ) ->      List[Any]:
						for attribute in key.split("""."""   ):
												__lowercase					=				getattr(lowercase__						,       lowercase__   )

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

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






def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      Tuple:
						for key in ignore_keys:
												if key.endswith(""".*"""   ):
																		if name.startswith(key[:-1]   ):
																								return True
												elif ".*." in key:
																		__lowercase	,			__lowercase					=				key.split(""".*."""   )
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      List[Any]:
						__lowercase					=				[]

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

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

												__lowercase					=				False
												for key, mapped_key in MAPPING.items():
																		if "*" in key:
																								__lowercase	,			__lowercase					=				key.split(""".*."""   )
																								if prefix in name and suffix in name:
																														__lowercase					=				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					=				True
																								if "*" in mapped_key:
																														__lowercase					=				name.split(lowercase__   )[0].split("""."""   )[-2]
																														__lowercase					=				mapped_key.replace("""*"""						,       lowercase__   )
																								if "weight_g" in name:
																														__lowercase					=				"""weight_g"""
																								elif "weight_v" in name:
																														__lowercase					=				"""weight_v"""
																								elif "weight_ih_l0" in name:
																														__lowercase					=				"""weight_ih_l0"""
																								elif "weight_hh_l0" in name:
																														__lowercase					=				"""weight_hh_l0"""
																								elif "bias_ih_l0" in name:
																														__lowercase					=				"""bias_ih_l0"""
																								elif "bias_hh_l0" in name:
																														__lowercase					=				"""bias_hh_l0"""
																								elif "weight_ih_l1" in name:
																														__lowercase					=				"""weight_ih_l1"""
																								elif "weight_hh_l1" in name:
																														__lowercase					=				"""weight_hh_l1"""
																								elif "bias_ih_l1" in name:
																														__lowercase					=				"""bias_ih_l1"""
																								elif "bias_hh_l1" in name:
																														__lowercase					=				"""bias_hh_l1"""
																								elif "bias" in name:
																														__lowercase					=				"""bias"""
																								elif "weight" in name:
																														__lowercase					=				"""weight"""
																								elif "running_mean" in name:
																														__lowercase					=				"""running_mean"""
																								elif "running_var" in name:
																														__lowercase					=				"""running_var"""
																								elif "num_batches_tracked" in name:
																														__lowercase					=				"""num_batches_tracked"""
																								else:
																														__lowercase					=				None
																								set_recursively(lowercase__						,       lowercase__						,       lowercase__						,       lowercase__						,       lowercase__   )
																		continue
												if not is_used:
																		unused_weights.append(lowercase__   )

						logger.warning(F"Unused weights: {unused_weights}"   )






@torch.no_grad()
def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__=None						,       lowercase__=None						,       ) ->      List[Any]:
						if config_path is not None:
												__lowercase					=				EncodecConfig.from_pretrained(lowercase__   )
						else:
												__lowercase					=				EncodecConfig()

						if model_name == "encodec_24khz":
												pass  # config is already correct
						elif model_name == "encodec_32khz":
												__lowercase					=				[8, 5, 4, 4]
												__lowercase					=				[2.2]
												__lowercase					=				64
												__lowercase					=				32_000
												__lowercase					=				2_048
												__lowercase					=				False
												__lowercase					=				False
												__lowercase					=				False
						elif model_name == "encodec_48khz":
												__lowercase					=				[8, 5, 4, 2]
												__lowercase					=				[3.0, 6.0, 12.0, 24.0]
												__lowercase					=				48_000
												__lowercase					=				2
												__lowercase					=				False
												__lowercase					=				"""time_group_norm"""
												__lowercase					=				True
												__lowercase					=				1.0
												__lowercase					=				0.01
						else:
												raise ValueError(F"Unknown model name: {model_name}"   )

						__lowercase					=				EncodecModel(lowercase__   )

						__lowercase					=				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(lowercase__   )

						__lowercase					=				torch.load(lowercase__   )
						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					=				original_checkpoint["""best_state"""]
						recursively_load_weights(lowercase__						,       lowercase__						,       lowercase__   )
						model.save_pretrained(lowercase__   )

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


if __name__ == "__main__":
			UpperCamelCase__ =						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__ =						parser.parse_args()
			convert_checkpoint(
			    args.model,
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.config_path,
			    args.push_to_hub,
			)










def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      int:
						if len(lowercase__   ) != len(lowercase__   ):
												raise ValueError("""String lengths must match!"""   )

						__lowercase					=				0

						for chara, chara in zip(lowercase__						,       lowercase__   ):
												if chara != chara:
																		count += 1

						return count


if __name__ == "__main__":
			import doctest

			doctest.testmod()





