





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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Optional[int]:

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

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

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





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Optional[Any]:

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





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =[]

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

								lowerCamelCase	 =False
								for key, mapped_key in MAPPING.items():
												if "*" in key:
																lowerCamelCase	 =key.split(""".*."""				)
																if prefix in name and suffix in name:
																				lowerCamelCase	 =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	 =True
																if "*" in mapped_key:
																				lowerCamelCase	 =name.split(SCREAMING_SNAKE_CASE_				)[0].split("""."""				)[-2]
																				lowerCamelCase	 =mapped_key.replace("""*"""			,      SCREAMING_SNAKE_CASE_				)
																if "weight_g" in name:
																				lowerCamelCase	 ="weight_g"
																elif "weight_v" in name:
																				lowerCamelCase	 ="weight_v"
																elif "weight_ih_l0" in name:
																				lowerCamelCase	 ="weight_ih_l0"
																elif "weight_hh_l0" in name:
																				lowerCamelCase	 ="weight_hh_l0"
																elif "bias_ih_l0" in name:
																				lowerCamelCase	 ="bias_ih_l0"
																elif "bias_hh_l0" in name:
																				lowerCamelCase	 ="bias_hh_l0"
																elif "weight_ih_l1" in name:
																				lowerCamelCase	 ="weight_ih_l1"
																elif "weight_hh_l1" in name:
																				lowerCamelCase	 ="weight_hh_l1"
																elif "bias_ih_l1" in name:
																				lowerCamelCase	 ="bias_ih_l1"
																elif "bias_hh_l1" in name:
																				lowerCamelCase	 ="bias_hh_l1"
																elif "bias" in name:
																				lowerCamelCase	 ="bias"
																elif "weight" in name:
																				lowerCamelCase	 ="weight"
																elif "running_mean" in name:
																				lowerCamelCase	 ="running_mean"
																elif "running_var" in name:
																				lowerCamelCase	 ="running_var"
																elif "num_batches_tracked" in name:
																				lowerCamelCase	 ="num_batches_tracked"
																else:
																				lowerCamelCase	 =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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=None			,      _UpperCAmelCase=None			,      )						->  Optional[int]:

				if config_path is not None:
								lowerCamelCase	 =EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
				else:
								lowerCamelCase	 =EncodecConfig()

				if model_name == "encodec_24khz":
								pass  # config is already correct
				elif model_name == "encodec_32khz":
								lowerCamelCase	 =[8, 5, 4, 4]
								lowerCamelCase	 =[2.2]
								lowerCamelCase	 =64
								lowerCamelCase	 =3_20_00
								lowerCamelCase	 =20_48
								lowerCamelCase	 =False
								lowerCamelCase	 =False
								lowerCamelCase	 =False
				elif model_name == "encodec_48khz":
								lowerCamelCase	 =[8, 5, 4, 2]
								lowerCamelCase	 =[3.0, 6.0, 1_2.0, 2_4.0]
								lowerCamelCase	 =4_80_00
								lowerCamelCase	 =2
								lowerCamelCase	 =False
								lowerCamelCase	 ="time_group_norm"
								lowerCamelCase	 =True
								lowerCamelCase	 =1.0
								lowerCamelCase	 =0.0_1
				else:
								raise ValueError(F"""Unknown model name: {model_name}"""				)

				lowerCamelCase	 =EncodecModel(SCREAMING_SNAKE_CASE_				)

				lowerCamelCase	 =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_				)

				lowerCamelCase	 =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
								lowerCamelCase	 =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__":
							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,
							)












import os

try:
							from .build_directory_md import good_file_paths
except ImportError:
							from build_directory_md import good_file_paths  # type: ignore

UpperCAmelCase__						:			List[Any]									=list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

UpperCAmelCase__						:			Dict									=[file for file in filepaths if file != file.lower()]
if upper_files:
							print(F"{len(upper_files)} files contain uppercase characters:")
							print('''\n'''.join(upper_files) + '''\n''')

UpperCAmelCase__						:			Dict									=[file for file in filepaths if ''' ''' in file]
if space_files:
							print(F"{len(space_files)} files contain space characters:")
							print('''\n'''.join(space_files) + '''\n''')

UpperCAmelCase__						:			Dict									=[file for file in filepaths if '''-''' in file]
if hyphen_files:
							print(F"{len(hyphen_files)} files contain hyphen characters:")
							print('''\n'''.join(hyphen_files) + '''\n''')

UpperCAmelCase__						:			Optional[int]									=[file for file in filepaths if os.sep not in file]
if nodir_files:
							print(F"{len(nodir_files)} files are not in a directory:")
							print('''\n'''.join(nodir_files) + '''\n''')

UpperCAmelCase__						:			str									=len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
							import sys

							sys.exit(bad_files)





