


"""simple docstring"""







import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       np.inf

					def set_batch_size(lowerCAmelCase_     ) -> None:
										nonlocal batch_size
										if isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ):
															__SCREAMING_SNAKE_CASE		     =       min(lowerCAmelCase_						,		config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS     )
										elif isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ):
															__SCREAMING_SNAKE_CASE		     =       min(lowerCAmelCase_						,		config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS     )
										elif isinstance(lowerCAmelCase_						,		lowerCAmelCase_     ) and feature.dtype == "binary":
															__SCREAMING_SNAKE_CASE		     =       min(lowerCAmelCase_						,		config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS     )

					_visit(lowerCAmelCase_						,		lowerCAmelCase_     )

					return None if batch_size is np.inf else batch_size




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	def __init__(				self    :       Any    ,    UpperCAmelCase__    :       NestedDataStructureLike[PathLike]    ,    UpperCAmelCase__    :       Optional[NamedSplit] = None    ,    UpperCAmelCase__    :       Optional[Features] = None    ,    UpperCAmelCase__    :       str = None    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       bool = False    ,    UpperCAmelCase__    :       Optional[int] = None    ,    **UpperCAmelCase__    :       List[Any]    ,    ) ->			int:
						super().__init__(
						    UpperCAmelCase__    ,    split=UpperCAmelCase__    ,    features=UpperCAmelCase__    ,    cache_dir=UpperCAmelCase__    ,    keep_in_memory=UpperCAmelCase__    ,    streaming=UpperCAmelCase__    ,    num_proc=UpperCAmelCase__    ,    **UpperCAmelCase__    ,    )
						__SCREAMING_SNAKE_CASE		     =       path_or_paths if isinstance(UpperCAmelCase__    ,    UpperCAmelCase__     ) else {self.split: path_or_paths}
						__SCREAMING_SNAKE_CASE		     =       _PACKAGED_DATASETS_MODULES["parquet"][1]
						__SCREAMING_SNAKE_CASE		     =       Parquet(
						    cache_dir=UpperCAmelCase__    ,    data_files=UpperCAmelCase__    ,    features=UpperCAmelCase__    ,    hash=UpperCAmelCase__    ,    **UpperCAmelCase__    ,    )




	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			str:
						# Build iterable dataset
						if self.streaming:
											__SCREAMING_SNAKE_CASE		     =       self.builder.as_streaming_dataset(split=self.split     )
						# Build regular (map-style) dataset
						else:
											__SCREAMING_SNAKE_CASE		     =       None
											__SCREAMING_SNAKE_CASE		     =       None
											__SCREAMING_SNAKE_CASE		     =       None
											__SCREAMING_SNAKE_CASE		     =       None

											self.builder.download_and_prepare(
											    download_config=UpperCAmelCase__    ,    download_mode=UpperCAmelCase__    ,    verification_mode=UpperCAmelCase__    ,    base_path=UpperCAmelCase__    ,    num_proc=self.num_proc    ,    )
											__SCREAMING_SNAKE_CASE		     =       self.builder.as_dataset(
											    split=self.split    ,    verification_mode=UpperCAmelCase__    ,    in_memory=self.keep_in_memory     )
						return dataset




class 				UpperCamelCase_			:

	"""simple docstring"""





	def __init__(				self    :       str    ,    UpperCAmelCase__    :       Dataset    ,    UpperCAmelCase__    :       Union[PathLike, BinaryIO]    ,    UpperCAmelCase__    :       Optional[int] = None    ,    **UpperCAmelCase__    :       Optional[int]    ,    ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       dataset
						__SCREAMING_SNAKE_CASE		     =       path_or_buf
						__SCREAMING_SNAKE_CASE		     =       batch_size or get_writer_batch_size(dataset.features     )
						__SCREAMING_SNAKE_CASE		     =       parquet_writer_kwargs




	def 						UpperCAmelCase_   (				self    :       Tuple     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

						if isinstance(self.path_or_buf    ,    (str, bytes, os.PathLike)     ):
											with open(self.path_or_buf    ,    "wb+"     ) as buffer:
																__SCREAMING_SNAKE_CASE		     =       self._write(file_obj=UpperCAmelCase__    ,    batch_size=UpperCAmelCase__    ,    **self.parquet_writer_kwargs     )
						else:
											__SCREAMING_SNAKE_CASE		     =       self._write(file_obj=self.path_or_buf    ,    batch_size=UpperCAmelCase__    ,    **self.parquet_writer_kwargs     )
						return written




	def 						UpperCAmelCase_   (				self    :       List[str]    ,    UpperCAmelCase__    :       BinaryIO    ,    UpperCAmelCase__    :       int    ,    **UpperCAmelCase__    :       int     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       0
						__SCREAMING_SNAKE_CASE		     =       parquet_writer_kwargs.pop("path_or_buf"    ,    UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       self.dataset.features.arrow_schema

						__SCREAMING_SNAKE_CASE		     =       pq.ParquetWriter(UpperCAmelCase__    ,    schema=UpperCAmelCase__    ,    **UpperCAmelCase__     )

						for offset in logging.tqdm(
						    range(0    ,    len(self.dataset     )    ,    UpperCAmelCase__     )    ,    unit="ba"    ,    disable=not logging.is_progress_bar_enabled()    ,    desc="Creating parquet from Arrow format"    ,    ):
											__SCREAMING_SNAKE_CASE		     =       query_table(
											    table=self.dataset._data    ,    key=slice(UpperCAmelCase__    ,    offset + batch_size     )    ,    indices=self.dataset._indices if self.dataset._indices is not None else None    ,    )
											writer.write_table(UpperCAmelCase__     )
											written += batch.nbytes
						writer.close()
						return written




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

a__       :							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''',
}
a__       :							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''',
}
a__       :							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''',
}
a__       :							List[Any]        =  {
    '''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''',
}
a__       :							Union[str, 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''',
}
a__       :							List[Any]        =  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
a__       :							Any        =  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
a__       :							List[Any]        =  []
a__       :							str        =  []




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					for attribute in key.split("."     ):
										__SCREAMING_SNAKE_CASE		     =       getattr(lowerCAmelCase_						,		lowerCAmelCase_     )

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

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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


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




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       []

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

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

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

																				__SCREAMING_SNAKE_CASE		     =       True
																				if "*" in mapped_key:
																									__SCREAMING_SNAKE_CASE		     =       name.split(lowerCAmelCase_     )[0].split("."     )[-2]
																									__SCREAMING_SNAKE_CASE		     =       mapped_key.replace("*"						,		lowerCAmelCase_     )
																				if "weight_g" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_g"
																				elif "weight_v" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_v"
																				elif "weight_ih_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_ih_l0"
																				elif "weight_hh_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_hh_l0"
																				elif "bias_ih_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_ih_l0"
																				elif "bias_hh_l0" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_hh_l0"
																				elif "weight_ih_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_ih_l1"
																				elif "weight_hh_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight_hh_l1"
																				elif "bias_ih_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_ih_l1"
																				elif "bias_hh_l1" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias_hh_l1"
																				elif "bias" in name:
																									__SCREAMING_SNAKE_CASE		     =       "bias"
																				elif "weight" in name:
																									__SCREAMING_SNAKE_CASE		     =       "weight"
																				elif "running_mean" in name:
																									__SCREAMING_SNAKE_CASE		     =       "running_mean"
																				elif "running_var" in name:
																									__SCREAMING_SNAKE_CASE		     =       "running_var"
																				elif "num_batches_tracked" in name:
																									__SCREAMING_SNAKE_CASE		     =       "num_batches_tracked"
																				else:
																									__SCREAMING_SNAKE_CASE		     =       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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_=None						,		lowerCAmelCase_=None						,		):




					'''simple docstring'''


					if config_path is not None:
										__SCREAMING_SNAKE_CASE		     =       EncodecConfig.from_pretrained(lowerCAmelCase_     )
					else:
										__SCREAMING_SNAKE_CASE		     =       EncodecConfig()

					if model_name == "encodec_24khz":
										pass  # config is already correct
					elif model_name == "encodec_32khz":
										__SCREAMING_SNAKE_CASE		     =       [8, 5, 4, 4]
										__SCREAMING_SNAKE_CASE		     =       [2.2]
										__SCREAMING_SNAKE_CASE		     =       64
										__SCREAMING_SNAKE_CASE		     =       3_2000
										__SCREAMING_SNAKE_CASE		     =       2048
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       False
					elif model_name == "encodec_48khz":
										__SCREAMING_SNAKE_CASE		     =       [8, 5, 4, 2]
										__SCREAMING_SNAKE_CASE		     =       [3.0, 6.0, 12.0, 24.0]
										__SCREAMING_SNAKE_CASE		     =       4_8000
										__SCREAMING_SNAKE_CASE		     =       2
										__SCREAMING_SNAKE_CASE		     =       False
										__SCREAMING_SNAKE_CASE		     =       "time_group_norm"
										__SCREAMING_SNAKE_CASE		     =       True
										__SCREAMING_SNAKE_CASE		     =       1.0
										__SCREAMING_SNAKE_CASE		     =       0.01
					else:
										raise ValueError(f"""Unknown model name: {model_name}"""     )

					__SCREAMING_SNAKE_CASE		     =       EncodecModel(lowerCAmelCase_     )

					__SCREAMING_SNAKE_CASE		     =       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_     )

					__SCREAMING_SNAKE_CASE		     =       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
										__SCREAMING_SNAKE_CASE		     =       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__":
		a__       :							List[str]        =  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.'''
		)

		a__       :							Optional[int]        =  parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)
