






import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


SCREAMING_SNAKE_CASE__   :      int				=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :      int				=		OrderedDict(
    [
        ("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
        ("""beit""", """BeitFeatureExtractor"""),
        ("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
        ("""clap""", """ClapFeatureExtractor"""),
        ("""clip""", """CLIPFeatureExtractor"""),
        ("""clipseg""", """ViTFeatureExtractor"""),
        ("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
        ("""convnext""", """ConvNextFeatureExtractor"""),
        ("""cvt""", """ConvNextFeatureExtractor"""),
        ("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
        ("""data2vec-vision""", """BeitFeatureExtractor"""),
        ("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
        ("""deit""", """DeiTFeatureExtractor"""),
        ("""detr""", """DetrFeatureExtractor"""),
        ("""dinat""", """ViTFeatureExtractor"""),
        ("""donut-swin""", """DonutFeatureExtractor"""),
        ("""dpt""", """DPTFeatureExtractor"""),
        ("""encodec""", """EncodecFeatureExtractor"""),
        ("""flava""", """FlavaFeatureExtractor"""),
        ("""glpn""", """GLPNFeatureExtractor"""),
        ("""groupvit""", """CLIPFeatureExtractor"""),
        ("""hubert""", """Wav2Vec2FeatureExtractor"""),
        ("""imagegpt""", """ImageGPTFeatureExtractor"""),
        ("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
        ("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
        ("""levit""", """LevitFeatureExtractor"""),
        ("""maskformer""", """MaskFormerFeatureExtractor"""),
        ("""mctct""", """MCTCTFeatureExtractor"""),
        ("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
        ("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
        ("""mobilevit""", """MobileViTFeatureExtractor"""),
        ("""nat""", """ViTFeatureExtractor"""),
        ("""owlvit""", """OwlViTFeatureExtractor"""),
        ("""perceiver""", """PerceiverFeatureExtractor"""),
        ("""poolformer""", """PoolFormerFeatureExtractor"""),
        ("""regnet""", """ConvNextFeatureExtractor"""),
        ("""resnet""", """ConvNextFeatureExtractor"""),
        ("""segformer""", """SegformerFeatureExtractor"""),
        ("""sew""", """Wav2Vec2FeatureExtractor"""),
        ("""sew-d""", """Wav2Vec2FeatureExtractor"""),
        ("""speech_to_text""", """Speech2TextFeatureExtractor"""),
        ("""speecht5""", """SpeechT5FeatureExtractor"""),
        ("""swiftformer""", """ViTFeatureExtractor"""),
        ("""swin""", """ViTFeatureExtractor"""),
        ("""swinv2""", """ViTFeatureExtractor"""),
        ("""table-transformer""", """DetrFeatureExtractor"""),
        ("""timesformer""", """VideoMAEFeatureExtractor"""),
        ("""tvlt""", """TvltFeatureExtractor"""),
        ("""unispeech""", """Wav2Vec2FeatureExtractor"""),
        ("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
        ("""van""", """ConvNextFeatureExtractor"""),
        ("""videomae""", """VideoMAEFeatureExtractor"""),
        ("""vilt""", """ViltFeatureExtractor"""),
        ("""vit""", """ViTFeatureExtractor"""),
        ("""vit_mae""", """ViTFeatureExtractor"""),
        ("""vit_msn""", """ViTFeatureExtractor"""),
        ("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
        ("""wavlm""", """Wav2Vec2FeatureExtractor"""),
        ("""whisper""", """WhisperFeatureExtractor"""),
        ("""xclip""", """CLIPFeatureExtractor"""),
        ("""yolos""", """YolosFeatureExtractor"""),
    ]
)

SCREAMING_SNAKE_CASE__   :      Any				=		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
								if class_name in extractors:
												__magic_name__		:Any   =   model_type_to_module_name(snake_case__			)

												__magic_name__		:Any   =   importlib.import_module(f'''.{module_name}''',	'''transformers.models'''			)
												try:
																return getattr(snake_case__,	snake_case__			)
												except AttributeError:
																continue

				for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
								if getattr(snake_case__,	'''__name__''',	snake_case__			) == class_name:
												return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
				__magic_name__		:Dict   =   importlib.import_module('''transformers'''			)
				if hasattr(snake_case__,	snake_case__			):
								return getattr(snake_case__,	snake_case__			)

				return None

def   __lowercase       (			snake_case,	snake_case = None,	snake_case = False,	snake_case = False,	snake_case = None,	snake_case = None,	snake_case = None,	snake_case = False,	**snake_case,	):







				"""simple docstring"""






				__magic_name__		:str   =   get_file_from_repo(
				    snake_case__,	snake_case__,	cache_dir=snake_case__,	force_download=snake_case__,	resume_download=snake_case__,	proxies=snake_case__,	use_auth_token=snake_case__,	revision=snake_case__,	local_files_only=snake_case__,	)
				if resolved_config_file is None:
								logger.info(
								    '''Could not locate the feature extractor configuration file, will try to use the model config instead.'''			)
								return {}

				with open(snake_case__,	encoding='''utf-8'''			) as reader:
								return json.load(snake_case__			)

class     lowerCamelCase_					:







				def __init__(    self  ):
								"""simple docstring"""
								raise EnvironmentError(
								    '''AutoFeatureExtractor is designed to be instantiated '''
								    '''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.'''  )







				@classmethod
				@replace_list_option_in_docstrings(__lowerCAmelCase  )
				def       A						(    cls							,       __lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Any   =   kwargs.pop('''config'''							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   kwargs.pop('''trust_remote_code'''							,       __lowerCAmelCase  )
								__magic_name__		:Optional[Any]   =   True

								__magic_name__		:int   =   FeatureExtractionMixin.get_feature_extractor_dict(__lowerCAmelCase							,       **__lowerCAmelCase  )
								__magic_name__		:Any   =   config_dict.get('''feature_extractor_type'''							,       __lowerCAmelCase  )
								__magic_name__		:List[Any]   =   None
								if "AutoFeatureExtractor" in config_dict.get('''auto_map'''							,       {}  ):
												__magic_name__		:Any   =   config_dict['''auto_map''']['''AutoFeatureExtractor''']

								# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
								if feature_extractor_class is None and feature_extractor_auto_map is None:
												if not isinstance(__lowerCAmelCase							,       __lowerCAmelCase  ):
																__magic_name__		:Optional[Any]   =   AutoConfig.from_pretrained(__lowerCAmelCase							,       **__lowerCAmelCase  )
												# It could be in `config.feature_extractor_type``
												__magic_name__		:str   =   getattr(__lowerCAmelCase							,       '''feature_extractor_type'''							,       __lowerCAmelCase  )
												if hasattr(__lowerCAmelCase							,       '''auto_map'''  ) and "AutoFeatureExtractor" in config.auto_map:
																__magic_name__		:Optional[Any]   =   config.auto_map['''AutoFeatureExtractor''']

								if feature_extractor_class is not None:
												__magic_name__		:List[str]   =   feature_extractor_class_from_name(__lowerCAmelCase  )

								__magic_name__		:str   =   feature_extractor_auto_map is not None
								__magic_name__		:int   =   feature_extractor_class is not None or type(__lowerCAmelCase  ) in FEATURE_EXTRACTOR_MAPPING
								__magic_name__		:Optional[int]   =   resolve_trust_remote_code(
								    __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  )

								if has_remote_code and trust_remote_code:
												__magic_name__		:int   =   get_class_from_dynamic_module(
												    __lowerCAmelCase							,       __lowerCAmelCase							,       **__lowerCAmelCase  )
												__magic_name__		:List[str]   =   kwargs.pop('''code_revision'''							,       __lowerCAmelCase  )
												if os.path.isdir(__lowerCAmelCase  ):
																feature_extractor_class.register_for_auto_class()
												return feature_extractor_class.from_dict(__lowerCAmelCase							,       **__lowerCAmelCase  )
								elif feature_extractor_class is not None:
												return feature_extractor_class.from_dict(__lowerCAmelCase							,       **__lowerCAmelCase  )
								# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
								elif type(__lowerCAmelCase  ) in FEATURE_EXTRACTOR_MAPPING:
												__magic_name__		:Optional[Any]   =   FEATURE_EXTRACTOR_MAPPING[type(__lowerCAmelCase  )]
												return feature_extractor_class.from_dict(__lowerCAmelCase							,       **__lowerCAmelCase  )

								raise ValueError(
								    F'''Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '''
								    F'''`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '''
								    F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()  )}'''  )







				@staticmethod
				def       A						(    __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								FEATURE_EXTRACTOR_MAPPING.register(__lowerCAmelCase							,       __lowerCAmelCase  )








'''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()
lowercase     :							List[str]       						=     logging.get_logger('transformers.models.encodec')

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







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    for attribute in key.split('''.'''		):
        A			:  int   =		getattr(snake_case__						,							snake_case__		)

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

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







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



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







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__		):




    '''simple docstring'''



    A			:  List[Any]   =		[]

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

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

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

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

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

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







@torch.no_grad()
def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__=None						,							snake_case__=None						,							):




    '''simple docstring'''



    if config_path is not None:
        A			:  Dict   =		EncodecConfig.from_pretrained(snake_case__		)
    else:
        A			:  Dict   =		EncodecConfig()

    if model_name == "encodec_24khz":
        pass  # config is already correct
    elif model_name == "encodec_32khz":
        A			:  Optional[Any]   =		[8, 5, 4, 4]
        A			:  List[Any]   =		[2.2]
        A			:  Tuple   =		64
        A			:  str   =		3_2000
        A			:  Optional[Any]   =		2048
        A			:  Optional[int]   =		False
        A			:  Optional[int]   =		False
        A			:  List[Any]   =		False
    elif model_name == "encodec_48khz":
        A			:  List[str]   =		[8, 5, 4, 2]
        A			:  Any   =		[3.0, 6.0, 12.0, 24.0]
        A			:  Tuple   =		4_8000
        A			:  Optional[int]   =		2
        A			:  Dict   =		False
        A			:  Union[str, Any]   =		'''time_group_norm'''
        A			:  List[Any]   =		True
        A			:  Union[str, Any]   =		1.0
        A			:  Union[str, Any]   =		0.01
    else:
        raise ValueError(F'Unknown model name: {model_name}'		)

    A			:  Optional[Any]   =		EncodecModel(snake_case__		)

    A			:  Optional[Any]   =		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(snake_case__		)

    A			:  Optional[int]   =		torch.load(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
        A			:  Union[str, Any]   =		original_checkpoint['''best_state''']
    recursively_load_weights(snake_case__						,							snake_case__						,							snake_case__		)
    model.save_pretrained(snake_case__		)

    if repo_id:
        print('''Pushing to the hub...'''		)
        feature_extractor.push_to_hub(snake_case__		)
        model.push_to_hub(snake_case__		)


if __name__ == "__main__":
      lowercase     :							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.'
      )

      lowercase     :							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,
      )




