






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

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[Any]:
		for attribute in key.split('.'  ):
				UpperCamelCase_: str   				=    getattr(UpperCAmelCase__     ,   UpperCAmelCase__  )

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

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

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    str:
		UpperCamelCase_: Dict   				=    []

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

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

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

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

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

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

@torch.no_grad()
def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__=None     ,   UpperCAmelCase__=None     ,   )	->    Optional[int]:
		if config_path is not None:
				UpperCamelCase_: Any   				=    EncodecConfig.from_pretrained(UpperCAmelCase__  )
		else:
				UpperCamelCase_: Optional[int]   				=    EncodecConfig()

		if model_name == "encodec_24khz":
				pass  # config is already correct
		elif model_name == "encodec_32khz":
				UpperCamelCase_: List[str]   				=    [8, 5, 4, 4]
				UpperCamelCase_: int   				=    [2.2]
				UpperCamelCase_: List[Any]   				=    6_4
				UpperCamelCase_: List[Any]   				=    3_2_0_0_0
				UpperCamelCase_: Optional[Any]   				=    2_0_4_8
				UpperCamelCase_: List[str]   				=    False
				UpperCamelCase_: Optional[Any]   				=    False
				UpperCamelCase_: Tuple   				=    False
		elif model_name == "encodec_48khz":
				UpperCamelCase_: Dict   				=    [8, 5, 4, 2]
				UpperCamelCase_: List[str]   				=    [3.0, 6.0, 12.0, 24.0]
				UpperCamelCase_: Optional[Any]   				=    4_8_0_0_0
				UpperCamelCase_: Tuple   				=    2
				UpperCamelCase_: Optional[Any]   				=    False
				UpperCamelCase_: Union[str, Any]   				=    'time_group_norm'
				UpperCamelCase_: str   				=    True
				UpperCamelCase_: str   				=    1.0
				UpperCamelCase_: Tuple   				=    0.01
		else:
				raise ValueError(F'''Unknown model name: {model_name}'''  )

		UpperCamelCase_: Union[str, Any]   				=    EncodecModel(UpperCAmelCase__  )

		UpperCamelCase_: Dict   				=    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(UpperCAmelCase__  )

		UpperCamelCase_: Any   				=    torch.load(UpperCAmelCase__  )
		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
				UpperCamelCase_: int   				=    original_checkpoint['best_state']
		recursively_load_weights(UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )
		model.save_pretrained(UpperCAmelCase__  )

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


if __name__ == "__main__":
			A_		:		Dict		     =			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[Any]		     =			parser.parse_args()
			convert_checkpoint(
			    args.model,
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.config_path,
			    args.push_to_hub,
			)







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_		:		List[Any]		     =			logging.get_logger(__name__)

A_		:		Optional[Any]		     =			{
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		str									='''convbert'''





       def __init__( self  ,      _lowerCamelCase=3_0_5_2_2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=1_2  ,      _lowerCamelCase=3_0_7_2  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=2  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=1e-12  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      _lowerCamelCase=7_6_8  ,      _lowerCamelCase=2  ,      _lowerCamelCase=9  ,      _lowerCamelCase=1  ,      _lowerCamelCase=None  ,      **_lowerCamelCase  ,      ):
         super().__init__(
             pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )

         UpperCamelCase_: Dict   				=    vocab_size
         UpperCamelCase_: Tuple   				=    hidden_size
         UpperCamelCase_: Optional[int]   				=    num_hidden_layers
         UpperCamelCase_: Optional[int]   				=    num_attention_heads
         UpperCamelCase_: Optional[Any]   				=    intermediate_size
         UpperCamelCase_: Tuple   				=    hidden_act
         UpperCamelCase_: Any   				=    hidden_dropout_prob
         UpperCamelCase_: Any   				=    attention_probs_dropout_prob
         UpperCamelCase_: List[Any]   				=    max_position_embeddings
         UpperCamelCase_: List[Any]   				=    type_vocab_size
         UpperCamelCase_: Optional[int]   				=    initializer_range
         UpperCamelCase_: Tuple   				=    layer_norm_eps
         UpperCamelCase_: List[str]   				=    embedding_size
         UpperCamelCase_: int   				=    head_ratio
         UpperCamelCase_: Dict   				=    conv_kernel_size
         UpperCamelCase_: List[Any]   				=    num_groups
         UpperCamelCase_: Dict   				=    classifier_dropout







class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       @property
       def       _a   ( self							):
         if self.task == "multiple-choice":
           UpperCamelCase_: Dict   				=    {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
           UpperCamelCase_: Dict   				=    {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ('input_ids', dynamic_axis),
                 ('attention_mask', dynamic_axis),
                 ('token_type_ids', dynamic_axis),
             ]							)