




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()
__lowerCamelCase   :						List[Any]				=			logging.get_logger('''transformers.models.encodec''')

__lowerCamelCase   :						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""",
}
__lowerCamelCase   :						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""",
}
__lowerCamelCase   :						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""",
}
__lowerCamelCase   :						Optional[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""",
}
__lowerCamelCase   :						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""",
}
__lowerCamelCase   :						List[Any]				=			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__lowerCamelCase   :						List[Any]				=			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__lowerCamelCase   :						Dict				=			[]
__lowerCamelCase   :						Any				=			[]







def 		lowercase__   (		__A:	List[Any]							,__A:	Optional[int]							,__A:	Optional[Any]							,__A:	Union[str, Any]							,__A:	Optional[Any]				):





     '''simple docstring'''






     for attribute in key.split('''.'''				):
          __magic_name__							:	List[Any]    							=							getattr(__A							,__A				)

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

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







def 		lowercase__   (		__A:	str							,__A:	Tuple				):





     '''simple docstring'''






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







def 		lowercase__   (		__A:	Union[str, Any]							,__A:	Union[str, Any]							,__A:	List[Any]				):





     '''simple docstring'''






     __magic_name__							:	List[str]    							=							[]

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

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

          __magic_name__							:	Optional[int]    							=							False
          for key, mapped_key in MAPPING.items():
               if "*" in key:
                    __magic_name__							,    __magic_name__							:	List[str]    							=							key.split('''.*.'''				)
                    if prefix in name and suffix in name:
                         __magic_name__							:	Union[str, Any]    							=							suffix

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

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

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







@torch.no_grad()
def 		lowercase__   (		__A:	Optional[Any]							,__A:	Dict							,__A:	Tuple							,__A:	Optional[Any]=None							,__A:	List[str]=None							,):





     '''simple docstring'''






     if config_path is not None:
          __magic_name__							:	Any    							=							EncodecConfig.from_pretrained(__A				)
     else:
          __magic_name__							:	List[str]    							=							EncodecConfig()

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

     __magic_name__							:	Any    							=							EncodecModel(__A				)

     __magic_name__							:	Tuple    							=							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(__A				)

     __magic_name__							:	Optional[Any]    							=							torch.load(__A				)
     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
          __magic_name__							:	Optional[Any]    							=							original_checkpoint['''best_state''']
     recursively_load_weights(__A							,__A							,__A				)
     model.save_pretrained(__A				)

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


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

 __lowerCamelCase   :						List[str]				=			parser.parse_args()
 convert_checkpoint(
     args.model,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )









from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase   :						Optional[int]				=			logging.get_logger(__name__)

__lowerCamelCase   :						Tuple				=			{
    '''shi-labs/nat-mini-in1k-224''': '''https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json''',
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class    lowerCamelCase    (		_lowerCamelCase				,_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								='''nat'''

			UpperCamelCase__								={
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}

			def __init__( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Dict=4       ,						lowerCamelCase_			:   Union[str, Any]=3       ,						lowerCamelCase_			:   List[str]=64       ,						lowerCamelCase_			:   Union[str, Any]=[3, 4, 6, 5]       ,						lowerCamelCase_			:   List[Any]=[2, 4, 8, 16]       ,						lowerCamelCase_			:   List[Any]=7       ,						lowerCamelCase_			:   Union[str, Any]=3.0       ,						lowerCamelCase_			:   int=True       ,						lowerCamelCase_			:   str=0.0       ,						lowerCamelCase_			:   int=0.0       ,						lowerCamelCase_			:   Dict=0.1       ,						lowerCamelCase_			:   Optional[int]="gelu"       ,						lowerCamelCase_			:   Tuple=0.0_2       ,						lowerCamelCase_			:   Tuple=1E-5       ,						lowerCamelCase_			:   List[Any]=0.0       ,						lowerCamelCase_			:   str=None       ,						lowerCamelCase_			:   Tuple=None       ,						**lowerCamelCase_			:   Any       ,						)						->   Any:
								super().__init__(**lowerCamelCase_  )

								__magic_name__							:	List[Any]    							=							patch_size
								__magic_name__							:	str    							=							num_channels
								__magic_name__							:	Union[str, Any]    							=							embed_dim
								__magic_name__							:	Dict    							=							depths
								__magic_name__							:	str    							=							len(lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							num_heads
								__magic_name__							:	Optional[int]    							=							kernel_size
								__magic_name__							:	Any    							=							mlp_ratio
								__magic_name__							:	Any    							=							qkv_bias
								__magic_name__							:	Union[str, Any]    							=							hidden_dropout_prob
								__magic_name__							:	Any    							=							attention_probs_dropout_prob
								__magic_name__							:	List[str]    							=							drop_path_rate
								__magic_name__							:	str    							=							hidden_act
								__magic_name__							:	Tuple    							=							layer_norm_eps
								__magic_name__							:	Any    							=							initializer_range
								# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								__magic_name__							:	str    							=							int(embed_dim * 2 ** (len(lowerCamelCase_  ) - 1)  )
								__magic_name__							:	str    							=							layer_scale_init_value
								__magic_name__							:	Dict    							=							['''stem'''] + [F'''stage{idx}''' for idx in range(1       ,						len(lowerCamelCase_  ) + 1  )]
								__magic_name__							,    __magic_name__							:	int    							=							get_aligned_output_features_output_indices(
								    out_features=lowerCamelCase_       ,						out_indices=lowerCamelCase_       ,						stage_names=self.stage_names  )



