


'''simple docstring'''




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


__snake_case    						=logging.get_logger(__name__)

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Optional[Any]		=     '''upernet'''





     def __init__( self       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[Any]=None    ,      UpperCAmelCase__       :      Tuple=5_1_2    ,      UpperCAmelCase__       :      List[Any]=0.02    ,      UpperCAmelCase__       :      str=[1, 2, 3, 6]    ,      UpperCAmelCase__       :      str=True    ,      UpperCAmelCase__       :      Dict=0.4    ,      UpperCAmelCase__       :      int=3_8_4    ,      UpperCAmelCase__       :      Dict=2_5_6    ,      UpperCAmelCase__       :      Optional[int]=1    ,      UpperCAmelCase__       :      List[str]=False    ,      UpperCAmelCase__       :      Any=2_5_5    ,      **UpperCAmelCase__       :      Union[str, Any]    ,      )				->  List[str]:
        super().__init__(**UpperCAmelCase__    )

        if backbone_config is None:
           logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'    )
           lowerCAmelCase     =			CONFIG_MAPPING['resnet'](out_features=['stage1', 'stage2', 'stage3', 'stage4']    )
        elif isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
           lowerCAmelCase     =			backbone_config.get('model_type'    )
           lowerCAmelCase     =			CONFIG_MAPPING[backbone_model_type]
           lowerCAmelCase     =			config_class.from_dict(UpperCAmelCase__    )

        lowerCAmelCase     =			backbone_config
        lowerCAmelCase     =			hidden_size
        lowerCAmelCase     =			initializer_range
        lowerCAmelCase     =			pool_scales
        lowerCAmelCase     =			use_auxiliary_head
        lowerCAmelCase     =			auxiliary_loss_weight
        lowerCAmelCase     =			auxiliary_in_channels
        lowerCAmelCase     =			auxiliary_channels
        lowerCAmelCase     =			auxiliary_num_convs
        lowerCAmelCase     =			auxiliary_concat_input
        lowerCAmelCase     =			loss_ignore_index





     def 							__UpperCAmelCase      ( self       :      Any    )				->  Tuple:
        lowerCAmelCase     =			copy.deepcopy(self.__dict__    )
        lowerCAmelCase     =			self.backbone_config.to_dict()
        lowerCAmelCase     =			self.__class__.model_type
        return output









'''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()
__snake_case    						=logging.get_logger("""transformers.models.encodec""")

__snake_case    						={
    """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""",
}
__snake_case    						={
    """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""",
}
__snake_case    						={
    """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""",
}
__snake_case    						={
    """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""",
}
__snake_case    						={
    """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""",
}
__snake_case    						={
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__snake_case    						={
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__snake_case    						=[]
__snake_case    						=[]





def 	a_					( lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     int	, lowerCamelCase	:     Any	, lowerCamelCase	:     List[str]				):
   for attribute in key.split('.'				):
      lowerCAmelCase     =			getattr(lowerCamelCase	, lowerCamelCase				)

   if weight_type is not None:
      lowerCAmelCase     =			getattr(lowerCamelCase	, lowerCamelCase				).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 	a_					( lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Optional[Any]				):
   for key in ignore_keys:
      if key.endswith('.*'				):
         if name.startswith(key[:-1]				):
            return True
      elif ".*." in key:
         lowerCAmelCase     ,  lowerCAmelCase     =			key.split('.*.'				)
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False





def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Any	, lowerCamelCase	:     str				):
   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(lowerCamelCase	, lowerCamelCase				):
         logger.info(f'''{name} was ignored'''				)
         continue

      lowerCAmelCase     =			False
      for key, mapped_key in MAPPING.items():
         if "*" in key:
            lowerCAmelCase     ,  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(lowerCamelCase				)[0].split('.'				)[-2]
               lowerCAmelCase     =			mapped_key.replace('*'	, lowerCamelCase				)
            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(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 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     str	, lowerCamelCase	:     Dict=None	, lowerCamelCase	:     Union[str, Any]=None	, ):
   if config_path is not None:
      lowerCAmelCase     =			EncodecConfig.from_pretrained(lowerCamelCase				)
   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     =			32000
      lowerCAmelCase     =			2048
      lowerCAmelCase     =			False
      lowerCAmelCase     =			False
      lowerCAmelCase     =			False
   elif model_name == "encodec_48khz":
      lowerCAmelCase     =			[8, 5, 4, 2]
      lowerCAmelCase     =			[3.0, 6.0, 12.0, 24.0]
      lowerCAmelCase     =			48000
      lowerCAmelCase     =			2
      lowerCAmelCase     =			False
      lowerCAmelCase     =			'time_group_norm'
      lowerCAmelCase     =			True
      lowerCAmelCase     =			1.0
      lowerCAmelCase     =			0.01
   else:
      raise ValueError(f'''Unknown model name: {model_name}'''				)

   lowerCAmelCase     =			EncodecModel(lowerCamelCase				)

   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(lowerCamelCase				)

   lowerCAmelCase     =			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
      lowerCAmelCase     =			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__":
       __snake_case    						=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."""
       )

       __snake_case    						=parser.parse_args()
       convert_checkpoint(
           args.model,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )





