



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

__lowerCamelCase         =			{
    '''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         =			{
    '''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         =			{
    '''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         =			{
    '''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         =			{
    '''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         =			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__lowerCamelCase         =			{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__lowerCamelCase         =			[]
__lowerCamelCase         =			[]







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case    )     ->     Optional[Any]:






      '''simple docstring'''







      for attribute in key.split("."    ):
            UpperCAmelCase_  :	Optional[int]				      =		getattr(__snake_case					,  __snake_case    )

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

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







def    _snake_case (       __snake_case					,  __snake_case    )     ->     Union[str, Any]:






      '''simple docstring'''







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







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     Union[str, Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		[]

      if model_name == "encodec_24khz" or "encodec_32khz":
            UpperCAmelCase_  :	Tuple				      =		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(__snake_case					,  __snake_case    ):
                  logger.info(F"""{name} was ignored"""    )
                  continue

            UpperCAmelCase_  :	List[Any]				      =		False
            for key, mapped_key in MAPPING.items():
                  if "*" in key:
                        UpperCAmelCase_      ,      UpperCAmelCase_  :	Union[str, Any]				      =		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_  :	Dict				      =		True
                        if "*" in mapped_key:
                              UpperCAmelCase_  :	Tuple				      =		name.split(__snake_case    )[0].split("."    )[-2]
                              UpperCAmelCase_  :	Optional[int]				      =		mapped_key.replace("*"					,  __snake_case    )
                        if "weight_g" in name:
                              UpperCAmelCase_  :	Optional[int]				      =		"weight_g"
                        elif "weight_v" in name:
                              UpperCAmelCase_  :	List[Any]				      =		"weight_v"
                        elif "weight_ih_l0" in name:
                              UpperCAmelCase_  :	Tuple				      =		"weight_ih_l0"
                        elif "weight_hh_l0" in name:
                              UpperCAmelCase_  :	Optional[Any]				      =		"weight_hh_l0"
                        elif "bias_ih_l0" in name:
                              UpperCAmelCase_  :	str				      =		"bias_ih_l0"
                        elif "bias_hh_l0" in name:
                              UpperCAmelCase_  :	Dict				      =		"bias_hh_l0"
                        elif "weight_ih_l1" in name:
                              UpperCAmelCase_  :	Tuple				      =		"weight_ih_l1"
                        elif "weight_hh_l1" in name:
                              UpperCAmelCase_  :	str				      =		"weight_hh_l1"
                        elif "bias_ih_l1" in name:
                              UpperCAmelCase_  :	List[Any]				      =		"bias_ih_l1"
                        elif "bias_hh_l1" in name:
                              UpperCAmelCase_  :	int				      =		"bias_hh_l1"
                        elif "bias" in name:
                              UpperCAmelCase_  :	Any				      =		"bias"
                        elif "weight" in name:
                              UpperCAmelCase_  :	List[str]				      =		"weight"
                        elif "running_mean" in name:
                              UpperCAmelCase_  :	Optional[Any]				      =		"running_mean"
                        elif "running_var" in name:
                              UpperCAmelCase_  :	Optional[int]				      =		"running_var"
                        elif "num_batches_tracked" in name:
                              UpperCAmelCase_  :	str				      =		"num_batches_tracked"
                        else:
                              UpperCAmelCase_  :	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    _snake_case (       __snake_case					,  __snake_case					,  __snake_case					,  __snake_case=None					,  __snake_case=None					,  )     ->     str:






      '''simple docstring'''







      if config_path is not None:
            UpperCAmelCase_  :	int				      =		EncodecConfig.from_pretrained(__snake_case    )
      else:
            UpperCAmelCase_  :	Optional[int]				      =		EncodecConfig()

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

      UpperCAmelCase_  :	Tuple				      =		EncodecModel(__snake_case    )

      UpperCAmelCase_  :	int				      =		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    )

      UpperCAmelCase_  :	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
            UpperCAmelCase_  :	Tuple				      =		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__":
   __lowerCamelCase         =			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         =			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         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}


class   snake_case_ (lowercase__ , lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""focalnet"""



       def __init__(     self     ,lowercase=224     ,lowercase=4     ,lowercase=3     ,lowercase=96     ,lowercase=False     ,lowercase=[192, 384, 768, 768]     ,lowercase=[2, 2, 6, 2]     ,lowercase=[2, 2, 2, 2]     ,lowercase=[3, 3, 3, 3]     ,lowercase="gelu"     ,lowercase=4.0     ,lowercase=0.0     ,lowercase=0.1     ,lowercase=False     ,lowercase=1E-4     ,lowercase=False     ,lowercase=False     ,lowercase=False     ,lowercase=0.02     ,lowercase=1E-5     ,lowercase=32     ,lowercase=None     ,lowercase=None     ,**lowercase     ,):

             """simple docstring"""





             super().__init__(**lowercase)

             UpperCAmelCase_  :	int				      =		image_size
             UpperCAmelCase_  :	Optional[Any]				      =		patch_size
             UpperCAmelCase_  :	Dict				      =		num_channels
             UpperCAmelCase_  :	Dict				      =		embed_dim
             UpperCAmelCase_  :	Optional[int]				      =		use_conv_embed
             UpperCAmelCase_  :	int				      =		hidden_sizes
             UpperCAmelCase_  :	Optional[int]				      =		depths
             UpperCAmelCase_  :	Optional[Any]				      =		focal_levels
             UpperCAmelCase_  :	Tuple				      =		focal_windows
             UpperCAmelCase_  :	int				      =		hidden_act
             UpperCAmelCase_  :	Any				      =		mlp_ratio
             UpperCAmelCase_  :	List[Any]				      =		hidden_dropout_prob
             UpperCAmelCase_  :	Tuple				      =		drop_path_rate
             UpperCAmelCase_  :	List[str]				      =		use_layerscale
             UpperCAmelCase_  :	List[str]				      =		layerscale_value
             UpperCAmelCase_  :	List[str]				      =		use_post_layernorm
             UpperCAmelCase_  :	Dict				      =		use_post_layernorm_in_modulation
             UpperCAmelCase_  :	int				      =		normalize_modulator
             UpperCAmelCase_  :	Dict				      =		initializer_range
             UpperCAmelCase_  :	str				      =		layer_norm_eps
             UpperCAmelCase_  :	Union[str, Any]				      =		encoder_stride
             UpperCAmelCase_  :	str				      =		["stem"] + [F"""stage{idx}""" for idx in range(1     ,len(self.depths) + 1)]
             UpperCAmelCase_      ,      UpperCAmelCase_  :	Any				      =		get_aligned_output_features_output_indices(
                 out_features=lowercase     ,out_indices=lowercase     ,stage_names=self.stage_names)
