

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

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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







     for attribute in key.split("."				):
          _lowerCAmelCase   :					int        =    getattr(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)

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

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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      str:

     """simple docstring"""







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







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Dict:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[Any]        =    []

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

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

          _lowerCAmelCase   :					Dict        =    False
          for key, mapped_key in MAPPING.items():
               if "*" in key:
                    _lowerCAmelCase      ,			_lowerCAmelCase   :					Dict        =    key.split(".*."				)
                    if prefix in name and suffix in name:
                         _lowerCAmelCase   :					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

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

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







@torch.no_grad()
def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=None		,							SCREAMING_SNAKE_CASE=None		,							)       ->      Union[str, Any]:

     """simple docstring"""







     if config_path is not None:
          _lowerCAmelCase   :					Optional[int]        =    EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE				)
     else:
          _lowerCAmelCase   :					Any        =    EncodecConfig()

     if model_name == "encodec_24khz":
          pass  # config is already correct
     elif model_name == "encodec_32khz":
          _lowerCAmelCase   :					Any        =    [8, 5, 4, 4]
          _lowerCAmelCase   :					Optional[int]        =    [2.2]
          _lowerCAmelCase   :					Dict        =    64
          _lowerCAmelCase   :					Tuple        =    32000
          _lowerCAmelCase   :					str        =    2048
          _lowerCAmelCase   :					Tuple        =    False
          _lowerCAmelCase   :					Optional[int]        =    False
          _lowerCAmelCase   :					Optional[Any]        =    False
     elif model_name == "encodec_48khz":
          _lowerCAmelCase   :					str        =    [8, 5, 4, 2]
          _lowerCAmelCase   :					Tuple        =    [3.0, 6.0, 12.0, 24.0]
          _lowerCAmelCase   :					Tuple        =    48000
          _lowerCAmelCase   :					Union[str, Any]        =    2
          _lowerCAmelCase   :					List[Any]        =    False
          _lowerCAmelCase   :					Dict        =    "time_group_norm"
          _lowerCAmelCase   :					Any        =    True
          _lowerCAmelCase   :					Union[str, Any]        =    1.0
          _lowerCAmelCase   :					List[Any]        =    0.01
     else:
          raise ValueError(f'''Unknown model name: {model_name}'''				)

     _lowerCAmelCase   :					Tuple        =    EncodecModel(SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					str        =    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(SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					List[str]        =    torch.load(SCREAMING_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
          _lowerCAmelCase   :					str        =    original_checkpoint["best_state"]
     recursively_load_weights(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     model.save_pretrained(SCREAMING_SNAKE_CASE				)

     if repo_id:
          print("Pushing to the hub..."				)
          feature_extractor.push_to_hub(SCREAMING_SNAKE_CASE				)
          model.push_to_hub(SCREAMING_SNAKE_CASE				)


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

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









# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

__UpperCAmelCase    				=     TypeVar('T')





class A__    ( Generic[T] ):



 """simple docstring"""







 def __init__(		self :	Optional[int]     ,						A_ :	bool = True       ):






      '''simple docstring'''




      _lowerCAmelCase   :					dict[T, list[T]]        =    {}  # dictionary of lists
      _lowerCAmelCase   :					List[Any]        =    directed

 def    __magic_name__       (		self :	Any     ,						A_ :	T     ,						A_ :	T       ):






      '''simple docstring'''




      if not self.directed:  # For undirected graphs
           # if both source vertex and destination vertex are both present in the
           # adjacency list, add destination vertex to source vertex list of adjacent
           # vertices and add source vertex to destination vertex list of adjacent
           # vertices.
           if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
                self.adj_list[destination_vertex].append(A_       )
           # if only source vertex is present in adjacency list, add destination vertex
           # to source vertex list of adjacent vertices, then create a new vertex with
           # destination vertex as key and assign a list containing the source vertex
           # as it's first adjacent vertex.
           elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
                _lowerCAmelCase   :					List[Any]        =    [source_vertex]
           # if only destination vertex is present in adjacency list, add source vertex
           # to destination vertex list of adjacent vertices, then create a new vertex
           # with source vertex as key and assign a list containing the source vertex
           # as it's first adjacent vertex.
           elif destination_vertex in self.adj_list:
                self.adj_list[destination_vertex].append(A_       )
                _lowerCAmelCase   :					List[str]        =    [destination_vertex]
           # if both source vertex and destination vertex are not present in adjacency
           # list, create a new vertex with source vertex as key and assign a list
           # containing the destination vertex as it's first adjacent vertex also
           # create a new vertex with destination vertex as key and assign a list
           # containing the source vertex as it's first adjacent vertex.
           else:
                _lowerCAmelCase   :					List[str]        =    [destination_vertex]
                _lowerCAmelCase   :					Dict        =    [source_vertex]
      else:  # For directed graphs
           # if both source vertex and destination vertex are present in adjacency
           # list, add destination vertex to source vertex list of adjacent vertices.
           if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
           # if only source vertex is present in adjacency list, add destination
           # vertex to source vertex list of adjacent vertices and create a new vertex
           # with destination vertex as key, which has no adjacent vertex
           elif source_vertex in self.adj_list:
                self.adj_list[source_vertex].append(A_       )
                _lowerCAmelCase   :					Tuple        =    []
           # if only destination vertex is present in adjacency list, create a new
           # vertex with source vertex as key and assign a list containing destination
           # vertex as first adjacent vertex
           elif destination_vertex in self.adj_list:
                _lowerCAmelCase   :					Dict        =    [destination_vertex]
           # if both source vertex and destination vertex are not present in adjacency
           # list, create a new vertex with source vertex as key and a list containing
           # destination vertex as it's first adjacent vertex. Then create a new vertex
           # with destination vertex as key, which has no adjacent vertex
           else:
                _lowerCAmelCase   :					Any        =    [destination_vertex]
                _lowerCAmelCase   :					Union[str, Any]        =    []

      return self


 def __repr__(		self :	List[str]       ):






      '''simple docstring'''




      return pformat(self.adj_list       )






