"""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()
__A			   = logging.get_logger("transformers.models.encodec")

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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  int:
   for attribute in key.split('''.''' ):
      lowercase__:   List[str]					=      getattr(__UpperCAmelCase       ,    __UpperCAmelCase )

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

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



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Tuple:
   for key in ignore_keys:
      if key.endswith('''.*''' ):
         if name.startswith(key[:-1] ):
            return True
      elif ".*." in key:
         lowercase__, lowercase__:   Any					=      key.split('''.*.''' )
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Optional[Any]:
   lowercase__:   List[str]					=      []

   if model_name == "encodec_24khz" or "encodec_32khz":
      lowercase__:   int					=      MAPPING_24K
   elif model_name == "encodec_48khz":
      lowercase__:   Optional[int]					=      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

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

            lowercase__:   List[Any]					=      True
            if "*" in mapped_key:
               lowercase__:   Dict					=      name.split(__UpperCAmelCase )[0].split('''.''' )[-2]
               lowercase__:   Dict					=      mapped_key.replace('''*'''       ,    __UpperCAmelCase )
            if "weight_g" in name:
               lowercase__:   str					=      '''weight_g'''
            elif "weight_v" in name:
               lowercase__:   Union[str, Any]					=      '''weight_v'''
            elif "weight_ih_l0" in name:
               lowercase__:   Tuple					=      '''weight_ih_l0'''
            elif "weight_hh_l0" in name:
               lowercase__:   List[Any]					=      '''weight_hh_l0'''
            elif "bias_ih_l0" in name:
               lowercase__:   Dict					=      '''bias_ih_l0'''
            elif "bias_hh_l0" in name:
               lowercase__:   List[Any]					=      '''bias_hh_l0'''
            elif "weight_ih_l1" in name:
               lowercase__:   Optional[int]					=      '''weight_ih_l1'''
            elif "weight_hh_l1" in name:
               lowercase__:   Any					=      '''weight_hh_l1'''
            elif "bias_ih_l1" in name:
               lowercase__:   List[str]					=      '''bias_ih_l1'''
            elif "bias_hh_l1" in name:
               lowercase__:   int					=      '''bias_hh_l1'''
            elif "bias" in name:
               lowercase__:   Tuple					=      '''bias'''
            elif "weight" in name:
               lowercase__:   int					=      '''weight'''
            elif "running_mean" in name:
               lowercase__:   Optional[Any]					=      '''running_mean'''
            elif "running_var" in name:
               lowercase__:   List[str]					=      '''running_var'''
            elif "num_batches_tracked" in name:
               lowercase__:   Tuple					=      '''num_batches_tracked'''
            else:
               lowercase__:   Union[str, Any]					=      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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    )  ->  int:
   if config_path is not None:
      lowercase__:   Optional[int]					=      EncodecConfig.from_pretrained(__UpperCAmelCase )
   else:
      lowercase__:   List[Any]					=      EncodecConfig()

   if model_name == "encodec_24khz":
      pass  # config is already correct
   elif model_name == "encodec_32khz":
      lowercase__:   str					=      [8, 5, 4, 4]
      lowercase__:   Union[str, Any]					=      [2.2]
      lowercase__:   List[Any]					=      6_4
      lowercase__:   Any					=      3_2_0_0_0
      lowercase__:   Union[str, Any]					=      2_0_4_8
      lowercase__:   Tuple					=      False
      lowercase__:   List[Any]					=      False
      lowercase__:   Any					=      False
   elif model_name == "encodec_48khz":
      lowercase__:   Optional[Any]					=      [8, 5, 4, 2]
      lowercase__:   int					=      [3.0, 6.0, 1_2.0, 2_4.0]
      lowercase__:   Union[str, Any]					=      4_8_0_0_0
      lowercase__:   Optional[int]					=      2
      lowercase__:   Tuple					=      False
      lowercase__:   Tuple					=      '''time_group_norm'''
      lowercase__:   List[str]					=      True
      lowercase__:   int					=      1.0
      lowercase__:   Optional[int]					=      0.0_1
   else:
      raise ValueError(F"""Unknown model name: {model_name}""" )

   lowercase__:   List[str]					=      EncodecModel(__UpperCAmelCase )

   lowercase__:   List[Any]					=      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 )

   lowercase__:   str					=      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
      lowercase__:   str					=      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			   = 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			   = parser.parse_args()
       convert_checkpoint(
           args.model,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )




'''simple docstring'''




def 	a_					( lowerCamelCase	:     Optional[Any]				):
   return [
       {
           0: [1, 2],
           1: [0, 2],
           2: [0, 1, 3, 5],
           3: [2, 4],
           4: [3],
           5: [2, 6, 8],
           6: [5, 7],
           7: [6, 8],
           8: [5, 7],
       },
       {
           0: [6],
           1: [9],
           2: [4, 5],
           3: [4],
           4: [2, 3],
           5: [2],
           6: [0, 7],
           7: [6],
           8: [],
           9: [1],
       },
       {
           0: [4],
           1: [6],
           2: [],
           3: [5, 6, 7],
           4: [0, 6],
           5: [3, 8, 9],
           6: [1, 3, 4, 7],
           7: [3, 6, 8, 9],
           8: [5, 7],
           9: [5, 7],
       },
       {
           0: [1, 3],
           1: [0, 2, 4],
           2: [1, 3, 4],
           3: [0, 2, 4],
           4: [1, 2, 3],
       },
   ][index]





def 	a_					( lowerCamelCase	:     dict[int, list[int]]				):
   lowerCAmelCase     =			0
   lowerCAmelCase     =			len(lowerCamelCase				)  # No of vertices in graph
   lowerCAmelCase     =			[0] * n
   lowerCAmelCase     =			[False] * n

   def dfs(lowerCamelCase	:     Tuple	, lowerCamelCase	:     str	, lowerCamelCase	:     Dict	, lowerCamelCase	:     str				):
      lowerCAmelCase     =			True
      lowerCAmelCase     =			id_
      id_ += 1
      for to in graph[at]:
         if to == parent:
            pass
         elif not visited[to]:
            dfs(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, id_				)
            lowerCAmelCase     =			min(low[at]	, low[to]				)
            if id_ <= low[to]:
               bridges.append((at, to) if at < to else (to, at)				)
         else:
            # This edge is a back edge and cannot be a bridge
            lowerCAmelCase     =			min(low[at]	, low[to]				)

   lowerCAmelCase     =			[]
   for i in range(lowerCamelCase				):
      if not visited[i]:
         dfs(lowerCamelCase	, -1	, lowerCamelCase	, id_				)
   return bridges


if __name__ == "__main__":
       import doctest

       doctest.testmod()





