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  _lowerCamelCase					(   a_				: Union[str, Any]						,			a_				: Union[str, Any]						,			a_				: Optional[Any]						,			a_				: Optional[int]						,			a_				: int):




   for attribute in key.split('''.'''):
      lowerCamelCase						:Optional[Any]						  =     getattr(_A						,			_A)

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

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





def  _lowerCamelCase					(   a_				: Tuple						,			a_				: Any):




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





def  _lowerCamelCase					(   a_				: List[Any]						,			a_				: Optional[int]						,			a_				: Optional[Any]):
   lowerCamelCase						:Dict						  =     []

   if model_name == "encodec_24khz" or "encodec_32khz":
      lowerCamelCase						:str						  =     MAPPING_24K
   elif model_name == "encodec_48khz":
      lowerCamelCase						:Any						  =     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

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

            lowerCamelCase						:Optional[Any]						  =     True
            if "*" in mapped_key:
               lowerCamelCase						:Optional[Any]						  =     name.split(_A)[0].split('''.''')[-2]
               lowerCamelCase						:str						  =     mapped_key.replace('''*'''						,			_A)
            if "weight_g" in name:
               lowerCamelCase						:Any						  =     "weight_g"
            elif "weight_v" in name:
               lowerCamelCase						:List[Any]						  =     "weight_v"
            elif "weight_ih_l0" in name:
               lowerCamelCase						:Union[str, Any]						  =     "weight_ih_l0"
            elif "weight_hh_l0" in name:
               lowerCamelCase						:Dict						  =     "weight_hh_l0"
            elif "bias_ih_l0" in name:
               lowerCamelCase						:Optional[Any]						  =     "bias_ih_l0"
            elif "bias_hh_l0" in name:
               lowerCamelCase						:Optional[int]						  =     "bias_hh_l0"
            elif "weight_ih_l1" in name:
               lowerCamelCase						:Dict						  =     "weight_ih_l1"
            elif "weight_hh_l1" in name:
               lowerCamelCase						:Optional[Any]						  =     "weight_hh_l1"
            elif "bias_ih_l1" in name:
               lowerCamelCase						:List[str]						  =     "bias_ih_l1"
            elif "bias_hh_l1" in name:
               lowerCamelCase						:List[Any]						  =     "bias_hh_l1"
            elif "bias" in name:
               lowerCamelCase						:str						  =     "bias"
            elif "weight" in name:
               lowerCamelCase						:List[Any]						  =     "weight"
            elif "running_mean" in name:
               lowerCamelCase						:Any						  =     "running_mean"
            elif "running_var" in name:
               lowerCamelCase						:List[Any]						  =     "running_var"
            elif "num_batches_tracked" in name:
               lowerCamelCase						:str						  =     "num_batches_tracked"
            else:
               lowerCamelCase						: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  _lowerCamelCase					(   a_				: Tuple						,			a_				: Union[str, Any]						,			a_				: List[str]						,			a_				: List[str]=None						,			a_				: Optional[int]=None						,			):




   if config_path is not None:
      lowerCamelCase						:str						  =     EncodecConfig.from_pretrained(_A)
   else:
      lowerCamelCase						:Optional[int]						  =     EncodecConfig()

   if model_name == "encodec_24khz":
      pass  # config is already correct
   elif model_name == "encodec_32khz":
      lowerCamelCase						:Union[str, Any]						  =     [8, 5, 4, 4]
      lowerCamelCase						:Tuple						  =     [2.2]
      lowerCamelCase						:int						  =     64
      lowerCamelCase						:List[Any]						  =     3_20_00
      lowerCamelCase						:Optional[int]						  =     20_48
      lowerCamelCase						:Optional[Any]						  =     False
      lowerCamelCase						:int						  =     False
      lowerCamelCase						:int						  =     False
   elif model_name == "encodec_48khz":
      lowerCamelCase						:Any						  =     [8, 5, 4, 2]
      lowerCamelCase						:str						  =     [3.0, 6.0, 12.0, 24.0]
      lowerCamelCase						:Dict						  =     4_80_00
      lowerCamelCase						:Optional[int]						  =     2
      lowerCamelCase						:Union[str, Any]						  =     False
      lowerCamelCase						:str						  =     "time_group_norm"
      lowerCamelCase						:int						  =     True
      lowerCamelCase						:Optional[Any]						  =     1.0
      lowerCamelCase						:Union[str, Any]						  =     0.01
   else:
      raise ValueError(F"Unknown model name: {model_name}")

   lowerCamelCase						:Dict						  =     EncodecModel(_A)

   lowerCamelCase						:Dict						  =     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)

   lowerCamelCase						:Dict						  =     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
      lowerCamelCase						:List[str]						  =     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__":
   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,
   )




import math





def  _lowerCamelCase					(   a_				: int):

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5						,			int(math.sqrt(a_) + 1)						,			6):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True





def  _lowerCamelCase					(   a_				: float = 0.1):
			lowerCamelCase						:Dict						  =     3
			lowerCamelCase						:List[Any]						  =     3

			while primes / (2 * j - 1) >= ratio:
						for i in range(j * j + j + 1						,			(j + 2) * (j + 2)						,			j + 1):
									primes += is_prime(a_)
						j += 2
			return j


if __name__ == "__main__":
			import doctest

			doctest.testmod()



