





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     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   for attribute in key.split("""."""     ):
      A_     :				List[str]			      =	getattr(_lowerCAmelCase  ,_lowerCAmelCase     )

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

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				int			      =	[]

   if model_name == "encodec_24khz" or "encodec_32khz":
      A_     :				Tuple			      =	MAPPING_24K
   elif model_name == "encodec_48khz":
      A_     :				int			      =	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

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

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


   if config_path is not None:
      A_     :				Tuple			      =	EncodecConfig.from_pretrained(_lowerCAmelCase     )
   else:
      A_     :				List[str]			      =	EncodecConfig()

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

   A_     :				Union[str, Any]			      =	EncodecModel(_lowerCAmelCase     )

   A_     :				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(_lowerCAmelCase     )

   A_     :				Dict			      =	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
      A_     :				List[str]			      =	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__":
      _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


_lowerCAmelCase 				=     logging.get_logger(__name__)

# TODO: upload to AWS
_lowerCAmelCase 				=     {
    """yjernite/retribert-base-uncased""": (
        """https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"""
    ),
}

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							'''retribert'''
      def __init__(		self      ,       a__=30522      ,       a__=768      ,       a__=8      ,       a__=12      ,       a__=3072      ,       a__="gelu"      ,       a__=0.1      ,       a__=0.1      ,       a__=512      ,       a__=2      ,       a__=0.02      ,       a__=1E-12      ,       a__=True      ,       a__=128      ,       a__=0      ,       **a__      ,       ):
         super().__init__(pad_token_id=a__      ,       **a__       )

         A_     :				Union[str, Any]			      =	vocab_size
         A_     :				Optional[Any]			      =	hidden_size
         A_     :				Optional[Any]			      =	num_hidden_layers
         A_     :				List[str]			      =	num_attention_heads
         A_     :				Optional[int]			      =	hidden_act
         A_     :				Any			      =	intermediate_size
         A_     :				Tuple			      =	hidden_dropout_prob
         A_     :				Optional[Any]			      =	attention_probs_dropout_prob
         A_     :				Dict			      =	max_position_embeddings
         A_     :				Dict			      =	type_vocab_size
         A_     :				List[str]			      =	initializer_range
         A_     :				Optional[int]			      =	layer_norm_eps
         A_     :				Optional[Any]			      =	share_encoders
         A_     :				Dict			      =	projection_dim






