





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 :  Union[str, Any]										=  logging.get_logger("""transformers.models.encodec""")

__lowerCamelCase :  str										=  {
    """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 :  Dict										=  {
    """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 :  Union[str, Any]										=  {
    """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 :  Optional[Any]										=  {
    """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 :  Union[str, Any]										=  {
    """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 :  Union[str, Any]										=  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__lowerCamelCase :  int										=  {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__lowerCamelCase :  List[Any]										=  []
__lowerCamelCase :  Any										=  []



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				List[Any]:
 for attribute in key.split("."  ):
  UpperCamelCase			:      int 			=     getattr(_lowerCAmelCase							,     _lowerCAmelCase  )

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

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



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



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase  )		->				Optional[int]:
 UpperCamelCase			:      Tuple 			=     []

 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(_lowerCAmelCase							,     _lowerCAmelCase  ):
   logger.info(F"""{name} was ignored"""  )
   continue

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

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

 if model_name == "encodec_24khz":
  pass  # config is already correct
 elif model_name == "encodec_32khz":
  UpperCamelCase			:      Dict 			=     [8, 5, 4, 4]
  UpperCamelCase			:      Dict 			=     [2.2]
  UpperCamelCase			:      Tuple 			=     64
  UpperCamelCase			:      Dict 			=     3_2000
  UpperCamelCase			:      str 			=     2048
  UpperCamelCase			:      Union[str, Any] 			=     False
  UpperCamelCase			:      Tuple 			=     False
  UpperCamelCase			:      Optional[Any] 			=     False
 elif model_name == "encodec_48khz":
  UpperCamelCase			:      Optional[int] 			=     [8, 5, 4, 2]
  UpperCamelCase			:      str 			=     [3.0, 6.0, 12.0, 24.0]
  UpperCamelCase			:      int 			=     4_8000
  UpperCamelCase			:      List[str] 			=     2
  UpperCamelCase			:      Union[str, Any] 			=     False
  UpperCamelCase			:      Optional[int] 			=     "time_group_norm"
  UpperCamelCase			:      str 			=     True
  UpperCamelCase			:      str 			=     1.0
  UpperCamelCase			:      List[str] 			=     0.01
 else:
  raise ValueError(F"""Unknown model name: {model_name}"""  )

 UpperCamelCase			:      Tuple 			=     EncodecModel(_lowerCAmelCase  )

 UpperCamelCase			:      Optional[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(_lowerCAmelCase  )

 UpperCamelCase			:      List[Any] 			=     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
  UpperCamelCase			:      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 :  List[str]										=  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 :  List[str]										=  parser.parse_args()
    convert_checkpoint(
        args.model,
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase :  Tuple										=  logging.get_logger(__name__)

__lowerCamelCase :  str										=  {
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Union[str, Any]							=     'camembert'





      def __init__(				self ,							A_=3_0522 ,							A_=768 ,							A_=12 ,							A_=12 ,							A_=3072 ,							A_="gelu" ,							A_=0.1 ,							A_=0.1 ,							A_=512 ,							A_=2 ,							A_=0.02 ,							A_=1e-12 ,							A_=1 ,							A_=0 ,							A_=2 ,							A_="absolute" ,							A_=True ,							A_=None ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(pad_token_id=A_ ,							bos_token_id=A_ ,							eos_token_id=A_ ,							**A_     )

       UpperCamelCase			:      List[str] 			=     vocab_size
       UpperCamelCase			:      Union[str, Any] 			=     hidden_size
       UpperCamelCase			:      Any 			=     num_hidden_layers
       UpperCamelCase			:      Union[str, Any] 			=     num_attention_heads
       UpperCamelCase			:      Dict 			=     hidden_act
       UpperCamelCase			:      str 			=     intermediate_size
       UpperCamelCase			:      str 			=     hidden_dropout_prob
       UpperCamelCase			:      Dict 			=     attention_probs_dropout_prob
       UpperCamelCase			:      Union[str, Any] 			=     max_position_embeddings
       UpperCamelCase			:      Optional[Any] 			=     type_vocab_size
       UpperCamelCase			:      int 			=     initializer_range
       UpperCamelCase			:      List[str] 			=     layer_norm_eps
       UpperCamelCase			:      Dict 			=     position_embedding_type
       UpperCamelCase			:      int 			=     use_cache
       UpperCamelCase			:      List[str] 			=     classifier_dropout



class       A__     ( __snake_case    ):





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self.task == "multiple-choice":
        UpperCamelCase			:      Optional[int] 			=     {0: "batch", 1: "choice", 2: "sequence"}
       else:
        UpperCamelCase			:      str 			=     {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]     )
