





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  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Optional[int]:
       for attribute in key.split("."  ):
              UpperCamelCase_          =  getattr(UpperCamelCase_					,		UpperCamelCase_  )

       if weight_type is not None:
              UpperCamelCase_          =  getattr(UpperCamelCase_					,		UpperCamelCase_  ).shape
       else:
              UpperCamelCase_          =  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_          =  value
       elif weight_type == "weight_g":
              UpperCamelCase_          =  value
       elif weight_type == "weight_v":
              UpperCamelCase_          =  value
       elif weight_type == "bias":
              UpperCamelCase_          =  value
       elif weight_type == "running_mean":
              UpperCamelCase_          =  value
       elif weight_type == "running_var":
              UpperCamelCase_          =  value
       elif weight_type == "num_batches_tracked":
              UpperCamelCase_          =  value
       elif weight_type == "weight_ih_l0":
              UpperCamelCase_          =  value
       elif weight_type == "weight_hh_l0":
              UpperCamelCase_          =  value
       elif weight_type == "bias_ih_l0":
              UpperCamelCase_          =  value
       elif weight_type == "bias_hh_l0":
              UpperCamelCase_          =  value
       elif weight_type == "weight_ih_l1":
              UpperCamelCase_          =  value
       elif weight_type == "weight_hh_l1":
              UpperCamelCase_          =  value
       elif weight_type == "bias_ih_l1":
              UpperCamelCase_          =  value
       elif weight_type == "bias_hh_l1":
              UpperCamelCase_          =  value
       else:
              UpperCamelCase_          =  value

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




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




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		Dict:
       UpperCamelCase_          =  []

       if model_name == "encodec_24khz" or "encodec_32khz":
              UpperCamelCase_          =  MAPPING_24K
       elif model_name == "encodec_48khz":
              UpperCamelCase_          =  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

              UpperCamelCase_          =  False
              for key, mapped_key in MAPPING.items():
                     if "*" in key:
                            UpperCamelCase_       , UpperCamelCase_          =  key.split(".*."  )
                            if prefix in name and suffix in name:
                                   UpperCamelCase_          =  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_          =  True
                            if "*" in mapped_key:
                                   UpperCamelCase_          =  name.split(UpperCamelCase_  )[0].split("."  )[-2]
                                   UpperCamelCase_          =  mapped_key.replace("*"					,		UpperCamelCase_  )
                            if "weight_g" in name:
                                   UpperCamelCase_          =  "weight_g"
                            elif "weight_v" in name:
                                   UpperCamelCase_          =  "weight_v"
                            elif "weight_ih_l0" in name:
                                   UpperCamelCase_          =  "weight_ih_l0"
                            elif "weight_hh_l0" in name:
                                   UpperCamelCase_          =  "weight_hh_l0"
                            elif "bias_ih_l0" in name:
                                   UpperCamelCase_          =  "bias_ih_l0"
                            elif "bias_hh_l0" in name:
                                   UpperCamelCase_          =  "bias_hh_l0"
                            elif "weight_ih_l1" in name:
                                   UpperCamelCase_          =  "weight_ih_l1"
                            elif "weight_hh_l1" in name:
                                   UpperCamelCase_          =  "weight_hh_l1"
                            elif "bias_ih_l1" in name:
                                   UpperCamelCase_          =  "bias_ih_l1"
                            elif "bias_hh_l1" in name:
                                   UpperCamelCase_          =  "bias_hh_l1"
                            elif "bias" in name:
                                   UpperCamelCase_          =  "bias"
                            elif "weight" in name:
                                   UpperCamelCase_          =  "weight"
                            elif "running_mean" in name:
                                   UpperCamelCase_          =  "running_mean"
                            elif "running_var" in name:
                                   UpperCamelCase_          =  "running_var"
                            elif "num_batches_tracked" in name:
                                   UpperCamelCase_          =  "num_batches_tracked"
                            else:
                                   UpperCamelCase_          =  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  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_=None					,		UpperCamelCase_=None					,		)     ->		Any:
       if config_path is not None:
              UpperCamelCase_          =  EncodecConfig.from_pretrained(UpperCamelCase_  )
       else:
              UpperCamelCase_          =  EncodecConfig()

       if model_name == "encodec_24khz":
              pass  # config is already correct
       elif model_name == "encodec_32khz":
              UpperCamelCase_          =  [8, 5, 4, 4]
              UpperCamelCase_          =  [2.2]
              UpperCamelCase_          =  64
              UpperCamelCase_          =  32000
              UpperCamelCase_          =  2048
              UpperCamelCase_          =  False
              UpperCamelCase_          =  False
              UpperCamelCase_          =  False
       elif model_name == "encodec_48khz":
              UpperCamelCase_          =  [8, 5, 4, 2]
              UpperCamelCase_          =  [3.0, 6.0, 12.0, 24.0]
              UpperCamelCase_          =  48000
              UpperCamelCase_          =  2
              UpperCamelCase_          =  False
              UpperCamelCase_          =  "time_group_norm"
              UpperCamelCase_          =  True
              UpperCamelCase_          =  1.0
              UpperCamelCase_          =  0.01
       else:
              raise ValueError(F'''Unknown model name: {model_name}'''  )

       UpperCamelCase_          =  EncodecModel(UpperCamelCase_  )

       UpperCamelCase_          =  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_  )

       UpperCamelCase_          =  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
              UpperCamelCase_          =  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__":
     _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,
     )








import argparse
import os
import re


_UpperCAmelCase        =				'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_UpperCAmelCase        =				re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
_UpperCAmelCase        =				re.compile(r'\s*\(\s*"(\S[^"]+)"')




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_ = False  )     ->		Union[str, Any]:
       with open(UpperCamelCase_					,		"r"					,		encoding="utf-8"  ) as f:
              UpperCamelCase_          =  f.read()

       UpperCamelCase_          =  content.split("\n"  )
       UpperCamelCase_          =  []
       UpperCamelCase_          =  0
       while line_idx < len(UpperCamelCase_  ):
              if _re_intro_mapping.search(lines[line_idx]  ) is not None:
                     UpperCamelCase_          =  len(re.search(r"^(\s*)\S"					,		lines[line_idx]  ).groups()[0]  ) + 8
                     # Start of a new mapping!
                     while not lines[line_idx].startswith(" " * indent + "("  ):
                            new_lines.append(lines[line_idx]  )
                            line_idx += 1

                     UpperCamelCase_          =  []
                     while lines[line_idx].strip() != "]":
                            # Blocks either fit in one line or not
                            if lines[line_idx].strip() == "(":
                                   UpperCamelCase_          =  line_idx
                                   while not lines[line_idx].startswith(" " * indent + ")"  ):
                                          line_idx += 1
                                   blocks.append("\n".join(lines[start_idx : line_idx + 1]  )  )
                            else:
                                   blocks.append(lines[line_idx]  )
                            line_idx += 1

                     # Sort blocks by their identifiers
                     UpperCamelCase_          =  sorted(UpperCamelCase_					,		key=lambda UpperCamelCase_  : _re_identifier.search(UpperCamelCase_  ).groups()[0]  )
                     new_lines += blocks
              else:
                     new_lines.append(lines[line_idx]  )
                     line_idx += 1

       if overwrite:
              with open(UpperCamelCase_					,		"w"					,		encoding="utf-8"  ) as f:
                     f.write("\n".join(UpperCamelCase_  )  )
       elif "\n".join(UpperCamelCase_  ) != content:
              return True




def  lowerCAmelCase_ (							UpperCamelCase_ = False  )     ->		List[Any]:
       UpperCamelCase_          =  [os.path.join(UpperCamelCase_					,		UpperCamelCase_  ) for f in os.listdir(UpperCamelCase_  ) if f.endswith(".py"  )]
       UpperCamelCase_          =  [sort_auto_mapping(UpperCamelCase_					,		overwrite=UpperCamelCase_  ) for fname in fnames]

       if not overwrite and any(UpperCamelCase_  ):
              UpperCamelCase_          =  [f for f, d in zip(UpperCamelCase_					,		UpperCamelCase_  ) if d]
              raise ValueError(
                  F'''The following files have auto mappings that need sorting: {", ".join(UpperCamelCase_  )}. Run `make style` to fix'''
                  " this."  )


if __name__ == "__main__":
     _UpperCAmelCase        =				argparse.ArgumentParser()
     parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
     _UpperCAmelCase        =				parser.parse_args()

     sort_all_auto_mappings(not args.check_only)

