





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A   :       List[str]    					=    logging.get_logger(__name__)

_A   :       Any    					=    {"vocab_file": "sentencepiece.bpe.model"}

_A   :       Any    					=    {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

_A   :       Optional[int]    					=    {
    "camembert-base": 5_12,
}

_A   :       int    					=    "▁"


class   __SCREAMING_SNAKE_CASE     (					lowerCAmelCase_   ):
       _UpperCAmelCase :    int					       =       VOCAB_FILES_NAMES
       _UpperCAmelCase :    Any					       =       PRETRAINED_VOCAB_FILES_MAP
       _UpperCAmelCase :    Any					       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCAmelCase :    Tuple					       =       ["input_ids", "attention_mask"]







       def __init__( self	:      Optional[Any]						,   A	:      str						,   A	:      Optional[Any]="<s>"						,   A	:      int="</s>"						,   A	:      Union[str, Any]="</s>"						,   A	:      Optional[int]="<s>"						,   A	:      Any="<unk>"						,   A	:      Any="<pad>"						,   A	:      str="<mask>"						,   A	:      Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]						,   A	:      Optional[Dict[str, Any]] = None						,   **A	:      Union[str, Any]						,   )  ->None:
          lowerCamelCase__  :		List[Any]     =				AddedToken(A						,   lstrip=A						,   rstrip=A     ) if isinstance(A						,   A     ) else mask_token

          lowerCamelCase__  :		str     =				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=A						,   eos_token=A						,   unk_token=A						,   sep_token=A						,   cls_token=A						,   pad_token=A						,   mask_token=A						,   additional_special_tokens=A						,   sp_model_kwargs=self.sp_model_kwargs						,   **A						,   )
          lowerCamelCase__  :		Union[str, Any]     =				spm.SentencePieceProcessor(**self.sp_model_kwargs     )
          self.sp_model.Load(str(A     )     )
          lowerCamelCase__  :		int     =				vocab_file
          # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
          # sentencepiece vocabulary (this is the case for <s> and </s>
          lowerCamelCase__  :		Dict     =				{'''<s>NOTUSED''': 0, '''<pad>''': 1, '''</s>NOTUSED''': 2, '''<unk>''': 3}
          lowerCamelCase__  :		Dict     =				len(self.fairseq_tokens_to_ids     )
          lowerCamelCase__  :		int     =				len(self.sp_model     ) + len(self.fairseq_tokens_to_ids     )
          lowerCamelCase__  :		int     =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}







       def 		__lowerCamelCase       ( self	:      List[str]						,   A	:      List[int]						,   A	:      Optional[List[int]] = None     )  ->List[int]:

          if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          lowerCamelCase__  :		Any     =				[self.cls_token_id]
          lowerCamelCase__  :		Tuple     =				[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep







       def 		__lowerCamelCase       ( self	:      Tuple						,   A	:      List[int]						,   A	:      Optional[List[int]] = None						,   A	:      bool = False     )  ->List[int]:

          if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=A						,   token_ids_a=A						,   already_has_special_tokens=A     )

          if token_ids_a is None:
             return [1] + ([0] * len(A     )) + [1]
          return [1] + ([0] * len(A     )) + [1, 1] + ([0] * len(A     )) + [1]







       def 		__lowerCamelCase       ( self	:      List[Any]						,   A	:      List[int]						,   A	:      Optional[List[int]] = None     )  ->List[int]:
          lowerCamelCase__  :		str     =				[self.sep_token_id]
          lowerCamelCase__  :		Union[str, Any]     =				[self.cls_token_id]

          if token_ids_a is None:
             return len(cls + token_ids_a + sep     ) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]







       @property
       def 		__lowerCamelCase       ( self	:      Optional[Any]     )  ->Dict:
          return len(self.fairseq_tokens_to_ids     ) + len(self.sp_model     )







       def 		__lowerCamelCase       ( self	:      str     )  ->Tuple:
          lowerCamelCase__  :		List[Any]     =				{self.convert_ids_to_tokens(A     ): i for i in range(self.vocab_size     )}
          vocab.update(self.added_tokens_encoder     )
          return vocab







       def 		__lowerCamelCase       ( self	:      str						,   A	:      str     )  ->List[str]:
          return self.sp_model.encode(A						,   out_type=A     )







       def 		__lowerCamelCase       ( self	:      List[str]						,   A	:      Tuple     )  ->List[str]:

          if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
          elif self.sp_model.PieceToId(A     ) == 0:
             # Convert sentence piece unk token to fairseq unk token index
             return self.unk_token_id
          return self.fairseq_offset + self.sp_model.PieceToId(A     )







       def 		__lowerCamelCase       ( self	:      Optional[int]						,   A	:      Optional[int]     )  ->List[str]:

          if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(index - self.fairseq_offset     )







       def 		__lowerCamelCase       ( self	:      Union[str, Any]						,   A	:      Union[str, Any]     )  ->str:
          lowerCamelCase__  :		List[Any]     =				[]
          lowerCamelCase__  :		Union[str, Any]     =				''''''
          lowerCamelCase__  :		int     =				False
          for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                if not prev_is_special:
                   out_string += " "
                out_string += self.sp_model.decode(A     ) + token
                lowerCamelCase__  :		Union[str, Any]     =				True
                lowerCamelCase__  :		Any     =				[]
             else:
                current_sub_tokens.append(A     )
                lowerCamelCase__  :		List[str]     =				False
          out_string += self.sp_model.decode(A     )
          return out_string.strip()







       def __getstate__( self	:      Tuple     )  ->Optional[int]:
          lowerCamelCase__  :		Tuple     =				self.__dict__.copy()
          lowerCamelCase__  :		str     =				None
          return state







       def __setstate__( self	:      Optional[Any]						,   A	:      Any     )  ->Optional[Any]:
          lowerCamelCase__  :		List[Any]     =				d

          # for backward compatibility
          if not hasattr(self						,   '''sp_model_kwargs'''     ):
             lowerCamelCase__  :		Any     =				{}

          lowerCamelCase__  :		List[Any]     =				spm.SentencePieceProcessor(**self.sp_model_kwargs     )
          self.sp_model.Load(self.vocab_file     )







       def 		__lowerCamelCase       ( self	:      Any						,   A	:      str						,   A	:      Optional[str] = None     )  ->Tuple[str]:

          if not os.path.isdir(A     ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"     )
             return
          lowerCamelCase__  :		Any     =				os.path.join(
              A						,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

          if os.path.abspath(self.vocab_file     ) != os.path.abspath(A     ) and os.path.isfile(self.vocab_file     ):
             copyfile(self.vocab_file						,   A     )
          elif not os.path.isfile(self.vocab_file     ):
             with open(A						,   '''wb'''     ) as fi:
                lowerCamelCase__  :		List[str]     =				self.sp_model.serialized_model_proto()
                fi.write(A     )

          return (out_vocab_file,)








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__   :					List[str] 						=      logging.get_logger("transformers.models.encodec")

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

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__    ,	A__    ,	A__						):
      for attribute in key.split('.'						):
            lowercase__  =	getattr(A__    ,	A__						)

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

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

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

def 	_lowerCAmelCase (    A__    ,	A__    ,	A__						):
      lowercase__  =	[]

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

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

      if model_name == "encodec_24khz":
            pass  # config is already correct
      elif model_name == "encodec_32khz":
            lowercase__  =	[8, 5, 4, 4]
            lowercase__  =	[2.2]
            lowercase__  =	64
            lowercase__  =	32_000
            lowercase__  =	2_048
            lowercase__  =	False
            lowercase__  =	False
            lowercase__  =	False
      elif model_name == "encodec_48khz":
            lowercase__  =	[8, 5, 4, 2]
            lowercase__  =	[3.0, 6.0, 12.0, 24.0]
            lowercase__  =	48_000
            lowercase__  =	2
            lowercase__  =	False
            lowercase__  =	'time_group_norm'
            lowercase__  =	True
            lowercase__  =	1.0
            lowercase__  =	0.01
      else:
            raise ValueError(F'''Unknown model name: {model_name}'''						)

      lowercase__  =	EncodecModel(A__						)

      lowercase__  =	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__						)

      lowercase__  =	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
            lowercase__  =	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__   :					Optional[int] 						=      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__   :					Optional[int] 						=      parser.parse_args()
       convert_checkpoint(
           args.model,
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.config_path,
           args.push_to_hub,
       )
