"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_nllb import NllbTokenizer
else:
 lowercase__					       =	None


lowercase__					       =	logging.get_logger(__name__)


lowercase__					       =	{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowercase__					       =	{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'
        ),
    },
}

lowercase__					       =	{
    'facebook/nllb-large-en-ro': 1024,
    'facebook/nllb-200-distilled-600M': 1024,
}

# fmt: off
lowercase__					       =	['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		VOCAB_FILES_NAMES
      a__        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__        =		PRETRAINED_VOCAB_FILES_MAP
      a__        =		["""input_ids""", """attention_mask"""]
      a__        =		NllbTokenizer

      a__        =		[]
      a__        =		[]



      def __init__(						self    ,			lowercase=None    ,			lowercase=None    ,			lowercase="<s>"    ,			lowercase="</s>"    ,			lowercase="</s>"    ,			lowercase="<s>"    ,			lowercase="<unk>"    ,			lowercase="<pad>"    ,			lowercase="<mask>"    ,			lowercase=None    ,			lowercase=None    ,			lowercase=None    ,			lowercase=False    ,			**lowercase    ,			)	->			str:





        '''simple docstring'''





        a__:     int										=	AddedToken(lowercase    ,			lstrip=lowercase    ,			rstrip=lowercase) if isinstance(lowercase    ,			lowercase) else mask_token
        a__:     Optional[int]										=	legacy_behaviour
        super().__init__(
            vocab_file=lowercase    ,			tokenizer_file=lowercase    ,			bos_token=lowercase    ,			eos_token=lowercase    ,			sep_token=lowercase    ,			cls_token=lowercase    ,			unk_token=lowercase    ,			pad_token=lowercase    ,			mask_token=lowercase    ,			src_lang=lowercase    ,			tgt_lang=lowercase    ,			additional_special_tokens=lowercase    ,			legacy_behaviour=lowercase    ,			**lowercase    ,			)

        a__:     List[Any]										=	vocab_file
        a__:     int										=	False if not self.vocab_file else True

        a__:     Any										=	FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
          # Only add those special tokens if they are not already there.
          _additional_special_tokens.extend(
              [t for t in additional_special_tokens if t not in _additional_special_tokens])

        self.add_special_tokens({'additional_special_tokens': _additional_special_tokens})
        a__:     int										=	{
            lang_code: self.convert_tokens_to_ids(lowercase) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        a__:     List[Any]										=	src_lang if src_lang is not None else 'eng_Latn'
        a__:     Union[str, Any]										=	self.convert_tokens_to_ids(self._src_lang)
        a__:     List[Any]										=	tgt_lang
        self.set_src_lang_special_tokens(self._src_lang)



      @property
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        return self._src_lang



      @src_lang.setter
      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Any										=	new_src_lang
        self.set_src_lang_special_tokens(self._src_lang)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = None)	->			List[int]:





        '''simple docstring'''





        if token_ids_a is None:
          return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = None)	->			List[int]:





        '''simple docstring'''





        a__:     Tuple										=	[self.sep_token_id]
        a__:     Tuple										=	[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]



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			**lowercase)	->			Dict:





        '''simple docstring'''





        if src_lang is None or tgt_lang is None:
          raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
        a__:     List[str]										=	src_lang
        a__:     Optional[Any]										=	self(lowercase    ,			add_special_tokens=lowercase    ,			return_tensors=lowercase    ,			**lowercase)
        a__:     Optional[int]										=	self.convert_tokens_to_ids(lowercase)
        a__:     str										=	tgt_lang_id
        return inputs



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = "eng_Latn"    ,			lowercase = None    ,			lowercase = "fra_Latn"    ,			**lowercase    ,			)	->			BatchEncoding:





        '''simple docstring'''





        a__:     str										=	src_lang
        a__:     str										=	tgt_lang
        return super().prepare_seqaseq_batch(lowercase    ,			lowercase    ,			**lowercase)



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        return self.set_src_lang_special_tokens(self.src_lang)



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        return self.set_tgt_lang_special_tokens(self.tgt_lang)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     int										=	self.convert_tokens_to_ids(lowercase)

        if self.legacy_behaviour:
          a__:     Optional[Any]										=	[]
          a__:     Dict										=	[self.eos_token_id, self.cur_lang_code]
        else:
          a__:     str										=	[self.cur_lang_code]
          a__:     int										=	[self.eos_token_id]

        a__:     Tuple										=	self.convert_ids_to_tokens(self.prefix_tokens)
        a__:     Union[str, Any]										=	self.convert_ids_to_tokens(self.suffix_tokens)

        a__:     str										=	processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str    ,			pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str    ,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,			self.prefix_tokens + self.suffix_tokens))    ,			)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     int										=	self.convert_tokens_to_ids(lowercase)
        if self.legacy_behaviour:
          a__:     int										=	[]
          a__:     Any										=	[self.eos_token_id, self.cur_lang_code]
        else:
          a__:     int										=	[self.cur_lang_code]
          a__:     int										=	[self.eos_token_id]

        a__:     Dict										=	self.convert_ids_to_tokens(self.prefix_tokens)
        a__:     Dict										=	self.convert_ids_to_tokens(self.suffix_tokens)

        a__:     Optional[int]										=	processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str    ,			pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str    ,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,			self.prefix_tokens + self.suffix_tokens))    ,			)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = None)	->			Tuple[str]:





        '''simple docstring'''





        if not self.can_save_slow_tokenizer:
          raise ValueError(
              'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
              'tokenizer.')

        if not os.path.isdir(lowercase):
          logger.error(f'Vocabulary path ({save_directory}) should be a directory.')
          return
        a__:     Dict										=	os.path.join(
            lowercase    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

        if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase):
          copyfile(self.vocab_file    ,			lowercase)

        return (out_vocab_file,)



"""simple docstring"""
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()
lowercase__					       =	logging.get_logger('transformers.models.encodec')

lowercase__					       =	{
    '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',
}
lowercase__					       =	{
    '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',
}
lowercase__					       =	{
    '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',
}
lowercase__					       =	{
    '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',
}
lowercase__					       =	{
    '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',
}
lowercase__					       =	{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
lowercase__					       =	{
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
lowercase__					       =	[]
lowercase__					       =	[]







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  for attribute in key.split('.'   ):
    a__:     str										=	getattr(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )

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

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







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Dict:
  for key in ignore_keys:
    if key.endswith('.*'   ):
      if name.startswith(key[:-1]   ):
        return True
    elif ".*." in key:
      a__							,    a__:     Optional[Any]										=	key.split('.*.'   )
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->List[str]:
  a__:     List[Any]										=	[]

  if model_name == "encodec_24khz" or "encodec_32khz":
    a__:     Optional[int]										=	MAPPING_24K
  elif model_name == "encodec_48khz":
    a__:     List[Any]										=	MAPPING_48K
  else:
    raise ValueError(F'Unsupported model: {model_name}'   )

  for name, value in orig_dict.items():
    if should_ignore(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
      logger.info(F'{name} was ignored'   )
      continue

    a__:     int										=	False
    for key, mapped_key in MAPPING.items():
      if "*" in key:
        a__							,    a__:     str										=	key.split('.*.'   )
        if prefix in name and suffix in name:
          a__:     List[str]										=	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__:     List[str]										=	True
        if "*" in mapped_key:
          a__:     List[str]										=	name.split(_SCREAMING_SNAKE_CASE   )[0].split('.'   )[-2]
          a__:     str										=	mapped_key.replace('*'		,     _SCREAMING_SNAKE_CASE   )
        if "weight_g" in name:
          a__:     int										=	'weight_g'
        elif "weight_v" in name:
          a__:     Dict										=	'weight_v'
        elif "weight_ih_l0" in name:
          a__:     int										=	'weight_ih_l0'
        elif "weight_hh_l0" in name:
          a__:     Union[str, Any]										=	'weight_hh_l0'
        elif "bias_ih_l0" in name:
          a__:     Optional[Any]										=	'bias_ih_l0'
        elif "bias_hh_l0" in name:
          a__:     Optional[int]										=	'bias_hh_l0'
        elif "weight_ih_l1" in name:
          a__:     Dict										=	'weight_ih_l1'
        elif "weight_hh_l1" in name:
          a__:     Optional[Any]										=	'weight_hh_l1'
        elif "bias_ih_l1" in name:
          a__:     List[str]										=	'bias_ih_l1'
        elif "bias_hh_l1" in name:
          a__:     Optional[Any]										=	'bias_hh_l1'
        elif "bias" in name:
          a__:     List[str]										=	'bias'
        elif "weight" in name:
          a__:     Any										=	'weight'
        elif "running_mean" in name:
          a__:     Dict										=	'running_mean'
        elif "running_var" in name:
          a__:     Dict										=	'running_var'
        elif "num_batches_tracked" in name:
          a__:     Dict										=	'num_batches_tracked'
        else:
          a__:     List[str]										=	None
        set_recursively(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      continue
    if not is_used:
      unused_weights.append(_SCREAMING_SNAKE_CASE   )

  logger.warning(F'Unused weights: {unused_weights}'   )







@torch.no_grad()
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE=None		,     )	->int:
  if config_path is not None:
    a__:     Dict										=	EncodecConfig.from_pretrained(_SCREAMING_SNAKE_CASE   )
  else:
    a__:     Tuple										=	EncodecConfig()

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

  a__:     Optional[int]										=	EncodecModel(_SCREAMING_SNAKE_CASE   )

  a__:     List[str]										=	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(_SCREAMING_SNAKE_CASE   )

  a__:     int										=	torch.load(_SCREAMING_SNAKE_CASE   )
  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__:     str										=	original_checkpoint['best_state']
  recursively_load_weights(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  model.save_pretrained(_SCREAMING_SNAKE_CASE   )

  if repo_id:
    print('Pushing to the hub...'   )
    feature_extractor.push_to_hub(_SCREAMING_SNAKE_CASE   )
    model.push_to_hub(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
 lowercase__					       =	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.'
 )

 lowercase__					       =	parser.parse_args()
 convert_checkpoint(
     args.model,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )


