






import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
 import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case__   :						Any													=   logging.get_logger(__name__)

snake_case__   :						Any													=   {"""vocab_file""": """vocab.txt"""}

snake_case__   :						int													=   {
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

snake_case__   :						int													=   {
    """openbmb/cpm-ant-10b""": 1_0_2_4,
}






def      _snake_case     (__lowercase):
   UpperCamelCase_						=  collections.OrderedDict()
   with open(__lowercase     ,   'r'     ,   encoding='utf-8') as reader:
      UpperCamelCase_						=  reader.readlines()
   for index, token in enumerate(__lowercase):
      UpperCamelCase_						=  token.rstrip('\n')
      UpperCamelCase_						=  index
   return vocab


class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase="<unk>"		,		_UpperCAmelCase=200			)      ->			Any:
     UpperCamelCase_						=  vocab
     UpperCamelCase_						=  unk_token
     UpperCamelCase_						=  max_input_chars_per_word




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Tuple:
     UpperCamelCase_						=  list(_UpperCAmelCase			)
     if len(_UpperCAmelCase			) > self.max_input_chars_per_word:
        return [self.unk_token]

     UpperCamelCase_						=  0
     UpperCamelCase_						=  []
     while start < len(_UpperCAmelCase			):
        UpperCamelCase_						=  len(_UpperCAmelCase			)
        UpperCamelCase_						=  None
        while start < end:
           UpperCamelCase_						=  ''.join(chars[start:end]			)
           if substr in self.vocab:
              UpperCamelCase_						=  substr
              break
           end -= 1
        if cur_substr is None:
           sub_tokens.append(self.unk_token			)
           start += 1
        else:
           sub_tokens.append(_UpperCAmelCase			)
           UpperCamelCase_						=  end

     return sub_tokens




class     _a							(	UpperCAmelCase__     ):






  """simple docstring"""





  A_						       = VOCAB_FILES_NAMES
  A_						       = PRETRAINED_VOCAB_FILES_MAP
  A_						       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_						       = ["""input_ids""", """attention_mask"""]
  A_						       = False




  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase="<d>"		,		_UpperCAmelCase="</d>"		,		_UpperCAmelCase="<s>"		,		_UpperCAmelCase="</s>"		,		_UpperCAmelCase="<pad>"		,		_UpperCAmelCase="<unk>"		,		_UpperCAmelCase="</n>"		,		_UpperCAmelCase="</_>"		,		_UpperCAmelCase="left"		,		**_UpperCAmelCase		,		)      ->			List[Any]:
     requires_backends(self		,		['jieba']			)
     super().__init__(
         bod_token=_UpperCAmelCase		,		eod_token=_UpperCAmelCase		,		bos_token=_UpperCAmelCase		,		eos_token=_UpperCAmelCase		,		pad_token=_UpperCAmelCase		,		unk_token=_UpperCAmelCase		,		line_token=_UpperCAmelCase		,		space_token=_UpperCAmelCase		,		padding_side=_UpperCAmelCase		,		**_UpperCAmelCase		,		)
     UpperCamelCase_						=  bod_token
     UpperCamelCase_						=  eod_token
     UpperCamelCase_						=  load_vocab(_UpperCAmelCase			)
     UpperCamelCase_						=  self.encoder[space_token]
     UpperCamelCase_						=  self.encoder[line_token]

     del self.encoder[space_token]
     del self.encoder[line_token]

     UpperCamelCase_						=  collections.OrderedDict(sorted(self.encoder.items()		,		key=lambda _UpperCAmelCase			: x[1]			)			)
     UpperCamelCase_						=  {v: k for k, v in self.encoder.items()}

     UpperCamelCase_						=  WordpieceTokenizer(vocab=self.encoder		,		unk_token=self.unk_token			)




  @property
  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     return self.encoder[self.bod_token]




  @property
  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     return self.encoder[self.eod_token]




  @property
  def  _UpperCAmelCase  (  self			)      ->			Any:
     return self.encoder["\n"]




  @property
  def  _UpperCAmelCase  (  self			)      ->			int:
     return len(self.encoder			)




  def  _UpperCAmelCase  (  self			)      ->			str:
     return dict(self.encoder		,		**self.added_tokens_encoder			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Optional[int]:
     UpperCamelCase_						=  []
     for x in jieba.cut(_UpperCAmelCase		,		cut_all=_UpperCAmelCase			):
        output_tokens.extend(self.wordpiece_tokenizer.tokenize(_UpperCAmelCase			)			)
     return output_tokens




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Optional[int]:
     UpperCamelCase_						=  [i for i in token_ids if i >= 0]
     UpperCamelCase_						=  [
         x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
     ]
     return super()._decode(_UpperCAmelCase		,		**_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			str:
     return token in self.encoder




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			str:
     return "".join(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			int:
     return self.encoder.get(_UpperCAmelCase		,		self.encoder.get(self.unk_token			)			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			Optional[Any]:
     return self.decoder.get(_UpperCAmelCase		,		self.unk_token			)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None			)      ->			Tuple[str]:
     if os.path.isdir(_UpperCAmelCase			):
        UpperCamelCase_						=  os.path.join(
            _UpperCAmelCase		,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
     else:
        UpperCamelCase_						=  (filename_prefix + '-' if filename_prefix else '') + save_directory
     UpperCamelCase_						=  0
     if " " in self.encoder:
        UpperCamelCase_						=  self.encoder[' ']
        del self.encoder[" "]
     if "\n" in self.encoder:
        UpperCamelCase_						=  self.encoder['\n']
        del self.encoder["\n"]
     UpperCamelCase_						=  collections.OrderedDict(sorted(self.encoder.items()		,		key=lambda _UpperCAmelCase			: x[1]			)			)
     with open(_UpperCAmelCase		,		'w'		,		encoding='utf-8'			) as writer:
        for token, token_index in self.encoder.items():
           if index != token_index:
              logger.warning(
                  f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                  ' Please check that the vocabulary is not corrupted!'			)
              UpperCamelCase_						=  token_index
           writer.write(token + '\n'			)
           index += 1
     return (vocab_file,)




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None			)      ->			List[int]:
     if token_ids_a is None:
        return [self.bos_token_id] + token_ids_a
     return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = False			)      ->			List[int]:

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

     if token_ids_a is not None:
        return [1] + ([0] * len(_UpperCAmelCase			)) + [1] + ([0] * len(_UpperCAmelCase			))
     return [1] + ([0] * len(_UpperCAmelCase			))









'''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()
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_ ( __UpperCamelCase					:       Tuple							,    __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       List[Any]							,    __UpperCamelCase					:       int							,    __UpperCamelCase					:       Dict		)					->	List[Any]:







     """simple docstring"""
     for attribute in key.split('.'		):
          _A				=							getattr(__UpperCamelCase							,    __UpperCamelCase		)

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

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

def 					lowerCamelCase_ ( __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       str		)					->	Dict:







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

def 					lowerCamelCase_ ( __UpperCamelCase					:       Dict							,    __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       int		)					->	List[Any]:







     """simple docstring"""
     _A				=							[]

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

          _A				=							False
          for key, mapped_key in MAPPING.items():
               if "*" in key:
                    _A		,				_A				=							key.split('.*.'		)
                    if prefix in name and suffix in name:
                         _A				=							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				=							True
                    if "*" in mapped_key:
                         _A				=							name.split(__UpperCamelCase		)[0].split('.'		)[-2]
                         _A				=							mapped_key.replace('*'							,    __UpperCamelCase		)
                    if "weight_g" in name:
                         _A				=							'weight_g'
                    elif "weight_v" in name:
                         _A				=							'weight_v'
                    elif "weight_ih_l0" in name:
                         _A				=							'weight_ih_l0'
                    elif "weight_hh_l0" in name:
                         _A				=							'weight_hh_l0'
                    elif "bias_ih_l0" in name:
                         _A				=							'bias_ih_l0'
                    elif "bias_hh_l0" in name:
                         _A				=							'bias_hh_l0'
                    elif "weight_ih_l1" in name:
                         _A				=							'weight_ih_l1'
                    elif "weight_hh_l1" in name:
                         _A				=							'weight_hh_l1'
                    elif "bias_ih_l1" in name:
                         _A				=							'bias_ih_l1'
                    elif "bias_hh_l1" in name:
                         _A				=							'bias_hh_l1'
                    elif "bias" in name:
                         _A				=							'bias'
                    elif "weight" in name:
                         _A				=							'weight'
                    elif "running_mean" in name:
                         _A				=							'running_mean'
                    elif "running_var" in name:
                         _A				=							'running_var'
                    elif "num_batches_tracked" in name:
                         _A				=							'num_batches_tracked'
                    else:
                         _A				=							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					:       str							,    __UpperCamelCase					:       Optional[Any]							,    __UpperCamelCase					:       Any							,    __UpperCamelCase					:       List[str]=None							,    __UpperCamelCase					:       List[str]=None							,    )					->	List[str]:







     """simple docstring"""
     if config_path is not None:
          _A				=							EncodecConfig.from_pretrained(__UpperCamelCase		)
     else:
          _A				=							EncodecConfig()

     if model_name == "encodec_24khz":
          pass  # config is already correct
     elif model_name == "encodec_32khz":
          _A				=							[8, 5, 4, 4]
          _A				=							[2.2]
          _A				=							6_4
          _A				=							3_2_0_0_0
          _A				=							2_0_4_8
          _A				=							False
          _A				=							False
          _A				=							False
     elif model_name == "encodec_48khz":
          _A				=							[8, 5, 4, 2]
          _A				=							[3.0, 6.0, 12.0, 24.0]
          _A				=							4_8_0_0_0
          _A				=							2
          _A				=							False
          _A				=							'time_group_norm'
          _A				=							True
          _A				=							1.0
          _A				=							0.01
     else:
          raise ValueError(F'Unknown model name: {model_name}'		)

     _A				=							EncodecModel(__UpperCamelCase		)

     _A				=							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		)

     _A				=							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
          _A				=							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__":
    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,
    )





