



"""simple docstring"""

import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer




def __lowercase			(							_a    ):
    # This defines a "chinese character" as anything in the CJK Unicode block:
    #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
    #
    # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
    # despite its name. The modern Korean Hangul alphabet is a different block,
    # as is Japanese Hiragana and Katakana. Those alphabets are used to write
    # space-separated words, so they are not treated specially and handled
    # like the all of the other languages.
    if (
        (cp >= 0x4e00 and cp <= 0x9fff)
        or (cp >= 0x3400 and cp <= 0x4dbf)  #
        or (cp >= 0x2_0000 and cp <= 0x2_a6df)  #
        or (cp >= 0x2_a700 and cp <= 0x2_b73f)  #
        or (cp >= 0x2_b740 and cp <= 0x2_b81f)  #
        or (cp >= 0x2_b820 and cp <= 0x2_ceaf)  #
        or (cp >= 0xf900 and cp <= 0xfaff)
        or (cp >= 0x2_f800 and cp <= 0x2_fa1f)  #
    ):  #
        return True

    return False




def __lowercase			(							_a    ):
    # word like '180' or '身高' or '神'
    for char in word:
        snake_case_ :     Any				=	ord(_SCREAMING_SNAKE_CASE    )
        if not _is_chinese_char(_SCREAMING_SNAKE_CASE    ):
            return 0
    return 1




def __lowercase			(							_a    ):
    snake_case_ :     Tuple				=	set()

    for token in tokens:
        snake_case_ :     List[Any]				=	len(_SCREAMING_SNAKE_CASE    ) > 1 and is_chinese(_SCREAMING_SNAKE_CASE    )
        if chinese_word:
            word_set.add(_SCREAMING_SNAKE_CASE    )
    snake_case_ :     List[str]				=	list(_SCREAMING_SNAKE_CASE    )
    return word_list




def __lowercase			(							_a		,			_a    ):
    if not chinese_word_set:
        return bert_tokens
    snake_case_ :     Tuple				=	max([len(_SCREAMING_SNAKE_CASE    ) for w in chinese_word_set]    )

    snake_case_ :     List[str]				=	bert_tokens
    snake_case_ :     Optional[int]				=	0, len(_SCREAMING_SNAKE_CASE    )
    while start < end:
        snake_case_ :     Optional[Any]				=	True
        if is_chinese(bert_word[start]    ):
            snake_case_ :     Tuple				=	min(end - start		,			_SCREAMING_SNAKE_CASE    )
            for i in range(_SCREAMING_SNAKE_CASE		,			1		,			-1    ):
                snake_case_ :     int				=	''.join(bert_word[start : start + i]    )
                if whole_word in chinese_word_set:
                    for j in range(start + 1		,			start + i    ):
                        snake_case_ :     List[Any]				=	'##' + bert_word[j]
                    snake_case_ :     List[Any]				=	start + i
                    snake_case_ :     List[str]				=	False
                    break
        if single_word:
            start += 1
    return bert_word




def __lowercase			(							_a		,			_a		,			_a    ):
    snake_case_ :     str				=	[]

    for i in range(0		,			len(_SCREAMING_SNAKE_CASE    )		,			100    ):
        snake_case_ :     Dict				=	ltp_tokenizer.pipeline(lines[i : i + 100]		,			tasks=['''cws''']    ).cws
        snake_case_ :     str				=	[get_chinese_word(_SCREAMING_SNAKE_CASE    ) for r in res]
        ltp_res.extend(_SCREAMING_SNAKE_CASE    )
    assert len(_SCREAMING_SNAKE_CASE    ) == len(_SCREAMING_SNAKE_CASE    )

    snake_case_ :     Dict				=	[]
    for i in range(0		,			len(_SCREAMING_SNAKE_CASE    )		,			100    ):
        snake_case_ :     int				=	bert_tokenizer(lines[i : i + 100]		,			add_special_tokens=_SCREAMING_SNAKE_CASE		,			truncation=_SCREAMING_SNAKE_CASE		,			max_length=512    )
        bert_res.extend(res['''input_ids''']    )
    assert len(_SCREAMING_SNAKE_CASE    ) == len(_SCREAMING_SNAKE_CASE    )

    snake_case_ :     Tuple				=	[]
    for input_ids, chinese_word in zip(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE    ):
        snake_case_ :     int				=	[]
        for id in input_ids:
            snake_case_ :     Union[str, Any]				=	bert_tokenizer._convert_id_to_token(_SCREAMING_SNAKE_CASE    )
            input_tokens.append(_SCREAMING_SNAKE_CASE    )
        snake_case_ :     List[Any]				=	add_sub_symbol(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE    )
        snake_case_ :     List[str]				=	[]
        # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
        for i, token in enumerate(_SCREAMING_SNAKE_CASE    ):
            if token[:2] == "##":
                snake_case_ :     Any				=	token[2:]
                # save chinese tokens' pos
                if len(_SCREAMING_SNAKE_CASE    ) == 1 and _is_chinese_char(ord(_SCREAMING_SNAKE_CASE    )    ):
                    ref_id.append(_SCREAMING_SNAKE_CASE    )
        ref_ids.append(_SCREAMING_SNAKE_CASE    )

    assert len(_SCREAMING_SNAKE_CASE    ) == len(_SCREAMING_SNAKE_CASE    )

    return ref_ids




def __lowercase			(							_a    ):
    # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
    # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
    with open(args.file_name		,			'''r'''		,			encoding='''utf-8'''    ) as f:
        snake_case_ :     Optional[Any]				=	f.readlines()
    snake_case_ :     Tuple				=	[line.strip() for line in data if len(_SCREAMING_SNAKE_CASE    ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
    snake_case_ :     Union[str, Any]				=	LTP(args.ltp    )  # faster in GPU device
    snake_case_ :     Tuple				=	BertTokenizer.from_pretrained(args.bert    )

    snake_case_ :     List[str]				=	prepare_ref(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE    )

    with open(args.save_path		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        snake_case_ :     Optional[int]				=	[json.dumps(_SCREAMING_SNAKE_CASE    ) + '\n' for ref in ref_ids]
        f.writelines(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
     lowercase__    :  List[Any]							    =	argparse.ArgumentParser(description='''prepare_chinese_ref''')
     parser.add_argument(
         '''--file_name''',
         required=False,
         type=str,
         default='''./resources/chinese-demo.txt''',
         help='''file need process, same as training data in lm''',
     )
     parser.add_argument(
         '''--ltp''',
         required=False,
         type=str,
         default='''./resources/ltp''',
         help='''resources for LTP tokenizer, usually a path''',
     )
     parser.add_argument(
         '''--bert''',
         required=False,
         type=str,
         default='''./resources/robert''',
         help='''resources for Bert tokenizer''',
     )
     parser.add_argument(
         '''--save_path''',
         required=False,
         type=str,
         default='''./resources/ref.txt''',
         help='''path to save res''',
     )

     lowercase__    :  Any							    =	parser.parse_args()
     main(args)






"""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,
 )


