




"""simple docstring"""


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


_A													=      logging.get_logger(__name__)

_A													=      {"""vocab_file""": """vocab.txt"""}

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

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



def 	lowercase_				(			__UpperCAmelCase				)						->    Optional[Any]:
      lowerCAmelCase__      :			str   =	collections.OrderedDict()
      with open(__UpperCAmelCase    ,		"""r"""    ,		encoding="""utf-8"""				) as reader:
            lowerCAmelCase__      :			List[str]   =	reader.readlines()
      for index, token in enumerate(__UpperCAmelCase				):
            lowerCAmelCase__      :			Tuple   =	token.rstrip("""\n"""				)
            lowerCAmelCase__      :			Any   =	index
      return vocab


class _lowerCamelCase    (					a_						):






   def __init__(		self       :		int   ,     UpperCamelCase       :		Optional[int]   ,     UpperCamelCase       :		List[str]="<unk>"   ,     UpperCamelCase       :		Union[str, Any]=2_00   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	vocab
         lowerCAmelCase__      :			List[Any]   =	unk_token
         lowerCAmelCase__      :			Optional[Any]   =	max_input_chars_per_word






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		Union[str, Any]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	list(UpperCamelCase   )
         if len(UpperCamelCase   ) > self.max_input_chars_per_word:
               return [self.unk_token]

         lowerCAmelCase__      :			str   =	0
         lowerCAmelCase__      :			List[str]   =	[]
         while start < len(UpperCamelCase   ):
               lowerCAmelCase__      :			Any   =	len(UpperCamelCase   )
               lowerCAmelCase__      :			Optional[Any]   =	None
               while start < end:
                     lowerCAmelCase__      :			str   =	"""""".join(chars[start:end]   )
                     if substr in self.vocab:
                           lowerCAmelCase__      :			List[str]   =	substr
                           break
                     end -= 1
               if cur_substr is None:
                     sub_tokens.append(self.unk_token   )
                     start += 1
               else:
                     sub_tokens.append(UpperCamelCase   )
                     lowerCAmelCase__      :			Any   =	end

         return sub_tokens






class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Any     				=     VOCAB_FILES_NAMES
   _lowerCamelCase    :Any     				=     PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase    :Dict     				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowerCamelCase    :List[str]     				=     ["input_ids", "attention_mask"]
   _lowerCamelCase    :str     				=     False






   def __init__(		self       :		Optional[Any]   ,     UpperCamelCase       :		Optional[Any]   ,     UpperCamelCase       :		Tuple="<d>"   ,     UpperCamelCase       :		int="</d>"   ,     UpperCamelCase       :		List[str]="<s>"   ,     UpperCamelCase       :		List[Any]="</s>"   ,     UpperCamelCase       :		int="<pad>"   ,     UpperCamelCase       :		List[Any]="<unk>"   ,     UpperCamelCase       :		List[Any]="</n>"   ,     UpperCamelCase       :		Dict="</_>"   ,     UpperCamelCase       :		Optional[Any]="left"   ,     **UpperCamelCase       :		List[Any]   ,     )			->						Any:

         """simple docstring"""
         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   ,     )
         lowerCAmelCase__      :			Dict   =	bod_token
         lowerCAmelCase__      :			Any   =	eod_token
         lowerCAmelCase__      :			List[Any]   =	load_vocab(UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	self.encoder[space_token]
         lowerCAmelCase__      :			Union[str, Any]   =	self.encoder[line_token]

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

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

         lowerCAmelCase__      :			str   =	WordpieceTokenizer(vocab=self.encoder   ,     unk_token=self.unk_token   )






   @property
   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[Any]:

         """simple docstring"""
         return self.encoder[self.bod_token]






   @property
   def 	_lowerCAmelCase   (		self       :		Any   )			->						Any:

         """simple docstring"""
         return self.encoder[self.eod_token]






   @property
   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Optional[int]:

         """simple docstring"""
         return self.encoder["\n"]






   @property
   def 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						int:

         """simple docstring"""
         return len(self.encoder   )






   def 	_lowerCAmelCase   (		self       :		int   )			->						Tuple:

         """simple docstring"""
         return dict(self.encoder   ,     **self.added_tokens_encoder   )






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		Optional[int]   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	[]
         for x in jieba.cut(UpperCamelCase   ,     cut_all=UpperCamelCase   ):
               output_tokens.extend(self.wordpiece_tokenizer.tokenize(UpperCamelCase   )   )
         return output_tokens






   def 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		Optional[Any]   ,     **UpperCamelCase       :		Optional[int]   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	[i for i in token_ids if i >= 0]
         lowerCAmelCase__      :			Optional[Any]   =	[
             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 	_lowerCAmelCase   (		self       :		List[str]   ,     UpperCamelCase       :		Optional[int]   )			->						str:

         """simple docstring"""
         return token in self.encoder






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		List[str]   )			->						str:

         """simple docstring"""
         return "".join(UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Any   ,     UpperCamelCase       :		Tuple   )			->						Optional[Any]:

         """simple docstring"""
         return self.encoder.get(UpperCamelCase   ,     self.encoder.get(self.unk_token   )   )






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

         """simple docstring"""
         return self.decoder.get(UpperCamelCase   ,     self.unk_token   )






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Optional[str] = None   )			->						Tuple[str]:

         """simple docstring"""
         if os.path.isdir(UpperCamelCase   ):
               lowerCAmelCase__      :			Optional[int]   =	os.path.join(
                   UpperCamelCase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
         else:
               lowerCAmelCase__      :			List[str]   =	(filename_prefix + """-""" if filename_prefix else """""") + save_directory
         lowerCAmelCase__      :			Optional[int]   =	0
         if " " in self.encoder:
               lowerCAmelCase__      :			Any   =	self.encoder[""" """]
               del self.encoder[" "]
         if "\n" in self.encoder:
               lowerCAmelCase__      :			Union[str, Any]   =	self.encoder["""\n"""]
               del self.encoder["\n"]
         lowerCAmelCase__      :			Any   =	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!"""   )
                           lowerCAmelCase__      :			Dict   =	token_index
                     writer.write(token + """\n"""   )
                     index += 1
         return (vocab_file,)






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		List[int]   ,     UpperCamelCase       :		List[int] = None   )			->						List[int]:

         """simple docstring"""
         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 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		List[int]   ,     UpperCamelCase       :		Optional[List[int]] = None   ,     UpperCamelCase       :		bool = False   )			->						List[int]:

         """simple docstring"""

         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 os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


_A													=      """base_with_context"""



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Dict:
      lowerCAmelCase__      :			Optional[int]   =	nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]				)				)
      lowerCAmelCase__      :			int   =	nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]				)    ,		requires_grad=__UpperCAmelCase				)
      for lyr_num, lyr in enumerate(model.encoders				):
            lowerCAmelCase__      :			str   =	weights[f"""layers_{lyr_num}"""]
            lowerCAmelCase__      :			Union[str, Any]   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]				)				)

            lowerCAmelCase__      :			str   =	ly_weight["""attention"""]
            lowerCAmelCase__      :			str   =	nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Optional[Any]   =	nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			str   =	nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T				)				)

            lowerCAmelCase__      :			int   =	nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]				)				)

            lowerCAmelCase__      :			Tuple   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[Any]   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T				)				)

      lowerCAmelCase__      :			Optional[Any]   =	nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]				)				)
      return model



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Optional[int]:
      lowerCAmelCase__      :			Dict   =	nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T				)				)

      lowerCAmelCase__      :			List[Any]   =	nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]				)    ,		requires_grad=__UpperCAmelCase				)

      for lyr_num, lyr in enumerate(model.encoders				):
            lowerCAmelCase__      :			int   =	weights[f"""layers_{lyr_num}"""]
            lowerCAmelCase__      :			Any   =	ly_weight["""attention"""]

            lowerCAmelCase__      :			int   =	nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			str   =	nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Dict   =	nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Any   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]				)				)

            lowerCAmelCase__      :			Any   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Optional[int]   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Any   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Optional[int]   =	nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]				)				)

      lowerCAmelCase__      :			Optional[int]   =	nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]				)				)

      return model



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    str:
      lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T				)				)
      lowerCAmelCase__      :			Union[str, Any]   =	nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T				)				)

      lowerCAmelCase__      :			List[str]   =	nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]				)    ,		requires_grad=__UpperCAmelCase				)

      lowerCAmelCase__      :			Dict   =	nn.Parameter(
          torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T				)				)

      for lyr_num, lyr in enumerate(model.decoders				):
            lowerCAmelCase__      :			List[Any]   =	weights[f"""layers_{lyr_num}"""]
            lowerCAmelCase__      :			Tuple   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""]				)				)

            lowerCAmelCase__      :			Union[str, Any]   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T				)				)

            lowerCAmelCase__      :			Tuple   =	ly_weight["""self_attention"""]
            lowerCAmelCase__      :			str   =	nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Dict   =	nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T				)				)

            lowerCAmelCase__      :			List[Any]   =	ly_weight["""MultiHeadDotProductAttention_0"""]
            lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Dict   =	nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Any   =	nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Optional[int]   =	nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Optional[int]   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]				)				)

            lowerCAmelCase__      :			Tuple   =	nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]				)				)
            lowerCAmelCase__      :			Dict   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Optional[int]   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			int   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Tuple   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T				)				)

      lowerCAmelCase__      :			str   =	nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]				)				)

      lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T				)				)

      return model



def 	lowercase_				(			__UpperCAmelCase				)						->    str:
      lowerCAmelCase__      :			Optional[int]   =	checkpoints.load_tax_checkpoint(args.checkpoint_path				)
      lowerCAmelCase__      :			Optional[int]   =	jnp.tree_util.tree_map(onp.array    ,		__UpperCAmelCase				)

      lowerCAmelCase__      :			Union[str, Any]   =	[
          """from __gin__ import dynamic_registration""",
          """from music_spectrogram_diffusion.models.diffusion import diffusion_utils""",
          """diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0""",
          """diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()""",
      ]

      lowerCAmelCase__      :			Dict   =	os.path.join(args.checkpoint_path    ,		""".."""    ,		"""config.gin"""				)
      lowerCAmelCase__      :			Tuple   =	inference.parse_training_gin_file(__UpperCAmelCase    ,		__UpperCAmelCase				)
      lowerCAmelCase__      :			Any   =	inference.InferenceModel(args.checkpoint_path    ,		__UpperCAmelCase				)

      lowerCAmelCase__      :			List[Any]   =	DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""    ,		variance_type="""fixed_large"""				)

      lowerCAmelCase__      :			List[Any]   =	SpectrogramNotesEncoder(
          max_length=synth_model.sequence_length["""inputs"""]    ,		vocab_size=synth_model.model.module.config.vocab_size    ,		d_model=synth_model.model.module.config.emb_dim    ,		dropout_rate=synth_model.model.module.config.dropout_rate    ,		num_layers=synth_model.model.module.config.num_encoder_layers    ,		num_heads=synth_model.model.module.config.num_heads    ,		d_kv=synth_model.model.module.config.head_dim    ,		d_ff=synth_model.model.module.config.mlp_dim    ,		feed_forward_proj="""gated-gelu"""    ,		)

      lowerCAmelCase__      :			List[str]   =	SpectrogramContEncoder(
          input_dims=synth_model.audio_codec.n_dims    ,		targets_context_length=synth_model.sequence_length["""targets_context"""]    ,		d_model=synth_model.model.module.config.emb_dim    ,		dropout_rate=synth_model.model.module.config.dropout_rate    ,		num_layers=synth_model.model.module.config.num_encoder_layers    ,		num_heads=synth_model.model.module.config.num_heads    ,		d_kv=synth_model.model.module.config.head_dim    ,		d_ff=synth_model.model.module.config.mlp_dim    ,		feed_forward_proj="""gated-gelu"""    ,		)

      lowerCAmelCase__      :			Optional[int]   =	TaFilmDecoder(
          input_dims=synth_model.audio_codec.n_dims    ,		targets_length=synth_model.sequence_length["""targets_context"""]    ,		max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time    ,		d_model=synth_model.model.module.config.emb_dim    ,		num_layers=synth_model.model.module.config.num_decoder_layers    ,		num_heads=synth_model.model.module.config.num_heads    ,		d_kv=synth_model.model.module.config.head_dim    ,		d_ff=synth_model.model.module.config.mlp_dim    ,		dropout_rate=synth_model.model.module.config.dropout_rate    ,		)

      lowerCAmelCase__      :			Optional[Any]   =	load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]    ,		__UpperCAmelCase				)
      lowerCAmelCase__      :			List[str]   =	load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]    ,		__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	load_decoder(ta_checkpoint["""target"""]["""decoder"""]    ,		__UpperCAmelCase				)

      lowerCAmelCase__      :			Any   =	OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""				)

      lowerCAmelCase__      :			Optional[Any]   =	SpectrogramDiffusionPipeline(
          notes_encoder=__UpperCAmelCase    ,		continuous_encoder=__UpperCAmelCase    ,		decoder=__UpperCAmelCase    ,		scheduler=__UpperCAmelCase    ,		melgan=__UpperCAmelCase    ,		)
      if args.save:
            pipe.save_pretrained(args.output_path				)


if __name__ == "__main__":
  _A													=      argparse.ArgumentParser()

  parser.add_argument("""--output_path""", default=None, type=str, required=True, help="""Path to the converted model.""")
  parser.add_argument(
      """--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
  )
  parser.add_argument(
      """--checkpoint_path""",
      default=f"""{MODEL}/checkpoint_500000""",
      type=str,
      required=False,
      help="""Path to the original jax model checkpoint.""",
  )
  _A													=      parser.parse_args()

  main(args)





