




"""simple docstring"""


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

import sentencepiece as spm

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


_A													=      logging.get_logger(__name__)
_A													=      {"""vocab_file""": """spiece.model"""}

_A													=      {
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

_A													=      {
    """albert-base-v1""": 5_1_2,
    """albert-large-v1""": 5_1_2,
    """albert-xlarge-v1""": 5_1_2,
    """albert-xxlarge-v1""": 5_1_2,
    """albert-base-v2""": 5_1_2,
    """albert-large-v2""": 5_1_2,
    """albert-xlarge-v2""": 5_1_2,
    """albert-xxlarge-v2""": 5_1_2,
}

_A													=      """▁"""


class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :int     				=     VOCAB_FILES_NAMES
   _lowerCamelCase    :Union[str, Any]     				=     PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase    :Tuple     				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






   def __init__(		self       :		Optional[Any]   ,     UpperCamelCase       :		List[str]   ,     UpperCamelCase       :		Tuple=True   ,     UpperCamelCase       :		int=True   ,     UpperCamelCase       :		List[str]=False   ,     UpperCamelCase       :		Optional[int]="[CLS]"   ,     UpperCamelCase       :		str="[SEP]"   ,     UpperCamelCase       :		Tuple="<unk>"   ,     UpperCamelCase       :		int="[SEP]"   ,     UpperCamelCase       :		Union[str, Any]="<pad>"   ,     UpperCamelCase       :		Dict="[CLS]"   ,     UpperCamelCase       :		Union[str, Any]="[MASK]"   ,     UpperCamelCase       :		Optional[Dict[str, Any]] = None   ,     **UpperCamelCase       :		Dict   ,     )			->						None:

         """simple docstring"""
         # Mask token behave like a normal word, i.e. include the space before it and
         # is included in the raw text, there should be a match in a non-normalized sentence.
         lowerCAmelCase__      :			Any   =	(
             AddedToken(UpperCamelCase   ,     lstrip=UpperCamelCase   ,     rstrip=UpperCamelCase   ,     normalized=UpperCamelCase   )
             if isinstance(UpperCamelCase   ,     UpperCamelCase   )
             else mask_token
         )

         lowerCAmelCase__      :			Tuple   =	{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             do_lower_case=UpperCamelCase   ,     remove_space=UpperCamelCase   ,     keep_accents=UpperCamelCase   ,     bos_token=UpperCamelCase   ,     eos_token=UpperCamelCase   ,     unk_token=UpperCamelCase   ,     sep_token=UpperCamelCase   ,     pad_token=UpperCamelCase   ,     cls_token=UpperCamelCase   ,     mask_token=UpperCamelCase   ,     sp_model_kwargs=self.sp_model_kwargs   ,     **UpperCamelCase   ,     )

         lowerCAmelCase__      :			List[Any]   =	do_lower_case
         lowerCAmelCase__      :			str   =	remove_space
         lowerCAmelCase__      :			str   =	keep_accents
         lowerCAmelCase__      :			Union[str, Any]   =	vocab_file

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






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

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






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

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	{self.convert_ids_to_tokens(UpperCamelCase   ): i for i in range(self.vocab_size   )}
         vocab.update(self.added_tokens_encoder   )
         return vocab






   def __getstate__(		self       :		List[Any]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	self.__dict__.copy()
         lowerCAmelCase__      :			List[Any]   =	None
         return state






   def __setstate__(		self       :		Tuple   ,     UpperCamelCase       :		List[str]   )			->						Optional[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	d

         # for backward compatibility
         if not hasattr(self   ,     """sp_model_kwargs"""   ):
               lowerCAmelCase__      :			Dict   =	{}

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






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

         """simple docstring"""
         if self.remove_space:
               lowerCAmelCase__      :			Union[str, Any]   =	""" """.join(inputs.strip().split()   )
         else:
               lowerCAmelCase__      :			List[Any]   =	inputs
         lowerCAmelCase__      :			int   =	outputs.replace("""``"""   ,     """\""""   ).replace("""''"""   ,     """\""""   )

         if not self.keep_accents:
               lowerCAmelCase__      :			str   =	unicodedata.normalize("""NFKD"""   ,     UpperCamelCase   )
               lowerCAmelCase__      :			Optional[Any]   =	"""""".join([c for c in outputs if not unicodedata.combining(UpperCamelCase   )]   )
         if self.do_lower_case:
               lowerCAmelCase__      :			Optional[Any]   =	outputs.lower()

         return outputs






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

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	self.preprocess_text(UpperCamelCase   )
         lowerCAmelCase__      :			int   =	self.sp_model.encode(UpperCamelCase   ,     out_type=UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	[]
         for piece in pieces:
               if len(UpperCamelCase   ) > 1 and piece[-1] == str(""","""   ) and piece[-2].isdigit():
                     lowerCAmelCase__      :			Union[str, Any]   =	self.sp_model.EncodeAsPieces(piece[:-1].replace(UpperCamelCase   ,     """"""   )   )
                     if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                           if len(cur_pieces[0]   ) == 1:
                                 lowerCAmelCase__      :			Tuple   =	cur_pieces[1:]
                           else:
                                 lowerCAmelCase__      :			Optional[Any]   =	cur_pieces[0][1:]
                     cur_pieces.append(piece[-1]   )
                     new_pieces.extend(UpperCamelCase   )
               else:
                     new_pieces.append(UpperCamelCase   )

         return new_pieces






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

         """simple docstring"""
         return self.sp_model.PieceToId(UpperCamelCase   )






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

         """simple docstring"""
         return self.sp_model.IdToPiece(UpperCamelCase   )






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

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	[]
         lowerCAmelCase__      :			Dict   =	""""""
         lowerCAmelCase__      :			Union[str, Any]   =	False
         for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                     if not prev_is_special:
                           out_string += " "
                     out_string += self.sp_model.decode(UpperCamelCase   ) + token
                     lowerCAmelCase__      :			Tuple   =	True
                     lowerCAmelCase__      :			Any   =	[]
               else:
                     current_sub_tokens.append(UpperCamelCase   )
                     lowerCAmelCase__      :			Any   =	False
         out_string += self.sp_model.decode(UpperCamelCase   )
         return out_string.strip()






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

         """simple docstring"""
         lowerCAmelCase__      :			List[Any]   =	[self.sep_token_id]
         lowerCAmelCase__      :			Any   =	[self.cls_token_id]
         if token_ids_a is None:
               return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     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   )) + [1]
         return [1] + ([0] * len(UpperCamelCase   )) + [1]






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

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	[self.sep_token_id]
         lowerCAmelCase__      :			Optional[int]   =	[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   ) * [0] + len(token_ids_a + sep   ) * [1]






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

         """simple docstring"""
         if not os.path.isdir(UpperCamelCase   ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
               return
         lowerCAmelCase__      :			List[str]   =	os.path.join(
             UpperCamelCase   ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

         if os.path.abspath(self.vocab_file   ) != os.path.abspath(UpperCamelCase   ) and os.path.isfile(self.vocab_file   ):
               copyfile(self.vocab_file   ,     UpperCamelCase   )
         elif not os.path.isfile(self.vocab_file   ):
               with open(UpperCamelCase   ,     """wb"""   ) as fi:
                     lowerCAmelCase__      :			int   =	self.sp_model.serialized_model_proto()
                     fi.write(UpperCamelCase   )

         return (out_vocab_file,)











"""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)





