




"""simple docstring"""


import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


_A :    Optional[int]													=      logging.getLogger(__name__)


class _lowerCamelCase    (					__snake_case						):
   _lowerCamelCase    :Union[str, Any]     				=     """token-classification"""






   def __init__(		self       :		int   ,     UpperCamelCase       :		Tuple   )			->						Tuple:

         """simple docstring"""


         if type(UpperCamelCase__   ) == dict:
               lowerCAmelCase__      :			Optional[Any]   =	Namespace(**UpperCamelCase__   )
         lowerCAmelCase__      :			Any   =	import_module("""tasks"""   )
         try:
               lowerCAmelCase__      :			Optional[int]   =	getattr(UpperCamelCase__   ,     hparams.task_type   )
               lowerCAmelCase__      :			TokenClassificationTask   =	token_classification_task_clazz()
         except AttributeError:
               raise ValueError(
                   f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
                   f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}"""   )
         lowerCAmelCase__      :			List[str]   =	self.token_classification_task.get_labels(hparams.labels   )
         lowerCAmelCase__      :			Any   =	CrossEntropyLoss().ignore_index
         super().__init__(UpperCamelCase__   ,     len(self.labels   )   ,     self.mode   )






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

         """simple docstring"""
         return self.model(**UpperCamelCase__   )






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

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
         if self.config.model_type != "distilbert":
               lowerCAmelCase__      :			int   =	(
                   batch[2] if self.config.model_type in ["bert", "xlnet"] else None
               )  # XLM and RoBERTa don"t use token_type_ids

         lowerCAmelCase__      :			Any   =	self(**UpperCamelCase__   )
         lowerCAmelCase__      :			Dict   =	outputs[0]
         # tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
         return {"loss": loss}






   def 	_lowerCAmelCase   (		self       :		str   )			->						Any:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	self.hparams
         for mode in ["train", "dev", "test"]:
               lowerCAmelCase__      :			Dict   =	self._feature_file(UpperCamelCase__   )
               if os.path.exists(UpperCamelCase__   ) and not args.overwrite_cache:
                     logger.info("""Loading features from cached file %s"""   ,     UpperCamelCase__   )
                     lowerCAmelCase__      :			List[str]   =	torch.load(UpperCamelCase__   )
               else:
                     logger.info("""Creating features from dataset file at %s"""   ,     args.data_dir   )
                     lowerCAmelCase__      :			Any   =	self.token_classification_task.read_examples_from_file(args.data_dir   ,     UpperCamelCase__   )
                     lowerCAmelCase__      :			Any   =	self.token_classification_task.convert_examples_to_features(
                         UpperCamelCase__   ,     self.labels   ,     args.max_seq_length   ,     self.tokenizer   ,     cls_token_at_end=bool(self.config.model_type in ["""xlnet"""]   )   ,     cls_token=self.tokenizer.cls_token   ,     cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0   ,     sep_token=self.tokenizer.sep_token   ,     sep_token_extra=UpperCamelCase__   ,     pad_on_left=bool(self.config.model_type in ["""xlnet"""]   )   ,     pad_token=self.tokenizer.pad_token_id   ,     pad_token_segment_id=self.tokenizer.pad_token_type_id   ,     pad_token_label_id=self.pad_token_label_id   ,     )
                     logger.info("""Saving features into cached file %s"""   ,     UpperCamelCase__   )
                     torch.save(UpperCamelCase__   ,     UpperCamelCase__   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		bool = False   )			->						DataLoader:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	self._feature_file(UpperCamelCase__   )
         logger.info("""Loading features from cached file %s"""   ,     UpperCamelCase__   )
         lowerCAmelCase__      :			Tuple   =	torch.load(UpperCamelCase__   )
         lowerCAmelCase__      :			Optional[Any]   =	torch.tensor([f.input_ids for f in features]   ,     dtype=torch.long   )
         lowerCAmelCase__      :			int   =	torch.tensor([f.attention_mask for f in features]   ,     dtype=torch.long   )
         if features[0].token_type_ids is not None:
               lowerCAmelCase__      :			int   =	torch.tensor([f.token_type_ids for f in features]   ,     dtype=torch.long   )
         else:
               lowerCAmelCase__      :			str   =	torch.tensor([0 for f in features]   ,     dtype=torch.long   )
               # HACK(we will not use this anymore soon)
         lowerCAmelCase__      :			List[str]   =	torch.tensor([f.label_ids for f in features]   ,     dtype=torch.long   )
         return DataLoader(
             TensorDataset(UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__   )   ,     batch_size=UpperCamelCase__   )






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

         """simple docstring"""
         """Compute validation""" ""
         lowerCAmelCase__      :			str   =	{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
         if self.config.model_type != "distilbert":
               lowerCAmelCase__      :			Dict   =	(
                   batch[2] if self.config.model_type in ["bert", "xlnet"] else None
               )  # XLM and RoBERTa don"t use token_type_ids
         lowerCAmelCase__      :			List[str]   =	self(**UpperCamelCase__   )
         lowerCAmelCase__      :			Any   =	outputs[:2]
         lowerCAmelCase__      :			Union[str, Any]   =	logits.detach().cpu().numpy()
         lowerCAmelCase__      :			Optional[Any]   =	inputs["labels"].detach().cpu().numpy()
         return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}






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

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	torch.stack([x["""val_loss"""] for x in outputs]   ).mean()
         lowerCAmelCase__      :			Dict   =	np.concatenate([x["""pred"""] for x in outputs]   ,     axis=0   )
         lowerCAmelCase__      :			List[Any]   =	np.argmax(UpperCamelCase__   ,     axis=2   )
         lowerCAmelCase__      :			List[str]   =	np.concatenate([x["""target"""] for x in outputs]   ,     axis=0   )

         lowerCAmelCase__      :			Tuple   =	dict(enumerate(self.labels   )   )
         lowerCAmelCase__      :			Union[str, Any]   =	[[] for _ in range(out_label_ids.shape[0]   )]
         lowerCAmelCase__      :			int   =	[[] for _ in range(out_label_ids.shape[0]   )]

         for i in range(out_label_ids.shape[0]   ):
               for j in range(out_label_ids.shape[1]   ):
                     if out_label_ids[i, j] != self.pad_token_label_id:
                           out_label_list[i].append(label_map[out_label_ids[i][j]]   )
                           preds_list[i].append(label_map[preds[i][j]]   )

         lowerCAmelCase__      :			Tuple   =	{
             "val_loss": val_loss_mean,
             "accuracy_score": accuracy_score(UpperCamelCase__   ,     UpperCamelCase__   ),
             "precision": precision_score(UpperCamelCase__   ,     UpperCamelCase__   ),
             "recall": recall_score(UpperCamelCase__   ,     UpperCamelCase__   ),
             "f1": fa_score(UpperCamelCase__   ,     UpperCamelCase__   ),
         }

         lowerCAmelCase__      :			str   =	dict(results.items()   )
         lowerCAmelCase__      :			List[str]   =	results
         return ret, preds_list, out_label_list






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

         """simple docstring"""
         lowerCAmelCase__      :			str   =	self._eval_end(UpperCamelCase__   )
         lowerCAmelCase__      :			Tuple   =	ret["log"]
         return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






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

         """simple docstring"""
         lowerCAmelCase__      :			List[str]   =	self._eval_end(UpperCamelCase__   )

         # Converting to the dict required by pl
         # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
         # pytorch_lightning/trainer/logging.py#L139
         lowerCAmelCase__      :			List[Any]   =	ret["log"]
         # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
         return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}






   @staticmethod
   def 	_lowerCAmelCase   (		UpperCamelCase       :		List[Any]   ,     UpperCamelCase       :		Optional[int]   )			->						Tuple:

         """simple docstring"""
         BaseTransformer.add_model_specific_args(UpperCamelCase__   ,     UpperCamelCase__   )
         parser.add_argument(
             """--task_type"""   ,     default="""NER"""   ,     type=UpperCamelCase__   ,     help="""Task type to fine tune in training (e.g. NER, POS, etc)"""   )
         parser.add_argument(
             """--max_seq_length"""   ,     default=1_28   ,     type=UpperCamelCase__   ,     help=(
                 """The maximum total input sequence length after tokenization. Sequences longer """
                 """than this will be truncated, sequences shorter will be padded."""
             )   ,     )

         parser.add_argument(
             """--labels"""   ,     default=""""""   ,     type=UpperCamelCase__   ,     help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""   ,     )
         parser.add_argument(
             """--gpus"""   ,     default=0   ,     type=UpperCamelCase__   ,     help="""The number of GPUs allocated for this, it is by default 0 meaning none"""   ,     )

         parser.add_argument(
             """--overwrite_cache"""   ,     action="""store_true"""   ,     help="""Overwrite the cached training and evaluation sets"""   )

         return parser


if __name__ == "__main__":
  _A :    Any													=      argparse.ArgumentParser()
  add_generic_args(parser, os.getcwd())
  _A :    Optional[int]													=      NERTransformer.add_model_specific_args(parser, os.getcwd())
  _A :    Dict													=      parser.parse_args()
  _A :    int													=      NERTransformer(args)
  _A :    int													=      generic_train(model, args)

  if args.do_predict:
    # See https://github.com/huggingface/transformers/issues/3159
    # pl use this default format to create a checkpoint:
    # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
    # /pytorch_lightning/callbacks/model_checkpoint.py#L322
    _A :    Any													=      sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
    _A :    List[str]													=      model.load_from_checkpoint(checkpoints[-1])
    trainer.test(model)











"""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)





