





"""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__      :	Optional[int]       				=      """base_with_context"""







def     A__			(       __lowerCamelCase,		__lowerCamelCase					):





  """simple docstring"""
  _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']					)					)
  _lowerCAmelCase	      =					nn.Parameter(
      torch.FloatTensor(weights['Embed_0']['embedding']					),		requires_grad=__lowerCamelCase					)
  for lyr_num, lyr in enumerate(model.encoders					):
    _lowerCAmelCase	      =					weights[F'''layers_{lyr_num}''']
    _lowerCAmelCase	      =					nn.Parameter(
        torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']					)					)

    _lowerCAmelCase	      =					ly_weight['''attention''']
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)

    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)

    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

  _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']					)					)
  return model







def     A__			(       __lowerCamelCase,		__lowerCamelCase					):





  """simple docstring"""
  _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T					)					)

  _lowerCAmelCase	      =					nn.Parameter(
      torch.FloatTensor(weights['Embed_0']['embedding']					),		requires_grad=__lowerCamelCase					)

  for lyr_num, lyr in enumerate(model.encoders					):
    _lowerCAmelCase	      =					weights[F'''layers_{lyr_num}''']
    _lowerCAmelCase	      =					ly_weight['''attention''']

    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(
        torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']					)					)

    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)

  _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']					)					)

  return model







def     A__			(       __lowerCamelCase,		__lowerCamelCase					):





  """simple docstring"""
  _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T					)					)
  _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T					)					)

  _lowerCAmelCase	      =					nn.Parameter(
      torch.FloatTensor(weights['Embed_0']['embedding']					),		requires_grad=__lowerCamelCase					)

  _lowerCAmelCase	      =					nn.Parameter(
      torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T					)					)

  for lyr_num, lyr in enumerate(model.decoders					):
    _lowerCAmelCase	      =					weights[F'''layers_{lyr_num}''']
    _lowerCAmelCase	      =					nn.Parameter(
        torch.FloatTensor(ly_weight['pre_self_attention_layer_norm']['scale']					)					)

    _lowerCAmelCase	      =					nn.Parameter(
        torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T					)					)

    _lowerCAmelCase	      =					ly_weight['''self_attention''']
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)

    _lowerCAmelCase	      =					ly_weight['''MultiHeadDotProductAttention_0''']
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(
        torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']					)					)

    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)
    _lowerCAmelCase	      =					nn.Parameter(
        torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
    _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

  _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']					)					)

  _lowerCAmelCase	      =					nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T					)					)

  return model







def     A__			(       __lowerCamelCase					):





  """simple docstring"""
  _lowerCAmelCase	      =					checkpoints.load_tax_checkpoint(args.checkpoint_path					)
  _lowerCAmelCase	      =					jnp.tree_util.tree_map(onp.array,		__lowerCamelCase					)

  _lowerCAmelCase	      =					[
      '''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	      =					os.path.join(args.checkpoint_path,		'..',		'config.gin'					)
  _lowerCAmelCase	      =					inference.parse_training_gin_file(__lowerCamelCase,		__lowerCamelCase					)
  _lowerCAmelCase	      =					inference.InferenceModel(args.checkpoint_path,		__lowerCamelCase					)

  _lowerCAmelCase	      =					DDPMScheduler(beta_schedule='squaredcos_cap_v2',		variance_type='fixed_large'					)

  _lowerCAmelCase	      =					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	      =					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	      =					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	      =					load_notes_encoder(ta_checkpoint['target']['token_encoder'],		__lowerCamelCase					)
  _lowerCAmelCase	      =					load_continuous_encoder(ta_checkpoint['target']['continuous_encoder'],		__lowerCamelCase					)
  _lowerCAmelCase	      =					load_decoder(ta_checkpoint['target']['decoder'],		__lowerCamelCase					)

  _lowerCAmelCase	      =					OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'					)

  _lowerCAmelCase	      =					SpectrogramDiffusionPipeline(
      notes_encoder=__lowerCamelCase,		continuous_encoder=__lowerCamelCase,		decoder=__lowerCamelCase,		scheduler=__lowerCamelCase,		melgan=__lowerCamelCase,		)
  if args.save:
    pipe.save_pretrained(args.output_path					)


if __name__ == "__main__":
   a__      :	str       				=      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__      :	Dict       				=      parser.parse_args()

   main(args)











def   __lowercase       (			snake_case			):







				"""simple docstring"""






				return "".join([hex(snake_case			)[2:].zfill(2			).upper() for byte in list(snake_case			)]			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				if (len(snake_case			) % 2) != 0:
								raise ValueError(
								    '''Base16 encoded data is invalid:
Data does not have an even number of hex digits.'''			)
				# Check the character set - the standard base16 alphabet
				# is uppercase according to RFC3548 section 6
				if not set(snake_case			) <= set('''0123456789ABCDEF'''			):
								raise ValueError(
								    '''Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.'''			)
				# For every two hexadecimal digits (= a byte), turn it into an integer.
				# Then, string the result together into bytes, and return it.
				return bytes(int(data[i] + data[i + 1],	1_6			) for i in range(0,	len(snake_case			),	2			)			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()






