


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


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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Optional[int]:




      """simple docstring"""

      snake_case_ =   nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']      )      )
      snake_case_ =   nn.Parameter(
          torch.FloatTensor(weights['''Embed_0''']['''embedding''']      )      ,      requires_grad=SCREAMING_SNAKE_CASE      )
      for lyr_num, lyr in enumerate(model.encoders      ):
            snake_case_ =   weights[f'''layers_{lyr_num}''']
            snake_case_ =   nn.Parameter(
                torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']      )      )

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

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

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

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Any:




      """simple docstring"""

      snake_case_ =   nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T      )      )

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

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

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

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

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

      return model






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T      )      )
      snake_case_ =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T      )      )

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

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

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

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

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

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

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

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

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

      return model






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   checkpoints.load_tax_checkpoint(args.checkpoint_path      )
      snake_case_ =   jnp.tree_util.tree_map(onp.array      ,      SCREAMING_SNAKE_CASE      )

      snake_case_ =   [
          '''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()''',
      ]

      snake_case_ =   os.path.join(args.checkpoint_path      ,      '''..'''      ,      '''config.gin'''      )
      snake_case_ =   inference.parse_training_gin_file(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
      snake_case_ =   inference.InferenceModel(args.checkpoint_path      ,      SCREAMING_SNAKE_CASE      )

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

      snake_case_ =   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'''      ,      )

      snake_case_ =   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'''      ,      )

      snake_case_ =   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      ,      )

      snake_case_ =   load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']      ,      SCREAMING_SNAKE_CASE      )
      snake_case_ =   load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']      ,      SCREAMING_SNAKE_CASE      )
      snake_case_ =   load_decoder(ta_checkpoint['''target''']['''decoder''']      ,      SCREAMING_SNAKE_CASE      )

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

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


if __name__ == "__main__":
   UpperCAmelCase     :   List[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.""",
   )
   UpperCAmelCase     :   Tuple    =					parser.parse_args()

   main(args)



from collections import namedtuple

UpperCAmelCase    =					namedtuple("""from_to""", """from_ to""")

UpperCAmelCase    =					{
    """cubicmeter""": from_to(1, 1),
    """litre""": from_to(0.001, 1000),
    """kilolitre""": from_to(1, 1),
    """gallon""": from_to(0.00_454, 264.172),
    """cubicyard""": from_to(0.76_455, 1.30_795),
    """cubicfoot""": from_to(0.028, 35.3_147),
    """cup""": from_to(0.000_236_588, 4_226.75),
}






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

      if from_type not in METRIC_CONVERSION:
            raise ValueError(
                f'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
                + ''', '''.join(SCREAMING_SNAKE_CASE      )      )
      if to_type not in METRIC_CONVERSION:
            raise ValueError(
                f'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
                + ''', '''.join(SCREAMING_SNAKE_CASE      )      )
      return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
   import doctest

   doctest.testmod()