'''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


__UpperCAmelCase												="base_with_context"






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   Any:
    __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']    )    )
    __lowerCamelCase          =  nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']    )    ,  requires_grad=UpperCamelCase__    )
    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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   Tuple:
    __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T    )    )

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

    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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[Any]:
    __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=UpperCamelCase__    )

    __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 __lowerCAmelCase			(							UpperCamelCase__    )     ->   Any:
    __lowerCamelCase          =  checkpoints.load_tax_checkpoint(args.checkpoint_path    )
    __lowerCamelCase          =  jnp.tree_util.tree_map(onp.array    ,  UpperCamelCase__    )

    __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(UpperCamelCase__    ,  UpperCamelCase__    )
    __lowerCamelCase          =  inference.InferenceModel(args.checkpoint_path    ,  UpperCamelCase__    )

    __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''']    ,  UpperCamelCase__    )
    __lowerCamelCase          =  load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']    ,  UpperCamelCase__    )
    __lowerCamelCase          =  load_decoder(ta_checkpoint['''target''']['''decoder''']    ,  UpperCamelCase__    )

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

    __lowerCamelCase          =  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__":
  __UpperCAmelCase												=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												=parser.parse_args()

  main(args)

'''simple docstring'''




def __lowerCAmelCase			(							UpperCamelCase__    )     ->   str:
    return "".join(chr(ord(UpperCamelCase__    ) - 32    ) if '''a''' <= char <= '''z''' else char for char in word    )


if __name__ == "__main__":
  from doctest import testmod

  testmod()
