





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


_snake_case           =							"""base_with_context"""


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

      lowercase__			       =  ly_weight["attention"]
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T	)	)

      lowercase__			       =  nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]	)	)

      lowercase__			       =  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T	)	)

   lowercase__			       =  nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]	)	)
   return model


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T	)	)

   lowercase__			       =  nn.Parameter(
       torch.FloatTensor(weights["Embed_0"]["embedding"]	)  , requires_grad=__magic_name__	)

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

      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(
          torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]	)	)

      lowercase__			       =  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]	)	)

   lowercase__			       =  nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]	)	)

   return model


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T	)	)
   lowercase__			       =  nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T	)	)

   lowercase__			       =  nn.Parameter(
       torch.FloatTensor(weights["Embed_0"]["embedding"]	)  , requires_grad=__magic_name__	)

   lowercase__			       =  nn.Parameter(
       torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T	)	)

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

      lowercase__			       =  nn.Parameter(
          torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T	)	)

      lowercase__			       =  ly_weight["self_attention"]
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T	)	)

      lowercase__			       =  ly_weight["MultiHeadDotProductAttention_0"]
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(
          torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]	)	)

      lowercase__			       =  nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]	)	)
      lowercase__			       =  nn.Parameter(
          torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T	)	)
      lowercase__			       =  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T	)	)

   lowercase__			       =  nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]	)	)

   lowercase__			       =  nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T	)	)

   return model


def _A       (   __magic_name__	):
   lowercase__			       =  checkpoints.load_tax_checkpoint(args.checkpoint_path	)
   lowercase__			       =  jnp.tree_util.tree_map(onp.array  , __magic_name__	)

   lowercase__			       =  [
       "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()",
   ]

   lowercase__			       =  os.path.join(args.checkpoint_path  , ".."  , "config.gin"	)
   lowercase__			       =  inference.parse_training_gin_file(__magic_name__  , __magic_name__	)
   lowercase__			       =  inference.InferenceModel(args.checkpoint_path  , __magic_name__	)

   lowercase__			       =  DDPMScheduler(beta_schedule="squaredcos_cap_v2"  , variance_type="fixed_large"	)

   lowercase__			       =  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"  , )

   lowercase__			       =  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"  , )

   lowercase__			       =  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  , )

   lowercase__			       =  load_notes_encoder(ta_checkpoint["target"]["token_encoder"]  , __magic_name__	)
   lowercase__			       =  load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]  , __magic_name__	)
   lowercase__			       =  load_decoder(ta_checkpoint["target"]["decoder"]  , __magic_name__	)

   lowercase__			       =  OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"	)

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


if __name__ == "__main__":
  _snake_case           =							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.""",
  )
  _snake_case           =							parser.parse_args()

  main(args)













def _A       (   __magic_name__  , __magic_name__  , __magic_name__  , __magic_name__	):
   if height >= 1:
      move_tower(height - 1  , __magic_name__  , __magic_name__  , __magic_name__	)
      move_disk(__magic_name__  , __magic_name__	)
      move_tower(height - 1  , __magic_name__  , __magic_name__  , __magic_name__	)


def _A       (   __magic_name__  , __magic_name__	):
   print("moving disk from"  , __magic_name__  , "to"  , __magic_name__	)


def _A       (   ):
   lowercase__			       =  int(input("Height of hanoi: "	).strip()	)
   move_tower(__magic_name__  , "A"  , "B"  , "C"	)


if __name__ == "__main__":
  main()






