





'''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__				:   int      ='''base_with_context'''
def    UpperCamelCase__      (		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    UpperCamelCase__      (		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    UpperCamelCase__      (		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    UpperCamelCase__      (		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__				:   int      =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__				:   int      =parser.parse_args()

      main(args)







"""simple docstring"""






from __future__ import annotations

import numpy as np


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[float]		) ->    Optional[Any]:
      '''simple docstring'''


      return np.maximum(0					,		lowerCAmelCase__		)


if __name__ == "__main__":
       print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



