





'''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


__lowerCAmelCase							:	Dict			="base_with_context"





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Union[str, Any]       ):
    A__				=			nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]       )       )
    A__				=			nn.Parameter(
        torch.FloatTensor(weights["Embed_0"]["embedding"]       )						,       requires_grad=_lowerCamelCase       )
    for lyr_num, lyr in enumerate(model.encoders       ):
        A__				=			weights[F"layers_{lyr_num}"]
        A__				=			nn.Parameter(
            torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]       )       )

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

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

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

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





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Optional[int]       ):
    A__				=			nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T       )       )

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

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

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

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

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

    return model





def 						UpperCamelCase				(							_lowerCamelCase					:					List[str]						,       _lowerCamelCase					:					List[Any]       ):
    A__				=			nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T       )       )
    A__				=			nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T       )       )

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

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

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

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

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

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

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

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

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

    return model





def 						UpperCamelCase				(							_lowerCamelCase					:					Dict       ):
    A__				=			checkpoints.load_tax_checkpoint(args.checkpoint_path       )
    A__				=			jnp.tree_util.tree_map(onp.array						,       _lowerCamelCase       )

    A__				=			[
        "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()",
    ]

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

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

    A__				=			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"						,       )

    A__				=			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"						,       )

    A__				=			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						,       )

    A__				=			load_notes_encoder(ta_checkpoint["target"]["token_encoder"]						,       _lowerCamelCase       )
    A__				=			load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]						,       _lowerCamelCase       )
    A__				=			load_decoder(ta_checkpoint["target"]["decoder"]						,       _lowerCamelCase       )

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

    A__				=			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__":
  __lowerCAmelCase							:	Any			=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.",
  )
  __lowerCAmelCase							:	Union[str, Any]			=parser.parse_args()

  main(args)










'''simple docstring'''

from ..utils import DummyObject, requires_backends






class    UpperCAmelCase	(       metaclass=UpperCamelCase__				):
 __lowercase   		=				["""note_seq"""]
 def __init__(				self      :Optional[Any]						,       *lowercase_      :List[Any]						,       **lowercase_      :List[str]							)->							int:
     requires_backends(self						,       ["note_seq"]							)
 @classmethod
 def      UpperCAmelCase_							(				cls      :str						,       *lowercase_      :Union[str, Any]						,       **lowercase_      :Any							)->							Optional[int]:
     requires_backends(cls						,       ["note_seq"]							)



 @classmethod
 def      UpperCAmelCase_							(				cls      :Dict						,       *lowercase_      :Tuple						,       **lowercase_      :List[Any]							)->							Optional[Any]:
     requires_backends(cls						,       ["note_seq"]							)



