

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


lowercase_									=   "base_with_context"
def      a      (			A__	:   Optional[int]      ,   A__	:   Union[str, Any]     )    ->      Optional[Any]:





      """simple docstring"""



      _lowercase	       =nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']     )     )
      _lowercase	       =nn.Parameter(
          torch.FloatTensor(weights['Embed_0']['embedding']     )      ,   requires_grad=_lowerCamelCase     )
      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      (			A__	:   Optional[int]      ,   A__	:   Optional[int]     )    ->      Dict:





      """simple docstring"""



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

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

      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      (			A__	:   List[str]      ,   A__	:   List[Any]     )    ->      Any:





      """simple docstring"""



      _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=_lowerCamelCase     )

      _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      (			A__	:   Dict     )    ->      Union[str, Any]:





      """simple docstring"""



      _lowercase	       =checkpoints.load_tax_checkpoint(args.checkpoint_path     )
      _lowercase	       =jnp.tree_util.tree_map(onp.array      ,   _lowerCamelCase     )

      _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(_lowerCamelCase      ,   _lowerCamelCase     )
      _lowercase	       =inference.InferenceModel(args.checkpoint_path      ,   _lowerCamelCase     )

      _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']      ,   _lowerCamelCase     )
      _lowercase	       =load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']      ,   _lowerCamelCase     )
      _lowercase	       =load_decoder(ta_checkpoint['target']['decoder']      ,   _lowerCamelCase     )

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

      _lowercase	       =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__":
  lowercase_									=   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.',
  )
  lowercase_									=   parser.parse_args()

  main(args)









'''simple docstring'''

from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__lowerCAmelCase							:	Optional[Any]			=logging.get_logger(__name__)






class    UpperCAmelCase	:
 __lowercase   		=				42
 __lowercase   		=				None
 @staticmethod
 def      UpperCAmelCase_							(				)->							Dict:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :str						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							str:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :int							)->							Any:
     raise NotImplementedError
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Optional[Any]:
     if not self.is_available():
         raise RuntimeError(
             F"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."							)



 @classmethod
 def      UpperCAmelCase_							(				cls      :int							)->							Any:
     return F"`pip install {cls.pip_package or cls.name}`"






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""optuna"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							int:
     return is_optuna_available()
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :str						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :List[Any]							)->							Tuple:
     return run_hp_search_optuna(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :str						,       lowercase_      :Optional[int]							)->							Optional[Any]:
     return default_hp_space_optuna(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""ray"""
 __lowercase   		=				"""'ray[tune]'"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							str:
     return is_ray_available()
 def      UpperCAmelCase_							(				self      :int						,       lowercase_      :Dict						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :List[str]							)->							int:
     return run_hp_search_ray(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Dict							)->							int:
     return default_hp_space_ray(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""sigopt"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							Union[str, Any]:
     return is_sigopt_available()
 def      UpperCAmelCase_							(				self      :Any						,       lowercase_      :Union[str, Any]						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							Dict:
     return run_hp_search_sigopt(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Optional[int]							)->							List[str]:
     return default_hp_space_sigopt(lowercase_							)






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""wandb"""
 @staticmethod
 def      UpperCAmelCase_							(				)->							List[str]:
     return is_wandb_available()
 def      UpperCAmelCase_							(				self      :Dict						,       lowercase_      :Optional[Any]						,       lowercase_      :int						,       lowercase_      :str						,       **lowercase_      :Dict							)->							List[str]:
     return run_hp_search_wandb(lowercase_						,       lowercase_						,       lowercase_						,       **lowercase_							)



 def      UpperCAmelCase_							(				self      :Union[str, Any]						,       lowercase_      :str							)->							Dict:
     return default_hp_space_wandb(lowercase_							)

__lowerCAmelCase							:	int			={
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}





def 						UpperCamelCase				(							):
    A__				=			[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
    if len(_lowerCamelCase       ) > 0:
        A__				=			available_backends[0].name
        if len(_lowerCamelCase       ) > 1:
            logger.info(
                F"{len(_lowerCamelCase       )} hyperparameter search backends available. Using {name} as the default."       )
        return name
    raise RuntimeError(
        "No hyperparameter search backend available.\n"
        + "\n".join(
            F" - To install {backend.name} run {backend.pip_install()}"
            for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()       )       )



