


"""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


snake_case__						:   str			 = '''base_with_context'''


def    _snake_case  (  _snake_case :	str       ,      _snake_case :	List[Any]	):
      lowerCAmelCase    :		Optional[Any]				=      nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']	)	)
      lowerCAmelCase    :		Optional[Any]				=      nn.Parameter(
          torch.FloatTensor(weights['''Embed_0''']['''embedding''']	)       ,      requires_grad=_snake_case	)
      for lyr_num, lyr in enumerate(model.encoders	):
            lowerCAmelCase    :		List[Any]				=      weights[f'''layers_{lyr_num}''']
            lowerCAmelCase    :		Any				=      nn.Parameter(
                torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']	)	)

            lowerCAmelCase    :		Dict				=      ly_weight['''attention''']
            lowerCAmelCase    :		str				=      nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
            lowerCAmelCase    :		Dict				=      nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
            lowerCAmelCase    :		str				=      nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
            lowerCAmelCase    :		Optional[int]				=      nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)

            lowerCAmelCase    :		str				=      nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']	)	)

            lowerCAmelCase    :		Any				=      nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T	)	)
            lowerCAmelCase    :		int				=      nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T	)	)
            lowerCAmelCase    :		List[str]				=      nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T	)	)

      lowerCAmelCase    :		List[str]				=      nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']	)	)
      return model


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	Optional[int]	):
      lowerCAmelCase    :		Optional[Any]				=      nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T	)	)

      lowerCAmelCase    :		str				=      nn.Parameter(
          torch.FloatTensor(weights['''Embed_0''']['''embedding''']	)       ,      requires_grad=_snake_case	)

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

            lowerCAmelCase    :		int				=      nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
            lowerCAmelCase    :		List[str]				=      nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
            lowerCAmelCase    :		List[str]				=      nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
            lowerCAmelCase    :		Any				=      nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)
            lowerCAmelCase    :		List[Any]				=      nn.Parameter(
                torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']	)	)

            lowerCAmelCase    :		str				=      nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T	)	)
            lowerCAmelCase    :		int				=      nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T	)	)
            lowerCAmelCase    :		Tuple				=      nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T	)	)
            lowerCAmelCase    :		str				=      nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']	)	)

      lowerCAmelCase    :		int				=      nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']	)	)

      return model


def    _snake_case  (  _snake_case :	List[str]       ,      _snake_case :	str	):
      lowerCAmelCase    :		Any				=      nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T	)	)
      lowerCAmelCase    :		Optional[Any]				=      nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T	)	)

      lowerCAmelCase    :		Tuple				=      nn.Parameter(
          torch.FloatTensor(weights['''Embed_0''']['''embedding''']	)       ,      requires_grad=_snake_case	)

      lowerCAmelCase    :		Any				=      nn.Parameter(
          torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T	)	)

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

            lowerCAmelCase    :		List[str]				=      nn.Parameter(
                torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T	)	)

            lowerCAmelCase    :		Any				=      ly_weight['''self_attention''']
            lowerCAmelCase    :		Union[str, Any]				=      nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
            lowerCAmelCase    :		Dict				=      nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
            lowerCAmelCase    :		Optional[Any]				=      nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
            lowerCAmelCase    :		Any				=      nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)

            lowerCAmelCase    :		str				=      ly_weight['''MultiHeadDotProductAttention_0''']
            lowerCAmelCase    :		List[Any]				=      nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
            lowerCAmelCase    :		Union[str, Any]				=      nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
            lowerCAmelCase    :		List[str]				=      nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
            lowerCAmelCase    :		int				=      nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)
            lowerCAmelCase    :		int				=      nn.Parameter(
                torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']	)	)

            lowerCAmelCase    :		List[Any]				=      nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']	)	)
            lowerCAmelCase    :		Any				=      nn.Parameter(
                torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T	)	)
            lowerCAmelCase    :		str				=      nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T	)	)
            lowerCAmelCase    :		str				=      nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T	)	)
            lowerCAmelCase    :		Union[str, Any]				=      nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T	)	)

      lowerCAmelCase    :		str				=      nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']	)	)

      lowerCAmelCase    :		Optional[Any]				=      nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T	)	)

      return model


def    _snake_case  (  _snake_case :	Tuple	):
      lowerCAmelCase    :		int				=      checkpoints.load_tax_checkpoint(args.checkpoint_path	)
      lowerCAmelCase    :		Any				=      jnp.tree_util.tree_map(onp.array       ,      _snake_case	)

      lowerCAmelCase    :		Union[str, Any]				=      [
          '''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    :		List[Any]				=      os.path.join(args.checkpoint_path       ,      '''..'''       ,      '''config.gin'''	)
      lowerCAmelCase    :		List[Any]				=      inference.parse_training_gin_file(_snake_case       ,      _snake_case	)
      lowerCAmelCase    :		List[Any]				=      inference.InferenceModel(args.checkpoint_path       ,      _snake_case	)

      lowerCAmelCase    :		Any				=      DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''       ,      variance_type='''fixed_large'''	)

      lowerCAmelCase    :		Optional[Any]				=      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    :		Tuple				=      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    :		Any				=      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    :		Union[str, Any]				=      load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']       ,      _snake_case	)
      lowerCAmelCase    :		Dict				=      load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']       ,      _snake_case	)
      lowerCAmelCase    :		Tuple				=      load_decoder(ta_checkpoint['''target''']['''decoder''']       ,      _snake_case	)

      lowerCAmelCase    :		int				=      OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''	)

      lowerCAmelCase    :		Tuple				=      SpectrogramDiffusionPipeline(
          notes_encoder=_snake_case       ,      continuous_encoder=_snake_case       ,      decoder=_snake_case       ,      scheduler=_snake_case       ,      melgan=_snake_case       ,      )
      if args.save:
            pipe.save_pretrained(args.output_path	)


if __name__ == "__main__":
 snake_case__						:   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.''',
 )
 snake_case__						:   Dict			 = parser.parse_args()

 main(args)






"""simple docstring"""





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 						snake_case_(				a__	):






     def __init__(		self     :    int    , UpperCamelCase_     :    VQModel    , UpperCamelCase_     :    UNetaDModel    , UpperCamelCase_     :    DDIMScheduler ):
           super().__init__()
           self.register_modules(vqvae=UpperCamelCase_    , unet=UpperCamelCase_    , scheduler=UpperCamelCase_ )






     @torch.no_grad()
     def __call__(		self     :    Union[str, Any]    , UpperCamelCase_     :    int = 1    , UpperCamelCase_     :    Optional[Union[torch.Generator, List[torch.Generator]]] = None    , UpperCamelCase_     :    float = 0.0    , UpperCamelCase_     :    int = 5_0    , UpperCamelCase_     :    Optional[str] = "pil"    , UpperCamelCase_     :    bool = True    , **UpperCamelCase_     :    Optional[int]    , ):
           lowerCAmelCase    :		Dict				=      randn_tensor(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    , generator=UpperCamelCase_    , )
           lowerCAmelCase    :		Optional[int]				=      latents.to(self.device )

           # scale the initial noise by the standard deviation required by the scheduler
           lowerCAmelCase    :		List[str]				=      latents * self.scheduler.init_noise_sigma

           self.scheduler.set_timesteps(UpperCamelCase_ )

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           lowerCAmelCase    :		Any				=      '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )

           lowerCAmelCase    :		List[str]				=      {}
           if accepts_eta:
                 lowerCAmelCase    :		List[Any]				=      eta

           for t in self.progress_bar(self.scheduler.timesteps ):
                 lowerCAmelCase    :		List[str]				=      self.scheduler.scale_model_input(UpperCamelCase_    , UpperCamelCase_ )
                 # predict the noise residual
                 lowerCAmelCase    :		Tuple				=      self.unet(UpperCamelCase_    , UpperCamelCase_ ).sample
                 # compute the previous noisy sample x_t -> x_t-1
                 lowerCAmelCase    :		Optional[Any]				=      self.scheduler.step(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , **UpperCamelCase_ ).prev_sample

           # decode the image latents with the VAE
           lowerCAmelCase    :		Dict				=      self.vqvae.decode(UpperCamelCase_ ).sample

           lowerCAmelCase    :		Dict				=      (image / 2 + 0.5).clamp(0    , 1 )
           lowerCAmelCase    :		Dict				=      image.cpu().permute(0    , 2    , 3    , 1 ).numpy()
           if output_type == "pil":
                 lowerCAmelCase    :		List[str]				=      self.numpy_to_pil(UpperCamelCase_ )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=UpperCamelCase_ )


