


'''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__						:   str				=    """base_with_context"""



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[int] ,       UpperCAmelCase_       :							Tuple				)	->						Union[str, Any]:
     __lowerCamelCase  :      int														=						nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']				)				)
     __lowerCamelCase  :      List[Any]														=						nn.Parameter(
         torch.FloatTensor(weights['Embed_0']['embedding']				) ,       requires_grad=UpperCAmelCase_				)
     for lyr_num, lyr in enumerate(model.encoders				):
          __lowerCamelCase  :      int														=						weights[F'layers_{lyr_num}']
          __lowerCamelCase  :      int														=						nn.Parameter(
              torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']				)				)

          __lowerCamelCase  :      Union[str, Any]														=						ly_weight['attention']
          __lowerCamelCase  :      Dict														=						nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T				)				)
          __lowerCamelCase  :      Optional[int]														=						nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T				)				)
          __lowerCamelCase  :      List[str]														=						nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T				)				)
          __lowerCamelCase  :      Tuple														=						nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T				)				)

          __lowerCamelCase  :      Tuple														=						nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']				)				)

          __lowerCamelCase  :      Optional[int]														=						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  :      int														=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T				)				)

     __lowerCamelCase  :      Optional[int]														=						nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']				)				)
     return model



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							Dict				)	->						Tuple:
     __lowerCamelCase  :      int														=						nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T				)				)

     __lowerCamelCase  :      List[str]														=						nn.Parameter(
         torch.FloatTensor(weights['Embed_0']['embedding']				) ,       requires_grad=UpperCAmelCase_				)

     for lyr_num, lyr in enumerate(model.encoders				):
          __lowerCamelCase  :      Optional[int]														=						weights[F'layers_{lyr_num}']
          __lowerCamelCase  :      Any														=						ly_weight['attention']

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

          __lowerCamelCase  :      Union[str, Any]														=						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  :      Optional[Any]														=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T				)				)
          __lowerCamelCase  :      Tuple														=						nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']				)				)

     __lowerCamelCase  :      str														=						nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']				)				)

     return model



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Union[str, Any] ,       UpperCAmelCase_       :							int				)	->						Dict:
     __lowerCamelCase  :      int														=						nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T				)				)
     __lowerCamelCase  :      Tuple														=						nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T				)				)

     __lowerCamelCase  :      List[str]														=						nn.Parameter(
         torch.FloatTensor(weights['Embed_0']['embedding']				) ,       requires_grad=UpperCAmelCase_				)

     __lowerCamelCase  :      List[str]														=						nn.Parameter(
         torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T				)				)

     for lyr_num, lyr in enumerate(model.decoders				):
          __lowerCamelCase  :      Optional[int]														=						weights[F'layers_{lyr_num}']
          __lowerCamelCase  :      Dict														=						nn.Parameter(
              torch.FloatTensor(ly_weight['pre_self_attention_layer_norm']['scale']				)				)

          __lowerCamelCase  :      Dict														=						nn.Parameter(
              torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T				)				)

          __lowerCamelCase  :      str														=						ly_weight['self_attention']
          __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  :      Union[str, Any]														=						nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T				)				)
          __lowerCamelCase  :      int														=						nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T				)				)

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

          __lowerCamelCase  :      Optional[int]														=						nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']				)				)
          __lowerCamelCase  :      Optional[int]														=						nn.Parameter(
              torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T				)				)
          __lowerCamelCase  :      List[Any]														=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T				)				)
          __lowerCamelCase  :      List[Any]														=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T				)				)
          __lowerCamelCase  :      List[Any]														=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T				)				)

     __lowerCamelCase  :      Union[str, Any]														=						nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']				)				)

     __lowerCamelCase  :      Tuple														=						nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T				)				)

     return model



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Any				)	->						Optional[Any]:
     __lowerCamelCase  :      Optional[Any]														=						checkpoints.load_tax_checkpoint(args.checkpoint_path				)
     __lowerCamelCase  :      Union[str, Any]														=						jnp.tree_util.tree_map(onp.array ,       UpperCAmelCase_				)

     __lowerCamelCase  :      str														=						[
         '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  :      Union[str, Any]														=						os.path.join(args.checkpoint_path ,       '..' ,       'config.gin'				)
     __lowerCamelCase  :      str														=						inference.parse_training_gin_file(UpperCAmelCase_ ,       UpperCAmelCase_				)
     __lowerCamelCase  :      Optional[int]														=						inference.InferenceModel(args.checkpoint_path ,       UpperCAmelCase_				)

     __lowerCamelCase  :      int														=						DDPMScheduler(beta_schedule='squaredcos_cap_v2' ,       variance_type='fixed_large'				)

     __lowerCamelCase  :      Tuple														=						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  :      List[str]														=						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  :      List[str]														=						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  :      List[Any]														=						load_notes_encoder(ta_checkpoint['target']['token_encoder'] ,       UpperCAmelCase_				)
     __lowerCamelCase  :      Dict														=						load_continuous_encoder(ta_checkpoint['target']['continuous_encoder'] ,       UpperCAmelCase_				)
     __lowerCamelCase  :      Union[str, Any]														=						load_decoder(ta_checkpoint['target']['decoder'] ,       UpperCAmelCase_				)

     __lowerCamelCase  :      Optional[Any]														=						OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'				)

     __lowerCamelCase  :      List[str]														=						SpectrogramDiffusionPipeline(
         notes_encoder=UpperCAmelCase_ ,       continuous_encoder=UpperCAmelCase_ ,       decoder=UpperCAmelCase_ ,       scheduler=UpperCAmelCase_ ,       melgan=UpperCAmelCase_ ,       )
     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__						:   str				=    parser.parse_args()

      main(args)










'''simple docstring'''



from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

A__						:   Dict				=    [
    """python""",
    """tqdm""",
    """regex""",
    """requests""",
    """packaging""",
    """filelock""",
    """numpy""",
    """tokenizers""",
    """huggingface-hub""",
    """safetensors""",
    """accelerate""",
    """pyyaml""",
]

for pkg in pkgs_to_check_at_runtime:
      if pkg in deps:
            if pkg == "tokenizers":
                  # must be loaded here, or else tqdm check may fail
                  from .utils import is_tokenizers_available

                  if not is_tokenizers_available():
                        continue  # not required, check version only if installed
            elif pkg == "accelerate":
                  # must be loaded here, or else tqdm check may fail
                  from .utils import is_accelerate_available

                  # Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
                  # Transformers with PyTorch
                  if not is_accelerate_available():
                        continue  # not required, check version only if installed

            require_version_core(deps[pkg])
      else:
            raise ValueError(f'''can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py''')



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							List[Any]=None				)	->						List[Any]:
     require_version(deps[pkg] ,       UpperCAmelCase_				)






