


'''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    :     List[str]					=						"""base_with_context"""






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Tuple   			=       nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']					)					)
       _lowerCAmelCase    :							Any   			=       nn.Parameter(
           torch.FloatTensor(weights['Embed_0']['embedding']					)   ,						requires_grad=_A					)
       for lyr_num, lyr in enumerate(model.encoders					):
              _lowerCAmelCase    :							str   			=       weights[f'layers_{lyr_num}']
              _lowerCAmelCase    :							Dict   			=       nn.Parameter(
                  torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']					)					)

              _lowerCAmelCase    :							List[Any]   			=       ly_weight['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    :							str   			=       nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
              _lowerCAmelCase    :							str   			=       nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)

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

              _lowerCAmelCase    :							Optional[Any]   			=       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
              _lowerCAmelCase    :							Union[str, Any]   			=       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
              _lowerCAmelCase    :							Tuple   			=       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

       _lowerCAmelCase    :							Any   			=       nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']					)					)
       return model






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Union[str, Any]   			=       nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T					)					)

       _lowerCAmelCase    :							Optional[Any]   			=       nn.Parameter(
           torch.FloatTensor(weights['Embed_0']['embedding']					)   ,						requires_grad=_A					)

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

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

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

       _lowerCAmelCase    :							List[Any]   			=       nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']					)					)

       return model






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       _lowerCAmelCase    :							List[Any]   			=       nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T					)					)
       _lowerCAmelCase    :							Optional[int]   			=       nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T					)					)

       _lowerCAmelCase    :							int   			=       nn.Parameter(
           torch.FloatTensor(weights['Embed_0']['embedding']					)   ,						requires_grad=_A					)

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

       for lyr_num, lyr in enumerate(model.decoders					):
              _lowerCAmelCase    :							str   			=       weights[f'layers_{lyr_num}']
              _lowerCAmelCase    :							List[str]   			=       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    :							Dict   			=       ly_weight['self_attention']
              _lowerCAmelCase    :							int   			=       nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
              _lowerCAmelCase    :							Tuple   			=       nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
              _lowerCAmelCase    :							Tuple   			=       nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
              _lowerCAmelCase    :							Any   			=       nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)

              _lowerCAmelCase    :							Union[str, Any]   			=       ly_weight['MultiHeadDotProductAttention_0']
              _lowerCAmelCase    :							List[str]   			=       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    :							Optional[Any]   			=       nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)
              _lowerCAmelCase    :							Optional[Any]   			=       nn.Parameter(
                  torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']					)					)

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

       _lowerCAmelCase    :							Any   			=       nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']					)					)

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

       return model






def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							Any   			=       checkpoints.load_tax_checkpoint(args.checkpoint_path					)
       _lowerCAmelCase    :							Union[str, Any]   			=       jnp.tree_util.tree_map(onp.array   ,						_A					)

       _lowerCAmelCase    :							Dict   			=       [
           '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[str]   			=       os.path.join(args.checkpoint_path   ,						'..'   ,						'config.gin'					)
       _lowerCAmelCase    :							Union[str, Any]   			=       inference.parse_training_gin_file(_A   ,						_A					)
       _lowerCAmelCase    :							Dict   			=       inference.InferenceModel(args.checkpoint_path   ,						_A					)

       _lowerCAmelCase    :							Optional[int]   			=       DDPMScheduler(beta_schedule='squaredcos_cap_v2'   ,						variance_type='fixed_large'					)

       _lowerCAmelCase    :							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    :							int   			=       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    :							Tuple   			=       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']   ,						_A					)
       _lowerCAmelCase    :							List[str]   			=       load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']   ,						_A					)
       _lowerCAmelCase    :							List[str]   			=       load_decoder(ta_checkpoint['target']['decoder']   ,						_A					)

       _lowerCAmelCase    :							str   			=       OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'					)

       _lowerCAmelCase    :							Any   			=       SpectrogramDiffusionPipeline(
           notes_encoder=_A   ,						continuous_encoder=_A   ,						decoder=_A   ,						scheduler=_A   ,						melgan=_A   ,						)
       if args.save:
              pipe.save_pretrained(args.output_path					)


if __name__ == "__main__":
       lowerCAmelCase    :     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.""",
       )
       lowerCAmelCase    :     int					=						parser.parse_args()

       main(args)





'''simple docstring'''







from typing import Any






def 			lowercase				(_A					):







       """simple docstring"""







       if not input_list:
              return []
       _lowerCAmelCase    :							Optional[int]   			=       [input_list.count(_A					) for value in input_list]
       _lowerCAmelCase    :							int   			=       max(_A					)  # Gets the maximum count in the input list.
       # Gets values of modes
       return sorted({input_list[i] for i, value in enumerate(_A					) if value == y}					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

