


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


UpperCAmelCase    =					"""base_with_context"""






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Optional[int]:




      """simple docstring"""

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

            snake_case_ =   ly_weight['''attention''']
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T      )      )

            snake_case_ =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']      )      )

            snake_case_ =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T      )      )

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Any:




      """simple docstring"""

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

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

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

            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(
                torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']      )      )

            snake_case_ =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']      )      )

      snake_case_ =   nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']      )      )

      return model






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T      )      )
      snake_case_ =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T      )      )

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

      snake_case_ =   nn.Parameter(
          torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T      )      )

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

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

            snake_case_ =   ly_weight['''self_attention''']
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T      )      )

            snake_case_ =   ly_weight['''MultiHeadDotProductAttention_0''']
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(
                torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']      )      )

            snake_case_ =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']      )      )
            snake_case_ =   nn.Parameter(
                torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T      )      )
            snake_case_ =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T      )      )

      snake_case_ =   nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']      )      )

      snake_case_ =   nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T      )      )

      return model






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   checkpoints.load_tax_checkpoint(args.checkpoint_path      )
      snake_case_ =   jnp.tree_util.tree_map(onp.array      ,      SCREAMING_SNAKE_CASE      )

      snake_case_ =   [
          '''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()''',
      ]

      snake_case_ =   os.path.join(args.checkpoint_path      ,      '''..'''      ,      '''config.gin'''      )
      snake_case_ =   inference.parse_training_gin_file(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )
      snake_case_ =   inference.InferenceModel(args.checkpoint_path      ,      SCREAMING_SNAKE_CASE      )

      snake_case_ =   DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''      ,      variance_type='''fixed_large'''      )

      snake_case_ =   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'''      ,      )

      snake_case_ =   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'''      ,      )

      snake_case_ =   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      ,      )

      snake_case_ =   load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']      ,      SCREAMING_SNAKE_CASE      )
      snake_case_ =   load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']      ,      SCREAMING_SNAKE_CASE      )
      snake_case_ =   load_decoder(ta_checkpoint['''target''']['''decoder''']      ,      SCREAMING_SNAKE_CASE      )

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

      snake_case_ =   SpectrogramDiffusionPipeline(
          notes_encoder=SCREAMING_SNAKE_CASE      ,      continuous_encoder=SCREAMING_SNAKE_CASE      ,      decoder=SCREAMING_SNAKE_CASE      ,      scheduler=SCREAMING_SNAKE_CASE      ,      melgan=SCREAMING_SNAKE_CASE      ,      )
      if args.save:
            pipe.save_pretrained(args.output_path      )


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

   main(args)



from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase    =					2_9979_2458

# Symbols
UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase    =					symbols("""ct x y z""")






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

      if velocity > c:
            raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''      )
      elif velocity < 1:
            # Usually the speed should be much higher than 1 (c order of magnitude)
            raise ValueError('''Speed must be greater than or equal to 1!'''      )

      return velocity / c






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

      return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE      ) ** 2      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						np.ndarray:




      """simple docstring"""

      return np.array(
          [
              [gamma(SCREAMING_SNAKE_CASE      ), -gamma(SCREAMING_SNAKE_CASE      ) * beta(SCREAMING_SNAKE_CASE      ), 0, 0],
              [-gamma(SCREAMING_SNAKE_CASE      ) * beta(SCREAMING_SNAKE_CASE      ), gamma(SCREAMING_SNAKE_CASE      ), 0, 0],
              [0, 0, 1, 0],
              [0, 0, 0, 1],
          ]      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = None      )->						np.ndarray:




      """simple docstring"""

      if event is None:
            snake_case_ =   np.array([ct, x, y, z]      )  # Symbolic four vector
      else:
            event[0] *= c  # x0 is ct (speed of light * time)

      return transformation_matrix(SCREAMING_SNAKE_CASE      ) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   UpperCAmelCase    =					transform(2997_9245)
   print("""Example of four vector: """)
   print(f'''ct\' = {four_vector[0]}''')
   print(f'''x\' = {four_vector[1]}''')
   print(f'''y\' = {four_vector[2]}''')
   print(f'''z\' = {four_vector[3]}''')

   # Substitute symbols with numerical values
   UpperCAmelCase    =					{ct: c, x: 1, y: 1, z: 1}
   UpperCAmelCase    =					[four_vector[i].subs(sub_dict) for i in range(4)]

   print(f'''\n{numerical_vector}''')