





def 	_a						(   UpperCAmelCase = 4000000					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							[0, 1]
   lowerCamelCase__       :						Tuple        =							0
   while fib[i] <= n:
      fib.append(fib[i] + fib[i + 1]					)
      if fib[i + 2] > n:
         break
      i += 1
   lowerCamelCase__       :						Optional[Any]        =							0
   for j in range(len(UpperCAmelCase					) - 1					):
      if fib[j] % 2 == 0:
         total += fib[j]

   return total


if __name__ == "__main__":
       print(F'''{solution() = }''')










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 :   List[Any]  			=  'base_with_context'



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']					)					)
   lowerCamelCase__       :						List[str]        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)
   for lyr_num, lyr in enumerate(model.encoders					):
      lowerCamelCase__       :						Any        =							weights[f"layers_{lyr_num}"]
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']					)					)

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

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

      lowerCamelCase__       :						List[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__       :						Any        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)

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



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







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

   lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)

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

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

      lowerCamelCase__       :						Tuple        =							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__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']					)					)

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

   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T					)					)
   lowerCamelCase__       :						int        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T					)					)

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

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

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

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

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

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

      lowerCamelCase__       :						str        =							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__       :						Optional[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__       :						Any        =							nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']					)					)

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

   return model



def 	_a						(   UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							checkpoints.load_tax_checkpoint(args.checkpoint_path					)
   lowerCamelCase__       :						Optional[int]        =							jnp.tree_util.tree_map(onp.array      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[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__       :						List[Any]        =							os.path.join(args.checkpoint_path      ,  '''..'''      ,  '''config.gin'''					)
   lowerCamelCase__       :						Optional[Any]        =							inference.parse_training_gin_file(UpperCAmelCase      ,  UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							inference.InferenceModel(args.checkpoint_path      ,  UpperCAmelCase					)

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

   lowerCamelCase__       :						str        =							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__       :						Optional[int]        =							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__       :						Optional[int]        =							load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						int        =							load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						List[str]        =							load_decoder(ta_checkpoint['''target''']['''decoder''']      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[str]        =							OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''					)

   lowerCamelCase__       :						List[Any]        =							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 :   Tuple  			=  parser.parse_args()

       main(args)



