






'''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


SCREAMING_SNAKE_CASE							=						'base_with_context'


def 				lowercase_					(     __A			:							Optional[Any]       ,   __A			:							str      )							->   List[str]:



 """simple docstring"""




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

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

  lowercase       : Dict						=nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']      )      )

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

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


def 				lowercase_					(     __A			:							Union[str, Any]       ,   __A			:							Dict      )							->   Union[str, Any]:



 """simple docstring"""




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

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

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

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

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

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

 return model


def 				lowercase_					(     __A			:							List[Any]       ,   __A			:							Any      )							->   Optional[Any]:



 """simple docstring"""




 lowercase       : Union[str, Any]						=nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T      )      )
 lowercase       : Dict						=nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T      )      )

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

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

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

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

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

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

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

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

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

 return model


def 				lowercase_					(     __A			:							Tuple      )							->   Tuple:



 """simple docstring"""




 lowercase       : str						=checkpoints.load_tax_checkpoint(args.checkpoint_path      )
 lowercase       : Optional[Any]						=jnp.tree_util.tree_map(onp.array       ,   __A      )

 lowercase       : 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()''',
 ]

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

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

 lowercase       : 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'''       ,   )

 lowercase       : Optional[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'''       ,   )

 lowercase       : Optional[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       ,   )

 lowercase       : str						=load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']       ,   __A      )
 lowercase       : Dict						=load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']       ,   __A      )
 lowercase       : Any						=load_decoder(ta_checkpoint['''target''']['''decoder''']       ,   __A      )

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

 lowercase       : int						=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__":
      SCREAMING_SNAKE_CASE							=						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.',
      )
      SCREAMING_SNAKE_CASE							=						parser.parse_args()

      main(args)







'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       int							,    __UpperCamelCase					:       int		)					->	int:







     """simple docstring"""
     while second != 0:
          _A				=							first & second
          first ^= second
          _A				=							c << 1
     return first


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase						=					int(input("""Enter the first number: """).strip())
    lowerCAmelCase						=					int(input("""Enter the second number: """).strip())
    print(F'{add(first, second) = }')





