



'''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


_UpperCamelCase											=						'base_with_context'

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

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

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

    __lowerCamelCase       :       Tuple           = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T       )       )
    __lowerCamelCase       :       Tuple           = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T       )       )
    __lowerCamelCase       :       Optional[int]           = 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_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      List[Any]:
  __lowerCamelCase       :       List[str]           = nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T       )       )

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

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

    __lowerCamelCase       :       Union[str, Any]           = nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T       )       )
    __lowerCamelCase       :       List[Any]           = nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T       )       )
    __lowerCamelCase       :       Tuple           = 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_attention_layer_norm']['scale']       )       )

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

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

  return model

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[Any]:
  __lowerCamelCase       :       List[str]           = nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T       )       )
  __lowerCamelCase       :       int           = nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T       )       )

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

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

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

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

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

    __lowerCamelCase       :       Dict           = ly_weight['MultiHeadDotProductAttention_0']
    __lowerCamelCase       :       Union[str, Any]           = nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T       )       )
    __lowerCamelCase       :       List[Any]           = nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T       )       )
    __lowerCamelCase       :       List[str]           = nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T       )       )
    __lowerCamelCase       :       List[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       :       int           = nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']       )       )
    __lowerCamelCase       :       Optional[Any]           = nn.Parameter(
        torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T       )       )
    __lowerCamelCase       :       Dict           = 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       :       Tuple           = nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T       )       )

  __lowerCamelCase       :       Tuple           = nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']       )       )

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

  return model

def        a_     (							_lowerCAmelCase       )      ->      List[Any]:
  __lowerCamelCase       :       Tuple           = checkpoints.load_tax_checkpoint(args.checkpoint_path       )
  __lowerCamelCase       :       int           = jnp.tree_util.tree_map(onp.array       ,_lowerCAmelCase       )

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

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

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

  __lowerCamelCase       :       List[str]           = SpectrogramDiffusionPipeline(
      notes_encoder=_lowerCAmelCase       ,continuous_encoder=_lowerCAmelCase       ,decoder=_lowerCAmelCase       ,scheduler=_lowerCAmelCase       ,melgan=_lowerCAmelCase       ,)
  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)











'''simple docstring'''





from collections.abc import Sequence

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase       )       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       Any           = 0.0
  for coeff in reversed(_lowerCAmelCase       ):
    __lowerCamelCase       :       Tuple           = result * x + coeff
  return result


if __name__ == "__main__":
   _UpperCamelCase											=						(0.0, 0.0, 5.0, 9.3, 7.0)
   _UpperCamelCase											=						1_0.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))






