





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     :     Dict							=	"""base_with_context"""
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[Any]	,	SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



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

    a__     : Optional[Any]  =ly_weight["attention"]
    a__     : Any  =nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
    a__     : Optional[Any]  =nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
    a__     : List[Any]  =nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
    a__     : Tuple  =nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)

    a__     : Any  =nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)

    a__     : Dict  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
    a__     : Optional[Any]  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
    a__     : List[Any]  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)

  a__     : int  =nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]						)						)
  return model
def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       Any						):





  """simple docstring"""



  a__     : List[str]  =nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T						)						)

  a__     : List[str]  =nn.Parameter(
      torch.FloatTensor(weights["Embed_0"]["embedding"]						)	,	requires_grad=SCREAMING_SNAKE_CASE						)

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

    a__     : Any  =nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
    a__     : Any  =nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
    a__     : Union[str, Any]  =nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
    a__     : Dict  =nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)
    a__     : int  =nn.Parameter(
        torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]						)						)

    a__     : Tuple  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
    a__     : Optional[int]  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
    a__     : Tuple  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)
    a__     : Tuple  =nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)

  a__     : List[str]  =nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]						)						)

  return model
def 					_A	(		SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Tuple						):





  """simple docstring"""



  a__     : Tuple  =nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T						)						)
  a__     : int  =nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T						)						)

  a__     : Dict  =nn.Parameter(
      torch.FloatTensor(weights["Embed_0"]["embedding"]						)	,	requires_grad=SCREAMING_SNAKE_CASE						)

  a__     : str  =nn.Parameter(
      torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T						)						)

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

    a__     : Any  =nn.Parameter(
        torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T						)						)

    a__     : Dict  =ly_weight["self_attention"]
    a__     : List[str]  =nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
    a__     : str  =nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
    a__     : List[str]  =nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
    a__     : Tuple  =nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)

    a__     : Tuple  =ly_weight["MultiHeadDotProductAttention_0"]
    a__     : Tuple  =nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
    a__     : Optional[int]  =nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
    a__     : Any  =nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
    a__     : str  =nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)
    a__     : List[str]  =nn.Parameter(
        torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]						)						)

    a__     : List[Any]  =nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)
    a__     : Any  =nn.Parameter(
        torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T						)						)
    a__     : str  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
    a__     : Any  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
    a__     : List[str]  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)

  a__     : Union[str, Any]  =nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]						)						)

  a__     : List[Any]  =nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T						)						)

  return model
def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict						):





  """simple docstring"""



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

  a__     : List[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()",
  ]

  a__     : Union[str, Any]  =os.path.join(args.checkpoint_path	,	".."	,	"config.gin"						)
  a__     : Tuple  =inference.parse_training_gin_file(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  a__     : Tuple  =inference.InferenceModel(args.checkpoint_path	,	SCREAMING_SNAKE_CASE						)

  a__     : Union[str, Any]  =DDPMScheduler(beta_schedule="squaredcos_cap_v2"	,	variance_type="fixed_large"						)

  a__     : 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"	,	)

  a__     : Optional[Any]  =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"	,	)

  a__     : 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	,	)

  a__     : Dict  =load_notes_encoder(ta_checkpoint["target"]["token_encoder"]	,	SCREAMING_SNAKE_CASE						)
  a__     : Tuple  =load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]	,	SCREAMING_SNAKE_CASE						)
  a__     : List[str]  =load_decoder(ta_checkpoint["target"]["decoder"]	,	SCREAMING_SNAKE_CASE						)

  a__     : Optional[int]  =OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"						)

  a__     : Optional[Any]  =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     :     List[Any]							=	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     :     Optional[Any]							=	parser.parse_args()

    main(args)













UpperCAmelCase     :     Dict							=	[0, 2, 4, 6, 8]
UpperCAmelCase     :     Tuple							=	[1, 3, 5, 7, 9]
def 					_A	(		SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       list[int]	,	SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  if remaining_length == 0:
    if digits[0] == 0 or digits[-1] == 0:
      return 0

    for i in range(length // 2 - 1	,	-1	,	-1						):
      remainder += digits[i] + digits[length - i - 1]

      if remainder % 2 == 0:
        return 0

      remainder //= 10

    return 1

  if remaining_length == 1:
    if remainder % 2 == 0:
      return 0

    a__     : str  =0
    for digit in range(10						):
      a__     : int  =digit
      result += reversible_numbers(
          0	,	(remainder + 2 * digit) // 10	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
    return result

  a__     : List[str]  =0
  for digita in range(10						):
    a__     : Optional[int]  =digita

    if (remainder + digita) % 2 == 0:
      a__     : Dict  =ODD_DIGITS
    else:
      a__     : Any  =EVEN_DIGITS

    for digita in other_parity_digits:
      a__     : Union[str, Any]  =digita
      result += reversible_numbers(
          remaining_length - 2	,	(remainder + digita + digita) // 10	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	)
  return result
def 					_A	(		SCREAMING_SNAKE_CASE					:       int = 9						):





  """simple docstring"""



  a__     : List[str]  =0
  for length in range(1	,	max_power + 1						):
    result += reversible_numbers(SCREAMING_SNAKE_CASE	,	0	,	[0] * length	,	SCREAMING_SNAKE_CASE						)
  return result


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






