





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									=				"base_with_context"





def      lowerCAmelCase_ (       __a	,    __a		)     ->		List[str]:







  """simple docstring"""





  lowerCamelCase__:      Optional[Any]			  =nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]		)		)
  lowerCamelCase__:      Any			  =nn.Parameter(
      torch.FloatTensor(weights["Embed_0"]["embedding"]		)	,    requires_grad=__a		)
  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__:      Union[str, Any]			  =ly_weight['''attention''']
    lowerCamelCase__:      Any			  =nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T		)		)
    lowerCamelCase__:      int			  =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__:      List[str]			  =nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]		)		)

    lowerCamelCase__:      List[str]			  =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__:      str			  =nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]		)		)
  return model





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Union[str, Any]:







  """simple docstring"""





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

  lowerCamelCase__:      Optional[Any]			  =nn.Parameter(
      torch.FloatTensor(weights["Embed_0"]["embedding"]		)	,    requires_grad=__a		)

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

    lowerCamelCase__:      Tuple			  =nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T		)		)
    lowerCamelCase__:      Dict			  =nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T		)		)
    lowerCamelCase__:      List[Any]			  =nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T		)		)
    lowerCamelCase__:      Tuple			  =nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T		)		)
    lowerCamelCase__:      Dict			  =nn.Parameter(
        torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]		)		)

    lowerCamelCase__:      Any			  =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__:      Tuple			  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T		)		)
    lowerCamelCase__:      Dict			  =nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]		)		)

  lowerCamelCase__:      Optional[int]			  =nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]		)		)

  return model





def      lowerCAmelCase_ (       __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      Optional[Any]			  =nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T		)		)
  lowerCamelCase__:      Optional[int]			  =nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T		)		)

  lowerCamelCase__:      Any			  =nn.Parameter(
      torch.FloatTensor(weights["Embed_0"]["embedding"]		)	,    requires_grad=__a		)

  lowerCamelCase__:      Optional[int]			  =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__:      Any			  =nn.Parameter(
        torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"]		)		)

    lowerCamelCase__:      Optional[int]			  =nn.Parameter(
        torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T		)		)

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

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

    lowerCamelCase__:      Optional[Any]			  =nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]		)		)
    lowerCamelCase__:      List[str]			  =nn.Parameter(
        torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T		)		)
    lowerCamelCase__:      Optional[int]			  =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__:      Union[str, Any]			  =nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T		)		)

  lowerCamelCase__:      List[Any]			  =nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]		)		)

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

  return model





def      lowerCAmelCase_ (       __a		)     ->		List[str]:







  """simple docstring"""





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

  lowerCamelCase__:      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()''',
  ]

  lowerCamelCase__:      Optional[int]			  =os.path.join(args.checkpoint_path	,    ".."	,    "config.gin"		)
  lowerCamelCase__:      Any			  =inference.parse_training_gin_file(__a	,    __a		)
  lowerCamelCase__:      Tuple			  =inference.InferenceModel(args.checkpoint_path	,    __a		)

  lowerCamelCase__:      List[str]			  =DDPMScheduler(beta_schedule="squaredcos_cap_v2"	,    variance_type="fixed_large"		)

  lowerCamelCase__:      List[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__:      Tuple			  =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__:      Dict			  =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"]	,    __a		)
  lowerCamelCase__:      List[Any]			  =load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]	,    __a		)
  lowerCamelCase__:      Optional[Any]			  =load_decoder(ta_checkpoint["target"]["decoder"]	,    __a		)

  lowerCamelCase__:      Optional[Any]			  =OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"		)

  lowerCamelCase__:      List[str]			  =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__":
     __A									=				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									=				parser.parse_args()

     main(args)


import random



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		a[left_index]
    __UpperCamelCase     :Any			  =		left_index + 1
    for j in range(left_index + 1  ,			SCREAMING_SNAKE_CASE	):
        if a[j] < pivot:
            __UpperCamelCase  ,       __UpperCamelCase     :str			  =		a[i], a[j]
            i += 1
    __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		a[i - 1], a[left_index]
    return i - 1



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    if left < right:
        __UpperCamelCase     :int			  =		random.randint(SCREAMING_SNAKE_CASE  ,			right - 1	)
        __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		(
            a[left],
            a[pivot],
        )  # switches the pivot with the left most bound
        __UpperCamelCase     :Dict			  =		partition(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
        quick_sort_random(
            SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)  # recursive quicksort to the left of the pivot point
        quick_sort_random(
            SCREAMING_SNAKE_CASE  ,			pivot_index + 1  ,			SCREAMING_SNAKE_CASE	)  # recursive quicksort to the right of the pivot point



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		input('''Enter numbers separated by a comma:\n'''	).strip()
    __UpperCamelCase     :Union[str, Any]			  =		[int(SCREAMING_SNAKE_CASE	) for item in user_input.split(''','''	)]

    quick_sort_random(SCREAMING_SNAKE_CASE  ,			0  ,			len(SCREAMING_SNAKE_CASE	)	)

    print(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    main()





