





'''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


a        =						"base_with_context"

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]      )      )
   __SCREAMING_SNAKE_CASE      							=       nn.Parameter(
       torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]      )							,	requires_grad=__UpperCAmelCase      )
   for lyr_num, lyr in enumerate(model.encoders      ):
      __SCREAMING_SNAKE_CASE      							=       weights[f"""layers_{lyr_num}"""]
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(
          torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]      )      )

      __SCREAMING_SNAKE_CASE      							=       ly_weight["""attention"""]
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T      )      )

      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]      )      )

      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T      )      )

   __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]      )      )
   return model

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T      )      )

   __SCREAMING_SNAKE_CASE      							=       nn.Parameter(
       torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]      )							,	requires_grad=__UpperCAmelCase      )

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

      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(
          torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]      )      )

      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]      )      )

   __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]      )      )

   return model

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T      )      )
   __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T      )      )

   __SCREAMING_SNAKE_CASE      							=       nn.Parameter(
       torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]      )							,	requires_grad=__UpperCAmelCase      )

   __SCREAMING_SNAKE_CASE      							=       nn.Parameter(
       torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T      )      )

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

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

      __SCREAMING_SNAKE_CASE      							=       ly_weight["""self_attention"""]
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T      )      )

      __SCREAMING_SNAKE_CASE      							=       ly_weight["""MultiHeadDotProductAttention_0"""]
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(
          torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]      )      )

      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(
          torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T      )      )
      __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T      )      )

   __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]      )      )

   __SCREAMING_SNAKE_CASE      							=       nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T      )      )

   return model

def 						__magic_name__       (							__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       checkpoints.load_tax_checkpoint(args.checkpoint_path      )
   __SCREAMING_SNAKE_CASE      							=       jnp.tree_util.tree_map(onp.array							,	__UpperCAmelCase      )

   __SCREAMING_SNAKE_CASE      							=       [
       """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()""",
   ]

   __SCREAMING_SNAKE_CASE      							=       os.path.join(args.checkpoint_path							,	""".."""							,	"""config.gin"""      )
   __SCREAMING_SNAKE_CASE      							=       inference.parse_training_gin_file(__UpperCAmelCase							,	__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       inference.InferenceModel(args.checkpoint_path							,	__UpperCAmelCase      )

   __SCREAMING_SNAKE_CASE      							=       DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""							,	variance_type="""fixed_large"""      )

   __SCREAMING_SNAKE_CASE      							=       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"""							,	)

   __SCREAMING_SNAKE_CASE      							=       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"""							,	)

   __SCREAMING_SNAKE_CASE      							=       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							,	)

   __SCREAMING_SNAKE_CASE      							=       load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]							,	__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]							,	__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       load_decoder(ta_checkpoint["""target"""]["""decoder"""]							,	__UpperCAmelCase      )

   __SCREAMING_SNAKE_CASE      							=       OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""      )

   __SCREAMING_SNAKE_CASE      							=       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        =						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)












'''simple docstring'''

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 						__magic_name__       (							__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   def wrapper(*__UpperCAmelCase							,	**__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer()
      __SCREAMING_SNAKE_CASE      							=       func(*__UpperCAmelCase							,	**__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer() - starttime
      return delta

   __SCREAMING_SNAKE_CASE      							=       func.__name__

   return wrapper

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       []
   __SCREAMING_SNAKE_CASE      							=       seq_shapes or {}
   for i in range(__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       {}
      for col_id, (k, v) in enumerate(features.items()      ):
         if isinstance(__UpperCAmelCase							,	_ArrayXD      ):
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*v.shape      ).astype(v.dtype      )
         elif isinstance(__UpperCAmelCase							,	datasets.Value      ):
            if v.dtype == "string":
               __SCREAMING_SNAKE_CASE      							=       """The small grey turtle was surprisingly fast when challenged."""
            else:
               __SCREAMING_SNAKE_CASE      							=       np.random.randint(10							,	size=1      ).astype(v.dtype      ).item()
         elif isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
            while isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
               __SCREAMING_SNAKE_CASE      							=       v.feature
            __SCREAMING_SNAKE_CASE      							=       seq_shapes[k]
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*__UpperCAmelCase      ).astype(v.dtype      )
         __SCREAMING_SNAKE_CASE      							=       data

      dummy_data.append((i, example)      )

   return dummy_data







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       generate_examples(__UpperCAmelCase							,	num_examples=__UpperCAmelCase							,	seq_shapes=__UpperCAmelCase      )

   with ArrowWriter(features=__UpperCAmelCase							,	path=__UpperCAmelCase      ) as writer:
      for key, record in dummy_data:
         __SCREAMING_SNAKE_CASE      							=       features.encode_example(__UpperCAmelCase      )
         writer.write(__UpperCAmelCase      )

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       writer.finalize()

   if not num_final_examples == num_examples:
      raise ValueError(
          f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""      )

   __SCREAMING_SNAKE_CASE      							=       datasets.Dataset.from_file(filename=__UpperCAmelCase							,	info=datasets.DatasetInfo(features=__UpperCAmelCase      )      )

   return dataset





