



'''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      lowercase_		(      lowerCAmelCase__		:     Optional[Any]	,							lowerCAmelCase__		:     Dict       ):

			"""simple docstring"""





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

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

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

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

			__UpperCAmelCase      :					Optional[Any]   =   nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]       )       )
			return model


def      lowercase_		(      lowerCAmelCase__		:     Any	,							lowerCAmelCase__		:     int       ):

			"""simple docstring"""





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

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

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

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

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

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

			return model


def      lowercase_		(      lowerCAmelCase__		:     str	,							lowerCAmelCase__		:     List[str]       ):

			"""simple docstring"""





			__UpperCAmelCase      :					List[str]   =   nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T       )       )
			__UpperCAmelCase      :					int   =   nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T       )       )

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

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

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

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

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

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

						__UpperCAmelCase      :					int   =   nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]       )       )
						__UpperCAmelCase      :					Optional[Any]   =   nn.Parameter(
						    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T       )       )
						__UpperCAmelCase      :					Dict   =   nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T       )       )
						__UpperCAmelCase      :					str   =   nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T       )       )
						__UpperCAmelCase      :					Tuple   =   nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T       )       )

			__UpperCAmelCase      :					Tuple   =   nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]       )       )

			__UpperCAmelCase      :					int   =   nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T       )       )

			return model


def      lowercase_		(      lowerCAmelCase__		:     Union[str, Any]       ):

			"""simple docstring"""





			__UpperCAmelCase      :					Tuple   =   checkpoints.load_tax_checkpoint(args.checkpoint_path       )
			__UpperCAmelCase      :					int   =   jnp.tree_util.tree_map(onp.array	,							_lowerCAmelCase       )

			__UpperCAmelCase      :					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()',
			]

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

			__UpperCAmelCase      :					str   =   DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""	,							variance_type="""fixed_large"""       )

			__UpperCAmelCase      :					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"""	,							)

			__UpperCAmelCase      :					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"""	,							)

			__UpperCAmelCase      :					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	,							)

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

			__UpperCAmelCase      :					str   =   OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""       )

			__UpperCAmelCase      :					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'''





def        a_     (							_lowerCAmelCase       )      ->      str:
  if not all(char in '01' for char in bin_string       ):
    raise ValueError('Non-binary value was passed to the function'       )
  if not bin_string:
    raise ValueError('Empty string was passed to the function'       )
  __lowerCamelCase       :       int           = ''
  while len(_lowerCAmelCase       ) % 3 != 0:
    __lowerCamelCase       :       str           = '0' + bin_string
  __lowerCamelCase       :       Union[str, Any]           = [
      bin_string[index : index + 3]
      for index in range(len(_lowerCAmelCase       )       )
      if index % 3 == 0
  ]
  for bin_group in bin_string_in_3_list:
    __lowerCamelCase       :       Tuple           = 0
    for index, val in enumerate(_lowerCAmelCase       ):
      oct_val += int(2 ** (2 - index) * int(_lowerCAmelCase       )       )
    oct_string += str(_lowerCAmelCase       )
  return oct_string


if __name__ == "__main__":
   from doctest import testmod

   testmod()






