






"""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      _lowerCAmelCase		(							UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					Any      )		->Optional[Any]:
						A__						:  Dict		  =  nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]      )      )
						A__						:  Union[str, Any]		  =  nn.Parameter(
						    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]      ),						requires_grad=_lowercase      )
						for lyr_num, lyr in enumerate(model.encoders      ):
												A__						:  Optional[int]		  =  weights[f'layers_{lyr_num}']
												A__						:  List[Any]		  =  nn.Parameter(
												    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]      )      )

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

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

												A__						:  List[str]		  =  nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T      )      )
												A__						:  str		  =  nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T      )      )
												A__						:  int		  =  nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T      )      )

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					str      )		->List[Any]:
						A__						:  List[str]		  =  nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T      )      )

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

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

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

												A__						:  Optional[Any]		  =  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[Any]		  =  nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T      )      )
												A__						:  Union[str, Any]		  =  nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]      )      )

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

						return model




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Optional[Any]      )		->Optional[Any]:
						A__						:  List[Any]		  =  nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T      )      )
						A__						:  Optional[int]		  =  nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T      )      )

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

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

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

												A__						:  List[str]		  =  nn.Parameter(
												    torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T      )      )

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

												A__						:  List[str]		  =  ly_weight['''MultiHeadDotProductAttention_0''']
												A__						:  Dict		  =  nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T      )      )
												A__						:  Dict		  =  nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T      )      )
												A__						:  int		  =  nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T      )      )
												A__						:  Any		  =  nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T      )      )
												A__						:  Tuple		  =  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__						:  Tuple		  =  nn.Parameter(
												    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T      )      )
												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__						:  Any		  =  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      _lowerCAmelCase		(							UpperCAmelCase__       :					Union[str, Any]      )		->Tuple:
						A__						:  Any		  =  checkpoints.load_tax_checkpoint(args.checkpoint_path      )
						A__						:  Tuple		  =  jnp.tree_util.tree_map(onp.array,						_lowercase      )

						A__						:  Optional[int]		  =  [
						    '''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__						:  int		  =  os.path.join(args.checkpoint_path,						"""..""",						"""config.gin"""      )
						A__						:  Union[str, Any]		  =  inference.parse_training_gin_file(_lowercase,						_lowercase      )
						A__						:  Optional[int]		  =  inference.InferenceModel(args.checkpoint_path,						_lowercase      )

						A__						:  Optional[Any]		  =  DDPMScheduler(beta_schedule="""squaredcos_cap_v2""",						variance_type="""fixed_large"""      )

						A__						:  Optional[int]		  =  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[int]		  =  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__						:  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,						)

						A__						:  Any		  =  load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""],						_lowercase      )
						A__						:  Union[str, Any]		  =  load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""],						_lowercase      )
						A__						:  Optional[Any]		  =  load_decoder(ta_checkpoint["""target"""]["""decoder"""],						_lowercase      )

						A__						:  Union[str, Any]		  =  OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""      )

						A__						:  List[Any]		  =  SpectrogramDiffusionPipeline(
						    notes_encoder=_lowercase,						continuous_encoder=_lowercase,						decoder=_lowercase,						scheduler=_lowercase,						melgan=_lowercase,						)
						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"""



from timeit import timeit




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->int:
						if number < 0:
												raise ValueError("""the value of input must not be negative"""      )
						A__						:  Optional[int]		  =  0
						while number:
												number &= number - 1
												result += 1
						return result




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->int:
						if number < 0:
												raise ValueError("""the value of input must not be negative"""      )
						A__						:  Tuple		  =  0
						while number:
												if number % 2 == 1:
																		result += 1
												number >>= 1
						return result




def      _lowerCAmelCase		(							)		->None:

						def do_benchmark(UpperCAmelCase__       :					int      ) -> None:
												A__						:  Optional[int]		  =  """import __main__ as z"""
												print(f'Benchmark when {number = }:'      )
												print(f'{get_set_bits_count_using_modulo_operator(UpperCAmelCase__      ) = }'      )
												A__						:  Any		  =  timeit("""z.get_set_bits_count_using_modulo_operator(25)""",						setup=UpperCAmelCase__      )
												print(f'timeit() runs in {timing} seconds'      )
												print(f'{get_set_bits_count_using_brian_kernighans_algorithm(UpperCAmelCase__      ) = }'      )
												A__						:  List[str]		  =  timeit(
												    """z.get_set_bits_count_using_brian_kernighans_algorithm(25)""",						setup=UpperCAmelCase__,						)
												print(f'timeit() runs in {timing} seconds'      )

						for number in (2_5, 3_7, 5_8, 0):
												do_benchmark(UpperCAmelCase__      )
												print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()






