


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


__lowerCAmelCase	: Optional[Any]  				='base_with_context'


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']      )      )
						__SCREAMING_SNAKE_CASE						:     int					=		nn.Parameter(
						    torch.FloatTensor(weights['''Embed_0''']['''embedding''']      )    ,       requires_grad=lowercase__      )
						for lyr_num, lyr in enumerate(model.encoders      ):
												__SCREAMING_SNAKE_CASE						:     str					=		weights[F'''layers_{lyr_num}''']
												__SCREAMING_SNAKE_CASE						:     int					=		nn.Parameter(
												    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']      )      )

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

												__SCREAMING_SNAKE_CASE						:     List[str]					=		nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']      )      )

												__SCREAMING_SNAKE_CASE						:     str					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     str					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T      )      )

						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']      )      )
						return model


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T      )      )

						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		nn.Parameter(
						    torch.FloatTensor(weights['''Embed_0''']['''embedding''']      )    ,       requires_grad=lowercase__      )

						for lyr_num, lyr in enumerate(model.encoders      ):
												__SCREAMING_SNAKE_CASE						:     List[str]					=		weights[F'''layers_{lyr_num}''']
												__SCREAMING_SNAKE_CASE						:     int					=		ly_weight['''attention''']

												__SCREAMING_SNAKE_CASE						:     List[Any]					=		nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Any					=		nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     List[str]					=		nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		nn.Parameter(
												    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']      )      )

												__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Any					=		nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']      )      )

						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']      )      )

						return model


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T      )      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T      )      )

						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		nn.Parameter(
						    torch.FloatTensor(weights['''Embed_0''']['''embedding''']      )    ,       requires_grad=lowercase__      )

						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		nn.Parameter(
						    torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T      )      )

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

												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		nn.Parameter(
												    torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T      )      )

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

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

												__SCREAMING_SNAKE_CASE						:     Tuple					=		nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']      )      )
												__SCREAMING_SNAKE_CASE						:     int					=		nn.Parameter(
												    torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     Dict					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     int					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T      )      )
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T      )      )

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

						__SCREAMING_SNAKE_CASE						:     int					=		nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T      )      )

						return model


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		checkpoints.load_tax_checkpoint(args.checkpoint_path      )
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		jnp.tree_util.tree_map(onp.array    ,       lowercase__      )

						__SCREAMING_SNAKE_CASE						:     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()''',
						]

						__SCREAMING_SNAKE_CASE						:     Dict					=		os.path.join(args.checkpoint_path    ,       '''..'''    ,       '''config.gin'''      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		inference.parse_training_gin_file(lowercase__    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Tuple					=		inference.InferenceModel(args.checkpoint_path    ,       lowercase__      )

						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''    ,       variance_type='''fixed_large'''      )

						__SCREAMING_SNAKE_CASE						:     List[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'''    ,       )

						__SCREAMING_SNAKE_CASE						:     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'''    ,       )

						__SCREAMING_SNAKE_CASE						:     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    ,       )

						__SCREAMING_SNAKE_CASE						:     int					=		load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Dict					=		load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']    ,       lowercase__      )
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		load_decoder(ta_checkpoint['''target''']['''decoder''']    ,       lowercase__      )

						__SCREAMING_SNAKE_CASE						:     int					=		OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''      )

						__SCREAMING_SNAKE_CASE						:     Dict					=		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__":
						__lowerCAmelCase	: str  				=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.',
						)
						__lowerCAmelCase	: Tuple  				=parser.parse_args()

						main(args)




'''simple docstring'''

def _SCREAMING_SNAKE_CASE      (A       )       ->       int:






       """simple docstring"""







       if not isinstance(A  , A       ):
              raise TypeError('''only integers accepted as input'''       )
       else:
              lowercase__      			= str(abs(A       )       )
              lowercase__      			= [list(A       ) for char in range(len(A       )       )]
              for index in range(len(A       )       ):
                     num_transpositions[index].pop(A       )
              return max(
                  int(''''''.join(list(A       )       )       ) for transposition in num_transpositions       )


if __name__ == "__main__":
 __import__('doctest').testmod()




