


from __future__ import annotations




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       # Checks if the entire collection has been sorted
       if len(snake_case					) <= 1 or n <= 1:
              return

       insert_next(snake_case      ,							n - 1					)
       rec_insertion_sort(snake_case      ,							n - 1					)




def        snake_case_					(  snake_case      ,							snake_case					)  ->    Tuple:
       # Checks order between adjacent elements
       if index >= len(snake_case					) or collection[index - 1] <= collection[index]:
              return

       # Swaps adjacent elements since they are not in ascending order
       lowercase__      ,						lowercase__:       Any        =       (
           collection[index],
           collection[index - 1],
       )

       insert_next(snake_case      ,							index + 1					)


if __name__ == "__main__":
 __lowerCAmelCase			=							input('''Enter integers separated by spaces: ''')
 __lowerCAmelCase			=							[int(num) for num in numbers.split()]
 rec_insertion_sort(number_list, len(number_list))
 print(number_list)






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			=							'''base_with_context'''




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       lowercase__:       Tuple        =       nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']					)					)
       lowercase__:       Optional[int]        =       nn.Parameter(
           torch.FloatTensor(weights['Embed_0']['embedding']					)      ,							requires_grad=snake_case					)
       for lyr_num, lyr in enumerate(model.encoders					):
              lowercase__:       List[str]        =       weights[f'layers_{lyr_num}']
              lowercase__:       List[Any]        =       nn.Parameter(
                  torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']					)					)

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

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

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

       lowercase__:       Any        =       nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']					)					)
       return model




def        snake_case_					(  snake_case      ,							snake_case					)  ->    List[str]:
       lowercase__:       str        =       nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T					)					)

       lowercase__:       Dict        =       nn.Parameter(
           torch.FloatTensor(weights['Embed_0']['embedding']					)      ,							requires_grad=snake_case					)

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

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

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

       lowercase__:       List[str]        =       nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']					)					)

       return model




def        snake_case_					(  snake_case      ,							snake_case					)  ->    Any:
       lowercase__:       int        =       nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T					)					)
       lowercase__:       Any        =       nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T					)					)

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

       lowercase__:       Dict        =       nn.Parameter(
           torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T					)					)

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

              lowercase__:       int        =       nn.Parameter(
                  torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T					)					)

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

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

              lowercase__:       List[str]        =       nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)
              lowercase__:       Union[str, Any]        =       nn.Parameter(
                  torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T					)					)
              lowercase__:       List[str]        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
              lowercase__:       int        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
              lowercase__:       str        =       nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

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

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

       return model




def        snake_case_					(  snake_case					)  ->    Any:
       lowercase__:       int        =       checkpoints.load_tax_checkpoint(args.checkpoint_path					)
       lowercase__:       Tuple        =       jnp.tree_util.tree_map(onp.array      ,							snake_case					)

       lowercase__:       List[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()',
       ]

       lowercase__:       List[Any]        =       os.path.join(args.checkpoint_path      ,							'..'      ,							'config.gin'					)
       lowercase__:       Optional[Any]        =       inference.parse_training_gin_file(snake_case      ,							snake_case					)
       lowercase__:       str        =       inference.InferenceModel(args.checkpoint_path      ,							snake_case					)

       lowercase__:       Dict        =       DDPMScheduler(beta_schedule='squaredcos_cap_v2'      ,							variance_type='fixed_large'					)

       lowercase__:       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'      ,							)

       lowercase__:       Dict        =       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'      ,							)

       lowercase__:       Optional[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      ,							)

       lowercase__:       Dict        =       load_notes_encoder(ta_checkpoint['target']['token_encoder']      ,							snake_case					)
       lowercase__:       int        =       load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']      ,							snake_case					)
       lowercase__:       Optional[int]        =       load_decoder(ta_checkpoint['target']['decoder']      ,							snake_case					)

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

       lowercase__:       List[Any]        =       SpectrogramDiffusionPipeline(
           notes_encoder=snake_case      ,							continuous_encoder=snake_case      ,							decoder=snake_case      ,							scheduler=snake_case      ,							melgan=snake_case      ,							)
       if args.save:
              pipe.save_pretrained(args.output_path					)


if __name__ == "__main__":
 __lowerCAmelCase			=							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			=							parser.parse_args()

 main(args)


