


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__    :int       =				'''base_with_context'''

def 			lowerCAmelCase__					(     a__:						int     ,    a__:						Union[str, Any]			)    ->						Any:


							'''simple docstring'''





							_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']			)			)
							_UpperCAmelCase											=     nn.Parameter(
							    torch.FloatTensor(weights['Embed_0']['embedding']			)     ,    requires_grad=a__			)
							for lyr_num, lyr in enumerate(model.encoders			):
														_UpperCAmelCase											=     weights[F'''layers_{lyr_num}''']
														_UpperCAmelCase											=     nn.Parameter(
														    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']			)			)

														_UpperCAmelCase											=     ly_weight['attention']
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T			)			)

														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']			)			)

														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T			)			)

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

def 			lowerCAmelCase__					(     a__:						Optional[Any]     ,    a__:						List[str]			)    ->						Dict:


							'''simple docstring'''





							_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T			)			)

							_UpperCAmelCase											=     nn.Parameter(
							    torch.FloatTensor(weights['Embed_0']['embedding']			)     ,    requires_grad=a__			)

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

														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(
														    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']			)			)

														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']			)			)

							_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']			)			)

							return model

def 			lowerCAmelCase__					(     a__:						Optional[int]     ,    a__:						Tuple			)    ->						Tuple:


							'''simple docstring'''





							_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T			)			)
							_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T			)			)

							_UpperCAmelCase											=     nn.Parameter(
							    torch.FloatTensor(weights['Embed_0']['embedding']			)     ,    requires_grad=a__			)

							_UpperCAmelCase											=     nn.Parameter(
							    torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T			)			)

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

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

														_UpperCAmelCase											=     ly_weight['self_attention']
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T			)			)

														_UpperCAmelCase											=     ly_weight['MultiHeadDotProductAttention_0']
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(
														    torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']			)			)

														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']			)			)
														_UpperCAmelCase											=     nn.Parameter(
														    torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T			)			)
														_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T			)			)

							_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']			)			)

							_UpperCAmelCase											=     nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T			)			)

							return model

def 			lowerCAmelCase__					(     a__:						Optional[int]			)    ->						List[Any]:


							'''simple docstring'''





							_UpperCAmelCase											=     checkpoints.load_tax_checkpoint(args.checkpoint_path			)
							_UpperCAmelCase											=     jnp.tree_util.tree_map(onp.array     ,    a__			)

							_UpperCAmelCase											=     [
							    '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											=     os.path.join(args.checkpoint_path     ,    '..'     ,    'config.gin'			)
							_UpperCAmelCase											=     inference.parse_training_gin_file(a__     ,    a__			)
							_UpperCAmelCase											=     inference.InferenceModel(args.checkpoint_path     ,    a__			)

							_UpperCAmelCase											=     DDPMScheduler(beta_schedule='squaredcos_cap_v2'     ,    variance_type='fixed_large'			)

							_UpperCAmelCase											=     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											=     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											=     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											=     load_notes_encoder(ta_checkpoint['target']['token_encoder']     ,    a__			)
							_UpperCAmelCase											=     load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']     ,    a__			)
							_UpperCAmelCase											=     load_decoder(ta_checkpoint['target']['decoder']     ,    a__			)

							_UpperCAmelCase											=     OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'			)

							_UpperCAmelCase											=     SpectrogramDiffusionPipeline(
							    notes_encoder=a__     ,    continuous_encoder=a__     ,    decoder=a__     ,    scheduler=a__     ,    melgan=a__     ,    )
							if args.save:
														pipe.save_pretrained(args.output_path			)


if __name__ == "__main__":
		lowerCAmelCase__    :List[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__    :int       =				parser.parse_args()

		main(args)




def 			lowerCAmelCase__					(     a__:						list			)    ->						list:


							'''simple docstring'''





							if len(a__			) < 2:
														return collection

							def circle_sort_util(a__:						list     ,    a__:						int     ,    a__:						int			) -> bool:
														_UpperCAmelCase											=     False

														if low == high:
																					return swapped

														_UpperCAmelCase											=     low
														_UpperCAmelCase											=     high

														while left < right:
																					if collection[left] > collection[right]:
																												_UpperCAmelCase						,			_UpperCAmelCase											=     (
																												    collection[right],
																												    collection[left],
																												)
																												_UpperCAmelCase											=     True

																					left += 1
																					right -= 1

														if left == right and collection[left] > collection[right + 1]:
																					_UpperCAmelCase						,			_UpperCAmelCase											=     (
																					    collection[right + 1],
																					    collection[left],
																					)

																					_UpperCAmelCase											=     True

														_UpperCAmelCase											=     low + int((high - low) / 2			)
														_UpperCAmelCase											=     circle_sort_util(a__     ,    a__     ,    a__			)
														_UpperCAmelCase											=     circle_sort_util(a__     ,    mid + 1     ,    a__			)

														return swapped or left_swap or right_swap

							_UpperCAmelCase											=     True

							while is_not_sorted is True:
														_UpperCAmelCase											=     circle_sort_util(a__     ,    0     ,    len(a__			) - 1			)

							return collection


if __name__ == "__main__":
		lowerCAmelCase__    :Tuple       =				input('''Enter numbers separated by a comma:\n''').strip()
		lowerCAmelCase__    :List[str]       =				[int(item) for item in user_input.split(''',''')]
		print(circle_sort(unsorted))
