


"""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


__lowerCAmelCase							:      Union[str, Any]						=      'base_with_context'







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Dict:
							snake_case_          =						nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]			)			)
							snake_case_          =						nn.Parameter(
							    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]			)			,	requires_grad=_SCREAMING_SNAKE_CASE			)
							for lyr_num, lyr in enumerate(model.encoders			):
														snake_case_          =						weights[f"""layers_{lyr_num}"""]
														snake_case_          =						nn.Parameter(
														    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]			)			)

														snake_case_          =						ly_weight["attention"]
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T			)			)

														snake_case_          =						nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]			)			)

														snake_case_          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T			)			)

							snake_case_          =						nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]			)			)
							return model







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Optional[Any]:
							snake_case_          =						nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T			)			)

							snake_case_          =						nn.Parameter(
							    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]			)			,	requires_grad=_SCREAMING_SNAKE_CASE			)

							for lyr_num, lyr in enumerate(model.encoders			):
														snake_case_          =						weights[f"""layers_{lyr_num}"""]
														snake_case_          =						ly_weight["attention"]

														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(
														    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]			)			)

														snake_case_          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]			)			)

							snake_case_          =						nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]			)			)

							return model







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[str]:
							snake_case_          =						nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T			)			)
							snake_case_          =						nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T			)			)

							snake_case_          =						nn.Parameter(
							    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]			)			,	requires_grad=_SCREAMING_SNAKE_CASE			)

							snake_case_          =						nn.Parameter(
							    torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T			)			)

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

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

														snake_case_          =						ly_weight["self_attention"]
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T			)			)

														snake_case_          =						ly_weight["MultiHeadDotProductAttention_0"]
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(
														    torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]			)			)

														snake_case_          =						nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]			)			)
														snake_case_          =						nn.Parameter(
														    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T			)			)
														snake_case_          =						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T			)			)

							snake_case_          =						nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]			)			)

							snake_case_          =						nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T			)			)

							return model







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Union[str, Any]:
							snake_case_          =						checkpoints.load_tax_checkpoint(args.checkpoint_path			)
							snake_case_          =						jnp.tree_util.tree_map(onp.array			,	_SCREAMING_SNAKE_CASE			)

							snake_case_          =						[
							    "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()",
							]

							snake_case_          =						os.path.join(args.checkpoint_path			,	""".."""			,	"""config.gin"""			)
							snake_case_          =						inference.parse_training_gin_file(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							snake_case_          =						inference.InferenceModel(args.checkpoint_path			,	_SCREAMING_SNAKE_CASE			)

							snake_case_          =						DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""			,	variance_type="""fixed_large"""			)

							snake_case_          =						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"""			,	)

							snake_case_          =						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"""			,	)

							snake_case_          =						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			,	)

							snake_case_          =						load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]			,	_SCREAMING_SNAKE_CASE			)
							snake_case_          =						load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]			,	_SCREAMING_SNAKE_CASE			)
							snake_case_          =						load_decoder(ta_checkpoint["""target"""]["""decoder"""]			,	_SCREAMING_SNAKE_CASE			)

							snake_case_          =						OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""			)

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


if __name__ == "__main__":
				__lowerCAmelCase							:      Union[str, Any]						=      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							:      List[str]						=      parser.parse_args()

				main(args)







"""simple docstring"""
def   _a			( _SCREAMING_SNAKE_CASE			)      ->			list[int]:
							snake_case_          =						len(_SCREAMING_SNAKE_CASE			)
							for i in range(_SCREAMING_SNAKE_CASE			):
														for j in range(i + 1			,	_SCREAMING_SNAKE_CASE			):
																					if numbers[j] < numbers[i]:
																												snake_case_    ,  snake_case_          =						numbers[j], numbers[i]
							return numbers


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



