





"""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


__magic_name__       :					int						=			"""base_with_context"""

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		int				   =			nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]     )     )
						UpperCamelCase				:		List[Any]				   =			nn.Parameter(
						    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]     )							,     requires_grad=SCREAMING_SNAKE_CASE     )
						for lyr_num, lyr in enumerate(model.encoders     ):
												UpperCamelCase				:		Optional[Any]				   =			weights[f"""layers_{lyr_num}"""]
												UpperCamelCase				:		List[Any]				   =			nn.Parameter(
												    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]     )     )

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

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

												UpperCamelCase				:		Union[str, Any]				   =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T     )     )
												UpperCamelCase				:		List[str]				   =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T     )     )
												UpperCamelCase				:		List[str]				   =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T     )     )

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		int				   =			nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T     )     )

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

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

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

												UpperCamelCase				:		int				   =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T     )     )
												UpperCamelCase				:		int				   =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T     )     )
												UpperCamelCase				:		Optional[Any]				   =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T     )     )
												UpperCamelCase				:		List[str]				   =			nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]     )     )

						UpperCamelCase				:		Union[str, Any]				   =			nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]     )     )

						return model

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Dict				   =			nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T     )     )
						UpperCamelCase				:		Dict				   =			nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T     )     )

						UpperCamelCase				:		Optional[Any]				   =			nn.Parameter(
						    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]     )							,     requires_grad=SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Union[str, Any]				   =			nn.Parameter(
						    torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T     )     )

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

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

												UpperCamelCase				:		Dict				   =			ly_weight["""self_attention"""]
												UpperCamelCase				:		str				   =			nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T     )     )
												UpperCamelCase				:		int				   =			nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T     )     )
												UpperCamelCase				:		str				   =			nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T     )     )
												UpperCamelCase				:		Union[str, Any]				   =			nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T     )     )

												UpperCamelCase				:		int				   =			ly_weight["""MultiHeadDotProductAttention_0"""]
												UpperCamelCase				:		Optional[int]				   =			nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T     )     )
												UpperCamelCase				:		Dict				   =			nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T     )     )
												UpperCamelCase				:		Tuple				   =			nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T     )     )
												UpperCamelCase				:		Tuple				   =			nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T     )     )
												UpperCamelCase				:		Union[str, Any]				   =			nn.Parameter(
												    torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]     )     )

												UpperCamelCase				:		Optional[Any]				   =			nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]     )     )
												UpperCamelCase				:		Dict				   =			nn.Parameter(
												    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T     )     )
												UpperCamelCase				:		Union[str, Any]				   =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T     )     )
												UpperCamelCase				:		int				   =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T     )     )
												UpperCamelCase				:		Tuple				   =			nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T     )     )

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

						UpperCamelCase				:		Tuple				   =			nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T     )     )

						return model

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[str]				   =			checkpoints.load_tax_checkpoint(args.checkpoint_path     )
						UpperCamelCase				:		int				   =			jnp.tree_util.tree_map(onp.array							,     SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Tuple				   =			[
						    """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				:		List[Any]				   =			os.path.join(args.checkpoint_path							,     """.."""							,     """config.gin"""     )
						UpperCamelCase				:		Any				   =			inference.parse_training_gin_file(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		List[Any]				   =			inference.InferenceModel(args.checkpoint_path							,     SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[int]				   =			DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""							,     variance_type="""fixed_large"""     )

						UpperCamelCase				:		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"""							,     )

						UpperCamelCase				:		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"""							,     )

						UpperCamelCase				:		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							,     )

						UpperCamelCase				:		Optional[int]				   =			load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]							,     SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Optional[Any]				   =			load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]							,     SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Any				   =			load_decoder(ta_checkpoint["""target"""]["""decoder"""]							,     SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		List[Any]				   =			OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""     )

						UpperCamelCase				:		str				   =			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__":
		__magic_name__       :					List[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.""",
		)
		__magic_name__       :					int						=			parser.parse_args()

		main(args)










"""simple docstring"""


import os

def      UpperCamelCase							():
						UpperCamelCase				:		Union[str, Any]				   =			os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE     )							,     """num.txt"""     )
						with open(SCREAMING_SNAKE_CASE     ) as file_hand:
												return str(sum(int(SCREAMING_SNAKE_CASE     ) for line in file_hand     )     )[:10]


if __name__ == "__main__":
		print(solution())



