"""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


_a  :  int=				"base_with_context"
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Dict    ,							UpperCAmelCase_					:       Optional[int]						)							->    Optional[Any]:

	'''simple docstring'''





	__snake_case    :							Optional[Any]   	=				nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']						)						)
	__snake_case    :							Optional[int]   	=				nn.Parameter(
	    torch.FloatTensor(weights['Embed_0']['embedding']						)    ,							requires_grad=UpperCAmelCase_						)
	for lyr_num, lyr in enumerate(model.encoders						):
		__snake_case    :							Union[str, Any]   	=				weights[F"layers_{lyr_num}"]
		__snake_case    :							Any   	=				nn.Parameter(
		    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']						)						)

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

		__snake_case    :							int   	=				nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']						)						)

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

	__snake_case    :							Dict   	=				nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']						)						)
	return model
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Dict    ,							UpperCAmelCase_					:       Optional[Any]						)							->    Tuple:

	'''simple docstring'''





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

	__snake_case    :							List[Any]   	=				nn.Parameter(
	    torch.FloatTensor(weights['Embed_0']['embedding']						)    ,							requires_grad=UpperCAmelCase_						)

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

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

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

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

	return model
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[int]    ,							UpperCAmelCase_					:       Optional[Any]						)							->    Dict:

	'''simple docstring'''





	__snake_case    :							List[Any]   	=				nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T						)						)
	__snake_case    :							str   	=				nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T						)						)

	__snake_case    :							List[str]   	=				nn.Parameter(
	    torch.FloatTensor(weights['Embed_0']['embedding']						)    ,							requires_grad=UpperCAmelCase_						)

	__snake_case    :							List[str]   	=				nn.Parameter(
	    torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T						)						)

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

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

		__snake_case    :							Optional[Any]   	=				ly_weight['self_attention']
		__snake_case    :							Any   	=				nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T						)						)
		__snake_case    :							Dict   	=				nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T						)						)
		__snake_case    :							Optional[Any]   	=				nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T						)						)
		__snake_case    :							Tuple   	=				nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T						)						)

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

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

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

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

	return model
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Optional[Any]						)							->    int:

	'''simple docstring'''





	__snake_case    :							Dict   	=				checkpoints.load_tax_checkpoint(args.checkpoint_path						)
	__snake_case    :							List[str]   	=				jnp.tree_util.tree_map(onp.array    ,							UpperCAmelCase_						)

	__snake_case    :							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()',
	]

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

	__snake_case    :							List[Any]   	=				DDPMScheduler(beta_schedule='squaredcos_cap_v2'    ,							variance_type='fixed_large'						)

	__snake_case    :							Optional[int]   	=				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    :							Optional[Any]   	=				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    :							Dict   	=				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    :							List[str]   	=				load_notes_encoder(ta_checkpoint['target']['token_encoder']    ,							UpperCAmelCase_						)
	__snake_case    :							Any   	=				load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']    ,							UpperCAmelCase_						)
	__snake_case    :							Optional[Any]   	=				load_decoder(ta_checkpoint['target']['decoder']    ,							UpperCAmelCase_						)

	__snake_case    :							Optional[int]   	=				OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'						)

	__snake_case    :							List[str]   	=				SpectrogramDiffusionPipeline(
	    notes_encoder=UpperCAmelCase_    ,							continuous_encoder=UpperCAmelCase_    ,							decoder=UpperCAmelCase_    ,							scheduler=UpperCAmelCase_    ,							melgan=UpperCAmelCase_    ,							)
	if args.save:
		pipe.save_pretrained(args.output_path						)


if __name__ == "__main__":
	_a  :  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.",
	)
	_a  :  str=				parser.parse_args()

	main(args)







"""simple docstring"""







from abc import ABC, abstractmethod
from argparse import ArgumentParser

class 	UpperCamelCase    (					lowercase		):



				@staticmethod
				@abstractmethod
				def       _lowercase  (_A       :   ArgumentParser)					->			Tuple:
					raise NotImplementedError()






				@abstractmethod
				def       _lowercase  (self       :   Any)					->			Optional[Any]:
					raise NotImplementedError()





