





'''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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Optional[int]:
	snake_case__	       =   nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']							)							)
	snake_case__	       =   nn.Parameter(
	    torch.FloatTensor(weights['Embed_0']['embedding']							)					,				requires_grad=UpperCAmelCase_							)
	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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Union[str, Any]:
	snake_case__	       =   nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T							)							)

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

	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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Optional[int]:
	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=UpperCAmelCase_							)

	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 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Dict:
	snake_case__	       =   checkpoints.load_tax_checkpoint(args.checkpoint_path							)
	snake_case__	       =   jnp.tree_util.tree_map(onp.array					,				UpperCAmelCase_							)

	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(UpperCAmelCase_					,				UpperCAmelCase_							)
	snake_case__	       =   inference.InferenceModel(args.checkpoint_path					,				UpperCAmelCase_							)

	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']					,				UpperCAmelCase_							)
	snake_case__	       =   load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']					,				UpperCAmelCase_							)
	snake_case__	       =   load_decoder(ta_checkpoint['target']['decoder']					,				UpperCAmelCase_							)

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

	snake_case__	       =   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__					:			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.''',
			)
			a__					:			int          =     parser.parse_args()

			main(args)










'''simple docstring'''




import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


a__					:			Optional[int]          =     version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=False					,				)   ->Optional[int]:
	output_path.parent.mkdir(parents=UpperCAmelCase_					,				exist_ok=UpperCAmelCase_							)
	# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
	# so we check the torch version for backwards compatibility
	if is_torch_less_than_1_11:
		export(
		    UpperCAmelCase_					,				UpperCAmelCase_					,				f=output_path.as_posix()					,				input_names=UpperCAmelCase_					,				output_names=UpperCAmelCase_					,				dynamic_axes=UpperCAmelCase_					,				do_constant_folding=UpperCAmelCase_					,				use_external_data_format=UpperCAmelCase_					,				enable_onnx_checker=UpperCAmelCase_					,				opset_version=UpperCAmelCase_					,				)
	else:
		export(
		    UpperCAmelCase_					,				UpperCAmelCase_					,				f=output_path.as_posix()					,				input_names=UpperCAmelCase_					,				output_names=UpperCAmelCase_					,				dynamic_axes=UpperCAmelCase_					,				do_constant_folding=UpperCAmelCase_					,				opset_version=UpperCAmelCase_					,				)




@torch.no_grad()
def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_ = False							)   ->Union[str, Any]:
	snake_case__	       =   torch.floataa if fpaa else torch.floataa
	if fpaa and torch.cuda.is_available():
		snake_case__	       =   'cuda'
	elif fpaa and not torch.cuda.is_available():
		raise ValueError('`float16` model export is only supported on GPUs with CUDA'							)
	else:
		snake_case__	       =   'cpu'
	snake_case__	       =   Path(UpperCAmelCase_							)

	# VAE DECODER
	snake_case__	       =   AutoencoderKL.from_pretrained(model_path + '/vae'							)
	snake_case__	       =   vae_decoder.config.latent_channels
	# forward only through the decoder part
	snake_case__	       =   vae_decoder.decode
	onnx_export(
	    UpperCAmelCase_					,				model_args=(
	        torch.randn(1					,				UpperCAmelCase_					,				25					,				25							).to(device=UpperCAmelCase_					,				dtype=UpperCAmelCase_							),
	        False,
	    )					,				output_path=output_path / 'vae_decoder' / 'model.onnx'					,				ordered_input_names=['latent_sample', 'return_dict']					,				output_names=['sample']					,				dynamic_axes={
	        'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
	    }					,				opset=UpperCAmelCase_					,				)
	del vae_decoder


if __name__ == "__main__":
			a__					:			Optional[int]          =     argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_path''',
			    type=str,
			    required=True,
			    help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
			)

			parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
			parser.add_argument(
			    '''--opset''',
			    default=14,
			    type=int,
			    help='''The version of the ONNX operator set to use.''',
			)
			parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')

			a__					:			List[str]          =     parser.parse_args()
			print(args.output_path)
			convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
			print('''SD: Done: ONNX''')



