

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:    List[str] 							=			"""base_with_context"""



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]    )    )
	UpperCAmelCase_       				=			nn.Parameter(
	    torch.FloatTensor(weights["Embed_0"]["embedding"]    )       ,     requires_grad=snake_case_    )
	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       ,     A    ):
	UpperCAmelCase_       				=			nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T    )    )

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

	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       ,     A    ):
	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=snake_case_    )

	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    ):
	UpperCAmelCase_       				=			checkpoints.load_tax_checkpoint(args.checkpoint_path    )
	UpperCAmelCase_       				=			jnp.tree_util.tree_map(onp.array       ,     snake_case_    )

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

	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"]       ,     snake_case_    )
	UpperCAmelCase_       				=			load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]       ,     snake_case_    )
	UpperCAmelCase_       				=			load_decoder(ta_checkpoint["target"]["decoder"]       ,     snake_case_    )

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

	UpperCAmelCase_       				=			SpectrogramDiffusionPipeline(
	    notes_encoder=snake_case_       ,     continuous_encoder=snake_case_       ,     decoder=snake_case_       ,     scheduler=snake_case_       ,     melgan=snake_case_       ,     )
	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:    List[str] 							=			parser.parse_args()

				main(args)









import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_       				=			OmegaConf.load(A    )
	if display:
		print(yaml.dump(OmegaConf.to_container(A    )    )    )
	return config



def       __lowerCAmelCase      (						A       ,     A=None       ,     A=None    ):
	if conf_path is None:
		UpperCAmelCase_       				=			"./model_checkpoints/vqgan_only.yaml"
	UpperCAmelCase_       				=			load_config(A       ,     display=A    )
	UpperCAmelCase_       				=			VQModel(**config.model.params    )
	if ckpt_path is None:
		UpperCAmelCase_       				=			"./model_checkpoints/vqgan_only.pt"
	UpperCAmelCase_       				=			torch.load(A       ,     map_location=A    )
	if ".ckpt" in ckpt_path:
		UpperCAmelCase_       				=			sd["state_dict"]
	model.load_state_dict(A       ,     strict=A    )
	model.to(A    )
	del sd
	return model



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			model.encode(A    )
	print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"    )
	UpperCAmelCase_       				=			model.decode(A    )
	return xrec



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_				,   UpperCAmelCase_       				=			string.rsplit("."       ,     1    )
	if reload:
		UpperCAmelCase_       				=			importlib.import_module(A    )
		importlib.reload(A    )
	return getattr(importlib.import_module(A       ,     package=A    )       ,     cls    )



def       __lowerCAmelCase      (						A    ):
	if "target" not in config:
		raise KeyError("Expected key `target` to instantiate."    )
	return get_obj_from_str(config["target"]    )(**config.get("params"       ,     {}    )    )



def       __lowerCAmelCase      (						A       ,     A       ,     A=True       ,     A=True    ):
	UpperCAmelCase_       				=			instantiate_from_config(A    )
	if sd is not None:
		model.load_state_dict(A    )
	if gpu:
		model.cuda()
	if eval_mode:
		model.eval()
	return {"model": model}




def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	# load the specified checkpoint
	if ckpt:
		UpperCAmelCase_       				=			torch.load(A       ,     map_location="cpu"    )
		UpperCAmelCase_       				=			pl_sd["global_step"]
		print(F"loaded model from global step {global_step}."    )
	else:
		UpperCAmelCase_       				=			{"state_dict": None}
		UpperCAmelCase_       				=			None
	UpperCAmelCase_       				=			load_model_from_config(config.model       ,     pl_sd["state_dict"]       ,     gpu=A       ,     eval_mode=A    )["model"]
	return model, global_step