
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


_UpperCAmelCase					: Any      =       "base_with_context"


def       A       (					lowercase    ,       lowercase )   ->  Any:
		'''simple docstring'''

		UpperCamelCase		=     nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding'] ) )
		UpperCamelCase		=     nn.Parameter(
		    torch.FloatTensor(weights['Embed_0']['embedding'] )    ,       requires_grad=lowercase )
		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       A       (					lowercase    ,       lowercase )   ->  List[str]:
		'''simple docstring'''

		UpperCamelCase		=     nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T ) )

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

		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       A       (					lowercase    ,       lowercase )   ->  Union[str, Any]:
		'''simple docstring'''

		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=lowercase )

		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       A       (					lowercase )   ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase		=     checkpoints.load_tax_checkpoint(args.checkpoint_path )
		UpperCamelCase		=     jnp.tree_util.tree_map(onp.array    ,       lowercase )

		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(lowercase    ,       lowercase )
		UpperCamelCase		=     inference.InferenceModel(args.checkpoint_path    ,       lowercase )

		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']    ,       lowercase )
		UpperCamelCase		=     load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']    ,       lowercase )
		UpperCamelCase		=     load_decoder(ta_checkpoint['target']['decoder']    ,       lowercase )

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

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


if __name__ == "__main__":
					_UpperCAmelCase					: 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.",
					)
					_UpperCAmelCase					: Optional[Any]      =       parser.parse_args()

					main(args)


import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


_UpperCAmelCase					: Optional[int]      =       HUGGINGFACE_HUB_CACHE


_UpperCAmelCase					: List[str]      =       "config.json"
_UpperCAmelCase					: Union[str, Any]      =       "diffusion_pytorch_model.bin"
_UpperCAmelCase					: List[Any]      =       "diffusion_flax_model.msgpack"
_UpperCAmelCase					: Optional[Any]      =       "model.onnx"
_UpperCAmelCase					: int      =       "diffusion_pytorch_model.safetensors"
_UpperCAmelCase					: Optional[Any]      =       "weights.pb"
_UpperCAmelCase					: Tuple      =       "https://huggingface.co"
_UpperCAmelCase					: Union[str, Any]      =       default_cache_path
_UpperCAmelCase					: Optional[Any]      =       "diffusers_modules"
_UpperCAmelCase					: List[Any]      =       os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
_UpperCAmelCase					: Tuple      =       ["fp16", "non-ema"]
_UpperCAmelCase					: Any      =       ".self_attn"
