
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


_lowerCamelCase	:    Any          =      '''base_with_context'''






def 	lowerCamelCase__		(	__lowercase       :     Union[str, Any]	, __lowercase       :     Tuple			)			->  Any:
			_snake_case          =      nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']			)			)
			_snake_case          =      nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']			)	, requires_grad=__lowercase			)
			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__		(	__lowercase       :     Union[str, Any]	, __lowercase       :     int			)			->  List[Any]:
			_snake_case          =      nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T			)			)

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

			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__		(	__lowercase       :     Dict	, __lowercase       :     Tuple			)			->  Dict:
			_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=__lowercase			)

			_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__		(	__lowercase       :     Union[str, Any]			)			->  Tuple:
			_snake_case          =      checkpoints.load_tax_checkpoint(args.checkpoint_path			)
			_snake_case          =      jnp.tree_util.tree_map(onp.array	, __lowercase			)

			_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(__lowercase	, __lowercase			)
			_snake_case          =      inference.InferenceModel(args.checkpoint_path	, __lowercase			)

			_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']	, __lowercase			)
			_snake_case          =      load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']	, __lowercase			)
			_snake_case          =      load_decoder(ta_checkpoint['target']['decoder']	, __lowercase			)

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

			_snake_case          =      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__":
			_lowerCamelCase	:    Dict          =      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.''',
			)
			_lowerCamelCase	:    Dict          =      parser.parse_args()

			main(args)

from functools import lru_cache






@lru_cache
def 	a_		(	__lowercase       :     int			)			->  int:
			if num < 0:
						raise ValueError('Number should not be negative.'			)

			return 1 if num in (0, 1) else num * factorial(num - 1			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()