




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


SCREAMING_SNAKE_CASE__     :     str		    =       "base_with_context"

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding'] ) )
			SCREAMING_SNAKE_CASE_								=						nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding'] )       ,    requires_grad=SCREAMING_SNAKE_CASE )
			for lyr_num, lyr in enumerate(model.encoders ):
						SCREAMING_SNAKE_CASE_								=						weights[F'layers_{lyr_num}']
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )

						SCREAMING_SNAKE_CASE_								=						ly_weight['attention']
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )

						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )

						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )

			SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale'] ) )
			return model

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







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

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

			for lyr_num, lyr in enumerate(model.encoders ):
						SCREAMING_SNAKE_CASE_								=						weights[F'layers_{lyr_num}']
						SCREAMING_SNAKE_CASE_								=						ly_weight['attention']

						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )

						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )

			SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale'] ) )

			return model

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T ) )
			SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T ) )

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

			SCREAMING_SNAKE_CASE_								=						nn.Parameter(
			    torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T ) )

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

						SCREAMING_SNAKE_CASE_								=						nn.Parameter(
						    torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T ) )

						SCREAMING_SNAKE_CASE_								=						ly_weight['self_attention']
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )

						SCREAMING_SNAKE_CASE_								=						ly_weight['MultiHeadDotProductAttention_0']
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale'] ) )

						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(
						    torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
						SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )

			SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale'] ) )

			SCREAMING_SNAKE_CASE_								=						nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T ) )

			return model

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Optional[int]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						checkpoints.load_tax_checkpoint(args.checkpoint_path )
			SCREAMING_SNAKE_CASE_								=						jnp.tree_util.tree_map(onp.array       ,    SCREAMING_SNAKE_CASE )

			SCREAMING_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()',
			]

			SCREAMING_SNAKE_CASE_								=						os.path.join(args.checkpoint_path       ,    '..'       ,    'config.gin' )
			SCREAMING_SNAKE_CASE_								=						inference.parse_training_gin_file(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						inference.InferenceModel(args.checkpoint_path       ,    SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE_								=						DDPMScheduler(beta_schedule='squaredcos_cap_v2'       ,    variance_type='fixed_large' )

			SCREAMING_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'       ,    )

			SCREAMING_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'       ,    )

			SCREAMING_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       ,    )

			SCREAMING_SNAKE_CASE_								=						load_notes_encoder(ta_checkpoint['target']['token_encoder']       ,    SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']       ,    SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						load_decoder(ta_checkpoint['target']['decoder']       ,    SCREAMING_SNAKE_CASE )

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

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


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     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.",
				)
				SCREAMING_SNAKE_CASE__     :     Tuple		    =       parser.parse_args()

				main(args)






def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			if index == r:
						for j in range(SCREAMING_SNAKE_CASE ):
									print(data[j]       ,    end=' ' )
						print(' ' )
						return
			#  When no more elements are there to put in data[]
			if i >= n:
						return
			# current is included, put next at next location
			SCREAMING_SNAKE_CASE_								=						arr[i]
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1       ,    SCREAMING_SNAKE_CASE       ,    i + 1 )
			# current is excluded, replace it with
			# next (Note that i+1 is passed, but
			# index is not changed)
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    i + 1 )
			# The main function that prints all combinations
			# of size r in arr[] of size n. This function
			# mainly uses combinationUtil()

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Any:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[0] * r
			# Print all combination using temporary array 'data[]'
			combination_util(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    0       ,    SCREAMING_SNAKE_CASE       ,    0 )


if __name__ == "__main__":
				# Driver code to check the function above
				SCREAMING_SNAKE_CASE__     :     Dict		    =       [10, 20, 30, 40, 50]
				print_combination(arr, len(arr), 3)
				# This code is contributed by Ambuj sahu
