






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					:		Tuple	  =						"""base_with_context"""
def      a_ (      _A      ,   _A  )			->			Optional[int]:







				"""simple docstring"""
				snake_case__									=     nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']  )  )
				snake_case__									=     nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding']  )      ,   requires_grad=_A  )
				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      a_ (      _A      ,   _A  )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T  )  )

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

				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      a_ (      _A      ,   _A  )			->			Any:







				"""simple docstring"""
				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=_A  )

				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      a_ (      _A  )			->			Optional[Any]:







				"""simple docstring"""
				snake_case__									=     checkpoints.load_tax_checkpoint(args.checkpoint_path  )
				snake_case__									=     jnp.tree_util.tree_map(onp.array      ,   _A  )

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

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

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

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


if __name__ == "__main__":
							__UpperCamelCase					:		Optional[int]	  =						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					:		Dict	  =						parser.parse_args()

							main(args)












def      a_ (      _A      ,   _A  )			->			float:







				"""simple docstring"""
				if digit_amount > 0:
								return round(number - int(_A  )      ,   _A  )
				return number - int(_A  )


if __name__ == "__main__":
							print(decimal_isolate(1.5_3, 0))
							print(decimal_isolate(3_5.3_4_5, 1))
							print(decimal_isolate(3_5.3_4_5, 2))
							print(decimal_isolate(3_5.3_4_5, 3))
							print(decimal_isolate(-1_4.7_8_9, 3))
							print(decimal_isolate(0, 2))
							print(decimal_isolate(-1_4.1_2_3, 1))
							print(decimal_isolate(-1_4.1_2_3, 2))
							print(decimal_isolate(-1_4.1_2_3, 3))




