




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						:							Union[str, Any]							=  """base_with_context"""
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''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__":
		__A						:							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.""",
		)
		__A						:							Any							=  parser.parse_args()

		main(args)












import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						:							List[Any]							=  logging.get_logger(__name__)

__A						:							Tuple							=  {
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''encodec'''





							def __init__(					self       :	Dict       ,     a       :	Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]       ,     a       :	Union[str, Any]=24_000       ,     a       :	List[Any]=1       ,     a       :	str=False       ,     a       :	List[Any]=None       ,     a       :	List[Any]=None       ,     a       :	Optional[int]=128       ,     a       :	int=32       ,     a       :	int=1       ,     a       :	Dict=[8, 5, 4, 2]       ,     a       :	List[str]="weight_norm"       ,     a       :	str=7       ,     a       :	int=7       ,     a       :	Optional[int]=3       ,     a       :	Optional[int]=2       ,     a       :	Optional[int]=True       ,     a       :	Union[str, Any]="reflect"       ,     a       :	Dict=2       ,     a       :	Union[str, Any]=2       ,     a       :	Optional[Any]=1.0       ,     a       :	List[Any]=1_024       ,     a       :	int=None       ,     a       :	Dict=True       ,     **a       :	Tuple       ,     )			->					List[Any]:
										SCREAMING_SNAKE_CASE           =					target_bandwidths
										SCREAMING_SNAKE_CASE           =					sampling_rate
										SCREAMING_SNAKE_CASE           =					audio_channels
										SCREAMING_SNAKE_CASE           =					normalize
										SCREAMING_SNAKE_CASE           =					chunk_length_s
										SCREAMING_SNAKE_CASE           =					overlap
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					num_filters
										SCREAMING_SNAKE_CASE           =					num_residual_layers
										SCREAMING_SNAKE_CASE           =					upsampling_ratios
										SCREAMING_SNAKE_CASE           =					norm_type
										SCREAMING_SNAKE_CASE           =					kernel_size
										SCREAMING_SNAKE_CASE           =					last_kernel_size
										SCREAMING_SNAKE_CASE           =					residual_kernel_size
										SCREAMING_SNAKE_CASE           =					dilation_growth_rate
										SCREAMING_SNAKE_CASE           =					use_causal_conv
										SCREAMING_SNAKE_CASE           =					pad_mode
										SCREAMING_SNAKE_CASE           =					compress
										SCREAMING_SNAKE_CASE           =					num_lstm_layers
										SCREAMING_SNAKE_CASE           =					trim_right_ratio
										SCREAMING_SNAKE_CASE           =					codebook_size
										SCREAMING_SNAKE_CASE           =					codebook_dim if codebook_dim is not None else hidden_size
										SCREAMING_SNAKE_CASE           =					use_conv_shortcut

										if self.norm_type not in ["weight_norm", "time_group_norm"]:
													raise ValueError(
													    f"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""			)

										super().__init__(**a			)





							@property
							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[int]:
										if self.chunk_length_s is None:
													return None
										else:
													return int(self.chunk_length_s * self.sampling_rate			)





							@property
							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Optional[int]:
										if self.chunk_length_s is None or self.overlap is None:
													return None
										else:
													return max(1       ,     int((1.0 - self.overlap) * self.chunk_length			)			)





							@property
							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					int:
										SCREAMING_SNAKE_CASE           =					np.prod(self.upsampling_ratios			)
										return math.ceil(self.sampling_rate / hop_length			)







							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					int:
										return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)			)






