



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


_snake_case	=						"base_with_context"






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]						)						)
							_lowerCAmelCase  :							Any				     =					nn.Parameter(
							    torch.FloatTensor(weights["Embed_0"]["embedding"]						)       ,				requires_grad=_lowerCamelCase						)
							for lyr_num, lyr in enumerate(model.encoders						):
														_lowerCAmelCase  :							Optional[Any]				     =					weights[F"layers_{lyr_num}"]
														_lowerCAmelCase  :							Union[str, Any]				     =					nn.Parameter(
														    torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]						)						)

														_lowerCAmelCase  :							Dict				     =					ly_weight["attention"]
														_lowerCAmelCase  :							List[str]				     =					nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
														_lowerCAmelCase  :							Optional[int]				     =					nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
														_lowerCAmelCase  :							Optional[int]				     =					nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
														_lowerCAmelCase  :							Optional[Any]				     =					nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)

														_lowerCAmelCase  :							Optional[Any]				     =					nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)

														_lowerCAmelCase  :							List[str]				     =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
														_lowerCAmelCase  :							int				     =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
														_lowerCAmelCase  :							Optional[Any]				     =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)

							_lowerCAmelCase  :							Optional[int]				     =					nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]						)						)
							return model






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[str]				     =					nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T						)						)

							_lowerCAmelCase  :							str				     =					nn.Parameter(
							    torch.FloatTensor(weights["Embed_0"]["embedding"]						)       ,				requires_grad=_lowerCamelCase						)

							for lyr_num, lyr in enumerate(model.encoders						):
														_lowerCAmelCase  :							List[str]				     =					weights[F"layers_{lyr_num}"]
														_lowerCAmelCase  :							List[Any]				     =					ly_weight["attention"]

														_lowerCAmelCase  :							Tuple				     =					nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
														_lowerCAmelCase  :							List[str]				     =					nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
														_lowerCAmelCase  :							Dict				     =					nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
														_lowerCAmelCase  :							Tuple				     =					nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)
														_lowerCAmelCase  :							Optional[Any]				     =					nn.Parameter(
														    torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]						)						)

														_lowerCAmelCase  :							int				     =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
														_lowerCAmelCase  :							Optional[int]				     =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
														_lowerCAmelCase  :							Any				     =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)
														_lowerCAmelCase  :							List[str]				     =					nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)

							_lowerCAmelCase  :							Optional[int]				     =					nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]						)						)

							return model






def        A       (      _lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[str]				     =					nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T						)						)
							_lowerCAmelCase  :							List[Any]				     =					nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T						)						)

							_lowerCAmelCase  :							Optional[Any]				     =					nn.Parameter(
							    torch.FloatTensor(weights["Embed_0"]["embedding"]						)       ,				requires_grad=_lowerCamelCase						)

							_lowerCAmelCase  :							Optional[Any]				     =					nn.Parameter(
							    torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T						)						)

							for lyr_num, lyr in enumerate(model.decoders						):
														_lowerCAmelCase  :							List[Any]				     =					weights[F"layers_{lyr_num}"]
														_lowerCAmelCase  :							Optional[Any]				     =					nn.Parameter(
														    torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"]						)						)

														_lowerCAmelCase  :							Any				     =					nn.Parameter(
														    torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T						)						)

														_lowerCAmelCase  :							int				     =					ly_weight["self_attention"]
														_lowerCAmelCase  :							Optional[Any]				     =					nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
														_lowerCAmelCase  :							Optional[Any]				     =					nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
														_lowerCAmelCase  :							Tuple				     =					nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
														_lowerCAmelCase  :							Optional[int]				     =					nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)

														_lowerCAmelCase  :							int				     =					ly_weight["MultiHeadDotProductAttention_0"]
														_lowerCAmelCase  :							Dict				     =					nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
														_lowerCAmelCase  :							List[str]				     =					nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
														_lowerCAmelCase  :							int				     =					nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
														_lowerCAmelCase  :							Optional[Any]				     =					nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)
														_lowerCAmelCase  :							int				     =					nn.Parameter(
														    torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]						)						)

														_lowerCAmelCase  :							List[Any]				     =					nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)
														_lowerCAmelCase  :							List[str]				     =					nn.Parameter(
														    torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T						)						)
														_lowerCAmelCase  :							List[str]				     =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
														_lowerCAmelCase  :							int				     =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
														_lowerCAmelCase  :							List[str]				     =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)

							_lowerCAmelCase  :							Union[str, Any]				     =					nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]						)						)

							_lowerCAmelCase  :							Union[str, Any]				     =					nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T						)						)

							return model






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Union[str, Any]				     =					checkpoints.load_tax_checkpoint(args.checkpoint_path						)
							_lowerCAmelCase  :							Optional[int]				     =					jnp.tree_util.tree_map(onp.array       ,				_lowerCamelCase						)

							_lowerCAmelCase  :							int				     =					[
							    "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()",
							]

							_lowerCAmelCase  :							Any				     =					os.path.join(args.checkpoint_path       ,				".."       ,				"config.gin"						)
							_lowerCAmelCase  :							Dict				     =					inference.parse_training_gin_file(_lowerCamelCase       ,				_lowerCamelCase						)
							_lowerCAmelCase  :							Union[str, Any]				     =					inference.InferenceModel(args.checkpoint_path       ,				_lowerCamelCase						)

							_lowerCAmelCase  :							str				     =					DDPMScheduler(beta_schedule="squaredcos_cap_v2"       ,				variance_type="fixed_large"						)

							_lowerCAmelCase  :							List[str]				     =					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"       ,				)

							_lowerCAmelCase  :							str				     =					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"       ,				)

							_lowerCAmelCase  :							Dict				     =					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       ,				)

							_lowerCAmelCase  :							List[str]				     =					load_notes_encoder(ta_checkpoint["target"]["token_encoder"]       ,				_lowerCamelCase						)
							_lowerCAmelCase  :							int				     =					load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]       ,				_lowerCamelCase						)
							_lowerCAmelCase  :							List[str]				     =					load_decoder(ta_checkpoint["target"]["decoder"]       ,				_lowerCamelCase						)

							_lowerCAmelCase  :							List[str]				     =					OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"						)

							_lowerCAmelCase  :							int				     =					SpectrogramDiffusionPipeline(
							    notes_encoder=_lowerCamelCase       ,				continuous_encoder=_lowerCamelCase       ,				decoder=_lowerCamelCase       ,				scheduler=_lowerCamelCase       ,				melgan=_lowerCamelCase       ,				)
							if args.save:
														pipe.save_pretrained(args.output_path						)


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

						main(args)






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_snake_case	=						logging.get_logger(__name__)



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'upernet'







							def __init__(     self,    __a=None,    __a=512,    __a=0.02,    __a=[1, 2, 3, 6],    __a=True,    __a=0.4,    __a=384,    __a=256,    __a=1,    __a=False,    __a=255,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														if backbone_config is None:
																					logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
																					_lowerCAmelCase  :							List[str]				     =					CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"])
														elif isinstance(__a,    __a):
																					_lowerCAmelCase  :							List[Any]				     =					backbone_config.get("model_type")
																					_lowerCAmelCase  :							Dict				     =					CONFIG_MAPPING[backbone_model_type]
																					_lowerCAmelCase  :							Optional[Any]				     =					config_class.from_dict(__a)

														_lowerCAmelCase  :							Tuple				     =					backbone_config
														_lowerCAmelCase  :							List[Any]				     =					hidden_size
														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							str				     =					pool_scales
														_lowerCAmelCase  :							List[str]				     =					use_auxiliary_head
														_lowerCAmelCase  :							Dict				     =					auxiliary_loss_weight
														_lowerCAmelCase  :							Tuple				     =					auxiliary_in_channels
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_channels
														_lowerCAmelCase  :							str				     =					auxiliary_num_convs
														_lowerCAmelCase  :							Union[str, Any]				     =					auxiliary_concat_input
														_lowerCAmelCase  :							Dict				     =					loss_ignore_index







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					copy.deepcopy(self.__dict__)
														_lowerCAmelCase  :							List[Any]				     =					self.backbone_config.to_dict()
														_lowerCAmelCase  :							Optional[Any]				     =					self.__class__.model_type
														return output
