




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_				:    Tuple										=     """base_with_context"""





def 						UpperCAmelCase				(			A__:    Any							,				A__:    str     )  ->     List[Any]:
			__lowerCamelCase					:	Any	       =				nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']     )     )
			__lowerCamelCase					:	List[str]	       =				nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']     )							,				requires_grad=A__     )
			for lyr_num, lyr in enumerate(model.encoders     ):
						__lowerCamelCase					:	Any	       =				weights[f'''layers_{lyr_num}''']
						__lowerCamelCase					:	Optional[int]	       =				nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']     )     )

						__lowerCamelCase					:	Tuple	       =				ly_weight['attention']
						__lowerCamelCase					:	Union[str, Any]	       =				nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T     )     )
						__lowerCamelCase					:	Any	       =				nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T     )     )
						__lowerCamelCase					:	Tuple	       =				nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T     )     )
						__lowerCamelCase					:	Any	       =				nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T     )     )

						__lowerCamelCase					:	Union[str, Any]	       =				nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']     )     )

						__lowerCamelCase					:	Dict	       =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T     )     )
						__lowerCamelCase					:	Optional[Any]	       =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T     )     )
						__lowerCamelCase					:	Dict	       =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T     )     )

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





def 						UpperCAmelCase				(			A__:    Tuple							,				A__:    List[str]     )  ->     List[str]:
			__lowerCamelCase					:	Optional[Any]	       =				nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T     )     )

			__lowerCamelCase					:	List[str]	       =				nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']     )							,				requires_grad=A__     )

			for lyr_num, lyr in enumerate(model.encoders     ):
						__lowerCamelCase					:	Dict	       =				weights[f'''layers_{lyr_num}''']
						__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					:	List[str]	       =				nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T     )     )
						__lowerCamelCase					:	Optional[int]	       =				nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T     )     )
						__lowerCamelCase					:	Optional[Any]	       =				nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']     )     )

						__lowerCamelCase					:	Optional[Any]	       =				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					:	Any	       =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T     )     )
						__lowerCamelCase					:	int	       =				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 						UpperCAmelCase				(			A__:    List[str]							,				A__:    Dict     )  ->     int:
			__lowerCamelCase					:	Any	       =				nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T     )     )
			__lowerCamelCase					:	List[str]	       =				nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T     )     )

			__lowerCamelCase					:	Tuple	       =				nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']     )							,				requires_grad=A__     )

			__lowerCamelCase					:	int	       =				nn.Parameter(
			    torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T     )     )

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

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

						__lowerCamelCase					:	Union[str, Any]	       =				ly_weight['self_attention']
						__lowerCamelCase					:	Optional[Any]	       =				nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T     )     )
						__lowerCamelCase					:	Optional[int]	       =				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					:	str	       =				ly_weight['MultiHeadDotProductAttention_0']
						__lowerCamelCase					:	List[Any]	       =				nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T     )     )
						__lowerCamelCase					:	List[str]	       =				nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T     )     )
						__lowerCamelCase					:	str	       =				nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T     )     )
						__lowerCamelCase					:	Optional[int]	       =				nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T     )     )
						__lowerCamelCase					:	Any	       =				nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']     )     )

						__lowerCamelCase					:	Dict	       =				nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']     )     )
						__lowerCamelCase					:	int	       =				nn.Parameter(
						    torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T     )     )
						__lowerCamelCase					:	List[Any]	       =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T     )     )
						__lowerCamelCase					:	str	       =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T     )     )
						__lowerCamelCase					:	Optional[int]	       =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T     )     )

			__lowerCamelCase					:	Tuple	       =				nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']     )     )

			__lowerCamelCase					:	Tuple	       =				nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T     )     )

			return model





def 						UpperCAmelCase				(			A__:    Dict     )  ->     Optional[int]:
			__lowerCamelCase					:	Optional[int]	       =				checkpoints.load_tax_checkpoint(args.checkpoint_path     )
			__lowerCamelCase					:	Any	       =				jnp.tree_util.tree_map(onp.array							,				A__     )

			__lowerCamelCase					:	Union[str, Any]	       =				[
			    '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					:	int	       =				inference.parse_training_gin_file(A__							,				A__     )
			__lowerCamelCase					:	int	       =				inference.InferenceModel(args.checkpoint_path							,				A__     )

			__lowerCamelCase					:	str	       =				DDPMScheduler(beta_schedule='squaredcos_cap_v2'							,				variance_type='fixed_large'     )

			__lowerCamelCase					:	Tuple	       =				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					:	Tuple	       =				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					:	Optional[Any]	       =				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					:	int	       =				load_notes_encoder(ta_checkpoint['target']['token_encoder']							,				A__     )
			__lowerCamelCase					:	Union[str, Any]	       =				load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']							,				A__     )
			__lowerCamelCase					:	Dict	       =				load_decoder(ta_checkpoint['target']['decoder']							,				A__     )

			__lowerCamelCase					:	Any	       =				OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'     )

			__lowerCamelCase					:	str	       =				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__":
					a_				:    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.''',
					)
					a_				:    Dict										=     parser.parse_args()

					main(args)









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a_				:    List[Any]										=     {
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    int										=     [
					    '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''EncodecModel''',
					    '''EncodecPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_encodec import (
					    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    EncodecConfig,
					)
					from .feature_extraction_encodec import EncodecFeatureExtractor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_encodec import (
										    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
										    EncodecModel,
										    EncodecPreTrainedModel,
										)

else:
					import sys

					a_				:    Tuple										=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



