



'''simple docstring'''






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__:     int										=    "base_with_context"


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

														UpperCAmelCase				: Optional[Any]											=	ly_weight['''attention''']
														UpperCAmelCase				: str											=	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T						)						)
														UpperCAmelCase				: str											=	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T						)						)
														UpperCAmelCase				: Optional[int]											=	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T						)						)
														UpperCAmelCase				: int											=	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T						)						)

														UpperCAmelCase				: Tuple											=	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']						)						)

														UpperCAmelCase				: List[Any]											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T						)						)
														UpperCAmelCase				: Tuple											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T						)						)
														UpperCAmelCase				: Union[str, Any]											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T						)						)

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


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

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

							for lyr_num, lyr in enumerate(model.encoders						):
														UpperCAmelCase				: List[str]											=	weights[f"""layers_{lyr_num}"""]
														UpperCAmelCase				: Tuple											=	ly_weight['''attention''']

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

														UpperCAmelCase				: str											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T						)						)
														UpperCAmelCase				: Union[str, Any]											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T						)						)
														UpperCAmelCase				: Tuple											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T						)						)
														UpperCAmelCase				: Optional[Any]											=	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']						)						)

							UpperCAmelCase				: List[Any]											=	nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']						)						)

							return model


def 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Tuple						)   ->     Any:
							UpperCAmelCase				: Optional[Any]											=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T						)						)
							UpperCAmelCase				: Dict											=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T						)						)

							UpperCAmelCase				: List[Any]											=	nn.Parameter(
							    torch.FloatTensor(weights['''Embed_0''']['''embedding''']						)     ,   requires_grad=_lowerCAmelCase						)

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

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

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

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

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

														UpperCAmelCase				: Union[str, Any]											=	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']						)						)
														UpperCAmelCase				: str											=	nn.Parameter(
														    torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T						)						)
														UpperCAmelCase				: List[Any]											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T						)						)
														UpperCAmelCase				: Tuple											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T						)						)
														UpperCAmelCase				: Dict											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T						)						)

							UpperCAmelCase				: List[Any]											=	nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']						)						)

							UpperCAmelCase				: Optional[Any]											=	nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T						)						)

							return model


def 				snake_case_  (      _lowerCAmelCase						:		str						)   ->     Optional[int]:
							UpperCAmelCase				: List[str]											=	checkpoints.load_tax_checkpoint(args.checkpoint_path						)
							UpperCAmelCase				: Optional[Any]											=	jnp.tree_util.tree_map(onp.array     ,   _lowerCAmelCase						)

							UpperCAmelCase				: 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()''',
							]

							UpperCAmelCase				: List[str]											=	os.path.join(args.checkpoint_path     ,   '''..'''     ,   '''config.gin'''						)
							UpperCAmelCase				: Optional[Any]											=	inference.parse_training_gin_file(_lowerCAmelCase     ,   _lowerCAmelCase						)
							UpperCAmelCase				: List[str]											=	inference.InferenceModel(args.checkpoint_path     ,   _lowerCAmelCase						)

							UpperCAmelCase				: Any											=	DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''     ,   variance_type='''fixed_large'''						)

							UpperCAmelCase				: Any											=	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'''     ,   )

							UpperCAmelCase				: Optional[Any]											=	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'''     ,   )

							UpperCAmelCase				: Tuple											=	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     ,   )

							UpperCAmelCase				: Any											=	load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']     ,   _lowerCAmelCase						)
							UpperCAmelCase				: int											=	load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']     ,   _lowerCAmelCase						)
							UpperCAmelCase				: Dict											=	load_decoder(ta_checkpoint['''target''']['''decoder''']     ,   _lowerCAmelCase						)

							UpperCAmelCase				: List[Any]											=	OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''						)

							UpperCAmelCase				: Optional[Any]											=	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__":
						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__:     Any										=    parser.parse_args()

						main(args)










'''simple docstring'''






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate

@dataclass(frozen=A__    )
class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= field(default="""language-modeling"""							,	metadata={"""include_in_asdict_even_if_is_default""": True}    )

	lowerCamelCase__    				= Features({"""text""": Value("""string"""    )}    )
	lowerCamelCase__    				= Features({}    )
	lowerCamelCase__    				= "text"







	@property
	def   A			(		self    :  Optional[Any]	)    ->	Dict[str, str]:
								return {self.text_column: "text"}





