
"""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


_A     :			Tuple  						=			"""base_with_context"""


def     __magic_name__      ( __snake_case							:			Dict	,					__snake_case							:			str    )					->							int:
       lowercase       :							str									=				nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]    )    )
       lowercase       :							Optional[Any]									=				nn.Parameter(
           torch.FloatTensor(weights["Embed_0"]["embedding"]    )	,					requires_grad=_lowerCamelCase    )
       for lyr_num, lyr in enumerate(model.encoders    ):
              lowercase       :							Optional[Any]									=				weights[f"""layers_{lyr_num}"""]
              lowercase       :							Tuple									=				nn.Parameter(
                  torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]    )    )

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

              lowercase       :							Dict									=				nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]    )    )

              lowercase       :							Tuple									=				nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T    )    )
              lowercase       :							str									=				nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T    )    )
              lowercase       :							str									=				nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T    )    )

       lowercase       :							List[str]									=				nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]    )    )
       return model


def     __magic_name__      ( __snake_case							:			Optional[Any]	,					__snake_case							:			Any    )					->							str:
       lowercase       :							List[Any]									=				nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T    )    )

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

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

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

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

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

       return model


def     __magic_name__      ( __snake_case							:			int	,					__snake_case							:			List[str]    )					->							Optional[Any]:
       lowercase       :							Tuple									=				nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T    )    )
       lowercase       :							Dict									=				nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T    )    )

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

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

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

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

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

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

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

       lowercase       :							int									=				nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]    )    )

       lowercase       :							int									=				nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T    )    )

       return model


def     __magic_name__      ( __snake_case							:			Tuple    )					->							Optional[int]:
       lowercase       :							Optional[Any]									=				checkpoints.load_tax_checkpoint(args.checkpoint_path    )
       lowercase       :							Any									=				jnp.tree_util.tree_map(onp.array	,					_lowerCamelCase    )

       lowercase       :							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()""",
       ]

       lowercase       :							str									=				os.path.join(args.checkpoint_path	,					".."	,					"config.gin"    )
       lowercase       :							Tuple									=				inference.parse_training_gin_file(_lowerCamelCase	,					_lowerCamelCase    )
       lowercase       :							Any									=				inference.InferenceModel(args.checkpoint_path	,					_lowerCamelCase    )

       lowercase       :							Tuple									=				DDPMScheduler(beta_schedule="squaredcos_cap_v2"	,					variance_type="fixed_large"    )

       lowercase       :							Dict									=				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"	,					)

       lowercase       :							List[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"	,					)

       lowercase       :							int									=				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	,					)

       lowercase       :							Any									=				load_notes_encoder(ta_checkpoint["target"]["token_encoder"]	,					_lowerCamelCase    )
       lowercase       :							Optional[Any]									=				load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]	,					_lowerCamelCase    )
       lowercase       :							Optional[int]									=				load_decoder(ta_checkpoint["target"]["decoder"]	,					_lowerCamelCase    )

       lowercase       :							Any									=				OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"    )

       lowercase       :							List[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__":
   _A     :			Tuple  						=			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     :			str  						=			parser.parse_args()

   main(args)









'''simple docstring'''






from __future__ import annotations







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase = None    ,	_lowerCamelCase = None ) ->				None:



			"""simple docstring"""


			if start is None:
						__snake_case					:      Optional[Any]							      =     0

			if end is None:
						__snake_case					:      Optional[Any]							      =     len(_lowerCamelCase ) - 1

			if start >= end:
						return

			__snake_case					:      Tuple							      =     (start + end) // 2

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )
			slowsort(_lowerCamelCase    ,	mid + 1    ,	_lowerCamelCase )

			if sequence[end] < sequence[mid]:
						__snake_case      ,     __snake_case					:      str							      =     sequence[mid], sequence[end]

			slowsort(_lowerCamelCase    ,	_lowerCamelCase    ,	end - 1 )


if __name__ == "__main__":
	from doctest import testmod

	testmod()


