




"""simple docstring"""


def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    int:
      while a != 0:
            lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[Any]   =	b % a, a
      return b



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    int:
      if gcd(__UpperCAmelCase    ,		__UpperCAmelCase				) != 1:
            lowerCAmelCase__      :			Optional[Any]   =	f"""mod inverse of {a!r} and {m!r} does not exist"""
            raise ValueError(__UpperCAmelCase				)
      lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Optional[int]   =	1, 0, a
      lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Any   =	0, 1, m
      while va != 0:
            lowerCAmelCase__      :			Optional[Any]   =	ua // va
            lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__      :			Dict   =	(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
      return ua % m











"""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													=      """base_with_context"""



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

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

            lowerCAmelCase__      :			int   =	nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]				)				)

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

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



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Optional[int]:
      lowerCAmelCase__      :			Dict   =	nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T				)				)

      lowerCAmelCase__      :			List[Any]   =	nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]				)    ,		requires_grad=__UpperCAmelCase				)

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

            lowerCAmelCase__      :			int   =	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__      :			Dict   =	nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Any   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]				)				)

            lowerCAmelCase__      :			Any   =	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__      :			Optional[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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase				)						->    str:
      lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T				)				)
      lowerCAmelCase__      :			Union[str, Any]   =	nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T				)				)

      lowerCAmelCase__      :			List[str]   =	nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]				)    ,		requires_grad=__UpperCAmelCase				)

      lowerCAmelCase__      :			Dict   =	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__      :			Tuple   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""]				)				)

            lowerCAmelCase__      :			Union[str, Any]   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T				)				)

            lowerCAmelCase__      :			Tuple   =	ly_weight["""self_attention"""]
            lowerCAmelCase__      :			str   =	nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Dict   =	nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			List[str]   =	nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T				)				)

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

            lowerCAmelCase__      :			Tuple   =	nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]				)				)
            lowerCAmelCase__      :			Dict   =	nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T				)				)
            lowerCAmelCase__      :			Optional[int]   =	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__      :			Tuple   =	nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T				)				)

      lowerCAmelCase__      :			str   =	nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]				)				)

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

      return model



def 	lowercase_				(			__UpperCAmelCase				)						->    str:
      lowerCAmelCase__      :			Optional[int]   =	checkpoints.load_tax_checkpoint(args.checkpoint_path				)
      lowerCAmelCase__      :			Optional[int]   =	jnp.tree_util.tree_map(onp.array    ,		__UpperCAmelCase				)

      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__      :			Dict   =	os.path.join(args.checkpoint_path    ,		""".."""    ,		"""config.gin"""				)
      lowerCAmelCase__      :			Tuple   =	inference.parse_training_gin_file(__UpperCAmelCase    ,		__UpperCAmelCase				)
      lowerCAmelCase__      :			Any   =	inference.InferenceModel(args.checkpoint_path    ,		__UpperCAmelCase				)

      lowerCAmelCase__      :			List[Any]   =	DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""    ,		variance_type="""fixed_large"""				)

      lowerCAmelCase__      :			List[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"""    ,		)

      lowerCAmelCase__      :			List[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__      :			Optional[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    ,		)

      lowerCAmelCase__      :			Optional[Any]   =	load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]    ,		__UpperCAmelCase				)
      lowerCAmelCase__      :			List[str]   =	load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]    ,		__UpperCAmelCase				)
      lowerCAmelCase__      :			Optional[Any]   =	load_decoder(ta_checkpoint["""target"""]["""decoder"""]    ,		__UpperCAmelCase				)

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

      lowerCAmelCase__      :			Optional[Any]   =	SpectrogramDiffusionPipeline(
          notes_encoder=__UpperCAmelCase    ,		continuous_encoder=__UpperCAmelCase    ,		decoder=__UpperCAmelCase    ,		scheduler=__UpperCAmelCase    ,		melgan=__UpperCAmelCase    ,		)
      if args.save:
            pipe.save_pretrained(args.output_path				)


if __name__ == "__main__":
  _A													=      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													=      parser.parse_args()

  main(args)





