


'''simple docstring'''







import mpmath  # for roots of unity
import numpy as np
class 					lowercase_     :


    def __init__(     self  ,					a=None  ,					a=None    ):
          # Input as list
          UpperCamelCase__										=				list(poly_a or [0]    )[:]
          UpperCamelCase__										=				list(poly_b or [0]    )[:]

          # Remove leading zero coefficients
          while self.polyA[-1] == 0:
                self.polyA.pop()
          UpperCamelCase__										=				len(self.polyA    )

          while self.polyB[-1] == 0:
                self.polyB.pop()
          UpperCamelCase__										=				len(self.polyB    )

          # Add 0 to make lengths equal a power of 2
          UpperCamelCase__										=				int(
              2 ** np.ceil(np.loga(len(self.polyA    ) + len(self.polyB    ) - 1    )    )    )

          while len(self.polyA    ) < self.c_max_length:
                self.polyA.append(0    )
          while len(self.polyB    ) < self.c_max_length:
                self.polyB.append(0    )
          # A complex root used for the fourier transform
          UpperCamelCase__										=				complex(mpmath.root(x=1  ,					n=self.c_max_length  ,					k=1    )    )

          # The product
          UpperCamelCase__										=				self.__multiply()


    def    __a							(     self  ,					a    ):
          UpperCamelCase__										=				[[x] for x in self.polyA] if which == "A" else [[x] for x in self.polyB]
          # Corner case
          if len(a    ) <= 1:
                return dft[0]
          #
          UpperCamelCase__										=				self.c_max_length // 2
          while next_ncol > 0:
                UpperCamelCase__										=				[[] for i in range(a    )]
                UpperCamelCase__										=				self.root**next_ncol

                # First half of next step
                UpperCamelCase__										=				1
                for j in range(self.c_max_length // (next_ncol * 2)    ):
                      for i in range(a    ):
                            new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j]    )
                      current_root *= root
                # Second half of next step
                UpperCamelCase__										=				1
                for j in range(self.c_max_length // (next_ncol * 2)    ):
                      for i in range(a    ):
                            new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j]    )
                      current_root *= root
                # Update
                UpperCamelCase__										=				new_dft
                UpperCamelCase__										=				next_ncol // 2
          return dft[0]


    def    __a							(     self    ):
          UpperCamelCase__										=				self.__dft("A"    )
          UpperCamelCase__										=				self.__dft("B"    )
          UpperCamelCase__										=				[[dft_a[i] * dft_b[i] for i in range(self.c_max_length    )]]
          del dft_a
          del dft_b

          # Corner Case
          if len(inverce_c[0]    ) <= 1:
                return inverce_c[0]
          # Inverse DFT
          UpperCamelCase__										=				2
          while next_ncol <= self.c_max_length:
                UpperCamelCase__										=				[[] for i in range(a    )]
                UpperCamelCase__										=				self.root ** (next_ncol // 2)
                UpperCamelCase__										=				1
                # First half of next step
                for j in range(self.c_max_length // next_ncol    ):
                      for i in range(next_ncol // 2    ):
                            # Even positions
                            new_inverse_c[i].append(
                                (
                                    inverce_c[i][j]
                                    + inverce_c[i][j + self.c_max_length // next_ncol]
                                )
                                / 2    )
                            # Odd positions
                            new_inverse_c[i + next_ncol // 2].append(
                                (
                                    inverce_c[i][j]
                                    - inverce_c[i][j + self.c_max_length // next_ncol]
                                )
                                / (2 * current_root)    )
                      current_root *= root
                # Update
                UpperCamelCase__										=				new_inverse_c
                next_ncol *= 2
          # Unpack
          UpperCamelCase__										=				[round(x[0].real  ,					8    ) + round(x[0].imag  ,					8    ) * 1j for x in inverce_c]

          # Remove leading 0's
          while inverce_c[-1] == 0:
                inverce_c.pop()
          return inverce_c


    def __str__(     self    ):
          UpperCamelCase__										=				"A = " + " + ".join(
              f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyA[: self.len_A]    )    )
          UpperCamelCase__										=				"B = " + " + ".join(
              f'''{coef}*x^{i}''' for coef, i in enumerate(self.polyB[: self.len_B]    )    )
          UpperCamelCase__										=				"A*B = " + " + ".join(
              f'''{coef}*x^{i}''' for coef, i in enumerate(self.product    )    )

          return f'''{a}\n{b}\n{c}'''


# Unit tests
if __name__ == "__main__":
  import doctest

  doctest.testmod()






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__											=					'base_with_context'




def    lowerCAmelCase_ (     __A,  __A							)     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]							)							)
      UpperCAmelCase__								=  nn.Parameter(
          torch.FloatTensor(weights["Embed_0"]["embedding"]							),  requires_grad=__A							)
      for lyr_num, lyr in enumerate(model.encoders							):
            UpperCAmelCase__								=  weights[f"""layers_{lyr_num}"""]
            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]							)							)

            UpperCAmelCase__								=  ly_weight["attention"]
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T							)							)

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]							)							)

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T							)							)

      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]							)							)
      return model




def    lowerCAmelCase_ (     __A,  __A							)     ->				Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T							)							)

      UpperCAmelCase__								=  nn.Parameter(
          torch.FloatTensor(weights["Embed_0"]["embedding"]							),  requires_grad=__A							)

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

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]							)							)

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]							)							)

      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]							)							)

      return model




def    lowerCAmelCase_ (     __A,  __A							)     ->				List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T							)							)
      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T							)							)

      UpperCAmelCase__								=  nn.Parameter(
          torch.FloatTensor(weights["Embed_0"]["embedding"]							),  requires_grad=__A							)

      UpperCAmelCase__								=  nn.Parameter(
          torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T							)							)

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

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

            UpperCAmelCase__								=  ly_weight["self_attention"]
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T							)							)

            UpperCAmelCase__								=  ly_weight["MultiHeadDotProductAttention_0"]
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]							)							)

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]							)							)
            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T							)							)

      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]							)							)

      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T							)							)

      return model




def    lowerCAmelCase_ (     __A							)     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  checkpoints.load_tax_checkpoint(args.checkpoint_path							)
      UpperCAmelCase__								=  jnp.tree_util.tree_map(onp.array,  __A							)

      UpperCAmelCase__								=  [
          "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__								=  os.path.join(args.checkpoint_path,  "..",  "config.gin"							)
      UpperCAmelCase__								=  inference.parse_training_gin_file(__A,  __A							)
      UpperCAmelCase__								=  inference.InferenceModel(args.checkpoint_path,  __A							)

      UpperCAmelCase__								=  DDPMScheduler(beta_schedule="squaredcos_cap_v2",  variance_type="fixed_large"							)

      UpperCAmelCase__								=  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__								=  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__								=  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__								=  load_notes_encoder(ta_checkpoint["target"]["token_encoder"],  __A							)
      UpperCAmelCase__								=  load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"],  __A							)
      UpperCAmelCase__								=  load_decoder(ta_checkpoint["target"]["decoder"],  __A							)

      UpperCAmelCase__								=  OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"							)

      UpperCAmelCase__								=  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__":
      UpperCamelCase__											=					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__											=					parser.parse_args()

      main(args)



