'''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												= "base_with_context"






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

        __lowerCamelCase          =  ly_weight['''attention''']
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )

        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )

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

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






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   Tuple:
    __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T    )    )

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

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

        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(
            torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']    )    )

        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )

    __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']    )    )

    return model






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[Any]:
    __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T    )    )
    __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T    )    )

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

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

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

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

        __lowerCamelCase          =  ly_weight['''self_attention''']
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )

        __lowerCamelCase          =  ly_weight['''MultiHeadDotProductAttention_0''']
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(
            torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']    )    )

        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )
        __lowerCamelCase          =  nn.Parameter(
            torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
        __lowerCamelCase          =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )

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

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

    return model






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   Any:
    __lowerCamelCase          =  checkpoints.load_tax_checkpoint(args.checkpoint_path    )
    __lowerCamelCase          =  jnp.tree_util.tree_map(onp.array    ,  UpperCamelCase__    )

    __lowerCamelCase          =  [
        '''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          =  os.path.join(args.checkpoint_path    ,  '''..'''    ,  '''config.gin'''    )
    __lowerCamelCase          =  inference.parse_training_gin_file(UpperCamelCase__    ,  UpperCamelCase__    )
    __lowerCamelCase          =  inference.InferenceModel(args.checkpoint_path    ,  UpperCamelCase__    )

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

    __lowerCamelCase          =  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          =  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          =  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          =  load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']    ,  UpperCamelCase__    )
    __lowerCamelCase          =  load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']    ,  UpperCamelCase__    )
    __lowerCamelCase          =  load_decoder(ta_checkpoint['''target''']['''decoder''']    ,  UpperCamelCase__    )

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

    __lowerCamelCase          =  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__":
  __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)

'''simple docstring'''




import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest




@require_torchsde
class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							=(DPMSolverSDEScheduler,)
     lowerCamelCase  :					List[str]							=1_0






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    **a		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  {
             '''num_train_timesteps''': 11_00,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''noise_sampler_seed''': 0,
         }

         config.update(**a   )
         return config






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         for timesteps in [10, 50, 1_00, 10_00]:
             self.check_over_configs(num_train_timesteps=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]  ,    [0.00_02, 0.0_02, 0.02]   ):
             self.check_over_configs(beta_start=a  ,    beta_end=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config()
         __lowerCamelCase          =  scheduler_class(**a   )

         scheduler.set_timesteps(self.num_inference_steps   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase          =  sample.to(a   )

         for i, t in enumerate(scheduler.timesteps   ):
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

         __lowerCamelCase          =  torch.sum(torch.abs(a   )   )
         __lowerCamelCase          =  torch.mean(torch.abs(a   )   )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75   ) < 1e-2
             assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77   ) < 1e-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06   ) < 1e-2
             assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52   ) < 1e-3
         else:
             assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62   ) < 1e-2
             assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26   ) < 1e-3






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config(prediction_type='''v_prediction'''   )
         __lowerCamelCase          =  scheduler_class(**a   )

         scheduler.set_timesteps(self.num_inference_steps   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter * scheduler.init_noise_sigma
         __lowerCamelCase          =  sample.to(a   )

         for i, t in enumerate(scheduler.timesteps   ):
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

         __lowerCamelCase          =  torch.sum(torch.abs(a   )   )
         __lowerCamelCase          =  torch.mean(torch.abs(a   )   )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53   ) < 1e-2
             assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84   ) < 1e-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03   ) < 1e-2
             assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97   ) < 1e-3
         else:
             assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25   ) < 1e-2
             assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21   ) < 1e-3






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config()
         __lowerCamelCase          =  scheduler_class(**a   )

         scheduler.set_timesteps(self.num_inference_steps  ,    device=a   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter.to(a   ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

         __lowerCamelCase          =  torch.sum(torch.abs(a   )   )
         __lowerCamelCase          =  torch.mean(torch.abs(a   )   )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38   ) < 1e-2
             assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35   ) < 1e-3
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12   ) < 1e-2
             assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71   ) < 1e-3
         else:
             assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62   ) < 1e-2
             assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26   ) < 1e-3






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.scheduler_classes[0]
         __lowerCamelCase          =  self.get_scheduler_config()
         __lowerCamelCase          =  scheduler_class(**a  ,    use_karras_sigmas=a   )

         scheduler.set_timesteps(self.num_inference_steps  ,    device=a   )

         __lowerCamelCase          =  self.dummy_model()
         __lowerCamelCase          =  self.dummy_sample_deter.to(a   ) * scheduler.init_noise_sigma
         __lowerCamelCase          =  sample.to(a   )

         for t in scheduler.timesteps:
             __lowerCamelCase          =  scheduler.scale_model_input(a  ,    a   )

             __lowerCamelCase          =  model(a  ,    a   )

             __lowerCamelCase          =  scheduler.step(a  ,    a  ,    a   )
             __lowerCamelCase          =  output.prev_sample

         __lowerCamelCase          =  torch.sum(torch.abs(a   )   )
         __lowerCamelCase          =  torch.mean(torch.abs(a   )   )

         if torch_device in ["mps"]:
             assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88   ) < 1e-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1e-2
         elif torch_device in ["cuda"]:
             assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25   ) < 1e-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1e-2
         else:
             assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72   ) < 1e-2
             assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11   ) < 1e-2
