

import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= (EulerDiscreteScheduler,)
  a									= 10






  def 				lowercase_		(							self						:		int     ,       **__lowerCamelCase						:		List[Any] )		->							str:
       SCREAMING_SNAKE_CASE__   =   {
           '''num_train_timesteps''': 1100,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
       }

       config.update(**lowercase_ )
       return config






  def 				lowercase_		(							self						:		int )		->							str:







       for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=lowercase_ )






  def 				lowercase_		(							self						:		List[str] )		->							Optional[Any]:







       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]     ,       [0.0002, 0.002, 0.02] ):
            self.check_over_configs(beta_start=lowercase_     ,       beta_end=lowercase_ )






  def 				lowercase_		(							self						:		Dict )		->							Optional[int]:







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






  def 				lowercase_		(							self						:		Optional[Any] )		->							Optional[int]:







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






  def 				lowercase_		(							self						:		Optional[int] )		->							str:
       SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__   =   scheduler_class(**lowercase_ )

       scheduler.set_timesteps(self.num_inference_steps )

       SCREAMING_SNAKE_CASE__   =   torch.manual_seed(0 )

       SCREAMING_SNAKE_CASE__   =   self.dummy_model()
       SCREAMING_SNAKE_CASE__   =   self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__   =   sample.to(lowercase_ )

       for i, t in enumerate(scheduler.timesteps ):
            SCREAMING_SNAKE_CASE__   =   scheduler.scale_model_input(lowercase_     ,       lowercase_ )

            SCREAMING_SNAKE_CASE__   =   model(lowercase_     ,       lowercase_ )

            SCREAMING_SNAKE_CASE__   =   scheduler.step(lowercase_     ,       lowercase_     ,       lowercase_     ,       generator=lowercase_ )
            SCREAMING_SNAKE_CASE__   =   output.prev_sample

       SCREAMING_SNAKE_CASE__   =   torch.sum(torch.abs(lowercase_ ) )
       SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(lowercase_ ) )

       assert abs(result_sum.item() - 10.0807 ) < 1e-2
       assert abs(result_mean.item() - 0.0131 ) < 1e-3






  def 				lowercase_		(							self						:		List[Any] )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config(prediction_type='''v_prediction''' )
       SCREAMING_SNAKE_CASE__   =   scheduler_class(**lowercase_ )

       scheduler.set_timesteps(self.num_inference_steps )

       SCREAMING_SNAKE_CASE__   =   torch.manual_seed(0 )

       SCREAMING_SNAKE_CASE__   =   self.dummy_model()
       SCREAMING_SNAKE_CASE__   =   self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__   =   sample.to(lowercase_ )

       for i, t in enumerate(scheduler.timesteps ):
            SCREAMING_SNAKE_CASE__   =   scheduler.scale_model_input(lowercase_     ,       lowercase_ )

            SCREAMING_SNAKE_CASE__   =   model(lowercase_     ,       lowercase_ )

            SCREAMING_SNAKE_CASE__   =   scheduler.step(lowercase_     ,       lowercase_     ,       lowercase_     ,       generator=lowercase_ )
            SCREAMING_SNAKE_CASE__   =   output.prev_sample

       SCREAMING_SNAKE_CASE__   =   torch.sum(torch.abs(lowercase_ ) )
       SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(lowercase_ ) )

       assert abs(result_sum.item() - 0.0002 ) < 1e-2
       assert abs(result_mean.item() - 2.26_76e-06 ) < 1e-3






  def 				lowercase_		(							self						:		Union[str, Any] )		->							Dict:
       SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__   =   scheduler_class(**lowercase_ )

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

       SCREAMING_SNAKE_CASE__   =   torch.manual_seed(0 )

       SCREAMING_SNAKE_CASE__   =   self.dummy_model()
       SCREAMING_SNAKE_CASE__   =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       SCREAMING_SNAKE_CASE__   =   sample.to(lowercase_ )

       for t in scheduler.timesteps:
            SCREAMING_SNAKE_CASE__   =   scheduler.scale_model_input(lowercase_     ,       lowercase_ )

            SCREAMING_SNAKE_CASE__   =   model(lowercase_     ,       lowercase_ )

            SCREAMING_SNAKE_CASE__   =   scheduler.step(lowercase_     ,       lowercase_     ,       lowercase_     ,       generator=lowercase_ )
            SCREAMING_SNAKE_CASE__   =   output.prev_sample

       SCREAMING_SNAKE_CASE__   =   torch.sum(torch.abs(lowercase_ ) )
       SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(lowercase_ ) )

       assert abs(result_sum.item() - 10.0807 ) < 1e-2
       assert abs(result_mean.item() - 0.0131 ) < 1e-3







  def 				lowercase_		(							self						:		Any )		->							str:
       SCREAMING_SNAKE_CASE__   =   self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__   =   self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__   =   scheduler_class(**lowercase_     ,       use_karras_sigmas=lowercase_ )

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

       SCREAMING_SNAKE_CASE__   =   torch.manual_seed(0 )

       SCREAMING_SNAKE_CASE__   =   self.dummy_model()
       SCREAMING_SNAKE_CASE__   =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       SCREAMING_SNAKE_CASE__   =   sample.to(lowercase_ )

       for t in scheduler.timesteps:
            SCREAMING_SNAKE_CASE__   =   scheduler.scale_model_input(lowercase_     ,       lowercase_ )

            SCREAMING_SNAKE_CASE__   =   model(lowercase_     ,       lowercase_ )

            SCREAMING_SNAKE_CASE__   =   scheduler.step(lowercase_     ,       lowercase_     ,       lowercase_     ,       generator=lowercase_ )
            SCREAMING_SNAKE_CASE__   =   output.prev_sample

       SCREAMING_SNAKE_CASE__   =   torch.sum(torch.abs(lowercase_ ) )
       SCREAMING_SNAKE_CASE__   =   torch.mean(torch.abs(lowercase_ ) )

       assert abs(result_sum.item() - 124.52_2994_9951_1719 ) < 1e-2
       assert abs(result_mean.item() - 0.16213932633399963 ) < 1e-3





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


_SCREAMING_SNAKE_CASE					:  Dict             =      '''base_with_context'''
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']    )    )
     SCREAMING_SNAKE_CASE__   =   nn.Parameter(
         torch.FloatTensor(weights['''Embed_0''']['''embedding''']    )				,		requires_grad=_A    )
     for lyr_num, lyr in enumerate(model.encoders    ):
          SCREAMING_SNAKE_CASE__   =   weights[F'''layers_{lyr_num}''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']    )    )

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

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

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

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']    )    )
     return model
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T    )    )

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

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

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

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

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

     return model
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T    )    )
     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T    )    )

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

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

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

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

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

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

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

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

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

     return model
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   checkpoints.load_tax_checkpoint(args.checkpoint_path    )
     SCREAMING_SNAKE_CASE__   =   jnp.tree_util.tree_map(onp.array				,		_A    )

     SCREAMING_SNAKE_CASE__   =   [
         '''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()''',
     ]

     SCREAMING_SNAKE_CASE__   =   os.path.join(args.checkpoint_path				,		'''..'''				,		'''config.gin'''    )
     SCREAMING_SNAKE_CASE__   =   inference.parse_training_gin_file(_A				,		_A    )
     SCREAMING_SNAKE_CASE__   =   inference.InferenceModel(args.checkpoint_path				,		_A    )

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

     SCREAMING_SNAKE_CASE__   =   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'''				,		)

     SCREAMING_SNAKE_CASE__   =   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'''				,		)

     SCREAMING_SNAKE_CASE__   =   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				,		)

     SCREAMING_SNAKE_CASE__   =   load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']				,		_A    )
     SCREAMING_SNAKE_CASE__   =   load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']				,		_A    )
     SCREAMING_SNAKE_CASE__   =   load_decoder(ta_checkpoint['''target''']['''decoder''']				,		_A    )

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

     SCREAMING_SNAKE_CASE__   =   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__":
       _SCREAMING_SNAKE_CASE					:  Dict             =      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.''',
       )
       _SCREAMING_SNAKE_CASE					:  Tuple             =      parser.parse_args()

       main(args)


