


'''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__  :					int        =       'base_with_context'







def _UpperCamelCase       (     __A      ,			__A    )							->		Dict:


      '''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 _UpperCamelCase       (     __A      ,			__A    )							->		int:


      '''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 _UpperCamelCase       (     __A      ,			__A    )							->		int:


      '''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 _UpperCamelCase       (     __A    )							->		Union[str, Any]:


      '''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__":
  a__  :					Optional[Any]        =       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__  :					Dict        =       parser.parse_args()

  main(args)










import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Tuple											=						(DDPMParallelScheduler,)



       def 		_lowerCamelCase			(		self							:     int     ,		**__SCREAMING_SNAKE_CASE							:     List[Any]):


           '''simple docstring'''

           __a						       =				{
               '''num_train_timesteps''': 1_000,
               '''beta_start''': 0.00_01,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''variance_type''': '''fixed_small''',
               '''clip_sample''': True,
           }

           config.update(**__SCREAMING_SNAKE_CASE)
           return config



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for timesteps in [1, 5, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]     ,		[0.0_02, 0.02, 0.2, 2]):
               self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE     ,		beta_end=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           for variance in ["fixed_small", "fixed_large", "other"]:
               self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE)
           for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=__SCREAMING_SNAKE_CASE     ,		prediction_type=__SCREAMING_SNAKE_CASE     ,		sample_max_value=__SCREAMING_SNAKE_CASE     ,		)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

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



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           for t in [0, 500, 999]:
               self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_09_79)) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1E-5



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				self.dummy_sample_deter + 0.1
           __a						       =				self.dummy_sample_deter - 0.1

           __a						       =				samplea.shape[0]
           __a						       =				torch.stack([samplea, samplea, samplea]     ,		dim=0)
           __a						       =				torch.arange(__SCREAMING_SNAKE_CASE)[0:3, None].repeat(1     ,		__SCREAMING_SNAKE_CASE)

           __a						       =				model(samples.flatten(0     ,		1)     ,		timesteps.flatten(0     ,		1))
           __a						       =				scheduler.batch_step_no_noise(__SCREAMING_SNAKE_CASE     ,		timesteps.flatten(0     ,		1)     ,		samples.flatten(0     ,		1))

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 11_53.18_33) < 1E-2
           assert abs(result_mean.item() - 0.50_05) < 1E-3



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				torch.manual_seed(0)

           for t in reversed(range(__SCREAMING_SNAKE_CASE)):
               # 1. predict noise residual
               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               # 2. predict previous mean of sample x_t-1
               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE).prev_sample

               __a						       =				pred_prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 2_58.96_06) < 1E-2
           assert abs(result_mean.item() - 0.33_72) < 1E-3



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config(prediction_type='''v_prediction''')
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				len(__SCREAMING_SNAKE_CASE)

           __a						       =				self.dummy_model()
           __a						       =				self.dummy_sample_deter
           __a						       =				torch.manual_seed(0)

           for t in reversed(range(__SCREAMING_SNAKE_CASE)):
               # 1. predict noise residual
               __a						       =				model(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               # 2. predict previous mean of sample x_t-1
               __a						       =				scheduler.step(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE).prev_sample

               __a						       =				pred_prev_sample

           __a						       =				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a						       =				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE))

           assert abs(result_sum.item() - 2_02.02_96) < 1E-2
           assert abs(result_mean.item() - 0.26_31) < 1E-3



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)

           __a						       =				scheduler.timesteps

           for i, timestep in enumerate(__SCREAMING_SNAKE_CASE):
               if i == len(__SCREAMING_SNAKE_CASE) - 1:
                   __a						       =				-1
               else:
                   __a						       =				timesteps[i + 1]

               __a						       =				scheduler.previous_timestep(__SCREAMING_SNAKE_CASE)
               __a						       =				prev_t.item()

               self.assertEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 51, 0]

           with self.assertRaises(__SCREAMING_SNAKE_CASE     ,		msg='''`custom_timesteps` must be in descending order.'''):
               scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[100, 87, 50, 1, 0]
           __a						       =				len(__SCREAMING_SNAKE_CASE)

           with self.assertRaises(__SCREAMING_SNAKE_CASE     ,		msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
               scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE     ,		timesteps=__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				self.scheduler_classes[0]
           __a						       =				self.get_scheduler_config()
           __a						       =				scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a						       =				[scheduler.config.num_train_timesteps]

           with self.assertRaises(
               __SCREAMING_SNAKE_CASE     ,		msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''     ,		):
               scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE)





