import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[Any]      =			(UnCLIPScheduler,)


   def 			lowercase_      (self     :						Union[str, Any]       ,  **__UpperCAmelCase     :						Optional[Any]		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  {
             "num_train_timesteps": 1_0_0_0,
             "variance_type": "fixed_small_log",
             "clip_sample": True,
             "clip_sample_range": 1.0,
             "prediction_type": "epsilon",
         }

         config.update(**__UpperCAmelCase		)
         return config


   def 			lowercase_      (self     :						Optional[int]		)						->				Tuple:
         """simple docstring"""



         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCAmelCase		)


   def 			lowercase_      (self     :						Optional[Any]		)						->				Any:
         """simple docstring"""



         for variance in ["fixed_small_log", "learned_range"]:
               self.check_over_configs(variance_type=__UpperCAmelCase		)


   def 			lowercase_      (self     :						int		)						->				Union[str, Any]:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Dict:
         """simple docstring"""



         for clip_sample_range in [1, 5, 1_0, 2_0]:
               self.check_over_configs(clip_sample_range=__UpperCAmelCase		)


   def 			lowercase_      (self     :						List[str]		)						->				Dict:
         """simple docstring"""



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


   def 			lowercase_      (self     :						Dict		)						->				List[Any]:
         """simple docstring"""



         for time_step in [0, 5_0_0, 9_9_9]:
               for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
                     if prev_timestep is not None and prev_timestep >= time_step:
                           continue

                     self.check_over_forward(time_step=__UpperCAmelCase       ,  prev_timestep=__UpperCAmelCase		)


   def 			lowercase_      (self     :						Dict		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  self.scheduler_classes[0]
         UpperCAmelCase__								=  self.get_scheduler_config(variance_type="fixed_small_log"		)
         UpperCAmelCase__								=  scheduler_class(**__UpperCAmelCase		)

         assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.0000E-10		)		) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7		) - 0.0549625		)		) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9		) - 0.9994987		)		) < 1E-5


   def 			lowercase_      (self     :						Dict		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  self.scheduler_classes[0]
         UpperCAmelCase__								=  self.get_scheduler_config(variance_type="learned_range"		)
         UpperCAmelCase__								=  scheduler_class(**__UpperCAmelCase		)

         UpperCAmelCase__								=  0.5

         assert scheduler._get_variance(1       ,  predicted_variance=__UpperCAmelCase		) - -10.1712790 < 1E-5
         assert scheduler._get_variance(4_8_7       ,  predicted_variance=__UpperCAmelCase		) - -5.7998052 < 1E-5
         assert scheduler._get_variance(9_9_9       ,  predicted_variance=__UpperCAmelCase		) - -0.0010011 < 1E-5


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



         UpperCAmelCase__								=  self.scheduler_classes[0]
         UpperCAmelCase__								=  self.get_scheduler_config()
         UpperCAmelCase__								=  scheduler_class(**__UpperCAmelCase		)

         UpperCAmelCase__								=  scheduler.timesteps

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

         for i, t in enumerate(__UpperCAmelCase		):
               # 1. predict noise residual
               UpperCAmelCase__								=  model(__UpperCAmelCase       ,  __UpperCAmelCase		)

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

               UpperCAmelCase__								=  pred_prev_sample

         UpperCAmelCase__								=  torch.sum(torch.abs(__UpperCAmelCase		)		)
         UpperCAmelCase__								=  torch.mean(torch.abs(__UpperCAmelCase		)		)

         assert abs(result_sum.item() - 252.2682495		) < 1E-2
         assert abs(result_mean.item() - 0.3284743		) < 1E-3


   def 			lowercase_      (self     :						Any		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.scheduler_classes[0]
         UpperCAmelCase__								=  self.get_scheduler_config()
         UpperCAmelCase__								=  scheduler_class(**__UpperCAmelCase		)

         scheduler.set_timesteps(2_5		)

         UpperCAmelCase__								=  scheduler.timesteps

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

         for i, t in enumerate(__UpperCAmelCase		):
               # 1. predict noise residual
               UpperCAmelCase__								=  model(__UpperCAmelCase       ,  __UpperCAmelCase		)

               if i + 1 == timesteps.shape[0]:
                     UpperCAmelCase__								=  None
               else:
                     UpperCAmelCase__								=  timesteps[i + 1]

               # 2. predict previous mean of sample x_t-1
               UpperCAmelCase__								=  scheduler.step(
                   __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  prev_timestep=__UpperCAmelCase       ,  generator=__UpperCAmelCase		).prev_sample

               UpperCAmelCase__								=  pred_prev_sample

         UpperCAmelCase__								=  torch.sum(torch.abs(__UpperCAmelCase		)		)
         UpperCAmelCase__								=  torch.mean(torch.abs(__UpperCAmelCase		)		)

         assert abs(result_sum.item() - 258.2044983		) < 1E-2
         assert abs(result_mean.item() - 0.3362038		) < 1E-3


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



         pass





   def 			lowercase_      (self     :						List[Any]		)						->				List[str]:
         """simple docstring"""



         pass




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)



