


'''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)













"""simple docstring"""






import random
from typing import Any



def 					_A			(__a							) ->       list[Any]:
    """simple docstring"""






    for _ in range(len(__a							)							):
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		data[b], data[a]
    return data


if __name__ == "__main__":
  UpperCAmelCase_   : Dict   			= [0, 1, 2, 3, 4, 5, 6, 7]
  UpperCAmelCase_   : Dict   			= ["""python""", """says""", """hello""", """!"""]
  print("""Fisher-Yates Shuffle:""")
  print("""List""", integers, strings)
  print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


