






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


lowerCAmelCase_	:    List[Any]            =      '''base_with_context'''






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]		)		)
       UpperCAmelCase	    =			nn.Parameter(
           torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]		)	,      requires_grad=lowerCAmelCase		)
       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       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T		)		)

       UpperCAmelCase	    =			nn.Parameter(
           torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]		)	,      requires_grad=lowerCAmelCase		)

       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       (      lowerCAmelCase	,      lowerCAmelCase		):







       '''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=lowerCAmelCase		)

       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       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			checkpoints.load_tax_checkpoint(args.checkpoint_path		)
       UpperCAmelCase	    =			jnp.tree_util.tree_map(onp.array	,      lowerCAmelCase		)

       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(lowerCAmelCase	,      lowerCAmelCase		)
       UpperCAmelCase	    =			inference.InferenceModel(args.checkpoint_path	,      lowerCAmelCase		)

       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"""]	,      lowerCAmelCase		)
       UpperCAmelCase	    =			load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]	,      lowerCAmelCase		)
       UpperCAmelCase	    =			load_decoder(ta_checkpoint["""target"""]["""decoder"""]	,      lowerCAmelCase		)

       UpperCAmelCase	    =			OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""		)

       UpperCAmelCase	    =			SpectrogramDiffusionPipeline(
           notes_encoder=lowerCAmelCase	,      continuous_encoder=lowerCAmelCase	,      decoder=lowerCAmelCase	,      scheduler=lowerCAmelCase	,      melgan=lowerCAmelCase	,      )
       if args.save:
              pipe.save_pretrained(args.output_path		)


if __name__ == "__main__":
 lowerCAmelCase_	:    Tuple            =      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.''',
 )
 lowerCAmelCase_	:    Tuple            =      parser.parse_args()

 main(args)








"""simple docstring"""







from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_	:    str            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Optional[int]            =      {
    '''huggingface/time-series-transformer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}





class   UpperCamelCase_ (       a_  ):
  _A       :					int				=	'time_series_transformer'
  _A       :					List[Any]				=	{
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
      'num_hidden_layers': 'encoder_layers',
  }



  def __init__(      self ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = "student_t" ,		snake_case__ = "nll" ,		snake_case__ = 1 ,		snake_case__ = [1, 2, 3, 4, 5, 6, 7] ,		snake_case__ = "mean" ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = 0 ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = 32 ,		snake_case__ = 32 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = 2 ,		snake_case__ = True ,		snake_case__ = "gelu" ,		snake_case__ = 64 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 0.1 ,		snake_case__ = 1_00 ,		snake_case__ = 0.02 ,		snake_case__=True ,		**snake_case__ ,		)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			prediction_length
         UpperCAmelCase	    =			context_length or prediction_length
         UpperCAmelCase	    =			distribution_output
         UpperCAmelCase	    =			loss
         UpperCAmelCase	    =			input_size
         UpperCAmelCase	    =			num_time_features
         UpperCAmelCase	    =			lags_sequence
         UpperCAmelCase	    =			scaling
         UpperCAmelCase	    =			num_dynamic_real_features
         UpperCAmelCase	    =			num_static_real_features
         UpperCAmelCase	    =			num_static_categorical_features
         if cardinality and num_static_categorical_features > 0:
                if len(snake_case__				) != num_static_categorical_features:
                       raise ValueError(
                           """The cardinality should be a list of the same length as `num_static_categorical_features`"""				)
                UpperCAmelCase	    =			cardinality
         else:
                UpperCAmelCase	    =			[0]
         if embedding_dimension and num_static_categorical_features > 0:
                if len(snake_case__				) != num_static_categorical_features:
                       raise ValueError(
                           """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""				)
                UpperCAmelCase	    =			embedding_dimension
         else:
                UpperCAmelCase	    =			[min(50 ,		(cat + 1) // 2				) for cat in self.cardinality]
         UpperCAmelCase	    =			num_parallel_samples

         # Transformer architecture configuration
         UpperCAmelCase	    =			input_size * len(snake_case__				) + self._number_of_features
         UpperCAmelCase	    =			d_model
         UpperCAmelCase	    =			encoder_attention_heads
         UpperCAmelCase	    =			decoder_attention_heads
         UpperCAmelCase	    =			encoder_ffn_dim
         UpperCAmelCase	    =			decoder_ffn_dim
         UpperCAmelCase	    =			encoder_layers
         UpperCAmelCase	    =			decoder_layers

         UpperCAmelCase	    =			dropout
         UpperCAmelCase	    =			attention_dropout
         UpperCAmelCase	    =			activation_dropout
         UpperCAmelCase	    =			encoder_layerdrop
         UpperCAmelCase	    =			decoder_layerdrop

         UpperCAmelCase	    =			activation_function
         UpperCAmelCase	    =			init_std

         UpperCAmelCase	    =			use_cache

         super().__init__(is_encoder_decoder=snake_case__ ,		**snake_case__				)



  @property
  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return (
             sum(self.embedding_dimension				)
             + self.num_dynamic_real_features
             + self.num_time_features
             + self.num_static_real_features
             + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
         )
