





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


lowercase      :       List[Any]									=  """base_with_context"""



def A_	(		A__		,      A__				)  ->		Any:
       a__						:	str =				nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']				)				)
       a__						:	List[str] =				nn.Parameter(
           torch.FloatTensor(weights['Embed_0']['embedding']				)		,      requires_grad=A__				)
       for lyr_num, lyr in enumerate(model.encoders				):
              a__						:	Tuple =				weights[F'layers_{lyr_num}']
              a__						:	Tuple =				nn.Parameter(
                  torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']				)				)

              a__						:	Optional[int] =				ly_weight['attention']
              a__						:	str =				nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T				)				)
              a__						:	Any =				nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T				)				)
              a__						:	Dict =				nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T				)				)
              a__						:	Tuple =				nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T				)				)

              a__						:	List[Any] =				nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']				)				)

              a__						:	int =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T				)				)
              a__						:	Optional[Any] =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T				)				)
              a__						:	str =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T				)				)

       a__						:	Union[str, Any] =				nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']				)				)
       return model



def A_	(		A__		,      A__				)  ->		Tuple:
       a__						:	Dict =				nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T				)				)

       a__						:	Optional[int] =				nn.Parameter(
           torch.FloatTensor(weights['Embed_0']['embedding']				)		,      requires_grad=A__				)

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

              a__						:	Optional[int] =				nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T				)				)
              a__						:	Any =				nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T				)				)
              a__						:	Optional[int] =				nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T				)				)
              a__						:	List[str] =				nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T				)				)
              a__						:	int =				nn.Parameter(
                  torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']				)				)

              a__						:	List[str] =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T				)				)
              a__						:	List[str] =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T				)				)
              a__						:	Any =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T				)				)
              a__						:	str =				nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']				)				)

       a__						:	Optional[int] =				nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']				)				)

       return model



def A_	(		A__		,      A__				)  ->		str:
       a__						:	List[Any] =				nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T				)				)
       a__						:	Optional[int] =				nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T				)				)

       a__						:	Tuple =				nn.Parameter(
           torch.FloatTensor(weights['Embed_0']['embedding']				)		,      requires_grad=A__				)

       a__						:	Optional[int] =				nn.Parameter(
           torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T				)				)

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

              a__						:	Optional[Any] =				nn.Parameter(
                  torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T				)				)

              a__						:	int =				ly_weight['self_attention']
              a__						:	Tuple =				nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T				)				)
              a__						:	Union[str, Any] =				nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T				)				)
              a__						:	str =				nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T				)				)
              a__						:	Optional[Any] =				nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T				)				)

              a__						:	Tuple =				ly_weight['MultiHeadDotProductAttention_0']
              a__						:	int =				nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T				)				)
              a__						:	Any =				nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T				)				)
              a__						:	List[str] =				nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T				)				)
              a__						:	int =				nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T				)				)
              a__						:	List[str] =				nn.Parameter(
                  torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']				)				)

              a__						:	Tuple =				nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']				)				)
              a__						:	Optional[int] =				nn.Parameter(
                  torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T				)				)
              a__						:	int =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T				)				)
              a__						:	List[str] =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T				)				)
              a__						:	Dict =				nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T				)				)

       a__						:	int =				nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']				)				)

       a__						:	str =				nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T				)				)

       return model



def A_	(		A__				)  ->		Optional[int]:
       a__						:	List[str] =				checkpoints.load_tax_checkpoint(args.checkpoint_path				)
       a__						:	Any =				jnp.tree_util.tree_map(onp.array		,      A__				)

       a__						:	Optional[Any] =				[
           '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()',
       ]

       a__						:	List[Any] =				os.path.join(args.checkpoint_path		,      '..'		,      'config.gin'				)
       a__						:	Union[str, Any] =				inference.parse_training_gin_file(A__		,      A__				)
       a__						:	Dict =				inference.InferenceModel(args.checkpoint_path		,      A__				)

       a__						:	Tuple =				DDPMScheduler(beta_schedule='squaredcos_cap_v2'		,      variance_type='fixed_large'				)

       a__						:	Optional[int] =				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'		,      )

       a__						:	Tuple =				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'		,      )

       a__						:	Any =				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		,      )

       a__						:	str =				load_notes_encoder(ta_checkpoint['target']['token_encoder']		,      A__				)
       a__						:	Tuple =				load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']		,      A__				)
       a__						:	Tuple =				load_decoder(ta_checkpoint['target']['decoder']		,      A__				)

       a__						:	int =				OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'				)

       a__						:	Optional[Any] =				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__":
    lowercase      :       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.""",
    )
    lowercase      :       Optional[int]									=  parser.parse_args()

    main(args)









import math
import random



def A_	(		A__		,      A__ = False				)  ->		float:
       if deriv:
              return value * (1 - value)
       return 1 / (1 + math.exp(-value				))


# Initial Value
lowercase      :       Optional[Any]									=  0.02



def A_	(		A__		,      A__				)  ->		float:
       a__						:	int =				float(2 * (random.randint(1		,      100				)) - 1				)

       for _ in range(A__				):
              # Forward propagation
              a__						:	Union[str, Any] =				sigmoid_function(INITIAL_VALUE * weight				)
              # How much did we miss?
              a__						:	Optional[Any] =				(expected / 100) - layer_a
              # Error delta
              a__						:	Optional[int] =				layer_1_error * sigmoid_function(A__		,      A__				)
              # Update weight
              weight += INITIAL_VALUE * layer_1_delta

       return layer_a * 100


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowercase      :       Dict									=  int(input("""Expected value: """))
    lowercase      :       Dict									=  int(input("""Number of propagations: """))
    print(forward_propagation(expected, number_propagations))


