
'''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


UpperCamelCase__      :		Optional[int]       							=  "base_with_context"
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]						)						)
   _SCREAMING_SNAKE_CASE      =				nn.Parameter(
       torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)    ,      requires_grad=SCREAMING_SNAKE_CASE_						)
   for lyr_num, lyr in enumerate(model.encoders						):
      _SCREAMING_SNAKE_CASE      =				weights[F"layers_{lyr_num}"]
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(
          torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

      _SCREAMING_SNAKE_CASE      =				ly_weight["""attention"""]
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)

      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)

      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)

   _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]						)						)
   return model
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T						)						)

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

   for lyr_num, lyr in enumerate(model.encoders						):
      _SCREAMING_SNAKE_CASE      =				weights[F"layers_{lyr_num}"]
      _SCREAMING_SNAKE_CASE      =				ly_weight["""attention"""]

      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(
          torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)

   _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]						)						)

   return model
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T						)						)
   _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T						)						)

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

   _SCREAMING_SNAKE_CASE      =				nn.Parameter(
       torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T						)						)

   for lyr_num, lyr in enumerate(model.decoders						):
      _SCREAMING_SNAKE_CASE      =				weights[F"layers_{lyr_num}"]
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(
          torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""]						)						)

      _SCREAMING_SNAKE_CASE      =				nn.Parameter(
          torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T						)						)

      _SCREAMING_SNAKE_CASE      =				ly_weight["""self_attention"""]
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)

      _SCREAMING_SNAKE_CASE      =				ly_weight["""MultiHeadDotProductAttention_0"""]
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(
          torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]						)						)

      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(
          torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T						)						)
      _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)

   _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]						)						)

   _SCREAMING_SNAKE_CASE      =				nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T						)						)

   return model
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Union[str, Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				checkpoints.load_tax_checkpoint(args.checkpoint_path						)
   _SCREAMING_SNAKE_CASE      =				jnp.tree_util.tree_map(onp.array    ,      SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				[
       """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()""",
   ]

   _SCREAMING_SNAKE_CASE      =				os.path.join(args.checkpoint_path    ,      """.."""    ,      """config.gin"""						)
   _SCREAMING_SNAKE_CASE      =				inference.parse_training_gin_file(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				inference.InferenceModel(args.checkpoint_path    ,      SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""    ,      variance_type="""fixed_large"""						)

   _SCREAMING_SNAKE_CASE      =				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"""    ,      )

   _SCREAMING_SNAKE_CASE      =				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"""    ,      )

   _SCREAMING_SNAKE_CASE      =				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    ,      )

   _SCREAMING_SNAKE_CASE      =				load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]    ,      SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]    ,      SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				load_decoder(ta_checkpoint["""target"""]["""decoder"""]    ,      SCREAMING_SNAKE_CASE_						)

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

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


if __name__ == "__main__":
     UpperCamelCase__      :		List[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.",
     )
     UpperCamelCase__      :		List[Any]       							=  parser.parse_args()

     main(args)








'''simple docstring'''



import sys

UpperCamelCase__      :		int       							=  (
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_ = N						)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				-sys.maxsize - 1
   for i in range(len(SCREAMING_SNAKE_CASE_						) - 12						):
      _SCREAMING_SNAKE_CASE      =				1
      for j in range(13						):
         product *= int(n[i + j]						)
      if product > largest_product:
         _SCREAMING_SNAKE_CASE      =				product
   return largest_product


if __name__ == "__main__":
     print(f"""{solution() = }""")






