


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_	   =     'base_with_context'
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict		 =						nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']						)						)
 __lowerCamelCase		:							Optional[Any]		 =						nn.Parameter(
     torch.FloatTensor(weights['Embed_0']['embedding']						)      ,	requires_grad=SCREAMING_SNAKE_CASE__						)
 for lyr_num, lyr in enumerate(model.encoders						):
  __lowerCamelCase		:							Any		 =						weights[f'layers_{lyr_num}']
  __lowerCamelCase		:							Tuple		 =						nn.Parameter(
      torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']						)						)

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

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

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

 __lowerCamelCase		:							Dict		 =						nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']						)						)
 return model
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[Any]		 =						nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T						)						)

 __lowerCamelCase		:							str		 =						nn.Parameter(
     torch.FloatTensor(weights['Embed_0']['embedding']						)      ,	requires_grad=SCREAMING_SNAKE_CASE__						)

 for lyr_num, lyr in enumerate(model.encoders						):
  __lowerCamelCase		:							Union[str, Any]		 =						weights[f'layers_{lyr_num}']
  __lowerCamelCase		:							Tuple		 =						ly_weight['attention']

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

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

 __lowerCamelCase		:							Tuple		 =						nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']						)						)

 return model
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T						)						)
 __lowerCamelCase		:							Any		 =						nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T						)						)

 __lowerCamelCase		:							Dict		 =						nn.Parameter(
     torch.FloatTensor(weights['Embed_0']['embedding']						)      ,	requires_grad=SCREAMING_SNAKE_CASE__						)

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

 for lyr_num, lyr in enumerate(model.decoders						):
  __lowerCamelCase		:							Tuple		 =						weights[f'layers_{lyr_num}']
  __lowerCamelCase		:							Tuple		 =						nn.Parameter(
      torch.FloatTensor(ly_weight['pre_self_attention_layer_norm']['scale']						)						)

  __lowerCamelCase		:							Tuple		 =						nn.Parameter(
      torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T						)						)

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

  __lowerCamelCase		:							List[Any]		 =						ly_weight['MultiHeadDotProductAttention_0']
  __lowerCamelCase		:							List[Any]		 =						nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T						)						)
  __lowerCamelCase		:							str		 =						nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T						)						)
  __lowerCamelCase		:							Optional[Any]		 =						nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T						)						)
  __lowerCamelCase		:							List[Any]		 =						nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T						)						)
  __lowerCamelCase		:							Union[str, Any]		 =						nn.Parameter(
      torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']						)						)

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

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

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

 return model
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict		 =						checkpoints.load_tax_checkpoint(args.checkpoint_path						)
 __lowerCamelCase		:							str		 =						jnp.tree_util.tree_map(onp.array      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							List[str]		 =						[
     '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()',
 ]

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

 __lowerCamelCase		:							Union[str, Any]		 =						DDPMScheduler(beta_schedule='squaredcos_cap_v2'      ,	variance_type='fixed_large'						)

 __lowerCamelCase		:							Tuple		 =						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'      ,	)

 __lowerCamelCase		:							str		 =						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'      ,	)

 __lowerCamelCase		:							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      ,	)

 __lowerCamelCase		:							str		 =						load_notes_encoder(ta_checkpoint['target']['token_encoder']      ,	SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							int		 =						load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']      ,	SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							str		 =						load_decoder(ta_checkpoint['target']['decoder']      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							str		 =						OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'						)

 __lowerCamelCase		:							Dict		 =						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__":
       lowercase_	   =     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_	   =     parser.parse_args()

       main(args)





def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							str		 =						0
 __lowerCamelCase		:							Tuple		 =						len(SCREAMING_SNAKE_CASE__						)

 for i in range(n - 1						):
  for j in range(i + 1      ,	SCREAMING_SNAKE_CASE__						):
   if arr[i] > arr[j]:
    num_inversions += 1

 return num_inversions
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 if len(SCREAMING_SNAKE_CASE__						) <= 1:
  return arr, 0
 __lowerCamelCase		:							Optional[int]		 =						len(SCREAMING_SNAKE_CASE__						) // 2
 __lowerCamelCase		:							Union[str, Any]		 =						arr[0:mid]
 __lowerCamelCase		:							List[Any]		 =						arr[mid:]

 __lowerCamelCase ,					__lowerCamelCase		:							Any		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							List[str]		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						_count_cross_inversions(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							List[str]		 =						inversion_p + inversions_q + cross_inversions
 return c, num_inversions
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						[]
 __lowerCamelCase		:							List[Any]		 =						0
 while i < len(SCREAMING_SNAKE_CASE__						) and j < len(SCREAMING_SNAKE_CASE__						):
  if p[i] > q[j]:
   # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
   # These are all inversions. The claim emerges from the
   # property that P is sorted.
   num_inversion += len(SCREAMING_SNAKE_CASE__						) - i
   r.append(q[j]						)
   j += 1
  else:
   r.append(p[i]						)
   i += 1

 if i < len(SCREAMING_SNAKE_CASE__						):
  r.extend(p[i:]						)
 else:
  r.extend(q[j:]						)

 return r, num_inversion
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							Optional[int]		 =						[10, 2, 1, 5, 5, 2, 11]

 # this arr has 8 inversions:
 # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

 __lowerCamelCase		:							Optional[Any]		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 8

 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)

 # testing an array with zero inversion (a sorted arr_1)

 arr_a.sort()
 __lowerCamelCase		:							Optional[Any]		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							int		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 0
 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)

 # an empty list should also have zero inversions
 __lowerCamelCase		:							List[str]		 =						[]
 __lowerCamelCase		:							Dict		 =						count_inversions_bf(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						count_inversions_recursive(SCREAMING_SNAKE_CASE__						)

 assert num_inversions_bf == num_inversions_recursive == 0
 print('number of inversions = '      ,	SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
       main()

