






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


snake_case__      :	List[str]       	=      """base_with_context"""






def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']))
   UpperCamelCase_									=     nn.Parameter(
       torch.FloatTensor(weights['Embed_0']['embedding'])    ,      requires_grad=__lowercase)
   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     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T))

   UpperCamelCase_									=     nn.Parameter(
       torch.FloatTensor(weights['Embed_0']['embedding'])    ,      requires_grad=__lowercase)

   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     _snake_case    (__lowercase    ,      __lowercase):
   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=__lowercase)

   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     _snake_case    (__lowercase):
   UpperCamelCase_									=     checkpoints.load_tax_checkpoint(args.checkpoint_path)
   UpperCamelCase_									=     jnp.tree_util.tree_map(onp.array    ,      __lowercase)

   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(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     inference.InferenceModel(args.checkpoint_path    ,      __lowercase)

   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']    ,      __lowercase)
   UpperCamelCase_									=     load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']    ,      __lowercase)
   UpperCamelCase_									=     load_decoder(ta_checkpoint['target']['decoder']    ,      __lowercase)

   UpperCamelCase_									=     OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder')

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


if __name__ == "__main__":
 snake_case__      :	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.""",
 )
 snake_case__      :	List[str]       	=      parser.parse_args()

 main(args)













import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
 from transformers import MarkupLMFeatureExtractor


class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     parent




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     return {}






def     _snake_case    ():
   UpperCamelCase_									=     '<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR="FFFFFF">\n    <HR>\n    <a href="http://google.com">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style="color:#0000FF">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>'

   UpperCamelCase_									=     '\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '

   return [html_string_a, html_string_a]


@require_bsa
class 						_a       (  UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   MarkupLMFeatureExtractor if is_bsa_available() else None




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     MarkupLMFeatureExtractionTester(self )




  @property
  def    _UpperCAmelCase     (     self )			-> Any:
     return self.feature_extract_tester.prepare_feat_extract_dict()




  def    _UpperCAmelCase     (     self )			-> Dict:
     # Initialize feature_extractor
     UpperCamelCase_									=     self.feature_extraction_class()

     # Test not batched input
     UpperCamelCase_									=     get_html_strings()[0]
     UpperCamelCase_									=     feature_extractor(_UpperCAmelCase )

     # fmt: off
     UpperCamelCase_									=     [['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
     UpperCamelCase_									=     [['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
     # fmt: on

     self.assertEqual(encoding.nodes		,		_UpperCAmelCase )
     self.assertEqual(encoding.xpaths		,		_UpperCAmelCase )

     # Test batched
     UpperCamelCase_									=     get_html_strings()
     UpperCamelCase_									=     feature_extractor(_UpperCAmelCase )

     # fmt: off
     UpperCamelCase_									=     expected_nodes + [['My First Heading', 'My first paragraph.']]
     UpperCamelCase_									=     expected_xpaths + [['/html/body/h1', '/html/body/p']]

     self.assertEqual(len(encoding.nodes )		,		2 )
     self.assertEqual(len(encoding.xpaths )		,		2 )

     self.assertEqual(encoding.nodes		,		_UpperCAmelCase )
     self.assertEqual(encoding.xpaths		,		_UpperCAmelCase )





