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






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]		)		)
  _UpperCAmelCase						 =      nn.Parameter(
      torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]		)   ,requires_grad=__A		)
  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     __UpperCAmelCase					(			lowercase   ,lowercase		):







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

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

  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     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """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=__A		)

  _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     __UpperCAmelCase					(			lowercase		):







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

  _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(__A   ,__A		)
  _UpperCAmelCase						 =      inference.InferenceModel(args.checkpoint_path   ,__A		)

  _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"""]   ,__A		)
  _UpperCAmelCase						 =      load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]   ,__A		)
  _UpperCAmelCase						 =      load_decoder(ta_checkpoint["""target"""]["""decoder"""]   ,__A		)

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

  _UpperCAmelCase						 =      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__":
    UpperCAmelCase__     :   str  		=						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__     :   Tuple  		=						parser.parse_args()

    main(args)

"""simple docstring"""


import datasets


UpperCAmelCase__  		=						"""\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

UpperCAmelCase__  		=						"""\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

UpperCAmelCase__  		=						"""
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  return (preds == labels).mean()


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,							_KWARGS_DESCRIPTION   )
class        a				(		datasets.Metric   ):


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      return datasets.MetricInfo(
          description=_DESCRIPTION   ,				citation=_CITATION   ,				inputs_description=_KWARGS_DESCRIPTION   ,				features=datasets.Features(
              {
                  """predictions""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32"""					),
                  """references""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32"""					),
              }					)   ,				codebase_urls=[]   ,				reference_urls=[]   ,				format="""numpy"""   ,				)


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Any					):
      return {"accuracy": simple_accuracy(__lowerCAmelCase   ,				__lowerCAmelCase					)}
