





'''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


__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				'''base_with_context'''
def 	UpperCAmelCase_							(					__lowercase							:      Union[str, Any]			,     __lowercase							:      str       )		->       Dict:


     '''simple docstring'''



     _UpperCAmelCase     							=   nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]       )       )
     _UpperCAmelCase     							=   nn.Parameter(
         torch.FloatTensor(weights["Embed_0"]["embedding"]       )			,     requires_grad=_lowerCamelCase       )
     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							:      int			,     __lowercase							:      str       )		->       Optional[int]:


     '''simple docstring'''



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

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

     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							:      str			,     __lowercase							:      Dict       )		->       Optional[int]:


     '''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=_lowerCamelCase       )

     _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							:      Optional[Any]       )		->       Union[str, Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   checkpoints.load_tax_checkpoint(args.checkpoint_path       )
     _UpperCAmelCase     							=   jnp.tree_util.tree_map(onp.array			,     _lowerCamelCase       )

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

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

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

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


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :Optional[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.''',
  )
  __SCREAMING_SNAKE_CASE     :Union[str, Any]       =				parser.parse_args()

  main(args)












"""simple docstring"""





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


_SCREAMING_SNAKE_CASE					:				Tuple					    =						version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
  from nltk import word_tokenize


_SCREAMING_SNAKE_CASE					:				Tuple					    =						'''\
@inproceedings{banarjee2005,
  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
  author    = {Banerjee, Satanjeev  and Lavie, Alon},
  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
  month     = jun,
  year      = {2005},
  address   = {Ann Arbor, Michigan},
  publisher = {Association for Computational Linguistics},
  url       = {https://www.aclweb.org/anthology/W05-0909},
  pages     = {65--72},
}
'''

_SCREAMING_SNAKE_CASE					:				int					    =						'''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''

_SCREAMING_SNAKE_CASE					:				List[Any]					    =						'''
Computes METEOR score of translated segments against one or more references.
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    \'meteor\': meteor score.
Examples:

    >>> meteor = datasets.load_metric(\'meteor\')
    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results["meteor"], 4))
    0.6944
'''






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






    def    UpperCamelCase       (       self     :							List[str]					)     ->		List[str]:
      return datasets.MetricInfo(
          description=_DESCRIPTION       ,						citation=_CITATION       ,						inputs_description=_KWARGS_DESCRIPTION       ,						features=datasets.Features(
              {
                  'predictions': datasets.Value('string'       ,						id='sequence'					),
                  'references': datasets.Value('string'       ,						id='sequence'					),
              }					)       ,						codebase_urls=['https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py']       ,						reference_urls=[
              'https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score',
              'https://en.wikipedia.org/wiki/METEOR',
          ]       ,						)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]					)     ->		Dict:
      import nltk

      nltk.download('wordnet'					)
      if NLTK_VERSION >= version.Version('3.6.5'					):
        nltk.download('punkt'					)
      if NLTK_VERSION >= version.Version('3.6.6'					):
        nltk.download('omw-1.4'					)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=0.9       ,						__SCREAMING_SNAKE_CASE     :							Dict=3       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=0.5					)     ->		int:
      if NLTK_VERSION >= version.Version('3.6.5'					):
        lowerCamelCase_         =						[
            meteor_score.single_meteor_score(
                word_tokenize(__SCREAMING_SNAKE_CASE					)       ,						word_tokenize(__SCREAMING_SNAKE_CASE					)       ,						alpha=__SCREAMING_SNAKE_CASE       ,						beta=__SCREAMING_SNAKE_CASE       ,						gamma=__SCREAMING_SNAKE_CASE					)
            for ref, pred in zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        ]
      else:
        lowerCamelCase_         =						[
            meteor_score.single_meteor_score(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						alpha=__SCREAMING_SNAKE_CASE       ,						beta=__SCREAMING_SNAKE_CASE       ,						gamma=__SCREAMING_SNAKE_CASE					)
            for ref, pred in zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        ]

      return {"meteor": np.mean(__SCREAMING_SNAKE_CASE					)}




