"""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


__lowerCAmelCase					: Tuple        ="""base_with_context"""


def     UpperCAmelCase__ (       lowerCAmelCase__  :int      ,	lowerCAmelCase__  :Union[str, Any]	)				->					List[Any]:
      '''simple docstring'''


      lowercase														=			nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]	)	)
      lowercase														=			nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]	)      ,	requires_grad=lowerCAmelCase__	)
      for lyr_num, lyr in enumerate(model.encoders	):
            lowercase														=			weights[f'layers_{lyr_num}']
            lowercase														=			nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]	)	)

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

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

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

      lowercase														=			nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]	)	)
      return model


def     UpperCAmelCase__ (       lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :str	)				->					Any:
      '''simple docstring'''


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

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

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

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

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

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

      return model


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :str	)				->					Optional[Any]:
      '''simple docstring'''


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

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

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

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

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

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

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

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

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

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

      return model


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any	)				->					Optional[int]:
      '''simple docstring'''


      lowercase														=			checkpoints.load_tax_checkpoint(args.checkpoint_path	)
      lowercase														=			jnp.tree_util.tree_map(onp.array      ,	lowerCAmelCase__	)

      lowercase														=			[
          """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()""",
      ]

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

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

      lowercase														=			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"""      ,	)

      lowercase														=			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"""      ,	)

      lowercase														=			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      ,	)

      lowercase														=			load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]      ,	lowerCAmelCase__	)
      lowercase														=			load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]      ,	lowerCAmelCase__	)
      lowercase														=			load_decoder(ta_checkpoint["""target"""]["""decoder"""]      ,	lowerCAmelCase__	)

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

      lowercase														=			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__":
       __lowerCAmelCase					: Union[str, 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.""",
       )
       __lowerCAmelCase					: int        =parser.parse_args()

       main(args)




"""simple docstring"""






import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


__lowerCAmelCase					: str        ="""\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

__lowerCAmelCase					: str        ="""\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

__lowerCAmelCase					: Optional[int]        ="""
Calculates average rouge scores for a list of hypotheses and 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.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""





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

      def        A__ (		self		):







            """simple docstring"""



            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/google-research/google-research/tree/master/rouge"""]	, reference_urls=[
                    """https://en.wikipedia.org/wiki/ROUGE_(metric)""",
                    """https://github.com/google-research/google-research/tree/master/rouge""",
                ]	, )







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase=None	, __lowerCAmelCase=True	, __lowerCAmelCase=False		):







            """simple docstring"""



            if rouge_types is None:
                  lowercase														=			["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]

            lowercase														=			rouge_scorer.RougeScorer(rouge_types=__lowerCAmelCase	, use_stemmer=__lowerCAmelCase		)
            if use_aggregator:
                  lowercase														=			scoring.BootstrapAggregator()
            else:
                  lowercase														=			[]

            for ref, pred in zip(__lowerCAmelCase	, __lowerCAmelCase		):
                  lowercase														=			scorer.score(__lowerCAmelCase	, __lowerCAmelCase		)
                  if use_aggregator:
                        aggregator.add_scores(__lowerCAmelCase		)
                  else:
                        scores.append(__lowerCAmelCase		)

            if use_aggregator:
                  lowercase														=			aggregator.aggregate()
            else:
                  lowercase														=			{}
                  for key in scores[0]:
                        lowercase														=			[score[key] for score in scores]

            return result



