





'''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	:int	    =							'''base_with_context'''
def       UpperCAmelCase_				(   __lowercase				: str     ,	__lowercase				: str			)						->			Tuple:


     '''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				: List[str]     ,	__lowercase				: Optional[Any]			)						->			int:


     '''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				: Tuple     ,	__lowercase				: List[Any]			)						->			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=_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				: Optional[Any]			)						->			List[Any]:


     '''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__":
  __SCREAMING_SNAKE_CASE	:int	    =							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	:Tuple	    =							parser.parse_args()

  main(args)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_      =     {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     ['PLBartTokenizer']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'PLBartForCausalLM',
			    'PLBartForConditionalGeneration',
			    'PLBartForSequenceClassification',
			    'PLBartModel',
			    'PLBartPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_plbart import PLBartTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_plbart import (
						    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PLBartForCausalLM,
						    PLBartForConditionalGeneration,
						    PLBartForSequenceClassification,
						    PLBartModel,
						    PLBartPreTrainedModel,
						)


else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure)