



"""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__        =				'''base_with_context'''






def        snake_case_  (      A_					:		Optional[Any],				A_					:		str						):


       '''simple docstring'''





       _lowerCamelCase						:				List[str]    			= nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']						)						)
       _lowerCamelCase						:				str    			= nn.Parameter(
           torch.FloatTensor(weights['''Embed_0''']['''embedding''']						),				requires_grad=A_						)
       for lyr_num, lyr in enumerate(model.encoders						):
              _lowerCamelCase						:				Union[str, Any]    			= weights[F'''layers_{lyr_num}''']
              _lowerCamelCase						:				Optional[Any]    			= nn.Parameter(
                  torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']						)						)

              _lowerCamelCase						:				Optional[int]    			= ly_weight['''attention''']
              _lowerCamelCase						:				Any    			= nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T						)						)
              _lowerCamelCase						:				List[Any]    			= nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T						)						)
              _lowerCamelCase						:				List[str]    			= nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Dict    			= nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T						)						)

              _lowerCamelCase						:				str    			= nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']						)						)

              _lowerCamelCase						:				Optional[int]    			= nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Dict    			= nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T						)						)
              _lowerCamelCase						:				List[Any]    			= nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T						)						)

       _lowerCamelCase						:				Dict    			= nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']						)						)
       return model






def        snake_case_  (      A_					:		int,				A_					:		List[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:				str    			= nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T						)						)

       _lowerCamelCase						:				List[Any]    			= nn.Parameter(
           torch.FloatTensor(weights['''Embed_0''']['''embedding''']						),				requires_grad=A_						)

       for lyr_num, lyr in enumerate(model.encoders						):
              _lowerCamelCase						:				int    			= weights[F'''layers_{lyr_num}''']
              _lowerCamelCase						:				List[str]    			= ly_weight['''attention''']

              _lowerCamelCase						:				Any    			= nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Dict    			= nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Dict    			= nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Optional[Any]    			= nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T						)						)
              _lowerCamelCase						:				str    			= nn.Parameter(
                  torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']						)						)

              _lowerCamelCase						:				Dict    			= nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Union[str, Any]    			= nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Tuple    			= nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Any    			= nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']						)						)

       _lowerCamelCase						:				int    			= nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']						)						)

       return model






def        snake_case_  (      A_					:		Dict,				A_					:		str						):


       '''simple docstring'''





       _lowerCamelCase						:				List[str]    			= nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T						)						)
       _lowerCamelCase						:				Tuple    			= nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T						)						)

       _lowerCamelCase						:				Union[str, Any]    			= nn.Parameter(
           torch.FloatTensor(weights['''Embed_0''']['''embedding''']						),				requires_grad=A_						)

       _lowerCamelCase						:				str    			= nn.Parameter(
           torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T						)						)

       for lyr_num, lyr in enumerate(model.decoders						):
              _lowerCamelCase						:				Dict    			= weights[F'''layers_{lyr_num}''']
              _lowerCamelCase						:				Optional[int]    			= nn.Parameter(
                  torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale''']						)						)

              _lowerCamelCase						:				str    			= nn.Parameter(
                  torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T						)						)

              _lowerCamelCase						:				List[str]    			= ly_weight['''self_attention''']
              _lowerCamelCase						:				Dict    			= nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Dict    			= nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T						)						)
              _lowerCamelCase						:				List[str]    			= nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T						)						)
              _lowerCamelCase						:				List[str]    			= nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T						)						)

              _lowerCamelCase						:				Union[str, Any]    			= ly_weight['''MultiHeadDotProductAttention_0''']
              _lowerCamelCase						:				Any    			= nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Tuple    			= nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Dict    			= nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T						)						)
              _lowerCamelCase						:				int    			= nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Tuple    			= nn.Parameter(
                  torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']						)						)

              _lowerCamelCase						:				Optional[Any]    			= nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']						)						)
              _lowerCamelCase						:				int    			= nn.Parameter(
                  torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Tuple    			= nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T						)						)
              _lowerCamelCase						:				List[str]    			= nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T						)						)
              _lowerCamelCase						:				Union[str, Any]    			= nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T						)						)

       _lowerCamelCase						:				int    			= nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']						)						)

       _lowerCamelCase						:				Dict    			= nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T						)						)

       return model






def        snake_case_  (      A_					:		int						):


       '''simple docstring'''





       _lowerCamelCase						:				Any    			= checkpoints.load_tax_checkpoint(args.checkpoint_path						)
       _lowerCamelCase						:				Optional[int]    			= jnp.tree_util.tree_map(onp.array,				A_						)

       _lowerCamelCase						:				Dict    			= [
           '''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()''',
       ]

       _lowerCamelCase						:				List[Any]    			= os.path.join(args.checkpoint_path,				'''..''',				'''config.gin'''						)
       _lowerCamelCase						:				Tuple    			= inference.parse_training_gin_file(A_,				A_						)
       _lowerCamelCase						:				Optional[Any]    			= inference.InferenceModel(args.checkpoint_path,				A_						)

       _lowerCamelCase						:				List[str]    			= DDPMScheduler(beta_schedule='''squaredcos_cap_v2''',				variance_type='''fixed_large'''						)

       _lowerCamelCase						:				List[Any]    			= 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''',				)

       _lowerCamelCase						:				List[str]    			= 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''',				)

       _lowerCamelCase						:				Dict    			= 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,				)

       _lowerCamelCase						:				Optional[Any]    			= load_notes_encoder(ta_checkpoint['''target''']['''token_encoder'''],				A_						)
       _lowerCamelCase						:				Any    			= load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''],				A_						)
       _lowerCamelCase						:				int    			= load_decoder(ta_checkpoint['''target''']['''decoder'''],				A_						)

       _lowerCamelCase						:				str    			= OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''						)

       _lowerCamelCase						:				Optional[int]    			= 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__":
   lowerCAmelCase__        =				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__        =				parser.parse_args()

   main(args)










"""simple docstring"""




from typing import TYPE_CHECKING

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


_snake_case          =   {
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   ['PerceiverFeatureExtractor']
 _snake_case          =   ['PerceiverImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'PerceiverForImageClassificationConvProcessing',
     'PerceiverForImageClassificationFourier',
     'PerceiverForImageClassificationLearned',
     'PerceiverForMaskedLM',
     'PerceiverForMultimodalAutoencoding',
     'PerceiverForOpticalFlow',
     'PerceiverForSequenceClassification',
     'PerceiverLayer',
     'PerceiverModel',
     'PerceiverPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
 from .tokenization_perceiver import PerceiverTokenizer

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_perceiver import PerceiverFeatureExtractor
  from .image_processing_perceiver import PerceiverImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_perceiver import (
      PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
      PerceiverForImageClassificationConvProcessing,
      PerceiverForImageClassificationFourier,
      PerceiverForImageClassificationLearned,
      PerceiverForMaskedLM,
      PerceiverForMultimodalAutoencoding,
      PerceiverForOpticalFlow,
      PerceiverForSequenceClassification,
      PerceiverLayer,
      PerceiverModel,
      PerceiverPreTrainedModel,
  )

else:
 import sys

 _snake_case          =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

