
'''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__    :		Optional[int]          =   """base_with_context"""

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	List[str]:


  '''simple docstring'''






  lowercase__  :   int    		=      nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]   )   )
  lowercase__  :   Tuple    		=      nn.Parameter(
      torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]   ) , requires_grad=lowercase_   )
  for lyr_num, lyr in enumerate(model.encoders   ):
    lowercase__  :   Tuple    		=      weights[F'layers_{lyr_num}']
    lowercase__  :   str    		=      nn.Parameter(
        torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]   )   )

    lowercase__  :   List[Any]    		=      ly_weight["""attention"""]
    lowercase__  :   Any    		=      nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T   )   )
    lowercase__  :   str    		=      nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T   )   )
    lowercase__  :   str    		=      nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T   )   )
    lowercase__  :   Optional[Any]    		=      nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T   )   )

    lowercase__  :   Optional[int]    		=      nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]   )   )

    lowercase__  :   Optional[Any]    		=      nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T   )   )
    lowercase__  :   List[Any]    		=      nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T   )   )
    lowercase__  :   List[Any]    		=      nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T   )   )

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

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Any:


  '''simple docstring'''






  lowercase__  :   List[Any]    		=      nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T   )   )

  lowercase__  :   Optional[int]    		=      nn.Parameter(
      torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]   ) , requires_grad=lowercase_   )

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

    lowercase__  :   Optional[int]    		=      nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T   )   )
    lowercase__  :   List[str]    		=      nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T   )   )
    lowercase__  :   List[Any]    		=      nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T   )   )
    lowercase__  :   str    		=      nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T   )   )
    lowercase__  :   List[str]    		=      nn.Parameter(
        torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]   )   )

    lowercase__  :   Dict    		=      nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T   )   )
    lowercase__  :   Optional[Any]    		=      nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T   )   )
    lowercase__  :   Optional[Any]    		=      nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T   )   )
    lowercase__  :   int    		=      nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]   )   )

  lowercase__  :   Optional[int]    		=      nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]   )   )

  return model

def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	int:


  '''simple docstring'''






  lowercase__  :   int    		=      nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T   )   )
  lowercase__  :   Dict    		=      nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T   )   )

  lowercase__  :   Optional[Any]    		=      nn.Parameter(
      torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]   ) , requires_grad=lowercase_   )

  lowercase__  :   int    		=      nn.Parameter(
      torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T   )   )

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

    lowercase__  :   Optional[int]    		=      nn.Parameter(
        torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T   )   )

    lowercase__  :   Union[str, Any]    		=      ly_weight["""self_attention"""]
    lowercase__  :   Any    		=      nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T   )   )
    lowercase__  :   Optional[Any]    		=      nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T   )   )
    lowercase__  :   Tuple    		=      nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T   )   )
    lowercase__  :   List[str]    		=      nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T   )   )

    lowercase__  :   Optional[Any]    		=      ly_weight["""MultiHeadDotProductAttention_0"""]
    lowercase__  :   Any    		=      nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T   )   )
    lowercase__  :   Tuple    		=      nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T   )   )
    lowercase__  :   str    		=      nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T   )   )
    lowercase__  :   str    		=      nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T   )   )
    lowercase__  :   Any    		=      nn.Parameter(
        torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]   )   )

    lowercase__  :   List[Any]    		=      nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]   )   )
    lowercase__  :   Optional[int]    		=      nn.Parameter(
        torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T   )   )
    lowercase__  :   str    		=      nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T   )   )
    lowercase__  :   Optional[Any]    		=      nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T   )   )
    lowercase__  :   Dict    		=      nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T   )   )

  lowercase__  :   List[Any]    		=      nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]   )   )

  lowercase__  :   Optional[Any]    		=      nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T   )   )

  return model

def 		UpperCamelCase	(      lowercase_   )		->	Dict:


  '''simple docstring'''






  lowercase__  :   Tuple    		=      checkpoints.load_tax_checkpoint(args.checkpoint_path   )
  lowercase__  :   Union[str, Any]    		=      jnp.tree_util.tree_map(onp.array , lowercase_   )

  lowercase__  :   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()""",
  ]

  lowercase__  :   List[str]    		=      os.path.join(args.checkpoint_path , """..""" , """config.gin"""   )
  lowercase__  :   Optional[Any]    		=      inference.parse_training_gin_file(lowercase_ , lowercase_   )
  lowercase__  :   Union[str, Any]    		=      inference.InferenceModel(args.checkpoint_path , lowercase_   )

  lowercase__  :   Any    		=      DDPMScheduler(beta_schedule="""squaredcos_cap_v2""" , variance_type="""fixed_large"""   )

  lowercase__  :   Dict    		=      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__  :   Optional[int]    		=      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__  :   str    		=      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__  :   Tuple    		=      load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""] , lowercase_   )
  lowercase__  :   int    		=      load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""] , lowercase_   )
  lowercase__  :   str    		=      load_decoder(ta_checkpoint["""target"""]["""decoder"""] , lowercase_   )

  lowercase__  :   List[Any]    		=      OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""   )

  lowercase__  :   str    		=      SpectrogramDiffusionPipeline(
      notes_encoder=lowercase_ , continuous_encoder=lowercase_ , decoder=lowercase_ , scheduler=lowercase_ , melgan=lowercase_ , )
  if args.save:
    pipe.save_pretrained(args.output_path   )


if __name__ == "__main__":
     lowerCamelCase__    :		Optional[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.""",
     )
     lowerCamelCase__    :		str          =   parser.parse_args()

     main(args)








lowerCamelCase__    :		Any          =   """0.18.2"""

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
     if not is_onnx_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_onnx_objects import *  # noqa F403
else:
     from .pipelines import OnnxRuntimeModel

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_pt_objects import *  # noqa F403
else:
     from .models import (
         AutoencoderKL,
         ControlNetModel,
         ModelMixin,
         PriorTransformer,
         TaFilmDecoder,
         TransformeraDModel,
         UNetaDModel,
         UNetaDConditionModel,
         UNetaDModel,
         UNetaDConditionModel,
         VQModel,
     )
     from .optimization import (
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
         get_scheduler,
     )
     from .pipelines import (
         AudioPipelineOutput,
         ConsistencyModelPipeline,
         DanceDiffusionPipeline,
         DDIMPipeline,
         DDPMPipeline,
         DiffusionPipeline,
         DiTPipeline,
         ImagePipelineOutput,
         KarrasVePipeline,
         LDMPipeline,
         LDMSuperResolutionPipeline,
         PNDMPipeline,
         RePaintPipeline,
         ScoreSdeVePipeline,
     )
     from .schedulers import (
         CMStochasticIterativeScheduler,
         DDIMInverseScheduler,
         DDIMParallelScheduler,
         DDIMScheduler,
         DDPMParallelScheduler,
         DDPMScheduler,
         DEISMultistepScheduler,
         DPMSolverMultistepInverseScheduler,
         DPMSolverMultistepScheduler,
         DPMSolverSinglestepScheduler,
         EulerAncestralDiscreteScheduler,
         EulerDiscreteScheduler,
         HeunDiscreteScheduler,
         IPNDMScheduler,
         KarrasVeScheduler,
         KDPMaAncestralDiscreteScheduler,
         KDPMaDiscreteScheduler,
         PNDMScheduler,
         RePaintScheduler,
         SchedulerMixin,
         ScoreSdeVeScheduler,
         UnCLIPScheduler,
         UniPCMultistepScheduler,
         VQDiffusionScheduler,
     )
     from .training_utils import EMAModel

try:
     if not (is_torch_available() and is_scipy_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
     from .schedulers import LMSDiscreteScheduler

try:
     if not (is_torch_available() and is_torchsde_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
     from .schedulers import DPMSolverSDEScheduler

try:
     if not (is_torch_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
     from .pipelines import (
         AltDiffusionImgaImgPipeline,
         AltDiffusionPipeline,
         AudioLDMPipeline,
         CycleDiffusionPipeline,
         IFImgaImgPipeline,
         IFImgaImgSuperResolutionPipeline,
         IFInpaintingPipeline,
         IFInpaintingSuperResolutionPipeline,
         IFPipeline,
         IFSuperResolutionPipeline,
         ImageTextPipelineOutput,
         KandinskyImgaImgPipeline,
         KandinskyInpaintPipeline,
         KandinskyPipeline,
         KandinskyPriorPipeline,
         KandinskyVaaControlnetImgaImgPipeline,
         KandinskyVaaControlnetPipeline,
         KandinskyVaaImgaImgPipeline,
         KandinskyVaaInpaintPipeline,
         KandinskyVaaPipeline,
         KandinskyVaaPriorEmbaEmbPipeline,
         KandinskyVaaPriorPipeline,
         LDMTextToImagePipeline,
         PaintByExamplePipeline,
         SemanticStableDiffusionPipeline,
         ShapEImgaImgPipeline,
         ShapEPipeline,
         StableDiffusionAttendAndExcitePipeline,
         StableDiffusionControlNetImgaImgPipeline,
         StableDiffusionControlNetInpaintPipeline,
         StableDiffusionControlNetPipeline,
         StableDiffusionDepthaImgPipeline,
         StableDiffusionDiffEditPipeline,
         StableDiffusionImageVariationPipeline,
         StableDiffusionImgaImgPipeline,
         StableDiffusionInpaintPipeline,
         StableDiffusionInpaintPipelineLegacy,
         StableDiffusionInstructPixaPixPipeline,
         StableDiffusionLatentUpscalePipeline,
         StableDiffusionLDMaDPipeline,
         StableDiffusionModelEditingPipeline,
         StableDiffusionPanoramaPipeline,
         StableDiffusionParadigmsPipeline,
         StableDiffusionPipeline,
         StableDiffusionPipelineSafe,
         StableDiffusionPixaPixZeroPipeline,
         StableDiffusionSAGPipeline,
         StableDiffusionUpscalePipeline,
         StableUnCLIPImgaImgPipeline,
         StableUnCLIPPipeline,
         TextToVideoSDPipeline,
         TextToVideoZeroPipeline,
         UnCLIPImageVariationPipeline,
         UnCLIPPipeline,
         UniDiffuserModel,
         UniDiffuserPipeline,
         UniDiffuserTextDecoder,
         VersatileDiffusionDualGuidedPipeline,
         VersatileDiffusionImageVariationPipeline,
         VersatileDiffusionPipeline,
         VersatileDiffusionTextToImagePipeline,
         VideoToVideoSDPipeline,
         VQDiffusionPipeline,
     )

try:
     if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
     from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
     if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
     from .pipelines import StableDiffusionKDiffusionPipeline

try:
     if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
     from .pipelines import (
         OnnxStableDiffusionImgaImgPipeline,
         OnnxStableDiffusionInpaintPipeline,
         OnnxStableDiffusionInpaintPipelineLegacy,
         OnnxStableDiffusionPipeline,
         OnnxStableDiffusionUpscalePipeline,
         StableDiffusionOnnxPipeline,
     )

try:
     if not (is_torch_available() and is_librosa_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
     from .pipelines import AudioDiffusionPipeline, Mel

try:
     if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
     from .pipelines import SpectrogramDiffusionPipeline

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_flax_objects import *  # noqa F403
else:
     from .models.controlnet_flax import FlaxControlNetModel
     from .models.modeling_flax_utils import FlaxModelMixin
     from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
     from .models.vae_flax import FlaxAutoencoderKL
     from .pipelines import FlaxDiffusionPipeline
     from .schedulers import (
         FlaxDDIMScheduler,
         FlaxDDPMScheduler,
         FlaxDPMSolverMultistepScheduler,
         FlaxKarrasVeScheduler,
         FlaxLMSDiscreteScheduler,
         FlaxPNDMScheduler,
         FlaxSchedulerMixin,
         FlaxScoreSdeVeScheduler,
     )


try:
     if not (is_flax_available() and is_transformers_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
     from .pipelines import (
         FlaxStableDiffusionControlNetPipeline,
         FlaxStableDiffusionImgaImgPipeline,
         FlaxStableDiffusionInpaintPipeline,
         FlaxStableDiffusionPipeline,
     )

try:
     if not (is_note_seq_available()):
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     from .utils.dummy_note_seq_objects import *  # noqa F403
else:
     from .pipelines import MidiProcessor






