

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


__UpperCAmelCase    				=     'base_with_context'







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Optional[int]:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]				)				)
     _lowerCAmelCase   :					Union[str, Any]        =    nn.Parameter(
         torch.FloatTensor(weights["Embed_0"]["embedding"]				)		,							requires_grad=SCREAMING_SNAKE_CASE				)
     for lyr_num, lyr in enumerate(model.encoders				):
          _lowerCAmelCase   :					Optional[int]        =    weights[f'''layers_{lyr_num}''']
          _lowerCAmelCase   :					Tuple        =    nn.Parameter(
              torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]				)				)

          _lowerCAmelCase   :					Optional[Any]        =    ly_weight["attention"]
          _lowerCAmelCase   :					Dict        =    nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T				)				)
          _lowerCAmelCase   :					Tuple        =    nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T				)				)
          _lowerCAmelCase   :					Optional[Any]        =    nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T				)				)
          _lowerCAmelCase   :					Optional[int]        =    nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T				)				)

          _lowerCAmelCase   :					str        =    nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]				)				)

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

     _lowerCAmelCase   :					Optional[Any]        =    nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]				)				)
     return model







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Dict:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T				)				)

     _lowerCAmelCase   :					Tuple        =    nn.Parameter(
         torch.FloatTensor(weights["Embed_0"]["embedding"]				)		,							requires_grad=SCREAMING_SNAKE_CASE				)

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

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

          _lowerCAmelCase   :					List[Any]        =    nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T				)				)
          _lowerCAmelCase   :					int        =    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(ly_weight["pre_mlp_layer_norm"]["scale"]				)				)

     _lowerCAmelCase   :					Any        =    nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]				)				)

     return model







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Tuple:

     """simple docstring"""







     _lowerCAmelCase   :					Dict        =    nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T				)				)
     _lowerCAmelCase   :					Optional[Any]        =    nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T				)				)

     _lowerCAmelCase   :					Dict        =    nn.Parameter(
         torch.FloatTensor(weights["Embed_0"]["embedding"]				)		,							requires_grad=SCREAMING_SNAKE_CASE				)

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

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

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

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

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

          _lowerCAmelCase   :					int        =    nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]				)				)
          _lowerCAmelCase   :					Optional[int]        =    nn.Parameter(
              torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T				)				)
          _lowerCAmelCase   :					List[Any]        =    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   :					List[str]        =    nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]				)				)

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

     return model







def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      int:

     """simple docstring"""







     _lowerCAmelCase   :					Dict        =    checkpoints.load_tax_checkpoint(args.checkpoint_path				)
     _lowerCAmelCase   :					str        =    jnp.tree_util.tree_map(onp.array		,							SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					Optional[int]        =    [
         "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   :					Any        =    os.path.join(args.checkpoint_path		,							".."		,							"config.gin"				)
     _lowerCAmelCase   :					Optional[Any]        =    inference.parse_training_gin_file(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					str        =    inference.InferenceModel(args.checkpoint_path		,							SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					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   :					Optional[Any]        =    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   :					List[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		,							)

     _lowerCAmelCase   :					List[str]        =    load_notes_encoder(ta_checkpoint["target"]["token_encoder"]		,							SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Optional[Any]        =    load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]		,							SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Dict        =    load_decoder(ta_checkpoint["target"]["decoder"]		,							SCREAMING_SNAKE_CASE				)

     _lowerCAmelCase   :					Union[str, Any]        =    OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"				)

     _lowerCAmelCase   :					Union[str, Any]        =    SpectrogramDiffusionPipeline(
         notes_encoder=SCREAMING_SNAKE_CASE		,							continuous_encoder=SCREAMING_SNAKE_CASE		,							decoder=SCREAMING_SNAKE_CASE		,							scheduler=SCREAMING_SNAKE_CASE		,							melgan=SCREAMING_SNAKE_CASE		,							)
     if args.save:
          pipe.save_pretrained(args.output_path				)


if __name__ == "__main__":
  __UpperCAmelCase    				=     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.',
  )
  __UpperCAmelCase    				=     parser.parse_args()

  main(args)









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__UpperCAmelCase    				=     logging.get_logger(__name__)





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

 def __init__(		self :	List[str]     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PIL.Image.BICUBIC     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	Union[int, float] = 1 / 2_5_5     ,						A_ :	bool = True     ,						A_ :	bool = True     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					str        =    size if size is not None else {"height": 2_5_6, "width": 2_5_6}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					str        =    do_resize
      _lowerCAmelCase   :					str        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Union[str, Any]        =    do_center_crop
      _lowerCAmelCase   :					Union[str, Any]        =    crop_size
      _lowerCAmelCase   :					List[Any]        =    do_rescale
      _lowerCAmelCase   :					Tuple        =    rescale_factor
      _lowerCAmelCase   :					Any        =    do_normalize
      _lowerCAmelCase   :					List[Any]        =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCAmelCase   :					List[str]        =    image_std if image_std is not None else IMAGENET_STANDARD_STD

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PIL.Image.BICUBIC     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	str     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
      return resize(
          A_     ,						size=(size["height"], size["width"])     ,						resample=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Any     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	str     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )
      if "height" not in size or "width" not in size:
           raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''       )
      return center_crop(A_     ,						size=(size["height"], size["width"])     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	List[Any]     ,						A_ :	np.ndarray     ,						A_ :	Union[int, float]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[Any]     ,						):






      '''simple docstring'''




      return rescale(A_     ,						scale=A_     ,						data_format=A_     ,						**A_       )

 def    __magic_name__       (		self :	Optional[Any]     ,						A_ :	np.ndarray     ,						A_ :	Union[float, List[float]]     ,						A_ :	Union[float, List[float]]     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




      return normalize(A_     ,						mean=A_     ,						std=A_     ,						data_format=A_     ,						**A_       )


 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	Union[str, Any]=None     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	bool = None     ,						A_ :	float = None     ,						A_ :	bool = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[float, List[float]]] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					str        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					int        =    do_center_crop if do_center_crop is not None else self.do_center_crop
      _lowerCAmelCase   :					str        =    do_rescale if do_rescale is not None else self.do_rescale
      _lowerCAmelCase   :					Optional[Any]        =    rescale_factor if rescale_factor is not None else self.rescale_factor
      _lowerCAmelCase   :					int        =    do_normalize if do_normalize is not None else self.do_normalize
      _lowerCAmelCase   :					str        =    image_mean if image_mean is not None else self.image_mean
      _lowerCAmelCase   :					List[Any]        =    image_std if image_std is not None else self.image_std

      _lowerCAmelCase   :					Optional[int]        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					int        =    crop_size if crop_size is not None else self.crop_size
      _lowerCAmelCase   :					List[str]        =    get_size_dict(A_     ,						param_name="crop_size"       )

      _lowerCAmelCase   :					Tuple        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."       )

      if do_resize and size is None or resample is None:
           raise ValueError("Size and resample must be specified if do_resize is True."       )

      if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."       )

      if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."       )

      if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Union[str, Any]        =    [to_numpy_array(A_       ) for image in images]

      if do_resize:
           _lowerCAmelCase   :					List[str]        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      if do_center_crop:
           _lowerCAmelCase   :					Any        =    [self.center_crop(image=A_     ,						size=A_       ) for image in images]

      if do_rescale:
           _lowerCAmelCase   :					str        =    [self.rescale(image=A_     ,						scale=A_       ) for image in images]

      if do_normalize:
           _lowerCAmelCase   :					Dict        =    [self.normalize(image=A_     ,						mean=A_     ,						std=A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					int        =    {"pixel_values": images}
      return BatchFeature(data=A_     ,						tensor_type=A_       )






