





'''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


__magic_name__   :						str    =			"""base_with_context"""




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

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

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

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

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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

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

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

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

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

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

  return model




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

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

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

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

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

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

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

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

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

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

  return model




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				checkpoints.load_tax_checkpoint(args.checkpoint_path       )
  _snake_case     		=				jnp.tree_util.tree_map(onp.array					,	SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				[
      "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()",
  ]

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

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

  _snake_case     		=				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"					,	)

  _snake_case     		=				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"					,	)

  _snake_case     		=				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					,	)

  _snake_case     		=				load_notes_encoder(ta_checkpoint["target"]["token_encoder"]					,	SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]					,	SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				load_decoder(ta_checkpoint["target"]["decoder"]					,	SCREAMING_SNAKE_CASE__       )

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

  _snake_case     		=				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__":
 __magic_name__   :						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.""",
 )
 __magic_name__   :						Optional[Any]    =			parser.parse_args()

 main(args)









'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__magic_name__   :						Dict    =			logging.get_logger(__name__)

__magic_name__   :						Dict    =			{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   '''mobilenet_v1'''

     def __init__(					self						,   lowerCamelCase=3						,   lowerCamelCase=224						,   lowerCamelCase=1.0						,   lowerCamelCase=8						,   lowerCamelCase="relu6"						,   lowerCamelCase=True						,   lowerCamelCase=0.999						,   lowerCamelCase=0.02						,   lowerCamelCase=0.001						,   **lowerCamelCase						,   ):
       super().__init__(**lowerCamelCase	)

       if depth_multiplier <= 0:
         raise ValueError("depth_multiplier must be greater than zero."	)

       _snake_case     		=				num_channels
       _snake_case     		=				image_size
       _snake_case     		=				depth_multiplier
       _snake_case     		=				min_depth
       _snake_case     		=				hidden_act
       _snake_case     		=				tf_padding
       _snake_case     		=				classifier_dropout_prob
       _snake_case     		=				initializer_range
       _snake_case     		=				layer_norm_eps







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						int      =   version.parse('''1.11'''			)

     @property
     def 	UpperCamelCase(					self	):
       return OrderedDict([("pixel_values", {0: "batch"})]	)

     @property
     def 	UpperCamelCase(					self	):
       if self.task == "image-classification":
         return OrderedDict([("logits", {0: "batch"})]	)
       else:
         return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]	)






     @property
     def 	UpperCamelCase(					self	):
       return 1e-4


