"""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


UpperCAmelCase__			=    """base_with_context"""






def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]		)		)
  _UpperCAmelCase    			=   nn.Parameter(
      torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]		) ,requires_grad=lowercase		)
  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 ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T		)		)

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

  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 ,lowercase		):







  """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=lowercase		)

  _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		):







  """simple docstring"""
  _UpperCAmelCase    			=   checkpoints.load_tax_checkpoint(args.checkpoint_path		)
  _UpperCAmelCase    			=   jnp.tree_util.tree_map(onp.array ,lowercase		)

  _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(lowercase ,lowercase		)
  _UpperCAmelCase    			=   inference.InferenceModel(args.checkpoint_path ,lowercase		)

  _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"""] ,lowercase		)
  _UpperCAmelCase    			=   load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""] ,lowercase		)
  _UpperCAmelCase    			=   load_decoder(ta_checkpoint["""target"""]["""decoder"""] ,lowercase		)

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

  _UpperCAmelCase    			=   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__":
    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)

"""simple docstring"""


from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


UpperCAmelCase__			=    logging.get_logger(__name__)

UpperCAmelCase__			=    {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	Tuple  				=				'efficientnet'


    def __init__(       self						:	int ,					__lowerCAmelCase						:	int = 3 ,					__lowerCAmelCase						:	int = 600 ,					__lowerCAmelCase						:	float = 2.0 ,					__lowerCAmelCase						:	float = 3.1 ,					__lowerCAmelCase						:	int = 8 ,					__lowerCAmelCase						:	List[int] = [3, 3, 5, 3, 5, 5, 3] ,					__lowerCAmelCase						:	List[int] = [32, 16, 24, 40, 80, 112, 192] ,					__lowerCAmelCase						:	List[int] = [16, 24, 40, 80, 112, 192, 320] ,					__lowerCAmelCase						:	List[int] = [] ,					__lowerCAmelCase						:	List[int] = [1, 2, 2, 2, 1, 2, 1] ,					__lowerCAmelCase						:	List[int] = [1, 2, 2, 3, 3, 4, 1] ,					__lowerCAmelCase						:	List[int] = [1, 6, 6, 6, 6, 6, 6] ,					__lowerCAmelCase						:	float = 0.25 ,					__lowerCAmelCase						:	str = "swish" ,					__lowerCAmelCase						:	int = 2560 ,					__lowerCAmelCase						:	str = "mean" ,					__lowerCAmelCase						:	float = 0.02 ,					__lowerCAmelCase						:	float = 0.001 ,					__lowerCAmelCase						:	float = 0.99 ,					__lowerCAmelCase						:	float = 0.5 ,					__lowerCAmelCase						:	float = 0.2 ,					**__lowerCAmelCase						:	Optional[Any] ,					):
      super().__init__(**__lowerCAmelCase       )

      _UpperCAmelCase    			=   num_channels
      _UpperCAmelCase    			=   image_size
      _UpperCAmelCase    			=   width_coefficient
      _UpperCAmelCase    			=   depth_coefficient
      _UpperCAmelCase    			=   depth_divisor
      _UpperCAmelCase    			=   kernel_sizes
      _UpperCAmelCase    			=   in_channels
      _UpperCAmelCase    			=   out_channels
      _UpperCAmelCase    			=   depthwise_padding
      _UpperCAmelCase    			=   strides
      _UpperCAmelCase    			=   num_block_repeats
      _UpperCAmelCase    			=   expand_ratios
      _UpperCAmelCase    			=   squeeze_expansion_ratio
      _UpperCAmelCase    			=   hidden_act
      _UpperCAmelCase    			=   hidden_dim
      _UpperCAmelCase    			=   pooling_type
      _UpperCAmelCase    			=   initializer_range
      _UpperCAmelCase    			=   batch_norm_eps
      _UpperCAmelCase    			=   batch_norm_momentum
      _UpperCAmelCase    			=   dropout_rate
      _UpperCAmelCase    			=   drop_connect_rate
      _UpperCAmelCase    			=   sum(__lowerCAmelCase       ) * 4



class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	Union[str, Any]  				=				version.parse('1.11' )


    @property
    def   lowerCAmelCase_	(       self						:	Any       ):
      return OrderedDict(
          [
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
          ]       )


    @property
    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      return 1e-5
