





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_A :   Optional[int]  			=  logging.get_logger(__name__)



def 	_a						(   UpperCAmelCase					)    ->    YolosConfig:







   """simple docstring"""
   lowerCamelCase__       :						Any        =							YolosConfig()

   # size of the architecture
   if "yolos_ti" in yolos_name:
      lowerCamelCase__       :						Any        =							192
      lowerCamelCase__       :						Any        =							768
      lowerCamelCase__       :						Optional[Any]        =							12
      lowerCamelCase__       :						Optional[Any]        =							3
      lowerCamelCase__       :						List[Any]        =							[800, 1333]
      lowerCamelCase__       :						List[Any]        =							False
   elif yolos_name == "yolos_s_dWr":
      lowerCamelCase__       :						int        =							330
      lowerCamelCase__       :						Any        =							14
      lowerCamelCase__       :						int        =							6
      lowerCamelCase__       :						Dict        =							1320
   elif "yolos_s" in yolos_name:
      lowerCamelCase__       :						Dict        =							384
      lowerCamelCase__       :						int        =							1536
      lowerCamelCase__       :						str        =							12
      lowerCamelCase__       :						Dict        =							6
   elif "yolos_b" in yolos_name:
      lowerCamelCase__       :						str        =							[800, 1344]

   lowerCamelCase__       :						List[str]        =							91
   lowerCamelCase__       :						List[Any]        =							'''huggingface/label-files'''
   lowerCamelCase__       :						Dict        =							'''coco-detection-id2label.json'''
   lowerCamelCase__       :						List[str]        =							json.load(open(hf_hub_download(UpperCAmelCase      ,  UpperCAmelCase      ,  repo_type='''dataset'''					)      ,  '''r'''					)					)
   lowerCamelCase__       :						Dict        =							{int(UpperCAmelCase					): v for k, v in idalabel.items()}
   lowerCamelCase__       :						Tuple        =							idalabel
   lowerCamelCase__       :						List[Any]        =							{v: k for k, v in idalabel.items()}

   return config



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    Optional[int]:







   """simple docstring"""
   for i in range(config.num_hidden_layers					):
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      lowerCamelCase__       :						Optional[Any]        =							state_dict.pop(f"blocks.{i}.attn.qkv.weight"					)
      lowerCamelCase__       :						List[Any]        =							state_dict.pop(f"blocks.{i}.attn.qkv.bias"					)
      # next, add query, keys and values (in that order) to the state dict
      lowerCamelCase__       :						Any        =							in_proj_weight[: config.hidden_size, :]
      lowerCamelCase__       :						str        =							in_proj_bias[: config.hidden_size]
      lowerCamelCase__       :						int        =							in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      lowerCamelCase__       :						Dict        =							in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      lowerCamelCase__       :						Union[str, Any]        =							in_proj_weight[-config.hidden_size :, :]
      lowerCamelCase__       :						List[Any]        =							in_proj_bias[-config.hidden_size :]



def 	_a						(   UpperCAmelCase					)    ->    str:







   """simple docstring"""
   if "backbone" in name:
      lowerCamelCase__       :						Optional[Any]        =							name.replace('''backbone'''      ,  '''vit'''					)
   if "cls_token" in name:
      lowerCamelCase__       :						str        =							name.replace('''cls_token'''      ,  '''embeddings.cls_token'''					)
   if "det_token" in name:
      lowerCamelCase__       :						Optional[int]        =							name.replace('''det_token'''      ,  '''embeddings.detection_tokens'''					)
   if "mid_pos_embed" in name:
      lowerCamelCase__       :						Union[str, Any]        =							name.replace('''mid_pos_embed'''      ,  '''encoder.mid_position_embeddings'''					)
   if "pos_embed" in name:
      lowerCamelCase__       :						int        =							name.replace('''pos_embed'''      ,  '''embeddings.position_embeddings'''					)
   if "patch_embed.proj" in name:
      lowerCamelCase__       :						int        =							name.replace('''patch_embed.proj'''      ,  '''embeddings.patch_embeddings.projection'''					)
   if "blocks" in name:
      lowerCamelCase__       :						List[Any]        =							name.replace('''blocks'''      ,  '''encoder.layer'''					)
   if "attn.proj" in name:
      lowerCamelCase__       :						Dict        =							name.replace('''attn.proj'''      ,  '''attention.output.dense'''					)
   if "attn" in name:
      lowerCamelCase__       :						List[str]        =							name.replace('''attn'''      ,  '''attention.self'''					)
   if "norm1" in name:
      lowerCamelCase__       :						Optional[int]        =							name.replace('''norm1'''      ,  '''layernorm_before'''					)
   if "norm2" in name:
      lowerCamelCase__       :						Union[str, Any]        =							name.replace('''norm2'''      ,  '''layernorm_after'''					)
   if "mlp.fc1" in name:
      lowerCamelCase__       :						List[Any]        =							name.replace('''mlp.fc1'''      ,  '''intermediate.dense'''					)
   if "mlp.fc2" in name:
      lowerCamelCase__       :						Dict        =							name.replace('''mlp.fc2'''      ,  '''output.dense'''					)
   if "class_embed" in name:
      lowerCamelCase__       :						Tuple        =							name.replace('''class_embed'''      ,  '''class_labels_classifier'''					)
   if "bbox_embed" in name:
      lowerCamelCase__       :						Any        =							name.replace('''bbox_embed'''      ,  '''bbox_predictor'''					)
   if "vit.norm" in name:
      lowerCamelCase__       :						str        =							name.replace('''vit.norm'''      ,  '''vit.layernorm'''					)

   return name



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    dict:







   """simple docstring"""
   for key in orig_state_dict.copy().keys():
      lowerCamelCase__       :						int        =							orig_state_dict.pop(UpperCAmelCase					)

      if "qkv" in key:
         lowerCamelCase__       :						Dict        =							key.split('''.'''					)
         lowerCamelCase__       :						Dict        =							int(key_split[2]					)
         lowerCamelCase__       :						Optional[Any]        =							model.vit.encoder.layer[layer_num].attention.attention.all_head_size
         if "weight" in key:
            lowerCamelCase__       :						int        =							val[:dim, :]
            lowerCamelCase__       :						Union[str, Any]        =							val[
                dim : dim * 2, :
            ]
            lowerCamelCase__       :						List[Any]        =							val[-dim:, :]
         else:
            lowerCamelCase__       :						Optional[int]        =							val[:dim]
            lowerCamelCase__       :						Optional[Any]        =							val[dim : dim * 2]
            lowerCamelCase__       :						Optional[Any]        =							val[-dim:]
      else:
         lowerCamelCase__       :						Tuple        =							val

   return orig_state_dict



def 	_a						(   )    ->    torch.Tensor:







   """simple docstring"""
   lowerCamelCase__       :						Union[str, Any]        =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
   lowerCamelCase__       :						Optional[int]        =							Image.open(requests.get(UpperCAmelCase      ,  stream=UpperCAmelCase					).raw					)
   return im



@torch.no_grad()
def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase = False					)    ->    Any:







   """simple docstring"""
   lowerCamelCase__       :						str        =							get_yolos_config(UpperCAmelCase					)

   # load original state_dict
   lowerCamelCase__       :						Tuple        =							torch.load(UpperCAmelCase      ,  map_location='''cpu'''					)['''model''']

   # load 🤗 model
   lowerCamelCase__       :						int        =							YolosForObjectDetection(UpperCAmelCase					)
   model.eval()
   lowerCamelCase__       :						str        =							convert_state_dict(UpperCAmelCase      ,  UpperCAmelCase					)
   model.load_state_dict(UpperCAmelCase					)

   # Check outputs on an image, prepared by YolosImageProcessor
   lowerCamelCase__       :						Union[str, Any]        =							800 if yolos_name != '''yolos_ti''' else 512
   lowerCamelCase__       :						int        =							YolosImageProcessor(format='''coco_detection'''      ,  size=UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							image_processor(images=prepare_img()      ,  return_tensors='''pt'''					)
   lowerCamelCase__       :						Optional[int]        =							model(**UpperCAmelCase					)
   lowerCamelCase__       ,							lowerCamelCase__       :						Tuple        =							outputs.logits, outputs.pred_boxes

   lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							None, None
   if yolos_name == "yolos_ti":
      lowerCamelCase__       :						Dict        =							torch.tensor(
          [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]					)
      lowerCamelCase__       :						int        =							torch.tensor(
          [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]					)
   elif yolos_name == "yolos_s_200_pre":
      lowerCamelCase__       :						Optional[int]        =							torch.tensor(
          [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]					)
      lowerCamelCase__       :						List[str]        =							torch.tensor(
          [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]					)
   elif yolos_name == "yolos_s_300_pre":
      lowerCamelCase__       :						int        =							torch.tensor(
          [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]					)
      lowerCamelCase__       :						Optional[int]        =							torch.tensor(
          [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]					)
   elif yolos_name == "yolos_s_dWr":
      lowerCamelCase__       :						Any        =							torch.tensor(
          [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]					)
      lowerCamelCase__       :						Any        =							torch.tensor(
          [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]					)
   elif yolos_name == "yolos_base":
      lowerCamelCase__       :						int        =							torch.tensor(
          [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]					)
      lowerCamelCase__       :						List[str]        =							torch.tensor(
          [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]					)
   else:
      raise ValueError(f"Unknown yolos_name: {yolos_name}"					)

   assert torch.allclose(logits[0, :3, :3]      ,  UpperCAmelCase      ,  atol=1E-4					)
   assert torch.allclose(pred_boxes[0, :3, :3]      ,  UpperCAmelCase      ,  atol=1E-4					)

   Path(UpperCAmelCase					).mkdir(exist_ok=UpperCAmelCase					)
   print(f"Saving model {yolos_name} to {pytorch_dump_folder_path}"					)
   model.save_pretrained(UpperCAmelCase					)
   print(f"Saving image processor to {pytorch_dump_folder_path}"					)
   image_processor.save_pretrained(UpperCAmelCase					)

   if push_to_hub:
      lowerCamelCase__       :						Optional[int]        =							{
          '''yolos_ti''': '''yolos-tiny''',
          '''yolos_s_200_pre''': '''yolos-small''',
          '''yolos_s_300_pre''': '''yolos-small-300''',
          '''yolos_s_dWr''': '''yolos-small-dwr''',
          '''yolos_base''': '''yolos-base''',
      }

      print('''Pushing to the hub...'''					)
      lowerCamelCase__       :						Union[str, Any]        =							model_mapping[yolos_name]
      image_processor.push_to_hub(UpperCAmelCase      ,  organization='''hustvl'''					)
      model.push_to_hub(UpperCAmelCase      ,  organization='''hustvl'''					)


if __name__ == "__main__":
       _A :   int  			=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--yolos_name',
           default='yolos_s_200_pre',
           type=str,
           help=(
               'Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','
               ' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'
           ),
       )
       parser.add_argument(
           '--checkpoint_path', default=None, type=str, help='Path to the original state dict (.pth file).'
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )
       parser.add_argument(
           '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
       )

       _A :   Union[str, Any]  			=  parser.parse_args()
       convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)










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


_A :   List[Any]  			=  'base_with_context'



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']					)					)
   lowerCamelCase__       :						List[str]        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)
   for lyr_num, lyr in enumerate(model.encoders					):
      lowerCamelCase__       :						Any        =							weights[f"layers_{lyr_num}"]
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']					)					)

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

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

      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__       :						Any        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)

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



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T					)					)

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

   for lyr_num, lyr in enumerate(model.encoders					):
      lowerCamelCase__       :						Tuple        =							weights[f"layers_{lyr_num}"]
      lowerCamelCase__       :						str        =							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__       :						Optional[Any]        =							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_attention_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[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 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T					)					)
   lowerCamelCase__       :						int        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T					)					)

   lowerCamelCase__       :						str        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)

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

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

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

      lowerCamelCase__       :						Optional[Any]        =							ly_weight['''self_attention''']
      lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)

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

      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[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[str]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)

   lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']					)					)

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

   return model



def 	_a						(   UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							checkpoints.load_tax_checkpoint(args.checkpoint_path					)
   lowerCamelCase__       :						Optional[int]        =							jnp.tree_util.tree_map(onp.array      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[str]        =							[
       '''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__       :						Optional[Any]        =							inference.parse_training_gin_file(UpperCAmelCase      ,  UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							inference.InferenceModel(args.checkpoint_path      ,  UpperCAmelCase					)

   lowerCamelCase__       :						int        =							DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''      ,  variance_type='''fixed_large'''					)

   lowerCamelCase__       :						str        =							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__       :						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'''      ,  )

   lowerCamelCase__       :						Optional[int]        =							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[int]        =							load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						int        =							load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						List[str]        =							load_decoder(ta_checkpoint['''target''']['''decoder''']      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[str]        =							OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''					)

   lowerCamelCase__       :						List[Any]        =							SpectrogramDiffusionPipeline(
       notes_encoder=UpperCAmelCase      ,  continuous_encoder=UpperCAmelCase      ,  decoder=UpperCAmelCase      ,  scheduler=UpperCAmelCase      ,  melgan=UpperCAmelCase      ,  )
   if args.save:
      pipe.save_pretrained(args.output_path					)


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

       main(args)



