

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 (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      logging.get_logger(__name__)
def     UpperCAmelCase_       (						_A				,		_A=False				,		_A=False				,		_A=False    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   []
     for i in range(config.num_hidden_layers    ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((F'''transformer.blocks.{i}.norm1.weight''', F'''vilt.encoder.layer.{i}.layernorm_before.weight''')    )
          rename_keys.append((F'''transformer.blocks.{i}.norm1.bias''', F'''vilt.encoder.layer.{i}.layernorm_before.bias''')    )
          rename_keys.append(
              (F'''transformer.blocks.{i}.attn.proj.weight''', F'''vilt.encoder.layer.{i}.attention.output.dense.weight''')    )
          rename_keys.append(
              (F'''transformer.blocks.{i}.attn.proj.bias''', F'''vilt.encoder.layer.{i}.attention.output.dense.bias''')    )
          rename_keys.append((F'''transformer.blocks.{i}.norm2.weight''', F'''vilt.encoder.layer.{i}.layernorm_after.weight''')    )
          rename_keys.append((F'''transformer.blocks.{i}.norm2.bias''', F'''vilt.encoder.layer.{i}.layernorm_after.bias''')    )
          rename_keys.append(
              (F'''transformer.blocks.{i}.mlp.fc1.weight''', F'''vilt.encoder.layer.{i}.intermediate.dense.weight''')    )
          rename_keys.append((F'''transformer.blocks.{i}.mlp.fc1.bias''', F'''vilt.encoder.layer.{i}.intermediate.dense.bias''')    )
          rename_keys.append((F'''transformer.blocks.{i}.mlp.fc2.weight''', F'''vilt.encoder.layer.{i}.output.dense.weight''')    )
          rename_keys.append((F'''transformer.blocks.{i}.mlp.fc2.bias''', F'''vilt.encoder.layer.{i}.output.dense.bias''')    )

     # embeddings
     rename_keys.extend(
         [
             # text embeddings
             ('''text_embeddings.word_embeddings.weight''', '''vilt.embeddings.text_embeddings.word_embeddings.weight'''),
             (
                 '''text_embeddings.position_embeddings.weight''',
                 '''vilt.embeddings.text_embeddings.position_embeddings.weight''',
             ),
             ('''text_embeddings.position_ids''', '''vilt.embeddings.text_embeddings.position_ids'''),
             (
                 '''text_embeddings.token_type_embeddings.weight''',
                 '''vilt.embeddings.text_embeddings.token_type_embeddings.weight''',
             ),
             ('''text_embeddings.LayerNorm.weight''', '''vilt.embeddings.text_embeddings.LayerNorm.weight'''),
             ('''text_embeddings.LayerNorm.bias''', '''vilt.embeddings.text_embeddings.LayerNorm.bias'''),
             # patch embeddings
             ('''transformer.cls_token''', '''vilt.embeddings.cls_token'''),
             ('''transformer.patch_embed.proj.weight''', '''vilt.embeddings.patch_embeddings.projection.weight'''),
             ('''transformer.patch_embed.proj.bias''', '''vilt.embeddings.patch_embeddings.projection.bias'''),
             ('''transformer.pos_embed''', '''vilt.embeddings.position_embeddings'''),
             # token type embeddings
             ('''token_type_embeddings.weight''', '''vilt.embeddings.token_type_embeddings.weight'''),
         ]    )

     # final layernorm + pooler
     rename_keys.extend(
         [
             ('''transformer.norm.weight''', '''vilt.layernorm.weight'''),
             ('''transformer.norm.bias''', '''vilt.layernorm.bias'''),
             ('''pooler.dense.weight''', '''vilt.pooler.dense.weight'''),
             ('''pooler.dense.bias''', '''vilt.pooler.dense.bias'''),
         ]    )

     # classifier head(s)
     if vqa_model:
          # classification head
          rename_keys.extend(
              [
                  ('''vqa_classifier.0.weight''', '''classifier.0.weight'''),
                  ('''vqa_classifier.0.bias''', '''classifier.0.bias'''),
                  ('''vqa_classifier.1.weight''', '''classifier.1.weight'''),
                  ('''vqa_classifier.1.bias''', '''classifier.1.bias'''),
                  ('''vqa_classifier.3.weight''', '''classifier.3.weight'''),
                  ('''vqa_classifier.3.bias''', '''classifier.3.bias'''),
              ]    )
     elif nlvr_model:
          # classification head
          rename_keys.extend(
              [
                  ('''nlvr2_classifier.0.weight''', '''classifier.0.weight'''),
                  ('''nlvr2_classifier.0.bias''', '''classifier.0.bias'''),
                  ('''nlvr2_classifier.1.weight''', '''classifier.1.weight'''),
                  ('''nlvr2_classifier.1.bias''', '''classifier.1.bias'''),
                  ('''nlvr2_classifier.3.weight''', '''classifier.3.weight'''),
                  ('''nlvr2_classifier.3.bias''', '''classifier.3.bias'''),
              ]    )
     else:
          pass

     return rename_keys
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     for i in range(config.num_hidden_layers    ):
          SCREAMING_SNAKE_CASE__   =   '''vilt.'''
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          SCREAMING_SNAKE_CASE__   =   state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.weight'''    )
          SCREAMING_SNAKE_CASE__   =   state_dict.pop(F'''transformer.blocks.{i}.attn.qkv.bias'''    )
          # next, add query, keys and values (in that order) to the state dict
          SCREAMING_SNAKE_CASE__   =   in_proj_weight[
              : config.hidden_size, :
          ]
          SCREAMING_SNAKE_CASE__   =   in_proj_bias[: config.hidden_size]
          SCREAMING_SNAKE_CASE__   =   in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          SCREAMING_SNAKE_CASE__   =   in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          SCREAMING_SNAKE_CASE__   =   in_proj_weight[
              -config.hidden_size :, :
          ]
          SCREAMING_SNAKE_CASE__   =   in_proj_bias[-config.hidden_size :]
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   ['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(_A				,		_A    )
def     UpperCAmelCase_       (						_A				,		_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   dct.pop(_A    )
     SCREAMING_SNAKE_CASE__   =   val
@torch.no_grad()
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   ViltConfig(image_size=3_84				,		patch_size=32				,		tie_word_embeddings=_A    )
     SCREAMING_SNAKE_CASE__   =   False
     SCREAMING_SNAKE_CASE__   =   False
     SCREAMING_SNAKE_CASE__   =   False
     SCREAMING_SNAKE_CASE__   =   False
     if "vqa" in checkpoint_url:
          SCREAMING_SNAKE_CASE__   =   True
          SCREAMING_SNAKE_CASE__   =   31_29
          SCREAMING_SNAKE_CASE__   =   '''huggingface/label-files'''
          SCREAMING_SNAKE_CASE__   =   '''vqa2-id2label.json'''
          SCREAMING_SNAKE_CASE__   =   json.load(open(hf_hub_download(_A				,		_A				,		repo_type='''dataset'''    )				,		'''r'''    )    )
          SCREAMING_SNAKE_CASE__   =   {int(_A    ): v for k, v in idalabel.items()}
          SCREAMING_SNAKE_CASE__   =   idalabel
          SCREAMING_SNAKE_CASE__   =   {v: k for k, v in idalabel.items()}
          SCREAMING_SNAKE_CASE__   =   ViltForQuestionAnswering(_A    )
     elif "nlvr" in checkpoint_url:
          SCREAMING_SNAKE_CASE__   =   True
          SCREAMING_SNAKE_CASE__   =   2
          SCREAMING_SNAKE_CASE__   =   {0: '''False''', 1: '''True'''}
          SCREAMING_SNAKE_CASE__   =   {v: k for k, v in config.idalabel.items()}
          SCREAMING_SNAKE_CASE__   =   3
          SCREAMING_SNAKE_CASE__   =   ViltForImagesAndTextClassification(_A    )
     elif "irtr" in checkpoint_url:
          SCREAMING_SNAKE_CASE__   =   True
          SCREAMING_SNAKE_CASE__   =   ViltForImageAndTextRetrieval(_A    )
     elif "mlm_itm" in checkpoint_url:
          SCREAMING_SNAKE_CASE__   =   True
          SCREAMING_SNAKE_CASE__   =   ViltForMaskedLM(_A    )
     else:
          raise ValueError('''Unknown model type'''    )

     # load state_dict of original model, remove and rename some keys
     SCREAMING_SNAKE_CASE__   =   torch.hub.load_state_dict_from_url(_A				,		map_location='''cpu'''    )['''state_dict''']
     SCREAMING_SNAKE_CASE__   =   create_rename_keys(_A				,		_A				,		_A				,		_A    )
     for src, dest in rename_keys:
          rename_key(_A				,		_A				,		_A    )
     read_in_q_k_v(_A				,		_A    )
     if mlm_model or irtr_model:
          SCREAMING_SNAKE_CASE__   =   ['''itm_score.fc.weight''', '''itm_score.fc.bias''']
          for k in ignore_keys:
               state_dict.pop(_A				,		_A    )

    # load state dict into HuggingFace model
     model.eval()
     if mlm_model:
          SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   model.load_state_dict(_A				,		strict=_A    )
          assert missing_keys == ["mlm_score.decoder.bias"]
     else:
          model.load_state_dict(_A    )

     # Define processor
     SCREAMING_SNAKE_CASE__   =   ViltImageProcessor(size=3_84    )
     SCREAMING_SNAKE_CASE__   =   BertTokenizer.from_pretrained('''bert-base-uncased'''    )
     SCREAMING_SNAKE_CASE__   =   ViltProcessor(_A				,		_A    )

     # Forward pass on example inputs (image + text)
     if nlvr_model:
          SCREAMING_SNAKE_CASE__   =   Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'''				,		stream=_A    ).raw    )
          SCREAMING_SNAKE_CASE__   =   Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'''				,		stream=_A    ).raw    )
          SCREAMING_SNAKE_CASE__   =   (
              '''The left image contains twice the number of dogs as the right image, and at least two dogs in total are'''
              ''' standing.'''
          )
          SCREAMING_SNAKE_CASE__   =   processor(_A				,		_A				,		return_tensors='''pt'''    )
          SCREAMING_SNAKE_CASE__   =   processor(_A				,		_A				,		return_tensors='''pt'''    )
          SCREAMING_SNAKE_CASE__   =   model(
              input_ids=encoding_a.input_ids				,		pixel_values=encoding_a.pixel_values				,		pixel_values_a=encoding_a.pixel_values				,		)
     else:
          SCREAMING_SNAKE_CASE__   =   Image.open(requests.get('''http://images.cocodataset.org/val2017/000000039769.jpg'''				,		stream=_A    ).raw    )
          if mlm_model:
               SCREAMING_SNAKE_CASE__   =   '''a bunch of [MASK] laying on a [MASK].'''
          else:
               SCREAMING_SNAKE_CASE__   =   '''How many cats are there?'''
          SCREAMING_SNAKE_CASE__   =   processor(_A				,		_A				,		return_tensors='''pt'''    )
          SCREAMING_SNAKE_CASE__   =   model(**_A    )

     # Verify outputs
     if mlm_model:
          SCREAMING_SNAKE_CASE__   =   torch.Size([1, 11, 3_05_22]    )
          SCREAMING_SNAKE_CASE__   =   torch.tensor([-1_2.5_0_6_1, -1_2.5_1_2_3, -1_2.5_1_7_4]    )
          assert outputs.logits.shape == expected_shape
          assert torch.allclose(outputs.logits[0, 0, :3]				,		_A				,		atol=1e-4    )

          # verify masked token prediction equals "cats"
          SCREAMING_SNAKE_CASE__   =   outputs.logits[0, 4, :].argmax(-1    ).item()
          assert tokenizer.decode([predicted_id]    ) == "cats"
     elif vqa_model:
          SCREAMING_SNAKE_CASE__   =   torch.Size([1, 31_29]    )
          SCREAMING_SNAKE_CASE__   =   torch.tensor([-1_5.9_4_9_5, -1_8.1_4_7_2, -1_0.3_0_4_1]    )
          assert torch.allclose(outputs.logits[0, :3]				,		_A				,		atol=1e-4    )
          assert outputs.logits.shape == expected_shape
          assert torch.allclose(outputs.logits[0, 0, :3]				,		_A				,		atol=1e-4    )

          # verify vqa prediction equals "2"
          SCREAMING_SNAKE_CASE__   =   outputs.logits.argmax(-1    ).item()
          assert model.config.idalabel[predicted_idx] == "2"
     elif nlvr_model:
          SCREAMING_SNAKE_CASE__   =   torch.Size([1, 2]    )
          SCREAMING_SNAKE_CASE__   =   torch.tensor([-2.8_7_2_1, 2.1_2_9_1]    )
          assert torch.allclose(outputs.logits[0, :3]				,		_A				,		atol=1e-4    )
          assert outputs.logits.shape == expected_shape

     Path(_A    ).mkdir(exist_ok=_A    )
     print(F'''Saving model and processor to {pytorch_dump_folder_path}'''    )
     model.save_pretrained(_A    )
     processor.save_pretrained(_A    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Tuple             =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint_url''',
           default='''https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt''',
           type=str,
           help='''URL of the checkpoint you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
       )

       _SCREAMING_SNAKE_CASE					:  List[str]             =      parser.parse_args()
       convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





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


_SCREAMING_SNAKE_CASE					:  Dict             =      '''base_with_context'''
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




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

          SCREAMING_SNAKE_CASE__   =   ly_weight['''attention''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']    )    )
     return model
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T    )    )

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

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

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']    )    )

     return model
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T    )    )
     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T    )    )

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

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(
         torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T    )    )

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

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

          SCREAMING_SNAKE_CASE__   =   ly_weight['''self_attention''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )

          SCREAMING_SNAKE_CASE__   =   ly_weight['''MultiHeadDotProductAttention_0''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T    )    )

     return model
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




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

     SCREAMING_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()''',
     ]

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

     SCREAMING_SNAKE_CASE__   =   DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''				,		variance_type='''fixed_large'''    )

     SCREAMING_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'''				,		)

     SCREAMING_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'''				,		)

     SCREAMING_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				,		)

     SCREAMING_SNAKE_CASE__   =   load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']				,		_A    )
     SCREAMING_SNAKE_CASE__   =   load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']				,		_A    )
     SCREAMING_SNAKE_CASE__   =   load_decoder(ta_checkpoint['''target''']['''decoder''']				,		_A    )

     SCREAMING_SNAKE_CASE__   =   OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''    )

     SCREAMING_SNAKE_CASE__   =   SpectrogramDiffusionPipeline(
         notes_encoder=_A				,		continuous_encoder=_A				,		decoder=_A				,		scheduler=_A				,		melgan=_A				,		)
     if args.save:
          pipe.save_pretrained(args.output_path    )


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

       main(args)


