






import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__				   =		logging.get_logger(__name__)
def 						_a    (      a :str )							->			Dict:
 a		  =      ASTConfig()

 if "10-10" in model_name:
  pass
 elif "speech-commands" in model_name:
  a		  =      128
 elif "12-12" in model_name:
  a		  =      12
  a		  =      12
 elif "14-14" in model_name:
  a		  =      14
  a		  =      14
 elif "16-16" in model_name:
  a		  =      16
  a		  =      16
 else:
  raise ValueError('''Model not supported''' )

 a		  =      '''huggingface/label-files'''
 if "speech-commands" in model_name:
  a		  =      35
  a		  =      '''speech-commands-v2-id2label.json'''
 else:
  a		  =      527
  a		  =      '''audioset-id2label.json'''

 a		  =      json.load(open(hf_hub_download(a	,							a	,							repo_type='''dataset''' )	,							'''r''' ) )
 a		  =      {int(a ): v for k, v in idalabel.items()}
 a		  =      idalabel
 a		  =      {v: k for k, v in idalabel.items()}

 return config
def 						_a    (      a :Optional[Any] )							->			Union[str, Any]:
 if "module.v" in name:
  a		  =      name.replace('''module.v'''	,							'''audio_spectrogram_transformer''' )
 if "cls_token" in name:
  a		  =      name.replace('''cls_token'''	,							'''embeddings.cls_token''' )
 if "dist_token" in name:
  a		  =      name.replace('''dist_token'''	,							'''embeddings.distillation_token''' )
 if "pos_embed" in name:
  a		  =      name.replace('''pos_embed'''	,							'''embeddings.position_embeddings''' )
 if "patch_embed.proj" in name:
  a		  =      name.replace('''patch_embed.proj'''	,							'''embeddings.patch_embeddings.projection''' )
 # transformer blocks
 if "blocks" in name:
  a		  =      name.replace('''blocks'''	,							'''encoder.layer''' )
 if "attn.proj" in name:
  a		  =      name.replace('''attn.proj'''	,							'''attention.output.dense''' )
 if "attn" in name:
  a		  =      name.replace('''attn'''	,							'''attention.self''' )
 if "norm1" in name:
  a		  =      name.replace('''norm1'''	,							'''layernorm_before''' )
 if "norm2" in name:
  a		  =      name.replace('''norm2'''	,							'''layernorm_after''' )
 if "mlp.fc1" in name:
  a		  =      name.replace('''mlp.fc1'''	,							'''intermediate.dense''' )
 if "mlp.fc2" in name:
  a		  =      name.replace('''mlp.fc2'''	,							'''output.dense''' )
 # final layernorm
 if "audio_spectrogram_transformer.norm" in name:
  a		  =      name.replace('''audio_spectrogram_transformer.norm'''	,							'''audio_spectrogram_transformer.layernorm''' )
 # classifier head
 if "module.mlp_head.0" in name:
  a		  =      name.replace('''module.mlp_head.0'''	,							'''classifier.layernorm''' )
 if "module.mlp_head.1" in name:
  a		  =      name.replace('''module.mlp_head.1'''	,							'''classifier.dense''' )

 return name
def 						_a    (      a :Optional[int]	,							a :List[str] )							->			Any:
 for key in orig_state_dict.copy().keys():
  a		  =      orig_state_dict.pop(a )

  if "qkv" in key:
   a		  =      key.split('''.''' )
   a		  =      int(key_split[3] )
   a		  =      config.hidden_size
   if "weight" in key:
    a		  =      val[:dim, :]
    a		  =      val[dim : dim * 2, :]
    a		  =      val[-dim:, :]
   else:
    a		  =      val[:dim]
    a		  =      val[dim : dim * 2]
    a		  =      val[-dim:]
  else:
   a		  =      val

 return orig_state_dict
def 						_a    (      a :Union[str, Any] )							->			Optional[Any]:
 a		  =      [
     '''module.v.head.weight''',
     '''module.v.head.bias''',
     '''module.v.head_dist.weight''',
     '''module.v.head_dist.bias''',
 ]
 for k in ignore_keys:
  state_dict.pop(a	,							a )
@torch.no_grad()
def 						_a    (      a :Union[str, Any]	,							a :Optional[int]	,							a :str=False )							->			Union[str, Any]:
 a		  =      get_audio_spectrogram_transformer_config(a )

 a		  =      {
     '''ast-finetuned-audioset-10-10-0.4593''': (
         '''https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'''
     ),
     '''ast-finetuned-audioset-10-10-0.450''': (
         '''https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'''
     ),
     '''ast-finetuned-audioset-10-10-0.448''': (
         '''https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'''
     ),
     '''ast-finetuned-audioset-10-10-0.448-v2''': (
         '''https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'''
     ),
     '''ast-finetuned-audioset-12-12-0.447''': (
         '''https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'''
     ),
     '''ast-finetuned-audioset-14-14-0.443''': (
         '''https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'''
     ),
     '''ast-finetuned-audioset-16-16-0.442''': (
         '''https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'''
     ),
     '''ast-finetuned-speech-commands-v2''': (
         '''https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'''
     ),
 }

 # load original state_dict
 a		  =      model_name_to_url[model_name]
 a		  =      torch.hub.load_state_dict_from_url(a	,							map_location='''cpu''' )
 # remove some keys
 remove_keys(a )
 # rename some keys
 a		  =      convert_state_dict(a	,							a )

 # load 🤗 model
 a		  =      ASTForAudioClassification(a )
 model.eval()

 model.load_state_dict(a )

 # verify outputs on dummy input
 # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
 a		  =      -4.2_677_393 if '''speech-commands''' not in model_name else -6.845_978
 a		  =      4.5_689_974 if '''speech-commands''' not in model_name else 5.5_654_526
 a		  =      1_024 if '''speech-commands''' not in model_name else 128
 a		  =      ASTFeatureExtractor(mean=a	,							std=a	,							max_length=a )

 if "speech-commands" in model_name:
  a		  =      load_dataset('''speech_commands'''	,							'''v0.02'''	,							split='''validation''' )
  a		  =      dataset[0]['''audio''']['''array''']
 else:
  a		  =      hf_hub_download(
      repo_id='''nielsr/audio-spectogram-transformer-checkpoint'''	,							filename='''sample_audio.flac'''	,							repo_type='''dataset'''	,							)

  a  ,     a		  =      torchaudio.load(a )
  a		  =      waveform.squeeze().numpy()

 a		  =      feature_extractor(a	,							sampling_rate=16_000	,							return_tensors='''pt''' )

 # forward pass
 a		  =      model(**a )
 a		  =      outputs.logits

 if model_name == "ast-finetuned-audioset-10-10-0.4593":
  a		  =      torch.tensor([-0.8_760, -7.0_042, -8.6_602] )
 elif model_name == "ast-finetuned-audioset-10-10-0.450":
  a		  =      torch.tensor([-1.1_986, -7.0_903, -8.2_718] )
 elif model_name == "ast-finetuned-audioset-10-10-0.448":
  a		  =      torch.tensor([-2.6_128, -8.0_080, -9.4_344] )
 elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
  a		  =      torch.tensor([-1.5_080, -7.4_534, -8.8_917] )
 elif model_name == "ast-finetuned-audioset-12-12-0.447":
  a		  =      torch.tensor([-0.5_050, -6.5_833, -8.0_843] )
 elif model_name == "ast-finetuned-audioset-14-14-0.443":
  a		  =      torch.tensor([-0.3_826, -7.0_336, -8.2_413] )
 elif model_name == "ast-finetuned-audioset-16-16-0.442":
  a		  =      torch.tensor([-1.2_113, -6.9_101, -8.3_470] )
 elif model_name == "ast-finetuned-speech-commands-v2":
  a		  =      torch.tensor([6.1_589, -8.0_566, -8.7_984] )
 else:
  raise ValueError('''Unknown model name''' )
 if not torch.allclose(logits[0, :3]	,							a	,							atol=1e-4 ):
  raise ValueError('''Logits don\'t match''' )
 print('''Looks ok!''' )

 if pytorch_dump_folder_path is not None:
  Path(a ).mkdir(exist_ok=a )
  print(F"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
  model.save_pretrained(a )
  print(F"""Saving feature extractor to {pytorch_dump_folder_path}""" )
  feature_extractor.save_pretrained(a )

 if push_to_hub:
  print('''Pushing model and feature extractor to the hub...''' )
  model.push_to_hub(F"""MIT/{model_name}""" )
  feature_extractor.push_to_hub(F"""MIT/{model_name}""" )


if __name__ == "__main__":
     UpperCAmelCase__				   =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--model_name",
         default="ast-finetuned-audioset-10-10-0.4593",
         type=str,
         help="Name of the Audio Spectrogram Transformer model you'd like to convert.",
     )
     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."
     )

     UpperCAmelCase__				   =		parser.parse_args()
     convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch






@require_torch
@require_tf
class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						str  )    ->int:




       """simple docstring"""


       a		  =      SMALL_MODEL_IDENTIFIER
       a		  =      '''pt'''
       a		  =      '''tf'''







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						str  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      AutoModel.from_pretrained(self.test_model  )
       model_pt.save_pretrained(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Any ,					__UpperCAmelCase      :						Union[str, Any]  )    ->List[str]:




       """simple docstring"""


       a		  =      TFAutoModel.from_pretrained(self.test_model ,					from_pt=__UpperCAmelCase  )
       model_tf.save_pretrained(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Any  )    ->int:




       """simple docstring"""


       a		  =      '''mock_framework'''

       # Framework provided - return whatever the user provides
       a		  =      FeaturesManager.determine_framework(self.test_model ,					__UpperCAmelCase  )
       self.assertEqual(__UpperCAmelCase ,					__UpperCAmelCase  )

       # Local checkpoint and framework provided - return provided framework
       # PyTorch checkpoint
       with TemporaryDirectory() as local_pt_ckpt:
        self._setup_pt_ckpt(__UpperCAmelCase  )
        a		  =      FeaturesManager.determine_framework(__UpperCAmelCase ,					__UpperCAmelCase  )
        self.assertEqual(__UpperCAmelCase ,					__UpperCAmelCase  )

       # TensorFlow checkpoint
       with TemporaryDirectory() as local_tf_ckpt:
        self._setup_tf_ckpt(__UpperCAmelCase  )
        a		  =      FeaturesManager.determine_framework(__UpperCAmelCase ,					__UpperCAmelCase  )
        self.assertEqual(__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						str  )    ->int:




       """simple docstring"""


       with TemporaryDirectory() as local_pt_ckpt:
        self._setup_pt_ckpt(__UpperCAmelCase  )
        a		  =      FeaturesManager.determine_framework(__UpperCAmelCase  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_pt  )

       # TensorFlow checkpoint
       with TemporaryDirectory() as local_tf_ckpt:
        self._setup_tf_ckpt(__UpperCAmelCase  )
        a		  =      FeaturesManager.determine_framework(__UpperCAmelCase  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_tf  )

       # Invalid local checkpoint
       with TemporaryDirectory() as local_invalid_ckpt:
        with self.assertRaises(__UpperCAmelCase  ):
         a		  =      FeaturesManager.determine_framework(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[str]:




       """simple docstring"""


       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       with patch('''transformers.onnx.features.is_tf_available''' ,					__UpperCAmelCase  ):
        a		  =      FeaturesManager.determine_framework(self.test_model  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_pt  )

       # PyTorch not in environment -> use TensorFlow
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       with patch('''transformers.onnx.features.is_torch_available''' ,					__UpperCAmelCase  ):
        a		  =      FeaturesManager.determine_framework(self.test_model  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_tf  )

       # Both in environment -> use PyTorch
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       with patch('''transformers.onnx.features.is_tf_available''' ,					__UpperCAmelCase  ), patch(
           '''transformers.onnx.features.is_torch_available''' ,					__UpperCAmelCase  ):
        a		  =      FeaturesManager.determine_framework(self.test_model  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_pt  )

       # Both not in environment -> raise error
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       with patch('''transformers.onnx.features.is_tf_available''' ,					__UpperCAmelCase  ), patch(
           '''transformers.onnx.features.is_torch_available''' ,					__UpperCAmelCase  ):
        with self.assertRaises(__UpperCAmelCase  ):
         a		  =      FeaturesManager.determine_framework(self.test_model  )


