






'''simple docstring'''



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()
SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)


def 				lowercase_					(     __A			:							Union[str, Any]      )							->   List[Any]:



 """simple docstring"""




 lowercase       : Dict						=ASTConfig()

 if "10-10" in model_name:
  pass
 elif "speech-commands" in model_name:
  lowercase       : Optional[Any]						=1_2_8
 elif "12-12" in model_name:
  lowercase       : Union[str, Any]						=1_2
  lowercase       : Tuple						=1_2
 elif "14-14" in model_name:
  lowercase       : Optional[Any]						=1_4
  lowercase       : List[str]						=1_4
 elif "16-16" in model_name:
  lowercase       : Union[str, Any]						=1_6
  lowercase       : Optional[int]						=1_6
 else:
  raise ValueError('''Model not supported'''      )

 lowercase       : List[Any]						='''huggingface/label-files'''
 if "speech-commands" in model_name:
  lowercase       : str						=3_5
  lowercase       : Union[str, Any]						='''speech-commands-v2-id2label.json'''
 else:
  lowercase       : Optional[int]						=5_2_7
  lowercase       : str						='''audioset-id2label.json'''

 lowercase       : List[str]						=json.load(open(hf_hub_download(__A       ,   __A       ,   repo_type='''dataset'''      )       ,   '''r'''      )      )
 lowercase       : Tuple						={int(__A      ): v for k, v in idalabel.items()}
 lowercase       : Dict						=idalabel
 lowercase       : List[str]						={v: k for k, v in idalabel.items()}

 return config


def 				lowercase_					(     __A			:							Dict      )							->   List[str]:



 """simple docstring"""




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

 return name


def 				lowercase_					(     __A			:							str       ,   __A			:							List[str]      )							->   Tuple:



 """simple docstring"""




 for key in orig_state_dict.copy().keys():
  lowercase       : Dict						=orig_state_dict.pop(__A      )

  if "qkv" in key:
   lowercase       : Union[str, Any]						=key.split('''.'''      )
   lowercase       : Any						=int(key_split[3]      )
   lowercase       : int						=config.hidden_size
   if "weight" in key:
    lowercase       : str						=val[:dim, :]
    lowercase       : int						=val[dim : dim * 2, :]
    lowercase       : Tuple						=val[-dim:, :]
   else:
    lowercase       : Dict						=val[:dim]
    lowercase       : str						=val[dim : dim * 2]
    lowercase       : Optional[int]						=val[-dim:]
  else:
   lowercase       : List[Any]						=val

 return orig_state_dict


def 				lowercase_					(     __A			:							Dict      )							->   List[str]:



 """simple docstring"""




 lowercase       : str						=[
     '''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 				lowercase_					(     __A			:							str       ,   __A			:							Optional[Any]       ,   __A			:							Optional[Any]=False      )							->   Optional[Any]:



 """simple docstring"""




 lowercase       : Any						=get_audio_spectrogram_transformer_config(__A      )

 lowercase       : List[Any]						={
     '''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
 lowercase       : Dict						=model_name_to_url[model_name]
 lowercase       : Optional[Any]						=torch.hub.load_state_dict_from_url(__A       ,   map_location='''cpu'''      )
 # remove some keys
 remove_keys(__A      )
 # rename some keys
 lowercase       : Optional[Any]						=convert_state_dict(__A       ,   __A      )

 # load 🤗 model
 lowercase       : str						=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
 lowercase       : List[Any]						=-4.2677393 if '''speech-commands''' not in model_name else -6.845978
 lowercase       : Union[str, Any]						=4.5689974 if '''speech-commands''' not in model_name else 5.5654526
 lowercase       : Optional[int]						=1_0_2_4 if '''speech-commands''' not in model_name else 1_2_8
 lowercase       : List[Any]						=ASTFeatureExtractor(mean=__A       ,   std=__A       ,   max_length=__A      )

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

  lowercase	,		lowercase       : Optional[int]						=torchaudio.load(__A      )
  lowercase       : Any						=waveform.squeeze().numpy()

 lowercase       : Optional[int]						=feature_extractor(__A       ,   sampling_rate=1_6_0_0_0       ,   return_tensors='''pt'''      )

 # forward pass
 lowercase       : Optional[int]						=model(**__A      )
 lowercase       : List[Any]						=outputs.logits

 if model_name == "ast-finetuned-audioset-10-10-0.4593":
  lowercase       : Union[str, Any]						=torch.tensor([-0.8760, -7.0042, -8.6602]      )
 elif model_name == "ast-finetuned-audioset-10-10-0.450":
  lowercase       : str						=torch.tensor([-1.1986, -7.0903, -8.2718]      )
 elif model_name == "ast-finetuned-audioset-10-10-0.448":
  lowercase       : Dict						=torch.tensor([-2.6128, -8.0080, -9.4344]      )
 elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
  lowercase       : List[str]						=torch.tensor([-1.5080, -7.4534, -8.8917]      )
 elif model_name == "ast-finetuned-audioset-12-12-0.447":
  lowercase       : str						=torch.tensor([-0.5050, -6.5833, -8.0843]      )
 elif model_name == "ast-finetuned-audioset-14-14-0.443":
  lowercase       : List[Any]						=torch.tensor([-0.3826, -7.0336, -8.2413]      )
 elif model_name == "ast-finetuned-audioset-16-16-0.442":
  lowercase       : Any						=torch.tensor([-1.2113, -6.9101, -8.3470]      )
 elif model_name == "ast-finetuned-speech-commands-v2":
  lowercase       : Optional[Any]						=torch.tensor([6.1589, -8.0566, -8.7984]      )
 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__":
      SCREAMING_SNAKE_CASE							=						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.'
      )

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








def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						A__	  =     _modexpt(__UpperCamelCase			,						exponent // 2			,						__UpperCamelCase       ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(__UpperCamelCase			,						exponent - 1			,						__UpperCamelCase       )) % modulo_value


def A    (							__UpperCamelCase = 1_777			,						__UpperCamelCase = 1_855			,						__UpperCamelCase = 8       )    ->   int:
			A__	  =     base
			for _ in range(1			,						__UpperCamelCase       ):
						A__	  =     _modexpt(__UpperCamelCase			,						__UpperCamelCase			,						10**digits       )

			return result


if __name__ == "__main__":
							print(f'{solution() = }')




