





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()
_A :   int  			=  logging.get_logger(__name__)



def 	_a						(   UpperCAmelCase					)    ->    str:







   """simple docstring"""
   lowerCamelCase__       :						List[str]        =							ASTConfig()

   if "10-10" in model_name:
      pass
   elif "speech-commands" in model_name:
      lowerCamelCase__       :						int        =							128
   elif "12-12" in model_name:
      lowerCamelCase__       :						Any        =							12
      lowerCamelCase__       :						List[str]        =							12
   elif "14-14" in model_name:
      lowerCamelCase__       :						List[str]        =							14
      lowerCamelCase__       :						Any        =							14
   elif "16-16" in model_name:
      lowerCamelCase__       :						str        =							16
      lowerCamelCase__       :						Union[str, Any]        =							16
   else:
      raise ValueError('''Model not supported'''					)

   lowerCamelCase__       :						Any        =							'''huggingface/label-files'''
   if "speech-commands" in model_name:
      lowerCamelCase__       :						Any        =							35
      lowerCamelCase__       :						str        =							'''speech-commands-v2-id2label.json'''
   else:
      lowerCamelCase__       :						Union[str, Any]        =							527
      lowerCamelCase__       :						Dict        =							'''audioset-id2label.json'''

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

   return config



def 	_a						(   UpperCAmelCase					)    ->    Any:







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

   return name



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Any:







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

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

   return orig_state_dict



def 	_a						(   UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							[
       '''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(UpperCAmelCase      ,  UpperCAmelCase					)



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







   """simple docstring"""
   lowerCamelCase__       :						Any        =							get_audio_spectrogram_transformer_config(UpperCAmelCase					)

   lowerCamelCase__       :						Tuple        =							{
       '''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
   lowerCamelCase__       :						Any        =							model_name_to_url[model_name]
   lowerCamelCase__       :						Optional[int]        =							torch.hub.load_state_dict_from_url(UpperCAmelCase      ,  map_location='''cpu'''					)
   # remove some keys
   remove_keys(UpperCAmelCase					)
   # rename some keys
   lowerCamelCase__       :						Tuple        =							convert_state_dict(UpperCAmelCase      ,  UpperCAmelCase					)

   # load 🤗 model
   lowerCamelCase__       :						List[str]        =							ASTForAudioClassification(UpperCAmelCase					)
   model.eval()

   model.load_state_dict(UpperCAmelCase					)

   # verify outputs on dummy input
   # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
   lowerCamelCase__       :						Union[str, Any]        =							-4.2_67_73_93 if '''speech-commands''' not in model_name else -6.84_59_78
   lowerCamelCase__       :						Dict        =							4.5_68_99_74 if '''speech-commands''' not in model_name else 5.5_65_45_26
   lowerCamelCase__       :						List[Any]        =							1024 if '''speech-commands''' not in model_name else 128
   lowerCamelCase__       :						Tuple        =							ASTFeatureExtractor(mean=UpperCAmelCase      ,  std=UpperCAmelCase      ,  max_length=UpperCAmelCase					)

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

      lowerCamelCase__       ,							lowerCamelCase__       :						Any        =							torchaudio.load(UpperCAmelCase					)
      lowerCamelCase__       :						Union[str, Any]        =							waveform.squeeze().numpy()

   lowerCamelCase__       :						Optional[Any]        =							feature_extractor(UpperCAmelCase      ,  sampling_rate=16000      ,  return_tensors='''pt'''					)

   # forward pass
   lowerCamelCase__       :						Optional[int]        =							model(**UpperCAmelCase					)
   lowerCamelCase__       :						List[str]        =							outputs.logits

   if model_name == "ast-finetuned-audioset-10-10-0.4593":
      lowerCamelCase__       :						Tuple        =							torch.tensor([-0.87_60, -7.00_42, -8.66_02]					)
   elif model_name == "ast-finetuned-audioset-10-10-0.450":
      lowerCamelCase__       :						Optional[Any]        =							torch.tensor([-1.19_86, -7.09_03, -8.27_18]					)
   elif model_name == "ast-finetuned-audioset-10-10-0.448":
      lowerCamelCase__       :						List[str]        =							torch.tensor([-2.61_28, -8.00_80, -9.43_44]					)
   elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
      lowerCamelCase__       :						List[str]        =							torch.tensor([-1.50_80, -7.45_34, -8.89_17]					)
   elif model_name == "ast-finetuned-audioset-12-12-0.447":
      lowerCamelCase__       :						Tuple        =							torch.tensor([-0.50_50, -6.58_33, -8.08_43]					)
   elif model_name == "ast-finetuned-audioset-14-14-0.443":
      lowerCamelCase__       :						Tuple        =							torch.tensor([-0.38_26, -7.03_36, -8.24_13]					)
   elif model_name == "ast-finetuned-audioset-16-16-0.442":
      lowerCamelCase__       :						List[Any]        =							torch.tensor([-1.21_13, -6.91_01, -8.34_70]					)
   elif model_name == "ast-finetuned-speech-commands-v2":
      lowerCamelCase__       :						Union[str, Any]        =							torch.tensor([6.15_89, -8.05_66, -8.79_84]					)
   else:
      raise ValueError('''Unknown model name'''					)
   if not torch.allclose(logits[0, :3]      ,  UpperCAmelCase      ,  atol=1E-4					):
      raise ValueError('''Logits don\'t match'''					)
   print('''Looks ok!'''					)

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

   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__":
       _A :   int  			=  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.'
       )

       _A :   Dict  			=  parser.parse_args()
       convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_A :   Optional[Any]  			=  {
    'text_branch': 'text_model',
    'audio_branch': 'audio_model.audio_encoder',
    'attn': 'attention.self',
    'self.proj': 'output.dense',
    'attention.self_mask': 'attn_mask',
    'mlp.fc1': 'intermediate.dense',
    'mlp.fc2': 'output.dense',
    'norm1': 'layernorm_before',
    'norm2': 'layernorm_after',
    'bn0': 'batch_norm',
}

_A :   Any  			=  AutoFeatureExtractor.from_pretrained('laion/clap-htsat-unfused', truncation='rand_trunc')



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    str:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							create_model(
       '''HTSAT-tiny'''      ,  '''roberta'''      ,  UpperCAmelCase      ,  precision='''fp32'''      ,  device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''      ,  enable_fusion=UpperCAmelCase      ,  fusion_type='''aff_2d''' if enable_fusion else None      ,  )
   return model, model_cfg



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







   """simple docstring"""
   lowerCamelCase__       :						Optional[int]        =							{}

   lowerCamelCase__       :						int        =							R'''.*sequential.(\d+).*'''
   lowerCamelCase__       :						Any        =							R'''.*_projection.(\d+).*'''

   for key, value in state_dict.items():
      # check if any key needs to be modified
      for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
         if key_to_modify in key:
            lowerCamelCase__       :						List[str]        =							key.replace(UpperCAmelCase      ,  UpperCAmelCase					)

      if re.match(UpperCAmelCase      ,  UpperCAmelCase					):
         # replace sequential layers with list
         lowerCamelCase__       :						List[Any]        =							re.match(UpperCAmelCase      ,  UpperCAmelCase					).group(1					)

         lowerCamelCase__       :						Optional[int]        =							key.replace(f"sequential.{sequential_layer}."      ,  f"layers.{int(UpperCAmelCase					)//3}.linear."					)
      elif re.match(UpperCAmelCase      ,  UpperCAmelCase					):
         lowerCamelCase__       :						Optional[int]        =							int(re.match(UpperCAmelCase      ,  UpperCAmelCase					).group(1					)					)

         # Because in CLAP they use `nn.Sequential`...
         lowerCamelCase__       :						str        =							1 if projecton_layer == 0 else 2

         lowerCamelCase__       :						List[str]        =							key.replace(f"_projection.{projecton_layer}."      ,  f"_projection.linear{transformers_projection_layer}."					)

      if "audio" and "qkv" in key:
         # split qkv into query key and value
         lowerCamelCase__       :						Optional[Any]        =							value
         lowerCamelCase__       :						Optional[Any]        =							mixed_qkv.size(0					) // 3

         lowerCamelCase__       :						Tuple        =							mixed_qkv[:qkv_dim]
         lowerCamelCase__       :						Dict        =							mixed_qkv[qkv_dim : qkv_dim * 2]
         lowerCamelCase__       :						int        =							mixed_qkv[qkv_dim * 2 :]

         lowerCamelCase__       :						Optional[int]        =							query_layer
         lowerCamelCase__       :						str        =							key_layer
         lowerCamelCase__       :						List[str]        =							value_layer
      else:
         lowerCamelCase__       :						Tuple        =							value

   return model_state_dict



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase=False					)    ->    List[Any]:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       :						Dict        =							init_clap(UpperCAmelCase      ,  enable_fusion=UpperCAmelCase					)

   clap_model.eval()
   lowerCamelCase__       :						List[Any]        =							clap_model.state_dict()
   lowerCamelCase__       :						Dict        =							rename_state_dict(UpperCAmelCase					)

   lowerCamelCase__       :						Optional[int]        =							ClapConfig()
   lowerCamelCase__       :						Optional[int]        =							enable_fusion
   lowerCamelCase__       :						Optional[Any]        =							ClapModel(UpperCAmelCase					)

   # ignore the spectrogram embedding layer
   model.load_state_dict(UpperCAmelCase      ,  strict=UpperCAmelCase					)

   model.save_pretrained(UpperCAmelCase					)
   transformers_config.save_pretrained(UpperCAmelCase					)


if __name__ == "__main__":
       _A :   int  			=  argparse.ArgumentParser()
       parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
       parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
       parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
       parser.add_argument('--enable_fusion', action='store_true', help='Whether to enable fusion or not')
       _A :   Tuple  			=  parser.parse_args()

       convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)



