"""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()
__magic_name__     					=       logging.get_logger(__name__)





def 	_lowerCamelCase			(			UpperCAmelCase__       )				->  str:


   '''simple docstring'''


   a__  					=		ASTConfig()

   if "10-10" in model_name:
      pass
   elif "speech-commands" in model_name:
      a__  					=		1_28
   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__  					=		5_27
      a__  					=		"audioset-id2label.json"

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

   return config





def 	_lowerCamelCase			(			UpperCAmelCase__       )				->  Any:


   '''simple docstring'''


   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 	_lowerCamelCase			(			UpperCAmelCase__,UpperCAmelCase__       )				->  Union[str, Any]:


   '''simple docstring'''


   for key in orig_state_dict.copy().keys():
      a__  					=		orig_state_dict.pop(__snake_case       )

      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 	_lowerCamelCase			(			UpperCAmelCase__       )				->  Optional[int]:


   '''simple docstring'''


   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(__snake_case,__snake_case       )





@torch.no_grad()
def 	_lowerCamelCase			(			UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__=False       )				->  Any:


   '''simple docstring'''


   a__  					=		get_audio_spectrogram_transformer_config(__snake_case       )

   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(__snake_case,map_location='cpu'       )
   # remove some keys
   remove_keys(__snake_case       )
   # rename some keys
   a__  					=		convert_state_dict(__snake_case,__snake_case       )

   # load 🤗 model
   a__  					=		ASTForAudioClassification(__snake_case       )
   model.eval()

   model.load_state_dict(__snake_case       )

   # verify outputs on dummy input
   # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
   a__  					=		-4.2677393 if "speech-commands" not in model_name else -6.845978
   a__  					=		4.5689974 if "speech-commands" not in model_name else 5.5654526
   a__  					=		10_24 if "speech-commands" not in model_name else 1_28
   a__  					=		ASTFeatureExtractor(mean=__snake_case,std=__snake_case,max_length=__snake_case       )

   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__  					=		torchaudio.load(__snake_case       )
      a__  					=		waveform.squeeze().numpy()

   a__  					=		feature_extractor(__snake_case,sampling_rate=1_60_00,return_tensors='pt'       )

   # forward pass
   a__  					=		model(**__snake_case       )
   a__  					=		outputs.logits

   if model_name == "ast-finetuned-audioset-10-10-0.4593":
      a__  					=		torch.tensor([-0.8760, -7.0042, -8.6602]       )
   elif model_name == "ast-finetuned-audioset-10-10-0.450":
      a__  					=		torch.tensor([-1.1986, -7.0903, -8.2718]       )
   elif model_name == "ast-finetuned-audioset-10-10-0.448":
      a__  					=		torch.tensor([-2.6128, -8.0080, -9.4344]       )
   elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
      a__  					=		torch.tensor([-1.5080, -7.4534, -8.8917]       )
   elif model_name == "ast-finetuned-audioset-12-12-0.447":
      a__  					=		torch.tensor([-0.5050, -6.5833, -8.0843]       )
   elif model_name == "ast-finetuned-audioset-14-14-0.443":
      a__  					=		torch.tensor([-0.3826, -7.0336, -8.2413]       )
   elif model_name == "ast-finetuned-audioset-16-16-0.442":
      a__  					=		torch.tensor([-1.2113, -6.9101, -8.3470]       )
   elif model_name == "ast-finetuned-speech-commands-v2":
      a__  					=		torch.tensor([6.1589, -8.0566, -8.7984]       )
   else:
      raise ValueError('Unknown model name'       )
   if not torch.allclose(logits[0, :3],__snake_case,atol=1e-4       ):
      raise ValueError('Logits don\'t match'       )
   print('Looks ok!'       )

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

   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__":
     __magic_name__     					=       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."
     )

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




"""simple docstring"""




from __future__ import annotations

from math import pow, sqrt


def     __magic_name__      ( __snake_case							:			float	,					__snake_case							:			float	,					__snake_case							:			float    )					->							dict[str, float]:
       if (resistance, reactance, impedance).count(0    ) != 1:
              raise ValueError("One and only one argument must be 0"    )
       if resistance == 0:
              return {"resistance": sqrt(pow(__snake_case	,					2    ) - pow(__snake_case	,					2    )    )}
       elif reactance == 0:
              return {"reactance": sqrt(pow(__snake_case	,					2    ) - pow(__snake_case	,					2    )    )}
       elif impedance == 0:
              return {"impedance": sqrt(pow(__snake_case	,					2    ) + pow(__snake_case	,					2    )    )}
       else:
              raise ValueError("Exactly one argument must be 0"    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


