



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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict				)  ->     Optional[int]:
   _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(_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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[Any]				)  ->     Tuple:
   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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any	,   _snake_case	:List[str]				)  ->     List[Any]:
   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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]				)  ->     Dict:
   _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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple	,   _snake_case	:Dict	,   _snake_case	:List[Any]=False				)  ->     Optional[Any]:
   _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      =       -4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
   _A      =       4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
   _A      =       1_024 if '''speech-commands''' not in model_name else 128
   _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  ,    _A      =       torchaudio.load(_snake_case				)
      _A      =       waveform.squeeze().numpy()

   _A      =       feature_extractor(_snake_case	,   sampling_rate=16_000	,   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__":
   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)








UpperCAmelCase_		 =					0  # The first color of the flag.
UpperCAmelCase_		 =					1  # The second color of the flag.
UpperCAmelCase_		 =					2  # The third color of the flag.
UpperCAmelCase_		 =					(red, white, blue)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     list:
   if not sequence:
      return []
   if len(_snake_case				) == 1:
      return list(_snake_case				)
   _A      =       0
   _A      =       len(_snake_case				) - 1
   _A      =       0
   while mid <= high:
      if sequence[mid] == colors[0]:
         _A  ,    _A      =       sequence[mid], sequence[low]
         low += 1
         mid += 1
      elif sequence[mid] == colors[1]:
         mid += 1
      elif sequence[mid] == colors[2]:
         _A  ,    _A      =       sequence[high], sequence[mid]
         high -= 1
      else:
         _A      =       F'''The elements inside the sequence must contains only {colors} values'''
         raise ValueError(_snake_case				)
   return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase_		 =					input("""Enter numbers separated by commas:\n""").strip()
   UpperCAmelCase_		 =					[int(item.strip()) for item in user_input.split(""",""")]
   print(f'{dutch_national_flag_sort(unsorted)}')



