
'''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()
__lowerCAmelCase         =    logging.get_logger(__name__)





def 					UpperCAmelCase_		(__a      :    Dict ):






   """simple docstring"""

   _a				:	Dict			=   ASTConfig()

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

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

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

   return config





def 					UpperCAmelCase_		(__a      :    Tuple ):






   """simple docstring"""

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

   return name





def 					UpperCAmelCase_		(__a      :    Union[str, Any] ,	__a      :    str ):






   """simple docstring"""

   for key in orig_state_dict.copy().keys():
      _a				:	List[Any]			=   orig_state_dict.pop(__a )

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

   return orig_state_dict





def 					UpperCAmelCase_		(__a      :    Any ):






   """simple docstring"""

   _a				:	Dict			=   [
       '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 					UpperCAmelCase_		(__a      :    List[Any] ,	__a      :    Dict ,	__a      :    Any=False ):






   """simple docstring"""

   _a				:	List[Any]			=   get_audio_spectrogram_transformer_config(__a )

   _a				:	List[str]			=   {
       '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				:	int			=   model_name_to_url[model_name]
   _a				:	List[Any]			=   torch.hub.load_state_dict_from_url(__a ,	map_location='cpu' )
   # remove some keys
   remove_keys(__a )
   # rename some keys
   _a				:	Any			=   convert_state_dict(__a ,	__a )

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

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

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

   _a				:	str			=   feature_extractor(__a ,	sampling_rate=1_6_0_0_0 ,	return_tensors='pt' )

   # forward pass
   _a				:	str			=   model(**__a )
   _a				:	Union[str, Any]			=   outputs.logits

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

  __lowerCAmelCase         =    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

__lowerCAmelCase         =    [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
__lowerCAmelCase         =    [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	Optional[int]			=   []
   _a				:	int			=   len(__a )

   for i in range(__a ):
      _a				:	float			=   -1
      for j in range(i + 1 ,	__a ):
         if arr[i] < arr[j]:
            _a				:	Any			=   arr[j]
            break
      result.append(__a )
   return result





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	Tuple			=   []
   for i, outer in enumerate(__a ):
      _a				:	float			=   -1
      for inner in arr[i + 1 :]:
         if outer < inner:
            _a				:	Dict			=   inner
            break
      result.append(__a )
   return result





def 					UpperCAmelCase_		(__a      :    list[float] ):






   """simple docstring"""

   _a				:	int			=   len(__a )
   _a				:	list[float]			=   []
   _a				:	list[float]			=   [-1] * arr_size

   for index in reversed(range(__a ) ):
      if stack:
         while stack[-1] <= arr[index]:
            stack.pop()
            if not stack:
               break
      if stack:
         _a				:	Dict			=   stack[-1]
      stack.append(arr[index] )
   return result


if __name__ == "__main__":
  from doctest import testmod
  from timeit import timeit

  testmod()
  print(next_greatest_element_slow(arr))
  print(next_greatest_element_fast(arr))
  print(next_greatest_element(arr))

  __lowerCAmelCase         =    (
      """from __main__ import arr, next_greatest_element_slow, """
      """next_greatest_element_fast, next_greatest_element"""
  )
  print(
      """next_greatest_element_slow():""",
      timeit("""next_greatest_element_slow(arr)""", setup=setup),
  )
  print(
      """next_greatest_element_fast():""",
      timeit("""next_greatest_element_fast(arr)""", setup=setup),
  )
  print(
      """     next_greatest_element():""",
      timeit("""next_greatest_element(arr)""", setup=setup),
  )



