

"""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_     :			int	  =			logging.get_logger(__name__)



def 	UpperCAmelCase__   (				A__   )      ->      Optional[int]:




   """simple docstring"""
   lowerCamelCase__						=			ASTConfig()

   if "10-10" in model_name:
      pass
   elif "speech-commands" in model_name:
      lowerCamelCase__						=			128
   elif "12-12" in model_name:
      lowerCamelCase__						=			12
      lowerCamelCase__						=			12
   elif "14-14" in model_name:
      lowerCamelCase__						=			14
      lowerCamelCase__						=			14
   elif "16-16" in model_name:
      lowerCamelCase__						=			16
      lowerCamelCase__						=			16
   else:
      raise ValueError("Model not supported"   )

   lowerCamelCase__						=			"huggingface/label-files"
   if "speech-commands" in model_name:
      lowerCamelCase__						=			35
      lowerCamelCase__						=			"speech-commands-v2-id2label.json"
   else:
      lowerCamelCase__						=			527
      lowerCamelCase__						=			"audioset-id2label.json"

   lowerCamelCase__						=			json.load(open(hf_hub_download(A__	,    A__	,    repo_type="dataset"   )	,    "r"   )   )
   lowerCamelCase__						=			{int(A__   ): v for k, v in idalabel.items()}
   lowerCamelCase__						=			idalabel
   lowerCamelCase__						=			{v: k for k, v in idalabel.items()}

   return config



def 	UpperCAmelCase__   (				A__   )      ->      str:




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

   return name



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




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

      if "qkv" in key:
         lowerCamelCase__						=			key.split("."   )
         lowerCamelCase__						=			int(key_split[3]   )
         lowerCamelCase__						=			config.hidden_size
         if "weight" in key:
            lowerCamelCase__						=			val[:dim, :]
            lowerCamelCase__						=			val[dim : dim * 2, :]
            lowerCamelCase__						=			val[-dim:, :]
         else:
            lowerCamelCase__						=			val[:dim]
            lowerCamelCase__						=			val[dim : dim * 2]
            lowerCamelCase__						=			val[-dim:]
      else:
         lowerCamelCase__						=			val

   return orig_state_dict



def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			[
       "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__	,    A__	,    A__=False   )      ->      str:




   """simple docstring"""
   lowerCamelCase__						=			get_audio_spectrogram_transformer_config(A__   )

   lowerCamelCase__						=			{
       "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__						=			model_name_to_url[model_name]
   lowerCamelCase__						=			torch.hub.load_state_dict_from_url(A__	,    map_location="cpu"   )
   # remove some keys
   remove_keys(A__   )
   # rename some keys
   lowerCamelCase__						=			convert_state_dict(A__	,    A__   )

   # load 🤗 model
   lowerCamelCase__						=			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
   lowerCamelCase__						=			-4.2677393 if "speech-commands" not in model_name else -6.845978
   lowerCamelCase__						=			4.5689974 if "speech-commands" not in model_name else 5.5654526
   lowerCamelCase__						=			1024 if "speech-commands" not in model_name else 128
   lowerCamelCase__						=			ASTFeatureExtractor(mean=A__	,    std=A__	,    max_length=A__   )

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

      lowerCamelCase__					,       lowerCamelCase__						=			torchaudio.load(A__   )
      lowerCamelCase__						=			waveform.squeeze().numpy()

   lowerCamelCase__						=			feature_extractor(A__	,    sampling_rate=1_6000	,    return_tensors="pt"   )

   # forward pass
   lowerCamelCase__						=			model(**A__   )
   lowerCamelCase__						=			outputs.logits

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

import bisect



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = 0	,    A__ = -1   )      ->      int:




   """simple docstring"""
   if hi < 0:
      lowerCamelCase__						=			len(A__   )

   while lo < hi:
      lowerCamelCase__						=			lo + (hi - lo) // 2
      if sorted_collection[mid] < item:
         lowerCamelCase__						=			mid + 1
      else:
         lowerCamelCase__						=			mid

   return lo



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = 0	,    A__ = -1   )      ->      int:




   """simple docstring"""
   if hi < 0:
      lowerCamelCase__						=			len(A__   )

   while lo < hi:
      lowerCamelCase__						=			lo + (hi - lo) // 2
      if sorted_collection[mid] <= item:
         lowerCamelCase__						=			mid + 1
      else:
         lowerCamelCase__						=			mid

   return lo



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = 0	,    A__ = -1   )      ->      None:




   """simple docstring"""
   sorted_collection.insert(bisect_left(A__	,    A__	,    A__	,    A__   )	,    A__   )



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = 0	,    A__ = -1   )      ->      None:




   """simple docstring"""
   sorted_collection.insert(bisect_right(A__	,    A__	,    A__	,    A__   )	,    A__   )



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      int | None:




   """simple docstring"""
   lowerCamelCase__						=			0
   lowerCamelCase__						=			len(A__   ) - 1

   while left <= right:
      lowerCamelCase__						=			left + (right - left) // 2
      lowerCamelCase__						=			sorted_collection[midpoint]
      if current_item == item:
         return midpoint
      elif item < current_item:
         lowerCamelCase__						=			midpoint - 1
      else:
         lowerCamelCase__						=			midpoint + 1
   return None



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      int | None:




   """simple docstring"""
   lowerCamelCase__						=			bisect.bisect_left(A__	,    A__   )
   if index != len(A__   ) and sorted_collection[index] == item:
      return index
   return None



def 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__   )      ->      int | None:




   """simple docstring"""
   if right < left:
      return None

   lowerCamelCase__						=			left + (right - left) // 2

   if sorted_collection[midpoint] == item:
      return midpoint
   elif sorted_collection[midpoint] > item:
      return binary_search_by_recursion(A__	,    A__	,    A__	,    midpoint - 1   )
   else:
      return binary_search_by_recursion(A__	,    A__	,    midpoint + 1	,    A__   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			input('''Enter numbers separated by comma:\n''').strip()
     SCREAMING_SNAKE_CASE_     :			int	  =			sorted(int(item) for item in user_input.split(''','''))
     SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			int(input('''Enter a single number to be found in the list:\n'''))
     SCREAMING_SNAKE_CASE_     :			Dict	  =			binary_search(collection, target)
     if result is None:
          print(F'''{target} was not found in {collection}.''')
     else:
          print(F'''{target} was found at position {result} in {collection}.''')




