


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()
snake_case							: List[str]      =   logging.get_logger(__name__)






def 							__lowercase     (				__lowerCAmelCase							:       Dict     ):
							a__    =  ASTConfig()

							if "10-10" in model_name:
														pass
							elif "speech-commands" in model_name:
														a__    =  1_2_8
							elif "12-12" in model_name:
														a__    =  1_2
														a__    =  1_2
							elif "14-14" in model_name:
														a__    =  1_4
														a__    =  1_4
							elif "16-16" in model_name:
														a__    =  1_6
														a__    =  1_6
							else:
														raise ValueError('Model not supported'     )

							a__    =  '''huggingface/label-files'''
							if "speech-commands" in model_name:
														a__    =  3_5
														a__    =  '''speech-commands-v2-id2label.json'''
							else:
														a__    =  5_2_7
														a__    =  '''audioset-id2label.json'''

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

							return config






def 							__lowercase     (				__lowerCAmelCase							:       List[str]     ):






							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 							__lowercase     (				__lowerCAmelCase							:       Tuple      ,					__lowerCAmelCase							:       List[Any]     ):






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

														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 							__lowercase     (				__lowerCAmelCase							:       str     ):
							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(a_      ,					a_     )






@torch.no_grad()
def 							__lowercase     (				__lowerCAmelCase							:       Optional[int]      ,					__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       Union[str, Any]=False     ):
							a__    =  get_audio_spectrogram_transformer_config(a_     )

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

							# load 🤗 model
							a__    =  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__    =  -4.2_677_393 if '''speech-commands''' not in model_name else -6.845_978
							a__    =  4.5_689_974 if '''speech-commands''' not in model_name else 5.5_654_526
							a__    =  1_0_2_4 if '''speech-commands''' not in model_name else 1_2_8
							a__    =  ASTFeatureExtractor(mean=a_      ,					std=a_      ,					max_length=a_     )

							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(a_     )
														a__    =  waveform.squeeze().numpy()

							a__    =  feature_extractor(a_      ,					sampling_rate=1_6_0_0_0      ,					return_tensors='pt'     )

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

							if model_name == "ast-finetuned-audioset-10-10-0.4593":
														a__    =  torch.tensor([-0.8_760, -7.0_042, -8.6_602]     )
							elif model_name == "ast-finetuned-audioset-10-10-0.450":
														a__    =  torch.tensor([-1.1_986, -7.0_903, -8.2_718]     )
							elif model_name == "ast-finetuned-audioset-10-10-0.448":
														a__    =  torch.tensor([-2.6_128, -8.0_080, -9.4_344]     )
							elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
														a__    =  torch.tensor([-1.5_080, -7.4_534, -8.8_917]     )
							elif model_name == "ast-finetuned-audioset-12-12-0.447":
														a__    =  torch.tensor([-0.5_050, -6.5_833, -8.0_843]     )
							elif model_name == "ast-finetuned-audioset-14-14-0.443":
														a__    =  torch.tensor([-0.3_826, -7.0_336, -8.2_413]     )
							elif model_name == "ast-finetuned-audioset-16-16-0.442":
														a__    =  torch.tensor([-1.2_113, -6.9_101, -8.3_470]     )
							elif model_name == "ast-finetuned-speech-commands-v2":
														a__    =  torch.tensor([6.1_589, -8.0_566, -8.7_984]     )
							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__":
						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.'''
						)

						snake_case							: Tuple      =   parser.parse_args()
						convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








from __future__ import annotations






def 							__lowercase     (				__lowerCAmelCase							:       list[int]     ):  # This function is recursive
							a__    =  len(__lowerCAmelCase     )
							# If the array contains only one element, we return it (it's the stop condition of
							# recursion)
							if array_length <= 1:
														return array
														# Else
							a__    =  array[0]
							a__    =  False
							a__    =  1
							a__    =  []
							while not is_found and i < array_length:
														if array[i] < pivot:
																					a__    =  True
																					a__    =  [element for element in array[i:] if element >= array[i]]
																					a__    =  longest_subsequence(__lowerCAmelCase     )
																					if len(__lowerCAmelCase     ) > len(__lowerCAmelCase     ):
																												a__    =  temp_array
														else:
																					i += 1

							a__    =  [element for element in array[1:] if element >= pivot]
							a__    =  [pivot, *longest_subsequence(__lowerCAmelCase     )]
							if len(__lowerCAmelCase     ) > len(__lowerCAmelCase     ):
														return temp_array
							else:
														return longest_subseq


if __name__ == "__main__":
						import doctest

						doctest.testmod()




