






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     a_   (__A      )				->						Any:



					"""simple docstring"""
					__a					:						int      =					ASTConfig()

					if "10-10" in model_name:
										pass
					elif "speech-commands" in model_name:
										__a					:						int      =					128
					elif "12-12" in model_name:
										__a					:						str      =					12
										__a					:						str      =					12
					elif "14-14" in model_name:
										__a					:						str      =					14
										__a					:						Dict      =					14
					elif "16-16" in model_name:
										__a					:						Dict      =					16
										__a					:						Optional[int]      =					16
					else:
										raise ValueError("Model not supported"      )

					__a					:						List[Any]      =					"huggingface/label-files"
					if "speech-commands" in model_name:
										__a					:						Any      =					35
										__a					:						int      =					"speech-commands-v2-id2label.json"
					else:
										__a					:						int      =					527
										__a					:						Tuple      =					"audioset-id2label.json"

					__a					:						str      =					json.load(open(hf_hub_download(__A				,       __A				,       repo_type="dataset"      )				,       "r"      )      )
					__a					:						List[Any]      =					{int(__A      ): v for k, v in idalabel.items()}
					__a					:						List[Any]      =					idalabel
					__a					:						List[str]      =					{v: k for k, v in idalabel.items()}

					return config







def     a_   (__A      )				->						Optional[int]:



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

					return name







def     a_   (__A				,       __A      )				->						Union[str, Any]:



					"""simple docstring"""
					for key in orig_state_dict.copy().keys():
										__a					:						List[Any]      =					orig_state_dict.pop(__A      )

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

					return orig_state_dict







def     a_   (__A      )				->						Union[str, Any]:



					"""simple docstring"""
					__a					:						Tuple      =					[
					    "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     a_   (__A				,       __A				,       __A=False      )				->						Union[str, Any]:



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

					# load 🤗 model
					__a					:						Optional[int]      =					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					:						List[Any]      =					-4.267_7393 if "speech-commands" not in model_name else -6.84_5978
					__a					:						int      =					4.568_9974 if "speech-commands" not in model_name else 5.565_4526
					__a					:						int      =					1_024 if "speech-commands" not in model_name else 128
					__a					:						Optional[int]      =					ASTFeatureExtractor(mean=__A				,       std=__A				,       max_length=__A      )

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

										__a      ,			__a					:						Optional[int]      =					torchaudio.load(__A      )
										__a					:						List[Any]      =					waveform.squeeze().numpy()

					__a					:						Optional[int]      =					feature_extractor(__A				,       sampling_rate=16_000				,       return_tensors="pt"      )

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

					if model_name == "ast-finetuned-audioset-10-10-0.4593":
										__a					:						Dict      =					torch.tensor([-0.8760, -7.0042, -8.6602]      )
					elif model_name == "ast-finetuned-audioset-10-10-0.450":
										__a					:						List[str]      =					torch.tensor([-1.1986, -7.0903, -8.2718]      )
					elif model_name == "ast-finetuned-audioset-10-10-0.448":
										__a					:						Dict      =					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					:						Tuple      =					torch.tensor([-0.5050, -6.5833, -8.0843]      )
					elif model_name == "ast-finetuned-audioset-14-14-0.443":
										__a					:						str      =					torch.tensor([-0.3826, -7.0336, -8.2413]      )
					elif model_name == "ast-finetuned-audioset-16-16-0.442":
										__a					:						int      =					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__":
			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)













def     a_   (__A      )				->						Dict:



					"""simple docstring"""
					if not head:
										return True
					# split the list to two parts
					__a      ,			__a					:						Any      =					head.next, head
					while fast and fast.next:
										__a					:						Optional[int]      =					fast.next.next
										__a					:						Optional[int]      =					slow.next
					__a					:						Optional[int]      =					slow.next
					__a					:						Tuple      =					None  # Don't forget here! But forget still works!
					# reverse the second part
					__a					:						Any      =					None
					while second:
										__a					:						int      =					second.next
										__a					:						int      =					node
										__a					:						Union[str, Any]      =					second
										__a					:						Union[str, Any]      =					nxt
					# compare two parts
					# second part has the same or one less node
					while node:
										if node.val != head.val:
															return False
										__a					:						int      =					node.next
										__a					:						Dict      =					head.next
					return True







def     a_   (__A      )				->						Union[str, Any]:



					"""simple docstring"""
					if not head or not head.next:
										return True

					# 1. Get the midpoint (slow)
					__a					:						Union[str, Any]      =					head
					while fast and fast.next:
										__a      ,			__a					:						List[str]      =					fast.next.next, slow.next

					# 2. Push the second half into the stack
					__a					:						Dict      =					[slow.val]
					while slow.next:
										__a					:						Optional[Any]      =					slow.next
										stack.append(slow.val      )

					# 3. Comparison
					while stack:
										if stack.pop() != cur.val:
															return False
										__a					:						List[str]      =					cur.next

					return True







def     a_   (__A      )				->						List[str]:



					"""simple docstring"""
					if not head or not head.next:
										return True
					__a					:						Optional[int]      =					{}
					__a					:						Optional[int]      =					0
					while head:
										if head.val in d:
															d[head.val].append(__A      )
										else:
															__a					:						Dict      =					[pos]
										__a					:						Dict      =					head.next
										pos += 1
					__a					:						Dict      =					pos - 1
					__a					:						Optional[int]      =					0
					for v in d.values():
										if len(__A      ) % 2 != 0:
															middle += 1
										else:
															__a					:						Dict      =					0
															for i in range(0				,       len(__A      )      ):
																				if v[i] + v[len(__A      ) - 1 - step] != checksum:
																									return False
																				step += 1
										if middle > 1:
															return False
					return True





