




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   :						int				=			logging.get_logger(__name__)







def 		lowercase__   (		__A:	Any				):





					'''simple docstring'''






					__magic_name__							:	List[str]    							=							ASTConfig()

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

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

					__magic_name__							:	Union[str, Any]    							=							json.load(open(hf_hub_download(__A							,__A							,repo_type='''dataset'''				)							,'''r'''				)				)
					__magic_name__							:	Optional[int]    							=							{int(__A				): v for k, v in idalabel.items()}
					__magic_name__							:	Optional[Any]    							=							idalabel
					__magic_name__							:	Union[str, Any]    							=							{v: k for k, v in idalabel.items()}

					return config







def 		lowercase__   (		__A:	Union[str, Any]				):





					'''simple docstring'''






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

					return name







def 		lowercase__   (		__A:	List[str]							,__A:	List[Any]				):





					'''simple docstring'''






					for key in orig_state_dict.copy().keys():
										__magic_name__							:	int    							=							orig_state_dict.pop(__A				)

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

					return orig_state_dict







def 		lowercase__   (		__A:	Optional[int]				):





					'''simple docstring'''






					__magic_name__							:	Optional[int]    							=							[
					    '''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__   (		__A:	Tuple							,__A:	str							,__A:	Any=False				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							get_audio_spectrogram_transformer_config(__A				)

					__magic_name__							:	Optional[Any]    							=							{
					    '''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
					__magic_name__							:	Union[str, Any]    							=							model_name_to_url[model_name]
					__magic_name__							:	Union[str, Any]    							=							torch.hub.load_state_dict_from_url(__A							,map_location='''cpu'''				)
					# remove some keys
					remove_keys(__A				)
					# rename some keys
					__magic_name__							:	List[str]    							=							convert_state_dict(__A							,__A				)

					# load 🤗 model
					__magic_name__							:	str    							=							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
					__magic_name__							:	Dict    							=							-4.2677393 if '''speech-commands''' not in model_name else -6.845978
					__magic_name__							:	Tuple    							=							4.5689974 if '''speech-commands''' not in model_name else 5.5654526
					__magic_name__							:	int    							=							1_0_2_4 if '''speech-commands''' not in model_name else 1_2_8
					__magic_name__							:	Tuple    							=							ASTFeatureExtractor(mean=__A							,std=__A							,max_length=__A				)

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

										__magic_name__							,    __magic_name__							:	str    							=							torchaudio.load(__A				)
										__magic_name__							:	Optional[int]    							=							waveform.squeeze().numpy()

					__magic_name__							:	Any    							=							feature_extractor(__A							,sampling_rate=1_6_0_0_0							,return_tensors='''pt'''				)

					# forward pass
					__magic_name__							:	Any    							=							model(**__A				)
					__magic_name__							:	List[str]    							=							outputs.logits

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









from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase   :						Optional[int]				=			logging.get_logger(__name__)

__lowerCamelCase   :						Dict				=			{}


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								='''llama'''
			UpperCamelCase__								=['''past_key_values''']

			def __init__( self			:   Dict       ,						lowerCamelCase_			:   List[str]=32000       ,						lowerCamelCase_			:   List[Any]=4096       ,						lowerCamelCase_			:   Any=11008       ,						lowerCamelCase_			:   Optional[Any]=32       ,						lowerCamelCase_			:   Dict=32       ,						lowerCamelCase_			:   Optional[Any]=None       ,						lowerCamelCase_			:   Dict="silu"       ,						lowerCamelCase_			:   Optional[int]=2048       ,						lowerCamelCase_			:   int=0.0_2       ,						lowerCamelCase_			:   Tuple=1E-6       ,						lowerCamelCase_			:   Tuple=True       ,						lowerCamelCase_			:   List[str]=0       ,						lowerCamelCase_			:   List[str]=1       ,						lowerCamelCase_			:   Optional[int]=2       ,						lowerCamelCase_			:   List[str]=1       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   int=None       ,						**lowerCamelCase_			:   Tuple       ,						)						->   Tuple:
								__magic_name__							:	Any    							=							vocab_size
								__magic_name__							:	Optional[int]    							=							max_position_embeddings
								__magic_name__							:	Union[str, Any]    							=							hidden_size
								__magic_name__							:	Dict    							=							intermediate_size
								__magic_name__							:	Union[str, Any]    							=							num_hidden_layers
								__magic_name__							:	Tuple    							=							num_attention_heads

								# for backward compatibility
								if num_key_value_heads is None:
													__magic_name__							:	Optional[int]    							=							num_attention_heads

								__magic_name__							:	Optional[int]    							=							num_key_value_heads
								__magic_name__							:	List[str]    							=							hidden_act
								__magic_name__							:	List[str]    							=							initializer_range
								__magic_name__							:	Tuple    							=							rms_norm_eps
								__magic_name__							:	Optional[int]    							=							pretraining_tp
								__magic_name__							:	Any    							=							use_cache
								__magic_name__							:	int    							=							rope_scaling
								self._rope_scaling_validation()

								super().__init__(
								    pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						tie_word_embeddings=lowerCamelCase_       ,						**lowerCamelCase_       ,						)


			def 							UpperCAmelCase__       ( self			:   str  )						->   Tuple:
								if self.rope_scaling is None:
													return

								if not isinstance(self.rope_scaling       ,						lowerCamelCase_  ) or len(self.rope_scaling  ) != 2:
													raise ValueError(
													    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
													    F'''got {self.rope_scaling}'''  )
								__magic_name__							:	List[str]    							=							self.rope_scaling.get('''type'''       ,						lowerCamelCase_  )
								__magic_name__							:	str    							=							self.rope_scaling.get('''factor'''       ,						lowerCamelCase_  )
								if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
													raise ValueError(
													    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''  )
								if rope_scaling_factor is None or not isinstance(lowerCamelCase_       ,						lowerCamelCase_  ) or rope_scaling_factor <= 1.0:
													raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''  )



