


"""simple docstring"""




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Tuple								=     """EncodecFeatureExtractor"""
		SCREAMING_SNAKE_CASE_						:     str								=     ("""T5Tokenizer""", """T5TokenizerFast""")







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							int:
								super().__init__(lowerCAmelCase__						,       lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.feature_extractor
								SCREAMING_SNAKE_CASE													=					False







		def 							__A						(	self						,       lowerCAmelCase__=None						,       lowerCAmelCase__=None						,       lowerCAmelCase__=True				)     ->							List[str]:
								return self.tokenizer.get_decoder_prompt_ids(task=lowerCAmelCase__						,       language=lowerCAmelCase__						,       no_timestamps=lowerCAmelCase__				)







		def __call__(	self						,       *lowerCAmelCase__						,       **lowerCAmelCase__				)     ->							List[str]:
								# For backward compatibility
								if self._in_target_context_manager:
														return self.current_processor(*lowerCAmelCase__						,       **lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					kwargs.pop('audio'						,       lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					kwargs.pop('sampling_rate'						,       lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					kwargs.pop('text'						,       lowerCAmelCase__				)
								if len(lowerCAmelCase__				) > 0:
														SCREAMING_SNAKE_CASE													=					args[0]
														SCREAMING_SNAKE_CASE													=					args[1:]

								if audio is None and text is None:
														raise ValueError('You need to specify either an `audio` or `text` input to process.'				)

								if text is not None:
														SCREAMING_SNAKE_CASE													=					self.tokenizer(lowerCAmelCase__						,       **lowerCAmelCase__				)

								if audio is not None:
														SCREAMING_SNAKE_CASE													=					self.feature_extractor(lowerCAmelCase__						,       *lowerCAmelCase__						,       sampling_rate=lowerCAmelCase__						,       **lowerCAmelCase__				)

								if audio is None:
														return inputs

								elif text is None:
														return audio_inputs

								else:
														SCREAMING_SNAKE_CASE													=					audio_inputs['input_values']
														if "padding_mask" in audio_inputs:
																				SCREAMING_SNAKE_CASE													=					audio_inputs['padding_mask']
														return inputs







		def 							__A						(	self						,       *lowerCAmelCase__						,       **lowerCAmelCase__				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					kwargs.pop('audio'						,       lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					kwargs.pop('padding_mask'						,       lowerCAmelCase__				)

								if len(lowerCAmelCase__				) > 0:
														SCREAMING_SNAKE_CASE													=					args[0]
														SCREAMING_SNAKE_CASE													=					args[1:]

								if audio_values is not None:
														return self._decode_audio(lowerCAmelCase__						,       padding_mask=lowerCAmelCase__				)
								else:
														return self.tokenizer.batch_decode(*lowerCAmelCase__						,       **lowerCAmelCase__				)







		def 							__A						(	self						,       *lowerCAmelCase__						,       **lowerCAmelCase__				)     ->							Any:
								return self.tokenizer.decode(*lowerCAmelCase__						,       **lowerCAmelCase__				)







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__ = None				)     ->							List[np.ndarray]:
								SCREAMING_SNAKE_CASE													=					to_numpy(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					audio_values.shape

								if padding_mask is None:
														return list(lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					to_numpy(lowerCAmelCase__				)

								# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
								# token (so that the generated audio values are **not** treated as padded tokens)
								SCREAMING_SNAKE_CASE													=					seq_len - padding_mask.shape[-1]
								SCREAMING_SNAKE_CASE													=					1 - self.feature_extractor.padding_value
								SCREAMING_SNAKE_CASE													=					np.pad(lowerCAmelCase__						,       ((0, 0), (0, difference))						,       'constant'						,       constant_values=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					audio_values.tolist()
								for i in range(lowerCAmelCase__				):
														SCREAMING_SNAKE_CASE													=					np.asarray(audio_values[i]				)[
														    padding_mask[i][None, :] != self.feature_extractor.padding_value
														]
														SCREAMING_SNAKE_CASE													=					sliced_audio.reshape(lowerCAmelCase__						,       -1				)

								return audio_values






"""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()
__UpperCamelCase						=      logging.get_logger(__name__)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						List[str]:
						SCREAMING_SNAKE_CASE													=					ASTConfig()

						if "10-10" in model_name:
												pass
						elif "speech-commands" in model_name:
												SCREAMING_SNAKE_CASE													=					1_28
						elif "12-12" in model_name:
												SCREAMING_SNAKE_CASE													=					12
												SCREAMING_SNAKE_CASE													=					12
						elif "14-14" in model_name:
												SCREAMING_SNAKE_CASE													=					14
												SCREAMING_SNAKE_CASE													=					14
						elif "16-16" in model_name:
												SCREAMING_SNAKE_CASE													=					16
												SCREAMING_SNAKE_CASE													=					16
						else:
												raise ValueError('Model not supported'  )

						SCREAMING_SNAKE_CASE													=					'huggingface/label-files'
						if "speech-commands" in model_name:
												SCREAMING_SNAKE_CASE													=					35
												SCREAMING_SNAKE_CASE													=					'speech-commands-v2-id2label.json'
						else:
												SCREAMING_SNAKE_CASE													=					5_27
												SCREAMING_SNAKE_CASE													=					'audioset-id2label.json'

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

						return config


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

						return name


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				Dict  )   ->						Dict:
						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE													=					orig_state_dict.pop(SCREAMING_SNAKE_CASE_  )

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

						return orig_state_dict


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE													=					[
						    '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(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )


@torch.no_grad()
def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]=False  )   ->						Optional[int]:
						SCREAMING_SNAKE_CASE													=					get_audio_spectrogram_transformer_config(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					{
						    '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
						SCREAMING_SNAKE_CASE													=					model_name_to_url[model_name]
						SCREAMING_SNAKE_CASE													=					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ ,			map_location='cpu'  )
						# remove some keys
						remove_keys(SCREAMING_SNAKE_CASE_  )
						# rename some keys
						SCREAMING_SNAKE_CASE													=					convert_state_dict(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

						# load 🤗 model
						SCREAMING_SNAKE_CASE													=					ASTForAudioClassification(SCREAMING_SNAKE_CASE_  )
						model.eval()

						model.load_state_dict(SCREAMING_SNAKE_CASE_  )

						# verify outputs on dummy input
						# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
						SCREAMING_SNAKE_CASE													=					-4.2_67_73_93 if 'speech-commands' not in model_name else -6.84_59_78
						SCREAMING_SNAKE_CASE													=					4.5_68_99_74 if 'speech-commands' not in model_name else 5.5_65_45_26
						SCREAMING_SNAKE_CASE													=					10_24 if 'speech-commands' not in model_name else 1_28
						SCREAMING_SNAKE_CASE													=					ASTFeatureExtractor(mean=SCREAMING_SNAKE_CASE_ ,			std=SCREAMING_SNAKE_CASE_ ,			max_length=SCREAMING_SNAKE_CASE_  )

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

												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					torchaudio.load(SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					waveform.squeeze().numpy()

						SCREAMING_SNAKE_CASE													=					feature_extractor(SCREAMING_SNAKE_CASE_ ,			sampling_rate=1_60_00 ,			return_tensors='pt'  )

						# forward pass
						SCREAMING_SNAKE_CASE													=					model(**SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					outputs.logits

						if model_name == "ast-finetuned-audioset-10-10-0.4593":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-0.87_60, -7.00_42, -8.66_02]  )
						elif model_name == "ast-finetuned-audioset-10-10-0.450":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-1.19_86, -7.09_03, -8.27_18]  )
						elif model_name == "ast-finetuned-audioset-10-10-0.448":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-2.61_28, -8.00_80, -9.43_44]  )
						elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-1.50_80, -7.45_34, -8.89_17]  )
						elif model_name == "ast-finetuned-audioset-12-12-0.447":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-0.50_50, -6.58_33, -8.08_43]  )
						elif model_name == "ast-finetuned-audioset-14-14-0.443":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-0.38_26, -7.03_36, -8.24_13]  )
						elif model_name == "ast-finetuned-audioset-16-16-0.442":
												SCREAMING_SNAKE_CASE													=					torch.tensor([-1.21_13, -6.91_01, -8.34_70]  )
						elif model_name == "ast-finetuned-speech-commands-v2":
												SCREAMING_SNAKE_CASE													=					torch.tensor([6.15_89, -8.05_66, -8.79_84]  )
						else:
												raise ValueError('Unknown model name'  )
						if not torch.allclose(logits[0, :3] ,			SCREAMING_SNAKE_CASE_ ,			atol=1E-4  ):
												raise ValueError('Logits don\'t match'  )
						print('Looks ok!'  )

						if pytorch_dump_folder_path is not None:
												Path(SCREAMING_SNAKE_CASE_  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )
												print(F'Saving model {model_name} to {pytorch_dump_folder_path}'  )
												model.save_pretrained(SCREAMING_SNAKE_CASE_  )
												print(F'Saving feature extractor to {pytorch_dump_folder_path}'  )
												feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_  )

						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)


