



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()
A__								=						logging.get_logger(__name__)



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Optional[int]:


				"""simple docstring"""

				snake_case__	:      Optional[Any]     		=	ASTConfig()

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

				snake_case__	:      List[Any]     		=	'''huggingface/label-files'''
				if "speech-commands" in model_name:
								snake_case__	:      Tuple     		=	35
								snake_case__	:      Any     		=	'''speech-commands-v2-id2label.json'''
				else:
								snake_case__	:      Tuple     		=	527
								snake_case__	:      Union[str, Any]     		=	'''audioset-id2label.json'''

				snake_case__	:      List[str]     		=	json.load(open(hf_hub_download(__lowerCAmelCase  ,  __lowerCAmelCase  ,  repo_type='''dataset'''      )  ,  '''r'''      )      )
				snake_case__	:      str     		=	{int(__lowerCAmelCase      ): v for k, v in idalabel.items()}
				snake_case__	:      int     		=	idalabel
				snake_case__	:      Optional[int]     		=	{v: k for k, v in idalabel.items()}

				return config



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Tuple:


				"""simple docstring"""

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

				return name



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Optional[int]:


				"""simple docstring"""

				for key in orig_state_dict.copy().keys():
								snake_case__	:      Optional[Any]     		=	orig_state_dict.pop(__lowerCAmelCase      )

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

				return orig_state_dict



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Any:


				"""simple docstring"""

				snake_case__	:      List[Any]     		=	[
				    '''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(__lowerCAmelCase  ,  __lowerCAmelCase      )



@torch.no_grad()
def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase=False      )   ->					Union[str, Any]:


				"""simple docstring"""

				snake_case__	:      Optional[Any]     		=	get_audio_spectrogram_transformer_config(__lowerCAmelCase      )

				snake_case__	:      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
				snake_case__	:      Optional[int]     		=	model_name_to_url[model_name]
				snake_case__	:      Tuple     		=	torch.hub.load_state_dict_from_url(__lowerCAmelCase  ,  map_location='''cpu'''      )
				# remove some keys
				remove_keys(__lowerCAmelCase      )
				# rename some keys
				snake_case__	:      Any     		=	convert_state_dict(__lowerCAmelCase  ,  __lowerCAmelCase      )

				# load 🤗 model
				snake_case__	:      str     		=	ASTForAudioClassification(__lowerCAmelCase      )
				model.eval()

				model.load_state_dict(__lowerCAmelCase      )

				# verify outputs on dummy input
				# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
				snake_case__	:      List[Any]     		=	-4.2_677_393 if '''speech-commands''' not in model_name else -6.845_978
				snake_case__	:      Union[str, Any]     		=	4.5_689_974 if '''speech-commands''' not in model_name else 5.5_654_526
				snake_case__	:      Any     		=	1024 if '''speech-commands''' not in model_name else 128
				snake_case__	:      Tuple     		=	ASTFeatureExtractor(mean=__lowerCAmelCase  ,  std=__lowerCAmelCase  ,  max_length=__lowerCAmelCase      )

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

								snake_case__		, snake_case__	:      List[Any]     		=	torchaudio.load(__lowerCAmelCase      )
								snake_case__	:      List[Any]     		=	waveform.squeeze().numpy()

				snake_case__	:      int     		=	feature_extractor(__lowerCAmelCase  ,  sampling_rate=16000  ,  return_tensors='''pt'''      )

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

				if model_name == "ast-finetuned-audioset-10-10-0.4593":
								snake_case__	:      Optional[Any]     		=	torch.tensor([-0.8_760, -7.0_042, -8.6_602]      )
				elif model_name == "ast-finetuned-audioset-10-10-0.450":
								snake_case__	:      str     		=	torch.tensor([-1.1_986, -7.0_903, -8.2_718]      )
				elif model_name == "ast-finetuned-audioset-10-10-0.448":
								snake_case__	:      Union[str, Any]     		=	torch.tensor([-2.6_128, -8.0_080, -9.4_344]      )
				elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
								snake_case__	:      int     		=	torch.tensor([-1.5_080, -7.4_534, -8.8_917]      )
				elif model_name == "ast-finetuned-audioset-12-12-0.447":
								snake_case__	:      List[str]     		=	torch.tensor([-0.5_050, -6.5_833, -8.0_843]      )
				elif model_name == "ast-finetuned-audioset-14-14-0.443":
								snake_case__	:      Tuple     		=	torch.tensor([-0.3_826, -7.0_336, -8.2_413]      )
				elif model_name == "ast-finetuned-audioset-16-16-0.442":
								snake_case__	:      Optional[Any]     		=	torch.tensor([-1.2_113, -6.9_101, -8.3_470]      )
				elif model_name == "ast-finetuned-speech-commands-v2":
								snake_case__	:      List[Any]     		=	torch.tensor([6.1_589, -8.0_566, -8.7_984]      )
				else:
								raise ValueError('''Unknown model name'''      )
				if not torch.allclose(logits[0, :3]  ,  __lowerCAmelCase  ,  atol=1E-4      ):
								raise ValueError('''Logits don\'t match'''      )
				print('''Looks ok!'''      )

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

				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__":
				A__								=						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.'''
				)

				A__								=						parser.parse_args()
				convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
A__								=						abspath(join(dirname(dirname(dirname(__file__))), '''src'''))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				from transformers.testing_utils import pytest_addoption_shared

				pytest_addoption_shared(__lowerCAmelCase      )





def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				from transformers.testing_utils import pytest_terminal_summary_main

				snake_case__	:      Dict     		=	terminalreporter.config.getoption('''--make-reports'''      )
				if make_reports:
								pytest_terminal_summary_main(__lowerCAmelCase  ,  id=__lowerCAmelCase      )

