
'''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()
lowercase     :							List[Any]       						=     logging.get_logger(__name__)







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  Union[str, Any]   =		ASTConfig()

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

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

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

    return config







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



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

    return name







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    for key in orig_state_dict.copy().keys():
        A			:  Tuple   =		orig_state_dict.pop(snake_case__		)

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

    return orig_state_dict







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    A			:  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(snake_case__						,							snake_case__		)







@torch.no_grad()
def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__=False		):




    '''simple docstring'''



    A			:  str   =		get_audio_spectrogram_transformer_config(snake_case__		)

    A			:  Optional[int]   =		{
        '''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			:  Dict   =		torch.hub.load_state_dict_from_url(snake_case__						,							map_location='''cpu'''		)
    # remove some keys
    remove_keys(snake_case__		)
    # rename some keys
    A			:  int   =		convert_state_dict(snake_case__						,							snake_case__		)

    # load 🤗 model
    A			:  Optional[int]   =		ASTForAudioClassification(snake_case__		)
    model.eval()

    model.load_state_dict(snake_case__		)

    # verify outputs on dummy input
    # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
    A			:  List[str]   =		-4.2_67_73_93 if '''speech-commands''' not in model_name else -6.84_59_78
    A			:  List[str]   =		4.5_68_99_74 if '''speech-commands''' not in model_name else 5.5_65_45_26
    A			:  str   =		1024 if '''speech-commands''' not in model_name else 128
    A			:  Union[str, Any]   =		ASTFeatureExtractor(mean=snake_case__						,							std=snake_case__						,							max_length=snake_case__		)

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

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

    A			:  Optional[Any]   =		feature_extractor(snake_case__						,							sampling_rate=1_6000						,							return_tensors='''pt'''		)

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

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

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

      lowercase     :							Any       						=     parser.parse_args()
      convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase     :							int       						=     logging.get_logger(__name__)

lowercase     :							Dict       						=     {'vocab_file': 'spiece.model'}

lowercase     :							Tuple       						=     {
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

lowercase     :							Tuple       						=     {'bert_for_seq_generation': 5_12}





class 					A							(				__snake_case		):
     __magic_name__          =						VOCAB_FILES_NAMES
     __magic_name__          =						PRETRAINED_VOCAB_FILES_MAP
     __magic_name__          =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__          =						[]
     __magic_name__          =						['''input_ids''', '''attention_mask''']





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE="<s>"		,     SCREAMING_SNAKE_CASE="</s>"		,     SCREAMING_SNAKE_CASE="<unk>"		,     SCREAMING_SNAKE_CASE="<pad>"		,     SCREAMING_SNAKE_CASE="<::::>"		,     SCREAMING_SNAKE_CASE = None		,     **SCREAMING_SNAKE_CASE		,     )					->							None:






         """simple docstring"""






         A			:  str   =		{} if sp_model_kwargs is None else sp_model_kwargs

         # Add extra_ids to the special token list
         super().__init__(
             bos_token=SCREAMING_SNAKE_CASE		,     eos_token=SCREAMING_SNAKE_CASE		,     unk_token=SCREAMING_SNAKE_CASE		,     pad_token=SCREAMING_SNAKE_CASE		,     sep_token=SCREAMING_SNAKE_CASE		,     sp_model_kwargs=self.sp_model_kwargs		,     **SCREAMING_SNAKE_CASE		,     )

         A			:  List[Any]   =		vocab_file

         A			:  List[Any]   =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
         self.sp_model.Load(SCREAMING_SNAKE_CASE      )





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return self.sp_model.get_piece_size()





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Any   =		{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE      ): i for i in range(self.vocab_size      )}
         vocab.update(self.added_tokens_encoder      )
         return vocab





     def __getstate__(				self      )					->							Tuple:






         """simple docstring"""






         A			:  Dict   =		self.__dict__.copy()
         A			:  int   =		None
         return state





     def __setstate__(				self		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  Union[str, Any]   =		d

         # for backward compatibility
         if not hasattr(self		,     '''sp_model_kwargs'''      ):
             A			:  Optional[int]   =		{}

         A			:  Optional[int]   =		spm.SentencePieceProcessor(**self.sp_model_kwargs      )
         self.sp_model.Load(self.vocab_file      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							List[str]:






         """simple docstring"""






         return self.sp_model.encode(SCREAMING_SNAKE_CASE		,     out_type=SCREAMING_SNAKE_CASE      )





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






         """simple docstring"""






         return self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """simple docstring"""






         A			:  Tuple   =		self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE      )
         return token





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Tuple:






         """simple docstring"""






         A			:  List[Any]   =		[]
         A			:  Dict   =		''''''
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE      ) + token
                 A			:  str   =		[]
             else:
                 current_sub_tokens.append(SCREAMING_SNAKE_CASE      )
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE      )
         return out_string.strip()





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE = None      )					->							Tuple[str]:






         """simple docstring"""






         if not os.path.isdir(SCREAMING_SNAKE_CASE      ):
             logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
             return
         A			:  int   =		os.path.join(
             SCREAMING_SNAKE_CASE		,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

         if os.path.abspath(self.vocab_file      ) != os.path.abspath(SCREAMING_SNAKE_CASE      ) and os.path.isfile(self.vocab_file      ):
             copyfile(self.vocab_file		,     SCREAMING_SNAKE_CASE      )
         elif not os.path.isfile(self.vocab_file      ):
             with open(SCREAMING_SNAKE_CASE		,     '''wb'''      ) as fi:
                 A			:  str   =		self.sp_model.serialized_model_proto()
                 fi.write(SCREAMING_SNAKE_CASE      )

         return (out_vocab_file,)




