





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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Optional[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     ASTConfig()

       if "10-10" in model_name:
              pass
       elif "speech-commands" in model_name:
              _UpperCAmelCase     =     128
       elif "12-12" in model_name:
              _UpperCAmelCase     =     12
              _UpperCAmelCase     =     12
       elif "14-14" in model_name:
              _UpperCAmelCase     =     14
              _UpperCAmelCase     =     14
       elif "16-16" in model_name:
              _UpperCAmelCase     =     16
              _UpperCAmelCase     =     16
       else:
              raise ValueError('Model not supported'							)

       _UpperCAmelCase     =     'huggingface/label-files'
       if "speech-commands" in model_name:
              _UpperCAmelCase     =     35
              _UpperCAmelCase     =     'speech-commands-v2-id2label.json'
       else:
              _UpperCAmelCase     =     527
              _UpperCAmelCase     =     'audioset-id2label.json'

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

       return config

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Optional[Any]:






       """simple docstring"""




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

       return name

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       List[Any]:






       """simple docstring"""




       for key in orig_state_dict.copy().keys():
              _UpperCAmelCase     =     orig_state_dict.pop(SCREAMING_SNAKE_CASE							)

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

       return orig_state_dict

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Optional[Any]:






       """simple docstring"""




       _UpperCAmelCase     =     [
           '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       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=False							)		->       Optional[int]:






       """simple docstring"""




       _UpperCAmelCase     =     get_audio_spectrogram_transformer_config(SCREAMING_SNAKE_CASE							)

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

       # load 🤗 model
       _UpperCAmelCase     =     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
       _UpperCAmelCase     =     -4.2_677_393 if 'speech-commands' not in model_name else -6.845_978
       _UpperCAmelCase     =     4.5_689_974 if 'speech-commands' not in model_name else 5.5_654_526
       _UpperCAmelCase     =     1024 if 'speech-commands' not in model_name else 128
       _UpperCAmelCase     =     ASTFeatureExtractor(mean=SCREAMING_SNAKE_CASE,std=SCREAMING_SNAKE_CASE,max_length=SCREAMING_SNAKE_CASE							)

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

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

       _UpperCAmelCase     =     feature_extractor(SCREAMING_SNAKE_CASE,sampling_rate=16000,return_tensors='pt'							)

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

       if model_name == "ast-finetuned-audioset-10-10-0.4593":
              _UpperCAmelCase     =     torch.tensor([-0.8_760, -7.0_042, -8.6_602]							)
       elif model_name == "ast-finetuned-audioset-10-10-0.450":
              _UpperCAmelCase     =     torch.tensor([-1.1_986, -7.0_903, -8.2_718]							)
       elif model_name == "ast-finetuned-audioset-10-10-0.448":
              _UpperCAmelCase     =     torch.tensor([-2.6_128, -8.0_080, -9.4_344]							)
       elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
              _UpperCAmelCase     =     torch.tensor([-1.5_080, -7.4_534, -8.8_917]							)
       elif model_name == "ast-finetuned-audioset-12-12-0.447":
              _UpperCAmelCase     =     torch.tensor([-0.5_050, -6.5_833, -8.0_843]							)
       elif model_name == "ast-finetuned-audioset-14-14-0.443":
              _UpperCAmelCase     =     torch.tensor([-0.3_826, -7.0_336, -8.2_413]							)
       elif model_name == "ast-finetuned-audioset-16-16-0.442":
              _UpperCAmelCase     =     torch.tensor([-1.2_113, -6.9_101, -8.3_470]							)
       elif model_name == "ast-finetuned-speech-commands-v2":
              _UpperCAmelCase     =     torch.tensor([6.1_589, -8.0_566, -8.7_984]							)
       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__":
    lowerCAmelCase_			=							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_			=							parser.parse_args()
    convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













"""simple docstring"""






import sys

lowerCAmelCase_			=							(
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     1
       for digit in s:
              product *= int(SCREAMING_SNAKE_CASE							)
       return product

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE = N							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     -sys.maxsize - 1
       _UpperCAmelCase     =     n[:13]
       _UpperCAmelCase     =     13
       while cur_index < len(SCREAMING_SNAKE_CASE							) - 13:
              if int(n[cur_index]							) >= int(substr[0]							):
                     _UpperCAmelCase     =     substr[1:] + n[cur_index]
                     cur_index += 1
              else:
                     _UpperCAmelCase     =     max(SCREAMING_SNAKE_CASE,str_eval(SCREAMING_SNAKE_CASE							)							)
                     _UpperCAmelCase     =     n[cur_index : cur_index + 13]
                     cur_index += 13
       return largest_product


if __name__ == "__main__":
    print(F'''{solution() = }''')






