




'''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()
A     =logging.get_logger(__name__)





def 						snake_case_							(_a	:							List[str]     ):
    UpperCAmelCase			       =		ASTConfig()

    if "10-10" in model_name:
        pass
    elif "speech-commands" in model_name:
        UpperCAmelCase			       =		1_2_8
    elif "12-12" in model_name:
        UpperCAmelCase			       =		1_2
        UpperCAmelCase			       =		1_2
    elif "14-14" in model_name:
        UpperCAmelCase			       =		1_4
        UpperCAmelCase			       =		1_4
    elif "16-16" in model_name:
        UpperCAmelCase			       =		1_6
        UpperCAmelCase			       =		1_6
    else:
        raise ValueError('''Model not supported'''     )

    UpperCAmelCase			       =		'''huggingface/label-files'''
    if "speech-commands" in model_name:
        UpperCAmelCase			       =		3_5
        UpperCAmelCase			       =		'''speech-commands-v2-id2label.json'''
    else:
        UpperCAmelCase			       =		5_2_7
        UpperCAmelCase			       =		'''audioset-id2label.json'''

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

    return config





def 						snake_case_							(_a	:							Tuple     ):
    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 						snake_case_							(_a	:							Dict    ,   _a	:							List[Any]     ):
    for key in orig_state_dict.copy().keys():
        UpperCAmelCase			       =		orig_state_dict.pop(_a     )

        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 						snake_case_							(_a	:							Tuple     ):
    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(_a    ,   _a     )





@torch.no_grad()
def 						snake_case_							(_a	:							int    ,   _a	:							Union[str, Any]    ,   _a	:							Dict=False     ):
    UpperCAmelCase			       =		get_audio_spectrogram_transformer_config(_a     )

    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(_a    ,   map_location='''cpu'''     )
    # remove some keys
    remove_keys(_a     )
    # rename some keys
    UpperCAmelCase			       =		convert_state_dict(_a    ,   _a     )

    # load 🤗 model
    UpperCAmelCase			       =		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
    UpperCAmelCase			       =		-4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
    UpperCAmelCase			       =		4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
    UpperCAmelCase			       =		1_0_2_4 if '''speech-commands''' not in model_name else 1_2_8
    UpperCAmelCase			       =		ASTFeatureExtractor(mean=_a    ,   std=_a    ,   max_length=_a     )

    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(_a     )
        UpperCAmelCase			       =		waveform.squeeze().numpy()

    UpperCAmelCase			       =		feature_extractor(_a    ,   sampling_rate=1_6_0_0_0    ,   return_tensors='''pt'''     )

    # forward pass
    UpperCAmelCase			       =		model(**_a     )
    UpperCAmelCase			       =		outputs.logits

    if model_name == "ast-finetuned-audioset-10-10-0.4593":
        UpperCAmelCase			       =		torch.tensor([-0.8760, -7.0042, -8.6602]     )
    elif model_name == "ast-finetuned-audioset-10-10-0.450":
        UpperCAmelCase			       =		torch.tensor([-1.1986, -7.0903, -8.2718]     )
    elif model_name == "ast-finetuned-audioset-10-10-0.448":
        UpperCAmelCase			       =		torch.tensor([-2.6128, -8.0080, -9.4344]     )
    elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
        UpperCAmelCase			       =		torch.tensor([-1.5080, -7.4534, -8.8917]     )
    elif model_name == "ast-finetuned-audioset-12-12-0.447":
        UpperCAmelCase			       =		torch.tensor([-0.5050, -6.5833, -8.0843]     )
    elif model_name == "ast-finetuned-audioset-14-14-0.443":
        UpperCAmelCase			       =		torch.tensor([-0.3826, -7.0336, -8.2413]     )
    elif model_name == "ast-finetuned-audioset-16-16-0.442":
        UpperCAmelCase			       =		torch.tensor([-1.2113, -6.9101, -8.3470]     )
    elif model_name == "ast-finetuned-speech-commands-v2":
        UpperCAmelCase			       =		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__":
   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)








"""simple docstring"""







from __future__ import annotations

from functools import lru_cache
from math import ceil

lowerCAmelCase__      				=   100

lowerCAmelCase__      				=   set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCAmelCase__      				=   42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
     if prime not in primes:
          continue
     primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))
@lru_cache(maxsize=1_0_0				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  if number_to_partition < 0:
    return set()
  elif number_to_partition == 0:
    return {1}

  lowerCAmelCase     :     set[int]								=							set()
  lowerCAmelCase     :     int
  lowerCAmelCase     :     int

  for prime in primes:
    if prime > number_to_partition:
      continue
    for sub in partition(number_to_partition - prime				):
      ret.add(sub * prime				)

  return ret
def 					a__			(				SCREAMING_SNAKE_CASE    :    int = 5_0_0_0				):


  '''simple docstring'''







  for number_to_partition in range(1				,      SCREAMING_SNAKE_CASE				):
    if len(partition(SCREAMING_SNAKE_CASE				)				) > number_unique_partitions:
      return number_to_partition
  return None


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





