




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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[Any]				)  ->							Optional[int]:
    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(lowercase__	,					lowercase__	,					repo_type='''dataset'''				)	,					'''r'''				)				)
    UpperCAmelCase_										=					{int(lowercase__				): v for k, v in idalabel.items()}
    UpperCAmelCase_										=					idalabel
    UpperCAmelCase_										=					{v: k for k, v in idalabel.items()}

    return config







def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int				)  ->							int:





    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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						Dict				)  ->							List[str]:





    for key in orig_state_dict.copy().keys():
        UpperCAmelCase_										=					orig_state_dict.pop(lowercase__				)

        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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple				)  ->							List[Any]:
    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(lowercase__	,					lowercase__				)







@torch.no_grad()
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						int=False				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					get_audio_spectrogram_transformer_config(lowercase__				)

    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(lowercase__	,					map_location='''cpu'''				)
    # remove some keys
    remove_keys(lowercase__				)
    # rename some keys
    UpperCAmelCase_										=					convert_state_dict(lowercase__	,					lowercase__				)

    # load 🤗 model
    UpperCAmelCase_										=					ASTForAudioClassification(lowercase__				)
    model.eval()

    model.load_state_dict(lowercase__				)

    # 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=lowercase__	,					std=lowercase__	,					max_length=lowercase__				)

    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_										=					torchaudio.load(lowercase__				)
        UpperCAmelCase_										=					waveform.squeeze().numpy()

    UpperCAmelCase_										=					feature_extractor(lowercase__	,					sampling_rate=1_6000	,					return_tensors='''pt'''				)

    # forward pass
    UpperCAmelCase_										=					model(**lowercase__				)
    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]	,					lowercase__	,					atol=1e-4				):
        raise ValueError('''Logits don\'t match'''				)
    print('''Looks ok!'''				)

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

    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)











def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict=2_8123				)  ->							str:
    UpperCAmelCase_										=					[1] * (limit + 1)

    for i in range(2	,					int(limit**0.5				) + 1				):
        sum_divs[i * i] += i
        for k in range(i + 1	,					limit // i + 1				):
            sum_divs[k * i] += k + i

    UpperCAmelCase_										=					set()
    UpperCAmelCase_										=					0

    for n in range(1	,					limit + 1				):
        if sum_divs[n] > n:
            abundants.add(__UpperCamelCase				)

        if not any((n - a in abundants) for a in abundants				):
            res += n

    return res


if __name__ == "__main__":
 print(solution())





