



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()
UpperCAmelCase							 =					logging.get_logger(__name__)

def 			A_      (	__a     :				Any		):

    """simple docstring"""
    a__				      =		ASTConfig()

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

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

    a__				      =		json.load(open(hf_hub_download(__a ,		__a ,		repo_type="""dataset"""		) ,		"""r"""		)		)
    a__				      =		{int(__a		): v for k, v in idalabel.items()}
    a__				      =		idalabel
    a__				      =		{v: k for k, v in idalabel.items()}

    return config

def 			A_      (	__a     :				Union[str, Any]		):

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

    return name

def 			A_      (	__a     :				int ,		__a     :				str		):

    """simple docstring"""
    for key in orig_state_dict.copy().keys():
        a__				      =		orig_state_dict.pop(__a		)

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

    return orig_state_dict

def 			A_      (	__a     :				Dict		):

    """simple docstring"""
    a__				      =		[
        """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 			A_      (	__a     :				int ,		__a     :				Union[str, Any] ,		__a     :				List[Any]=False		):

    """simple docstring"""
    a__				      =		get_audio_spectrogram_transformer_config(__a		)

    a__				      =		{
        """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__				      =		model_name_to_url[model_name]
    a__				      =		torch.hub.load_state_dict_from_url(__a ,		map_location="""cpu"""		)
    # remove some keys
    remove_keys(__a		)
    # rename some keys
    a__				      =		convert_state_dict(__a ,		__a		)

    # load 🤗 model
    a__				      =		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
    a__				      =		-4.2_6_7_7_3_9_3 if """speech-commands""" not in model_name else -6.8_4_5_9_7_8
    a__				      =		4.5_6_8_9_9_7_4 if """speech-commands""" not in model_name else 5.5_6_5_4_5_2_6
    a__				      =		1_024 if """speech-commands""" not in model_name else 128
    a__				      =		ASTFeatureExtractor(mean=__a ,		std=__a ,		max_length=__a		)

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

        a__      ,			a__				      =		torchaudio.load(__a		)
        a__				      =		waveform.squeeze().numpy()

    a__				      =		feature_extractor(__a ,		sampling_rate=16_000 ,		return_tensors="""pt"""		)

    # forward pass
    a__				      =		model(**__a		)
    a__				      =		outputs.logits

    if model_name == "ast-finetuned-audioset-10-10-0.4593":
        a__				      =		torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2]		)
    elif model_name == "ast-finetuned-audioset-10-10-0.450":
        a__				      =		torch.tensor([-1.1_9_8_6, -7.0_9_0_3, -8.2_7_1_8]		)
    elif model_name == "ast-finetuned-audioset-10-10-0.448":
        a__				      =		torch.tensor([-2.6_1_2_8, -8.0_0_8_0, -9.4_3_4_4]		)
    elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
        a__				      =		torch.tensor([-1.5_0_8_0, -7.4_5_3_4, -8.8_9_1_7]		)
    elif model_name == "ast-finetuned-audioset-12-12-0.447":
        a__				      =		torch.tensor([-0.5_0_5_0, -6.5_8_3_3, -8.0_8_4_3]		)
    elif model_name == "ast-finetuned-audioset-14-14-0.443":
        a__				      =		torch.tensor([-0.3_8_2_6, -7.0_3_3_6, -8.2_4_1_3]		)
    elif model_name == "ast-finetuned-audioset-16-16-0.442":
        a__				      =		torch.tensor([-1.2_1_1_3, -6.9_1_0_1, -8.3_4_7_0]		)
    elif model_name == "ast-finetuned-speech-commands-v2":
        a__				      =		torch.tensor([6.1_5_8_9, -8.0_5_6_6, -8.7_9_8_4]		)
    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__":
 UpperCAmelCase							 =					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."""
 )

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






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

