
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 		lowercase_    (     A__	)							->							int:
    """simple docstring"""

    snake_case				  =      ASTConfig()

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

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

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

    return config



def 		lowercase_    (     A__	)							->							Dict:
    """simple docstring"""

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

    return name



def 		lowercase_    (     A__				,	A__	)							->							Tuple:
    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        snake_case				  =      orig_state_dict.pop(A__	)

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

    return orig_state_dict



def 		lowercase_    (     A__	)							->							Union[str, Any]:
    """simple docstring"""

    snake_case				  =      [
        "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 		lowercase_    (     A__				,	A__				,	A__=False	)							->							Dict:
    """simple docstring"""

    snake_case				  =      get_audio_spectrogram_transformer_config(A__	)

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

    # load 🤗 model
    snake_case				  =      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
    snake_case				  =      -4.2677393 if "speech-commands" not in model_name else -6.845978
    snake_case				  =      4.5689974 if "speech-commands" not in model_name else 5.5654526
    snake_case				  =      1024 if "speech-commands" not in model_name else 128
    snake_case				  =      ASTFeatureExtractor(mean=A__				,	std=A__				,	max_length=A__	)

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

        snake_case  ,							snake_case				  =      torchaudio.load(A__	)
        snake_case				  =      waveform.squeeze().numpy()

    snake_case				  =      feature_extractor(A__				,	sampling_rate=1_6000				,	return_tensors="pt"	)

    # forward pass
    snake_case				  =      model(**A__	)
    snake_case				  =      outputs.logits

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



def 		lowercase_    (     A__				,	A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      [0 for i in range(r + 1	)]
    # nc0 = 1
    snake_case				  =      1
    for i in range(1				,	n + 1	):
        # to compute current row from previous row.
        snake_case				  =      min(A__				,	A__	)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]





print(binomial_coefficient(n=10, r=5))

