


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







def 						__lowerCAmelCase		(		lowercase						:						List[Any]				)			->		Any:


    """simple docstring"""





    snake_case					:   List[Any]         =					ASTConfig()

    if "10-10" in model_name:
        pass
    elif "speech-commands" in model_name:
        snake_case					:   Union[str, Any]         =					128
    elif "12-12" in model_name:
        snake_case					:   Tuple         =					12
        snake_case					:   Union[str, Any]         =					12
    elif "14-14" in model_name:
        snake_case					:   str         =					14
        snake_case					:   Dict         =					14
    elif "16-16" in model_name:
        snake_case					:   List[str]         =					16
        snake_case					:   Optional[Any]         =					16
    else:
        raise ValueError("Model not supported"				)

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

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

    return config







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]				)			->		Union[str, Any]:


    """simple docstring"""





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

    return name







def 						__lowerCAmelCase		(		lowercase						:						int		,							lowercase						:						str				)			->		Any:


    """simple docstring"""





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

        if "qkv" in key:
            snake_case					:   str         =					key.split("."				)
            snake_case					:   Union[str, Any]         =					int(key_split[3]				)
            snake_case					:   List[Any]         =					config.hidden_size
            if "weight" in key:
                snake_case					:   Optional[Any]         =					val[:dim, :]
                snake_case					:   Dict         =					val[dim : dim * 2, :]
                snake_case					:   List[Any]         =					val[-dim:, :]
            else:
                snake_case					:   Tuple         =					val[:dim]
                snake_case					:   Union[str, Any]         =					val[dim : dim * 2]
                snake_case					:   Tuple         =					val[-dim:]
        else:
            snake_case					:   int         =					val

    return orig_state_dict







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]				)			->		List[Any]:


    """simple docstring"""





    snake_case					:   List[str]         =					[
        "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 						__lowerCAmelCase		(		lowercase						:						List[Any]		,							lowercase						:						Dict		,							lowercase						:						Union[str, Any]=False				)			->		Optional[int]:


    """simple docstring"""





    snake_case					:   Optional[int]         =					get_audio_spectrogram_transformer_config(lowercase				)

    snake_case					:   int         =					{
        "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					:   Tuple         =					model_name_to_url[model_name]
    snake_case					:   Optional[Any]         =					torch.hub.load_state_dict_from_url(lowercase		,							map_location="cpu"				)
    # remove some keys
    remove_keys(lowercase				)
    # rename some keys
    snake_case					:   List[Any]         =					convert_state_dict(lowercase		,							lowercase				)

    # load 🤗 model
    snake_case					:   Dict         =					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
    snake_case					:   Dict         =					-4.267_7393 if "speech-commands" not in model_name else -6.84_5978
    snake_case					:   Any         =					4.568_9974 if "speech-commands" not in model_name else 5.565_4526
    snake_case					:   Union[str, Any]         =					1024 if "speech-commands" not in model_name else 128
    snake_case					:   List[Any]         =					ASTFeatureExtractor(mean=lowercase		,							std=lowercase		,							max_length=lowercase				)

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

        snake_case      ,snake_case					:   Tuple         =					torchaudio.load(lowercase				)
        snake_case					:   Union[str, Any]         =					waveform.squeeze().numpy()

    snake_case					:   Tuple         =					feature_extractor(lowercase		,							sampling_rate=1_6000		,							return_tensors="pt"				)

    # forward pass
    snake_case					:   Any         =					model(**lowercase				)
    snake_case					:   Tuple         =					outputs.logits

    if model_name == "ast-finetuned-audioset-10-10-0.4593":
        snake_case					:   Optional[int]         =					torch.tensor([-0.8760, -7.0042, -8.6602]				)
    elif model_name == "ast-finetuned-audioset-10-10-0.450":
        snake_case					:   List[str]         =					torch.tensor([-1.1986, -7.0903, -8.2718]				)
    elif model_name == "ast-finetuned-audioset-10-10-0.448":
        snake_case					:   Tuple         =					torch.tensor([-2.6128, -8.0080, -9.4344]				)
    elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
        snake_case					:   Optional[int]         =					torch.tensor([-1.5080, -7.4534, -8.8917]				)
    elif model_name == "ast-finetuned-audioset-12-12-0.447":
        snake_case					:   int         =					torch.tensor([-0.5050, -6.5833, -8.0843]				)
    elif model_name == "ast-finetuned-audioset-14-14-0.443":
        snake_case					:   List[str]         =					torch.tensor([-0.3826, -7.0336, -8.2413]				)
    elif model_name == "ast-finetuned-audioset-16-16-0.442":
        snake_case					:   Optional[Any]         =					torch.tensor([-1.2113, -6.9101, -8.3470]				)
    elif model_name == "ast-finetuned-speech-commands-v2":
        snake_case					:   Dict         =					torch.tensor([6.1589, -8.0566, -8.7984]				)
    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__":
  __snake_case         =					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."""
  )

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









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Optional[Any]													=						'''big_bird'''







   def __init__(			self		,			UpperCamelCase__=5_0358		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu_new"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=4096		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=True		,			UpperCamelCase__=0		,			UpperCamelCase__=1		,			UpperCamelCase__=2		,			UpperCamelCase__=66		,			UpperCamelCase__="block_sparse"		,			UpperCamelCase__=True		,			UpperCamelCase__=False		,			UpperCamelCase__=64		,			UpperCamelCase__=3		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	Tuple:

       '''simple docstring'''




       super().__init__(
           pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			sep_token_id=UpperCamelCase__		,			**UpperCamelCase__		,			)

       snake_case					:   Union[str, Any]         =					vocab_size
       snake_case					:   List[Any]         =					max_position_embeddings
       snake_case					:   int         =					hidden_size
       snake_case					:   str         =					num_hidden_layers
       snake_case					:   Any         =					num_attention_heads
       snake_case					:   int         =					intermediate_size
       snake_case					:   Union[str, Any]         =					hidden_act
       snake_case					:   Optional[Any]         =					hidden_dropout_prob
       snake_case					:   List[Any]         =					attention_probs_dropout_prob
       snake_case					:   int         =					initializer_range
       snake_case					:   List[str]         =					type_vocab_size
       snake_case					:   Optional[Any]         =					layer_norm_eps
       snake_case					:   Optional[Any]         =					use_cache

       snake_case					:   List[Any]         =					rescale_embeddings
       snake_case					:   Any         =					attention_type
       snake_case					:   List[Any]         =					use_bias
       snake_case					:   int         =					block_size
       snake_case					:   int         =					num_random_blocks
       snake_case					:   Optional[int]         =					classifier_dropout







class _lowerCAmelCase       (   snake_case_			):







   @property
   def 				lowerCamelCase	(			self		)			->	Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case					:   int         =					{0: "batch", 1: "choice", 2: "sequence"}
       else:
           snake_case					:   Optional[Any]         =					{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]		)





