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			:							Dict     =logging.get_logger(__name__)







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Union[str, Any] 						=						ASTConfig()

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

    _lowerCamelCase     :		Dict 						=						"""huggingface/label-files"""
    if "speech-commands" in model_name:
        _lowerCamelCase     :		str 						=						35
        _lowerCamelCase     :		Union[str, Any] 						=						"""speech-commands-v2-id2label.json"""
    else:
        _lowerCamelCase     :		List[Any] 						=						527
        _lowerCamelCase     :		Union[str, Any] 						=						"""audioset-id2label.json"""

    _lowerCamelCase     :		Optional[Any] 						=						json.load(open(hf_hub_download(__A   , __A   , repo_type="""dataset"""	)   , """r"""	)	)
    _lowerCamelCase     :		int 						=						{int(__A	): v for k, v in idalabel.items()}
    _lowerCamelCase     :		Any 						=						idalabel
    _lowerCamelCase     :		List[str] 						=						{v: k for k, v in idalabel.items()}

    return config







def 						A__  (					__A	):







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

    return name







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    for key in orig_state_dict.copy().keys():
        _lowerCamelCase     :		Dict 						=						orig_state_dict.pop(__A	)

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

    return orig_state_dict







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Union[str, Any] 						=						[
        """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   , __A   , __A=False	):







    '''simple docstring'''
    _lowerCamelCase     :		Union[str, Any] 						=						get_audio_spectrogram_transformer_config(__A	)

    _lowerCamelCase     :		List[str] 						=						{
        """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
    _lowerCamelCase     :		List[Any] 						=						model_name_to_url[model_name]
    _lowerCamelCase     :		Union[str, Any] 						=						torch.hub.load_state_dict_from_url(__A   , map_location="""cpu"""	)
    # remove some keys
    remove_keys(__A	)
    # rename some keys
    _lowerCamelCase     :		str 						=						convert_state_dict(__A   , __A	)

    # load 🤗 model
    _lowerCamelCase     :		Tuple 						=						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
    _lowerCamelCase     :		Optional[int] 						=						-4.2_677_393 if """speech-commands""" not in model_name else -6.845_978
    _lowerCamelCase     :		Dict 						=						4.5_689_974 if """speech-commands""" not in model_name else 5.5_654_526
    _lowerCamelCase     :		int 						=						1_024 if """speech-commands""" not in model_name else 128
    _lowerCamelCase     :		List[Any] 						=						ASTFeatureExtractor(mean=__A   , std=__A   , max_length=__A	)

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

        _lowerCamelCase						,							_lowerCamelCase     :		Optional[Any] 						=						torchaudio.load(__A	)
        _lowerCamelCase     :		int 						=						waveform.squeeze().numpy()

    _lowerCamelCase     :		Optional[Any] 						=						feature_extractor(__A   , sampling_rate=16_000   , return_tensors="""pt"""	)

    # forward pass
    _lowerCamelCase     :		Optional[int] 						=						model(**__A	)
    _lowerCamelCase     :		Union[str, Any] 						=						outputs.logits

    if model_name == "ast-finetuned-audioset-10-10-0.4593":
        _lowerCamelCase     :		Optional[Any] 						=						torch.tensor([-0.8_760, -7.0_042, -8.6_602]	)
    elif model_name == "ast-finetuned-audioset-10-10-0.450":
        _lowerCamelCase     :		Any 						=						torch.tensor([-1.1_986, -7.0_903, -8.2_718]	)
    elif model_name == "ast-finetuned-audioset-10-10-0.448":
        _lowerCamelCase     :		Tuple 						=						torch.tensor([-2.6_128, -8.0_080, -9.4_344]	)
    elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
        _lowerCamelCase     :		Any 						=						torch.tensor([-1.5_080, -7.4_534, -8.8_917]	)
    elif model_name == "ast-finetuned-audioset-12-12-0.447":
        _lowerCamelCase     :		Optional[Any] 						=						torch.tensor([-0.5_050, -6.5_833, -8.0_843]	)
    elif model_name == "ast-finetuned-audioset-14-14-0.443":
        _lowerCamelCase     :		Any 						=						torch.tensor([-0.3_826, -7.0_336, -8.2_413]	)
    elif model_name == "ast-finetuned-audioset-16-16-0.442":
        _lowerCamelCase     :		int 						=						torch.tensor([-1.2_113, -6.9_101, -8.3_470]	)
    elif model_name == "ast-finetuned-speech-commands-v2":
        _lowerCamelCase     :		Optional[Any] 						=						torch.tensor([6.1_589, -8.0_566, -8.7_984]	)
    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__":
    lowerCAmelCase			:							int     =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			:							Tuple     =parser.parse_args()
    convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_tf_available():
    import tensorflow as tf

    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
    from ..tf_utils import stable_softmax

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCAmelCase			:							Optional[Any]     =logging.get_logger(__name__)




@add_end_docstrings(__lowerCAmelCase		)
class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				str    ,  *_UpperCamelCase     :				int    ,  **_UpperCamelCase     :				List[str])				->Tuple:


          """simple docstring"""


          super().__init__(*_UpperCamelCase    ,  **_UpperCamelCase)
          requires_backends(self    ,  """vision""")
          self.check_model_type(
              TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
              if self.framework == """tf"""
              else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				List[str]=None)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						{}
          if top_k is not None:
              _lowerCamelCase     :		str 						=						top_k
          return {}, {}, postprocess_params



      def __call__(					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,  **_UpperCamelCase     :				Optional[int])				->Dict:


          """simple docstring"""


          return super().__call__(_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[int])				->str:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						load_image(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						self.image_processor(images=_UpperCamelCase    ,  return_tensors=self.framework)
          return model_inputs



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				Union[str, Any])				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.model(**_UpperCamelCase)
          return model_outputs




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				List[str]=5)				->str:


          """simple docstring"""


          if top_k > self.model.config.num_labels:
              _lowerCamelCase     :		Union[str, Any] 						=						self.model.config.num_labels

          if self.framework == "pt":
              _lowerCamelCase     :		Optional[Any] 						=						model_outputs.logits.softmax(-1)[0]
              _lowerCamelCase						,							_lowerCamelCase     :		Dict 						=						probs.topk(_UpperCamelCase)
          elif self.framework == "tf":
              _lowerCamelCase     :		List[Any] 						=						stable_softmax(model_outputs.logits    ,  axis=-1)[0]
              _lowerCamelCase     :		List[Any] 						=						tf.math.top_k(_UpperCamelCase    ,  k=_UpperCamelCase)
              _lowerCamelCase						,							_lowerCamelCase     :		str 						=						topk.values.numpy(), topk.indices.numpy()
          else:
              raise ValueError(F"""Unsupported framework: {self.framework}""")

          _lowerCamelCase     :		str 						=						scores.tolist()
          _lowerCamelCase     :		str 						=						ids.tolist()
          return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase    ,  _UpperCamelCase)]




