



'''simple docstring'''

from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__lowerCAmelCase									=   logging.get_logger(__name__)

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    str   				=					['input_values', 'padding_mask']







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       int = 1	,_UpperCAmelCase					:       int = 24000	,_UpperCAmelCase					:       float = 0.0	,_UpperCAmelCase					:       float = None	,_UpperCAmelCase					:       float = None	,**_UpperCAmelCase					:       str	,):
									super().__init__(feature_size=_UpperCAmelCase	,sampling_rate=_UpperCAmelCase	,padding_value=_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      Optional[Any]        =							chunk_length_s
									_a     :      str        =							overlap







							@property
							def 							__lowercase			(							self					:       Union[str, Any]		):
									if self.chunk_length_s is None:
											return None
									else:
											return int(self.chunk_length_s * self.sampling_rate		)







							@property
							def 							__lowercase			(							self					:       Optional[int]		):
									if self.chunk_length_s is None or self.overlap is None:
											return None
									else:
											return max(1	,int((1.0 - self.overlap) * self.chunk_length		)		)







							def __call__(							self					:       Tuple	,_UpperCAmelCase					:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,_UpperCAmelCase					:       Optional[Union[bool, str, PaddingStrategy]] = None	,_UpperCAmelCase					:       Optional[bool] = False	,_UpperCAmelCase					:       Optional[int] = None	,_UpperCAmelCase					:       Optional[Union[str, TensorType]] = None	,_UpperCAmelCase					:       Optional[int] = None	,):
									if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
													raise ValueError(
													    F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
													    F""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
													    F""" {self.sampling_rate} and not {sampling_rate}."""		)
									else:
											logger.warning(
											    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
											    'Failing to do so can result in silent errors that might be hard to debug.'		)

									if padding and truncation:
											raise ValueError('Both padding and truncation were set. Make sure you only set one.'		)
									elif padding is None:
											# by default let's pad the inputs
											_a     :      Union[str, Any]        =							True

									_a     :      Optional[int]        =							bool(
									    isinstance(_UpperCAmelCase	,(list, tuple)		) and (isinstance(raw_audio[0]	,(np.ndarray, tuple, list)		))		)

									if is_batched:
											_a     :      Tuple        =							[np.asarray(_UpperCAmelCase	,dtype=np.floataa		).T for audio in raw_audio]
									elif not is_batched and not isinstance(_UpperCAmelCase	,np.ndarray		):
											_a     :      List[str]        =							np.asarray(_UpperCAmelCase	,dtype=np.floataa		)
									elif isinstance(_UpperCAmelCase	,np.ndarray		) and raw_audio.dtype is np.dtype(np.floataa		):
											_a     :      Optional[int]        =							raw_audio.astype(np.floataa		)

									# always return batch
									if not is_batched:
											_a     :      int        =							[np.asarray(_UpperCAmelCase		).T]

									# verify inputs are valid
									for idx, example in enumerate(_UpperCAmelCase		):
											if example.ndim > 2:
													raise ValueError(F"""Expected input shape (channels, length) but got shape {example.shape}"""		)
											if self.feature_size == 1 and example.ndim != 1:
													raise ValueError(F"""Expected mono audio but example has {example.shape[-1]} channels"""		)
											if self.feature_size == 2 and example.shape[-1] != 2:
													raise ValueError(F"""Expected stereo audio but example has {example.shape[-1]} channels"""		)

									_a     :      Tuple        =							None
									_a     :      str        =							BatchFeature({'input_values': raw_audio}		)
									if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
											if truncation:
													_a     :      Optional[Any]        =							min(array.shape[0] for array in raw_audio		)
													_a     :      Optional[int]        =							int(np.floor(max_length / self.chunk_stride		)		)
													_a     :      int        =							(nb_step - 1) * self.chunk_stride + self.chunk_length
											elif padding:
													_a     :      str        =							max(array.shape[0] for array in raw_audio		)
													_a     :      Optional[int]        =							int(np.ceil(max_length / self.chunk_stride		)		)
													_a     :      Optional[Any]        =							(nb_step - 1) * self.chunk_stride + self.chunk_length
													_a     :      str        =							'max_length'
											else:
													_a     :      Dict        =							input_values

        # normal padding on batch
									if padded_inputs is None:
											_a     :      Tuple        =							self.pad(
											    _UpperCAmelCase	,max_length=_UpperCAmelCase	,truncation=_UpperCAmelCase	,padding=_UpperCAmelCase	,return_attention_mask=_UpperCAmelCase	,)
											if padding:
													_a     :      Optional[Any]        =							padded_inputs.pop('attention_mask'		)

									_a     :      Any        =							[]
									for example in padded_inputs.pop('input_values'		):
											if self.feature_size == 1:
													_a     :      Optional[int]        =							example[..., None]
											input_values.append(example.T		)

									_a     :      Optional[Any]        =							input_values
									if return_tensors is not None:
											_a     :      str        =							padded_inputs.convert_to_tensors(_UpperCAmelCase		)

									return padded_inputs








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


def      lowercase			(				A_       )->      Dict:
  '''simple docstring'''







  a						:		str      							=			ASTConfig()

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

  a						:		Optional[int]      							=			"huggingface/label-files"
  if "speech-commands" in model_name:
    a						:		Optional[int]      							=			35
    a						:		List[str]      							=			"speech-commands-v2-id2label.json"
  else:
    a						:		Optional[Any]      							=			527
    a						:		Tuple      							=			"audioset-id2label.json"

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

  return config


def      lowercase			(				A_       )->      Tuple:
  '''simple docstring'''







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

  return name


def      lowercase			(				A_  ,   A_       )->      Any:
  '''simple docstring'''







  for key in orig_state_dict.copy().keys():
    a						:		str      							=			orig_state_dict.pop(A_       )

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

  return orig_state_dict


def      lowercase			(				A_       )->      Dict:
  '''simple docstring'''







  a						:		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      lowercase			(				A_  ,   A_  ,   A_=False       )->      Optional[int]:
  '''simple docstring'''







  a						:		Optional[int]      							=			get_audio_spectrogram_transformer_config(A_       )

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

  # load 🤗 model
  a						:		List[str]      							=			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						:		Tuple      							=			-4.2_6_7_7_3_9_3 if "speech-commands" not in model_name else -6.8_4_5_9_7_8
  a						:		Union[str, Any]      							=			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						:		str      							=			1_024 if "speech-commands" not in model_name else 128
  a						:		List[Any]      							=			ASTFeatureExtractor(mean=A_  ,   std=A_  ,   max_length=A_       )

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

    a							,		a						:		Tuple      							=			torchaudio.load(A_       )
    a						:		Optional[Any]      							=			waveform.squeeze().numpy()

  a						:		Union[str, Any]      							=			feature_extractor(A_  ,   sampling_rate=16_000  ,   return_tensors="pt"       )

  # forward pass
  a						:		Optional[Any]      							=			model(**A_       )
  a						:		List[str]      							=			outputs.logits

  if model_name == "ast-finetuned-audioset-10-10-0.4593":
    a						:		Any      							=			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						:		Optional[int]      							=			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						:		List[str]      							=			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						:		Tuple      							=			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						:		int      							=			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						:		Any      							=			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						:		Dict      							=			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						:		Union[str, Any]      							=			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__":
 __lowercase    					=      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."""
 )

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






