



"""simple docstring"""


import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


snake_case_				     =       NewType("""DataClass""", Any)
snake_case_				     =       NewType("""DataClassType""", Any)

def  _lowerCAmelCase      (   lowercase_						):







						if isinstance(A_		,	A_						):
												return v
						if v.lower() in ("yes", "true", "t", "y", "1"):
												return True
						elif v.lower() in ("no", "false", "f", "n", "0"):
												return False
						else:
												raise ArgumentTypeError(
												    F"""Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive)."""						)

def  _lowerCAmelCase      (   lowercase_						):
						UpperCAmelCase             = {str(A_						): choice for choice in choices}
						return lambda lowercase_						: str_to_choice.get(A_		,	A_						)

def  _lowerCAmelCase      (   *,
    lowercase_ = None		,	lowercase_ = None		,	lowercase_ = dataclasses.MISSING		,	lowercase_ = dataclasses.MISSING		,	lowercase_ = None		,	**lowercase_		,	):







						if metadata is None:
												# Important, don't use as default param in function signature because dict is mutable and shared across function calls
												UpperCAmelCase             = {}
						if aliases is not None:
												UpperCAmelCase             = aliases
						if help is not None:
												UpperCAmelCase             = help

						return dataclasses.field(metadata=A_		,	default=A_		,	default_factory=A_		,	**A_						)







class       A_			(     _a				):



						"""simple docstring"""
						__UpperCamelCase						=							42






						def __init__(					self :Optional[int]   ,  lowercase_ :Union[DataClassType, Iterable[DataClassType]]   ,  **lowercase_ :Union[str, Any]					)				->			List[str]:
												# To make the default appear when using --help
												if "formatter_class" not in kwargs:
																		UpperCAmelCase             = ArgumentDefaultsHelpFormatter
												super().__init__(**__UpperCAmelCase					)
												if dataclasses.is_dataclass(__UpperCAmelCase					):
																		UpperCAmelCase             = [dataclass_types]
												UpperCAmelCase             = list(__UpperCAmelCase					)
												for dtype in self.dataclass_types:
																		self._add_dataclass_arguments(__UpperCAmelCase					)






						@staticmethod
						def 	UpperCAmelCase__     (					lowercase_ :ArgumentParser   ,  lowercase_ :dataclasses.Field					)				->			Union[str, Any]:
												UpperCAmelCase             = f"""--{field.name}"""
												UpperCAmelCase             = field.metadata.copy()
												# field.metadata is not used at all by Data Classes,
												# it is provided as a third-party extension mechanism.
												if isinstance(field.type   ,  __UpperCAmelCase					):
																		raise RuntimeError(
																		    'Unresolved type detected, which should have been done with the help of '
																		    '`typing.get_type_hints` method by default'					)

												UpperCAmelCase             = kwargs.pop('aliases'   ,  []					)
												if isinstance(__UpperCAmelCase   ,  __UpperCAmelCase					):
																		UpperCAmelCase             = [aliases]

												UpperCAmelCase             = getattr(field.type   ,  '__origin__'   ,  field.type					)
												if origin_type is Union or (hasattr(__UpperCAmelCase   ,  'UnionType'					) and isinstance(__UpperCAmelCase   ,  types.UnionType					)):
																		if str not in field.type.__args__ and (
																		    len(field.type.__args__					) != 2 or type(__UpperCAmelCase					) not in field.type.__args__
																		):
																								raise ValueError(
																								    'Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'
																								    ' the argument parser only supports one type per argument.'
																								    f""" Problem encountered in field \'{field.name}\'."""					)
																		if type(__UpperCAmelCase					) not in field.type.__args__:
																								# filter `str` in Union
																								UpperCAmelCase             = field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
																								UpperCAmelCase             = getattr(field.type   ,  '__origin__'   ,  field.type					)
																		elif bool not in field.type.__args__:
																								# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
																								UpperCAmelCase             = (
																								    field.type.__args__[0] if isinstance(__UpperCAmelCase   ,  field.type.__args__[1]					) else field.type.__args__[1]
																								)
																								UpperCAmelCase             = getattr(field.type   ,  '__origin__'   ,  field.type					)

																		# A variable to store kwargs for a boolean field, if needed
																		# so that we can init a `no_*` complement argument (see below)
												UpperCAmelCase             = {}
												if origin_type is Literal or (isinstance(field.type   ,  __UpperCAmelCase					) and issubclass(field.type   ,  __UpperCAmelCase					)):
																		if origin_type is Literal:
																								UpperCAmelCase             = field.type.__args__
																		else:
																								UpperCAmelCase             = [x.value for x in field.type]

																		UpperCAmelCase             = make_choice_type_function(kwargs['choices']					)

																		if field.default is not dataclasses.MISSING:
																								UpperCAmelCase             = field.default
																		else:
																								UpperCAmelCase             = True
												elif field.type is bool or field.type == Optional[bool]:
																		# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
																		# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
																		UpperCAmelCase             = copy(__UpperCAmelCase					)

																		# Hack because type=bool in argparse does not behave as we want.
																		UpperCAmelCase             = string_to_bool
																		if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
																								# Default value is False if we have no default when of type bool.
																								UpperCAmelCase             = False if field.default is dataclasses.MISSING else field.default
																								# This is the value that will get picked if we don't include --field_name in any way
																								UpperCAmelCase             = default
																								# This tells argparse we accept 0 or 1 value after --field_name
																								UpperCAmelCase             = "?"
																								# This is the value that will get picked if we do --field_name (without value)
																								UpperCAmelCase             = True
												elif isclass(__UpperCAmelCase					) and issubclass(__UpperCAmelCase   ,  __UpperCAmelCase					):
																		UpperCAmelCase             = field.type.__args__[0]
																		UpperCAmelCase             = "+"
																		if field.default_factory is not dataclasses.MISSING:
																								UpperCAmelCase             = field.default_factory()
																		elif field.default is dataclasses.MISSING:
																								UpperCAmelCase             = True
												else:
																		UpperCAmelCase             = field.type
																		if field.default is not dataclasses.MISSING:
																								UpperCAmelCase             = field.default
																		elif field.default_factory is not dataclasses.MISSING:
																								UpperCAmelCase             = field.default_factory()
																		else:
																								UpperCAmelCase             = True
												parser.add_argument(__UpperCAmelCase   ,  *__UpperCAmelCase   ,  **__UpperCAmelCase					)

												# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
												# Order is important for arguments with the same destination!
												# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
												# here and we do not need those changes/additional keys.
												if field.default is True and (field.type is bool or field.type == Optional[bool]):
																		UpperCAmelCase             = False
																		parser.add_argument(f"""--no_{field.name}"""   ,  action='store_false'   ,  dest=field.name   ,  **__UpperCAmelCase					)






						def 	UpperCAmelCase__     (					self :Dict   ,  lowercase_ :DataClassType					)				->			Union[str, Any]:
												if hasattr(__UpperCAmelCase   ,  '_argument_group_name'					):
																		UpperCAmelCase             = self.add_argument_group(dtype._argument_group_name					)
												else:
																		UpperCAmelCase             = self

												try:
																		UpperCAmelCase             = get_type_hints(__UpperCAmelCase					)
												except NameError:
																		raise RuntimeError(
																		    f"""Type resolution failed for {dtype}. Try declaring the class in global scope or """
																		    'removing line of `from __future__ import annotations` which opts in Postponed '
																		    'Evaluation of Annotations (PEP 563)'					)
												except TypeError as ex:
																		# Remove this block when we drop Python 3.9 support
																		if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__UpperCAmelCase					):
																								UpperCAmelCase             = ".".join(map(__UpperCAmelCase   ,  sys.version_info[:3]					)					)
																								raise RuntimeError(
																								    f"""Type resolution failed for {dtype} on Python {python_version}. Try removing """
																								    'line of `from __future__ import annotations` which opts in union types as '
																								    '`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '
																								    'support Python versions that lower than 3.10, you need to use '
																								    '`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '
																								    '`X | None`.'					) from ex
																		raise

												for field in dataclasses.fields(__UpperCAmelCase					):
																		if not field.init:
																								continue
																		UpperCAmelCase             = type_hints[field.name]
																		self._parse_dataclass_field(__UpperCAmelCase   ,  __UpperCAmelCase					)






						def 	UpperCAmelCase__     (					self :str   ,  lowercase_ :str=None   ,  lowercase_ :List[str]=False   ,  lowercase_ :Dict=True   ,  lowercase_ :List[str]=None   ,  lowercase_ :Dict=None   ,  )				->			str:

												if args_file_flag or args_filename or (look_for_args_file and len(sys.argv					)):
																		UpperCAmelCase             = []

																		if args_filename:
																								args_files.append(Path(__UpperCAmelCase					)					)
																		elif look_for_args_file and len(sys.argv					):
																								args_files.append(Path(sys.argv[0]					).with_suffix('.args'					)					)

																		# args files specified via command line flag should overwrite default args files so we add them last
																		if args_file_flag:
																								# Create special parser just to extract the args_file_flag values
																								UpperCAmelCase             = ArgumentParser()
																								args_file_parser.add_argument(__UpperCAmelCase   ,  type=__UpperCAmelCase   ,  action='append'					)

																								# Use only remaining args for further parsing (remove the args_file_flag)
																								UpperCAmelCase             = args_file_parser.parse_known_args(args=__UpperCAmelCase					)
																								UpperCAmelCase             = vars(__UpperCAmelCase					).get(args_file_flag.lstrip('-'					)   ,  __UpperCAmelCase					)

																								if cmd_args_file_paths:
																														args_files.extend([Path(__UpperCAmelCase					) for p in cmd_args_file_paths]					)

																		UpperCAmelCase             = []
																		for args_file in args_files:
																								if args_file.exists():
																														file_args += args_file.read_text().split()

																														# in case of duplicate arguments the last one has precedence
																														# args specified via the command line should overwrite args from files, so we add them last
																		UpperCAmelCase             = file_args + args if args is not None else file_args + sys.argv[1:]
												UpperCAmelCase             = self.parse_known_args(args=__UpperCAmelCase					)
												UpperCAmelCase             = []
												for dtype in self.dataclass_types:
																		UpperCAmelCase             = {f.name for f in dataclasses.fields(__UpperCAmelCase					) if f.init}
																		UpperCAmelCase             = {k: v for k, v in vars(__UpperCAmelCase					).items() if k in keys}
																		for k in keys:
																								delattr(__UpperCAmelCase   ,  __UpperCAmelCase					)
																		UpperCAmelCase             = dtype(**__UpperCAmelCase					)
																		outputs.append(__UpperCAmelCase					)
												if len(namespace.__dict__					) > 0:
																		# additional namespace.
																		outputs.append(__UpperCAmelCase					)
												if return_remaining_strings:
																		return (*outputs, remaining_args)
												else:
																		if remaining_args:
																								raise ValueError(f"""Some specified arguments are not used by the HfArgumentParser: {remaining_args}"""					)

																		return (*outputs,)






						def 	UpperCAmelCase__     (					self :Optional[Any]   ,  lowercase_ :Dict[str, Any]   ,  lowercase_ :bool = False					)				->			str:
												UpperCAmelCase             = set(args.keys()					)
												UpperCAmelCase             = []
												for dtype in self.dataclass_types:
																		UpperCAmelCase             = {f.name for f in dataclasses.fields(__UpperCAmelCase					) if f.init}
																		UpperCAmelCase             = {k: v for k, v in args.items() if k in keys}
																		unused_keys.difference_update(inputs.keys()					)
																		UpperCAmelCase             = dtype(**__UpperCAmelCase					)
																		outputs.append(__UpperCAmelCase					)
												if not allow_extra_keys and unused_keys:
																		raise ValueError(f"""Some keys are not used by the HfArgumentParser: {sorted(__UpperCAmelCase					)}"""					)
												return tuple(__UpperCAmelCase					)






						def 	UpperCAmelCase__     (					self :Union[str, Any]   ,  lowercase_ :str   ,  lowercase_ :bool = False					)				->			List[Any]:
												with open(Path(__UpperCAmelCase					)   ,  encoding='utf-8'					) as open_json_file:
																		UpperCAmelCase             = json.loads(open_json_file.read()					)
												UpperCAmelCase             = self.parse_dict(__UpperCAmelCase   ,  allow_extra_keys=__UpperCAmelCase					)
												return tuple(__UpperCAmelCase					)






						def 	UpperCAmelCase__     (					self :List[Any]   ,  lowercase_ :str   ,  lowercase_ :bool = False					)				->			Dict:
												UpperCAmelCase             = self.parse_dict(yaml.safe_load(Path(__UpperCAmelCase					).read_text()					)   ,  allow_extra_keys=__UpperCAmelCase					)
												return tuple(__UpperCAmelCase					)









"""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)






