






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


lowerCamelCase       :       List[Any]        =				NewType('''DataClass''', Any)
lowerCamelCase       :       Optional[int]        =				NewType('''DataClassType''', Any)
def 					snake_case_						(   lowerCAmelCase_				:  Optional[int]					):
							if isinstance(lowerCAmelCase_				,				lowerCAmelCase_					):
														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 					snake_case_						(   lowerCAmelCase_				:  list					):
							__lowercase					:							Optional[Any]			=   {str(lowerCAmelCase_					): choice for choice in choices}
							return lambda lowerCAmelCase_					: str_to_choice.get(lowerCAmelCase_				,				lowerCAmelCase_					)
def 					snake_case_						(   *,
    lowerCAmelCase_				:  Union[str, List[str]] = None				,				lowerCAmelCase_				:  str = None				,				lowerCAmelCase_				:  Any = dataclasses.MISSING				,				lowerCAmelCase_				:  Callable[[], Any] = dataclasses.MISSING				,				lowerCAmelCase_				:  dict = None				,				**lowerCAmelCase_				:  Optional[Any]				,				):
							if metadata is None:
														# Important, don't use as default param in function signature because dict is mutable and shared across function calls
														__lowercase					:							Dict			=   {}
							if aliases is not None:
														__lowercase					:							int			=   aliases
							if help is not None:
														__lowercase					:							Tuple			=   help

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



class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	Iterable[DataClassType]



					def __init__(     self :		Dict						,   __a :		Union[DataClassType, Iterable[DataClassType]]						,   **__a :		Dict							)	->     Optional[Any]:


												"""simple docstring"""

												if "formatter_class" not in kwargs:
																			__lowercase					:							Optional[Any]			=   ArgumentDefaultsHelpFormatter
												super().__init__(**__a							)
												if dataclasses.is_dataclass(__a							):
																			__lowercase					:							str			=   [dataclass_types]
												__lowercase					:							str			=   list(__a							)
												for dtype in self.dataclass_types:
																			self._add_dataclass_arguments(__a							)



					@staticmethod
					def 					lowerCAmelCase (     __a :		ArgumentParser						,   __a :		dataclasses.Field							)	->     str:


												"""simple docstring"""

												__lowercase					:							int			=   F"--{field.name}"
												__lowercase					:							int			=   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						,   __a							):
																			raise RuntimeError(
																			    """Unresolved type detected, which should have been done with the help of """
																			    """`typing.get_type_hints` method by default"""							)

												__lowercase					:							str			=   kwargs.pop("""aliases"""						,   []							)
												if isinstance(__a						,   __a							):
																			__lowercase					:							Tuple			=   [aliases]

												__lowercase					:							Optional[int]			=   getattr(field.type						,   """__origin__"""						,   field.type							)
												if origin_type is Union or (hasattr(__a						,   """UnionType"""							) and isinstance(__a						,   types.UnionType							)):
																			if str not in field.type.__args__ and (
																			    len(field.type.__args__							) != 2 or type(__a							) 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(__a							) not in field.type.__args__:
																										# filter `str` in Union
																										__lowercase					:							Optional[int]			=   field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
																										__lowercase					:							Dict			=   getattr(field.type						,   """__origin__"""						,   field.type							)
																			elif bool not in field.type.__args__:
																										# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
																										__lowercase					:							Dict			=   (
																										    field.type.__args__[0] if isinstance(__a						,   field.type.__args__[1]							) else field.type.__args__[1]
																										)
																										__lowercase					:							int			=   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)
												__lowercase					:							List[str]			=   {}
												if origin_type is Literal or (isinstance(field.type						,   __a							) and issubclass(field.type						,   __a							)):
																			if origin_type is Literal:
																										__lowercase					:							Union[str, Any]			=   field.type.__args__
																			else:
																										__lowercase					:							int			=   [x.value for x in field.type]

																			__lowercase					:							List[Any]			=   make_choice_type_function(kwargs["""choices"""]							)

																			if field.default is not dataclasses.MISSING:
																										__lowercase					:							List[Any]			=   field.default
																			else:
																										__lowercase					:							int			=   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
																			__lowercase					:							List[Any]			=   copy(__a							)

																			# Hack because type=bool in argparse does not behave as we want.
																			__lowercase					:							Tuple			=   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.
																										__lowercase					:							Union[str, Any]			=   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
																										__lowercase					:							int			=   default
																										# This tells argparse we accept 0 or 1 value after --field_name
																										__lowercase					:							Any			=   """?"""
																										# This is the value that will get picked if we do --field_name (without value)
																										__lowercase					:							str			=   True
												elif isclass(__a							) and issubclass(__a						,   __a							):
																			__lowercase					:							Optional[int]			=   field.type.__args__[0]
																			__lowercase					:							Union[str, Any]			=   """+"""
																			if field.default_factory is not dataclasses.MISSING:
																										__lowercase					:							Union[str, Any]			=   field.default_factory()
																			elif field.default is dataclasses.MISSING:
																										__lowercase					:							List[str]			=   True
												else:
																			__lowercase					:							Optional[int]			=   field.type
																			if field.default is not dataclasses.MISSING:
																										__lowercase					:							Dict			=   field.default
																			elif field.default_factory is not dataclasses.MISSING:
																										__lowercase					:							Optional[int]			=   field.default_factory()
																			else:
																										__lowercase					:							int			=   True
												parser.add_argument(__a						,   *__a						,   **__a							)

												# 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]):
																			__lowercase					:							int			=   False
																			parser.add_argument(F"--no_{field.name}"						,   action="""store_false"""						,   dest=field.name						,   **__a							)



					def 					lowerCAmelCase (     self :		Tuple						,   __a :		DataClassType							)	->     Optional[Any]:


												"""simple docstring"""

												if hasattr(__a						,   """_argument_group_name"""							):
																			__lowercase					:							Optional[Any]			=   self.add_argument_group(dtype._argument_group_name							)
												else:
																			__lowercase					:							Union[str, Any]			=   self

												try:
																			__lowercase					:							Dict[str, type]			=   get_type_hints(__a							)
												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(__a							):
																										__lowercase					:							List[str]			=   """.""".join(map(__a						,   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(__a							):
																			if not field.init:
																										continue
																			__lowercase					:							Optional[Any]			=   type_hints[field.name]
																			self._parse_dataclass_field(__a						,   __a							)



					def 					lowerCAmelCase (     self :		List[Any]						,   __a :		str=None						,   __a :		Optional[int]=False						,   __a :		Union[str, Any]=True						,   __a :		Tuple=None						,   __a :		Tuple=None						,   )	->     Tuple[DataClass, ...]:


												"""simple docstring"""

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

																			if args_filename:
																										args_files.append(Path(__a							)							)
																			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
																										__lowercase					:							Any			=   ArgumentParser()
																										args_file_parser.add_argument(__a						,   type=__a						,   action="""append"""							)

																										# Use only remaining args for further parsing (remove the args_file_flag)
																										__lowercase	,	__lowercase					:							Optional[int]			=   args_file_parser.parse_known_args(args=__a							)
																										__lowercase					:							Optional[Any]			=   vars(__a							).get(args_file_flag.lstrip("""-"""							)						,   __a							)

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

																			__lowercase					:							Optional[Any]			=   []
																			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
																			__lowercase					:							Union[str, Any]			=   file_args + args if args is not None else file_args + sys.argv[1:]
												__lowercase	,	__lowercase					:							List[str]			=   self.parse_known_args(args=__a							)
												__lowercase					:							Any			=   []
												for dtype in self.dataclass_types:
																			__lowercase					:							Dict			=   {f.name for f in dataclasses.fields(__a							) if f.init}
																			__lowercase					:							Optional[int]			=   {k: v for k, v in vars(__a							).items() if k in keys}
																			for k in keys:
																										delattr(__a						,   __a							)
																			__lowercase					:							str			=   dtype(**__a							)
																			outputs.append(__a							)
												if len(namespace.__dict__							) > 0:
																			# additional namespace.
																			outputs.append(__a							)
												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 					lowerCAmelCase (     self :		str						,   __a :		Dict[str, Any]						,   __a :		bool = False							)	->     Tuple[DataClass, ...]:


												"""simple docstring"""

												__lowercase					:							Optional[int]			=   set(args.keys()							)
												__lowercase					:							Dict			=   []
												for dtype in self.dataclass_types:
																			__lowercase					:							int			=   {f.name for f in dataclasses.fields(__a							) if f.init}
																			__lowercase					:							str			=   {k: v for k, v in args.items() if k in keys}
																			unused_keys.difference_update(inputs.keys()							)
																			__lowercase					:							Tuple			=   dtype(**__a							)
																			outputs.append(__a							)
												if not allow_extra_keys and unused_keys:
																			raise ValueError(F"Some keys are not used by the HfArgumentParser: {sorted(__a							)}"							)
												return tuple(__a							)



					def 					lowerCAmelCase (     self :		Tuple						,   __a :		str						,   __a :		bool = False							)	->     Tuple[DataClass, ...]:


												"""simple docstring"""

												with open(Path(__a							)						,   encoding="""utf-8"""							) as open_json_file:
																			__lowercase					:							Optional[int]			=   json.loads(open_json_file.read()							)
												__lowercase					:							Optional[int]			=   self.parse_dict(__a						,   allow_extra_keys=__a							)
												return tuple(__a							)



					def 					lowerCAmelCase (     self :		int						,   __a :		str						,   __a :		bool = False							)	->     Tuple[DataClass, ...]:


												"""simple docstring"""

												__lowercase					:							Optional[Any]			=   self.parse_dict(yaml.safe_load(Path(__a							).read_text()							)						,   allow_extra_keys=__a							)
												return tuple(__a							)







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       :       List[str]        =				logging.get_logger(__name__)
def 					snake_case_						(   lowerCAmelCase_				:  List[Any]					):
							__lowercase					:							Optional[Any]			=   ASTConfig()

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

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

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

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

							return name
def 					snake_case_						(   lowerCAmelCase_				:  Tuple				,				lowerCAmelCase_				:  List[str]					):
							for key in orig_state_dict.copy().keys():
														__lowercase					:							str			=   orig_state_dict.pop(lowerCAmelCase_					)

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

							return orig_state_dict
def 					snake_case_						(   lowerCAmelCase_				:  Optional[int]					):
							__lowercase					:							int			=   [
							    """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(lowerCAmelCase_				,				lowerCAmelCase_					)
@torch.no_grad()
def 					snake_case_						(   lowerCAmelCase_				:  Optional[Any]				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  int=False					):
							__lowercase					:							List[Any]			=   get_audio_spectrogram_transformer_config(lowerCAmelCase_					)

							__lowercase					:							Tuple			=   {
							    """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
							__lowercase					:							str			=   model_name_to_url[model_name]
							__lowercase					:							Dict			=   torch.hub.load_state_dict_from_url(lowerCAmelCase_				,				map_location="""cpu"""					)
							# remove some keys
							remove_keys(lowerCAmelCase_					)
							# rename some keys
							__lowercase					:							Optional[Any]			=   convert_state_dict(lowerCAmelCase_				,				lowerCAmelCase_					)

							# load 🤗 model
							__lowercase					:							str			=   ASTForAudioClassification(lowerCAmelCase_					)
							model.eval()

							model.load_state_dict(lowerCAmelCase_					)

							# verify outputs on dummy input
							# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
							__lowercase					:							Tuple			=   -4.2_677_393 if """speech-commands""" not in model_name else -6.845_978
							__lowercase					:							Optional[int]			=   4.5_689_974 if """speech-commands""" not in model_name else 5.5_654_526
							__lowercase					:							Union[str, Any]			=   1024 if """speech-commands""" not in model_name else 128
							__lowercase					:							Any			=   ASTFeatureExtractor(mean=lowerCAmelCase_				,				std=lowerCAmelCase_				,				max_length=lowerCAmelCase_					)

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

														__lowercase	,	__lowercase					:							Union[str, Any]			=   torchaudio.load(lowerCAmelCase_					)
														__lowercase					:							Union[str, Any]			=   waveform.squeeze().numpy()

							__lowercase					:							int			=   feature_extractor(lowerCAmelCase_				,				sampling_rate=16000				,				return_tensors="""pt"""					)

							# forward pass
							__lowercase					:							Tuple			=   model(**lowerCAmelCase_					)
							__lowercase					:							int			=   outputs.logits

							if model_name == "ast-finetuned-audioset-10-10-0.4593":
														__lowercase					:							Union[str, Any]			=   torch.tensor([-0.8_760, -7.0_042, -8.6_602]					)
							elif model_name == "ast-finetuned-audioset-10-10-0.450":
														__lowercase					:							Optional[int]			=   torch.tensor([-1.1_986, -7.0_903, -8.2_718]					)
							elif model_name == "ast-finetuned-audioset-10-10-0.448":
														__lowercase					:							Union[str, Any]			=   torch.tensor([-2.6_128, -8.0_080, -9.4_344]					)
							elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
														__lowercase					:							Optional[Any]			=   torch.tensor([-1.5_080, -7.4_534, -8.8_917]					)
							elif model_name == "ast-finetuned-audioset-12-12-0.447":
														__lowercase					:							List[Any]			=   torch.tensor([-0.5_050, -6.5_833, -8.0_843]					)
							elif model_name == "ast-finetuned-audioset-14-14-0.443":
														__lowercase					:							str			=   torch.tensor([-0.3_826, -7.0_336, -8.2_413]					)
							elif model_name == "ast-finetuned-audioset-16-16-0.442":
														__lowercase					:							List[str]			=   torch.tensor([-1.2_113, -6.9_101, -8.3_470]					)
							elif model_name == "ast-finetuned-speech-commands-v2":
														__lowercase					:							List[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]				,				lowerCAmelCase_				,				atol=1e-4					):
														raise ValueError("""Logits don't match"""					)
							print("""Looks ok!"""					)

							if pytorch_dump_folder_path is not None:
														Path(lowerCAmelCase_					).mkdir(exist_ok=lowerCAmelCase_					)
														print(F"Saving model {model_name} to {pytorch_dump_folder_path}"					)
														model.save_pretrained(lowerCAmelCase_					)
														print(F"Saving feature extractor to {pytorch_dump_folder_path}"					)
														feature_extractor.save_pretrained(lowerCAmelCase_					)

							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)