
"""simple docstring"""
def     __lowerCamelCase  (		UpperCamelCase__ = 99_8001      ):


	"""simple docstring"""






	_UpperCAmelCase =						0
	for i in range(999				,     99				,     -1      ):  # 3 digit numbers range from 999 down to 100
		for j in range(999				,     99				,     -1      ):
			_UpperCAmelCase =						str(i * j      )
			if product_string == product_string[::-1] and i * j < n:
				_UpperCAmelCase =						max(UpperCamelCase__				,     i * j      )
	return answer


if __name__ == "__main__":
			print(f'''{solution() = }''')






"""simple docstring"""
import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


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

__magic_name__		       =				logging.get_logger(__name__)




def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):


	"""simple docstring"""






	_UpperCAmelCase =						f"{sampling_rate}"
	_UpperCAmelCase =						"1"
	_UpperCAmelCase =						"f32le"
	_UpperCAmelCase =						[
	    "ffmpeg",
	    "-i",
	    "pipe:0",
	    "-ac",
	    ac,
	    "-ar",
	    ar,
	    "-f",
	    format_for_conversion,
	    "-hide_banner",
	    "-loglevel",
	    "quiet",
	    "pipe:1",
	]

	try:
		_UpperCAmelCase =						subprocess.Popen(UpperCamelCase__				,     stdin=subprocess.PIPE				,     stdout=subprocess.PIPE      )
	except FileNotFoundError:
		raise ValueError("ffmpeg was not found but is required to load audio files from filename"      )
	_UpperCAmelCase =						ffmpeg_process.communicate(UpperCamelCase__      )
	_UpperCAmelCase =						output_stream[0]

	_UpperCAmelCase =						np.frombuffer(UpperCamelCase__				,     np.floataa      )
	if audio.shape[0] == 0:
		raise ValueError("Malformed soundfile"      )
	return audio



@add_end_docstrings(lowerCamelCase						)
class    _lowerCAmelCase						(						lowerCamelCase						):



							def __init__(      self ,						*a_ ,						**a_   )							->							Union[str, Any]:
								# Default, might be overriden by the model.config.
								_UpperCAmelCase =						5
								super().__init__(*a_ ,						**a_   )

								if self.framework != "pt":
									raise ValueError(f"The {self.__class__} is only available in PyTorch."   )

								self.check_model_type(a_   )



							def __call__(      self ,						a_ ,						**a_ ,						)							->							int:
								return super().__call__(a_ ,						**a_   )



							def       _a   (      self ,						a_=None ,						**a_   )							->							Dict:
								# No parameters on this pipeline right now
								_UpperCAmelCase =						{}
								if top_k is not None:
									if top_k > self.model.config.num_labels:
										_UpperCAmelCase =						self.model.config.num_labels
									_UpperCAmelCase =						top_k
								return {}, {}, postprocess_params



							def       _a   (      self ,						a_   )							->							str:
								if isinstance(a_ ,						a_   ):
									if inputs.startswith("http://"   ) or inputs.startswith("https://"   ):
										# We need to actually check for a real protocol, otherwise it's impossible to use a local file
										# like http_huggingface_co.png
										_UpperCAmelCase =						requests.get(a_   ).content
									else:
										with open(a_ ,						"rb"   ) as f:
											_UpperCAmelCase =						f.read()

								if isinstance(a_ ,						a_   ):
									_UpperCAmelCase =						ffmpeg_read(a_ ,						self.feature_extractor.sampling_rate   )

								if isinstance(a_ ,						a_   ):
									# Accepting `"array"` which is the key defined in `datasets` for
									# better integration
									if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
										raise ValueError(
										    "When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a "
										    "\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, "
										    "containing the sampling_rate associated with that array"   )

									_UpperCAmelCase =						inputs.pop("raw" ,						a_   )
									if _inputs is None:
										# Remove path which will not be used from `datasets`.
										inputs.pop("path" ,						a_   )
										_UpperCAmelCase =						inputs.pop("array" ,						a_   )
									_UpperCAmelCase =						inputs.pop("sampling_rate"   )
									_UpperCAmelCase =						_inputs
									if in_sampling_rate != self.feature_extractor.sampling_rate:
										import torch

										if is_torchaudio_available():
											from torchaudio import functional as F
										else:
											raise ImportError(
											    "torchaudio is required to resample audio samples in AudioClassificationPipeline. "
											    "The torchaudio package can be installed through: `pip install torchaudio`."   )

										_UpperCAmelCase =						F.resample(
										    torch.from_numpy(a_   ) ,						a_ ,						self.feature_extractor.sampling_rate   ).numpy()

								if not isinstance(a_ ,						np.ndarray   ):
									raise ValueError("We expect a numpy ndarray as input"   )
								if len(inputs.shape   ) != 1:
									raise ValueError("We expect a single channel audio input for AudioClassificationPipeline"   )

								_UpperCAmelCase =						self.feature_extractor(
								    a_ ,						sampling_rate=self.feature_extractor.sampling_rate ,						return_tensors="pt"   )
								return processed



							def       _a   (      self ,						a_   )							->							List[str]:
								_UpperCAmelCase =						self.model(**a_   )
								return model_outputs



							def       _a   (      self ,						a_ ,						a_=5   )							->							int:
								_UpperCAmelCase =						model_outputs.logits[0].softmax(-1   )
								_UpperCAmelCase ,      _UpperCAmelCase =						probs.topk(a_   )

								_UpperCAmelCase =						scores.tolist()
								_UpperCAmelCase =						ids.tolist()

								_UpperCAmelCase =						[{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(a_ ,						a_   )]

								return labels




