




def     lowerCAmelCase		(		_lowerCAmelCase				: int = 10**6						):




 """simple docstring"""







 if n <= 0:
  raise ValueError("Please enter an integer greater than 0"						)

 UpperCAmelCase__							=    list(range(n + 1						)						)
 for number in range(2    ,				n + 1						):
  if phi[number] == number:
   phi[number] -= 1
   for multiple in range(number * 2    ,				n + 1    ,				_lowerCAmelCase						):
    UpperCAmelCase__							=    (phi[multiple] // number) * (number - 1)

 UpperCAmelCase__							=    1
 for number in range(1    ,				n + 1						):
  if (answer / phi[answer]) < (number / phi[number]):
   UpperCAmelCase__							=    number

 return answer


if __name__ == "__main__":
  print(solution())











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

_lowerCAmelCase    : List[str]		= logging.get_logger(__name__)



def     lowerCAmelCase		(		_lowerCAmelCase				: bytes    ,				_lowerCAmelCase				: int						):




 """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(_lowerCAmelCase    ,				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(_lowerCAmelCase						)
 UpperCAmelCase__							=    output_stream[0]

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


@add_end_docstrings(lowerCAmelCase		)
class       _UpperCamelCase   (					lowerCAmelCase		):
      def __init__(      self :Any			,   *lowerCamelCase :Any			,   **lowerCamelCase :Optional[Any]   )  ->      Optional[int]:
       # Default, might be overriden by the model.config.
       UpperCAmelCase__							=    5
       super().__init__(*lowerCamelCase			,   **lowerCamelCase   )

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

       self.check_model_type(lowerCamelCase   )
      def __call__(      self :Optional[Any]			,   lowerCamelCase :Union[np.ndarray, bytes, str]			,   **lowerCamelCase :List[Any]			,   )  ->      Tuple:
       return super().__call__(lowerCamelCase			,   **lowerCamelCase   )
      def 						UpperCAmelCase_    (      self :str			,   lowerCamelCase :Dict=None			,   **lowerCamelCase :str   )  ->      int:
       # 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 						UpperCAmelCase_    (      self :Optional[Any]			,   lowerCamelCase :Dict   )  ->      Optional[int]:
       if isinstance(lowerCamelCase			,   lowerCamelCase   ):
        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(lowerCamelCase   ).content
        else:
         with open(lowerCamelCase			,   "rb"   ) as f:
          UpperCAmelCase__							=    f.read()

       if isinstance(lowerCamelCase			,   lowerCamelCase   ):
        UpperCAmelCase__							=    ffmpeg_read(lowerCamelCase			,   self.feature_extractor.sampling_rate   )

       if isinstance(lowerCamelCase			,   lowerCamelCase   ):
        # 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"			,   lowerCamelCase   )
        if _inputs is None:
         # Remove path which will not be used from `datasets`.
         inputs.pop("path"			,   lowerCamelCase   )
         UpperCAmelCase__							=    inputs.pop("array"			,   lowerCamelCase   )
        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(lowerCamelCase   )			,   lowerCamelCase			,   self.feature_extractor.sampling_rate   ).numpy()

       if not isinstance(lowerCamelCase			,   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(
           lowerCamelCase			,   sampling_rate=self.feature_extractor.sampling_rate			,   return_tensors="pt"   )
       return processed
      def 						UpperCAmelCase_    (      self :Tuple			,   lowerCamelCase :Union[str, Any]   )  ->      List[Any]:
       UpperCAmelCase__							=    self.model(**lowerCamelCase   )
       return model_outputs







      def 						UpperCAmelCase_    (      self :List[str]			,   lowerCamelCase :Optional[Any]			,   lowerCamelCase :List[Any]=5   )  ->      Tuple:
       UpperCAmelCase__							=    model_outputs.logits[0].softmax(-1   )
       UpperCAmelCase__			,			UpperCAmelCase__							=    probs.topk(lowerCamelCase   )

       UpperCAmelCase__							=    scores.tolist()
       UpperCAmelCase__							=    ids.tolist()

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

       return labels





