code: 
  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
  
  UpperCamelCase_	:    List[Any]  					=       logging.get_logger(__name__)
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      bytes   , _UpperCAmelCase     :      int     )      -> np.array:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  str    			=      f"""{sampling_rate}"""
  						_lowercase					:  Optional[int]    			=      "1"
  						_lowercase					:  int    			=      "f32le"
  						_lowercase					:  Dict    			=      [
  						    "ffmpeg",
  						    "-i",
  						    "pipe:0",
  						    "-ac",
  						    ac,
  						    "-ar",
  						    ar,
  						    "-f",
  						    format_for_conversion,
  						    "-hide_banner",
  						    "-loglevel",
  						    "quiet",
  						    "pipe:1",
  						]
  
  						try:
  												_lowercase					:  str    			=      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"     )
  						_lowercase					:  List[Any]    			=      ffmpeg_process.communicate(_UpperCAmelCase     )
  						_lowercase					:  Tuple    			=      output_stream[0]
  
  						_lowercase					:  Optional[int]    			=      np.frombuffer(_UpperCAmelCase   , np.floataa     )
  						if audio.shape[0] == 0:
  												raise ValueError("Malformed soundfile"     )
  						return audio
  
  
  
  
  @add_end_docstrings(__snake_case					)
  class      __lowercase	(	__snake_case					):
  
  
  
  
  
  
  	def __init__(self							:   List[Any]			,  *snake_case							:   Optional[int]			,  **snake_case							:   Optional[int]						)   -> int:
  							# Default, might be overriden by the model.config.
  							_lowercase					:  List[Any]    			=      5
  							super().__init__(*snake_case			,  **snake_case						)
  
  							if self.framework != "pt":
  													raise ValueError(F"""The {self.__class__} is only available in PyTorch."""						)
  
  							self.check_model_type(snake_case						)
  
  
  
  
  
  
  	def __call__(self							:   Dict			,  snake_case							:   Union[np.ndarray, bytes, str]			,  **snake_case							:   str			,  )   -> Tuple:
  							return super().__call__(snake_case			,  **snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   List[Any]			,  snake_case							:   Union[str, Any]=None			,  **snake_case							:   Optional[Any]						)   -> Optional[int]:
  							# No parameters on this pipeline right now
  							_lowercase					:  str    			=      {}
  							if top_k is not None:
  													if top_k > self.model.config.num_labels:
  																			_lowercase					:  Dict    			=      self.model.config.num_labels
  													_lowercase					:  str    			=      top_k
  							return {}, {}, postprocess_params
  
  
  
  
  
  
  	def 			_a(self							:   Tuple			,  snake_case							:   Dict						)   -> str:
  							if isinstance(snake_case			,  snake_case						):
  													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
  																			_lowercase					:  Union[str, Any]    			=      requests.get(snake_case						).content
  													else:
  																			with open(snake_case			,  "rb"						) as f:
  																									_lowercase					:  List[str]    			=      f.read()
  
  							if isinstance(snake_case			,  snake_case						):
  													_lowercase					:  List[str]    			=      ffmpeg_read(snake_case			,  self.feature_extractor.sampling_rate						)
  
  							if isinstance(snake_case			,  snake_case						):
  													# 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"						)
  
  													_lowercase					:  str    			=      inputs.pop("raw"			,  snake_case						)
  													if _inputs is None:
  																			# Remove path which will not be used from `datasets`.
  																			inputs.pop("path"			,  snake_case						)
  																			_lowercase					:  Any    			=      inputs.pop("array"			,  snake_case						)
  													_lowercase					:  Any    			=      inputs.pop("sampling_rate"						)
  													_lowercase					:  Union[str, Any]    			=      _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`."						)
  
  																			_lowercase					:  List[str]    			=      F.resample(
  																			    torch.from_numpy(snake_case						)			,  snake_case			,  self.feature_extractor.sampling_rate						).numpy()
  
  							if not isinstance(snake_case			,  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"						)
  
  							_lowercase					:  Dict    			=      self.feature_extractor(
  							    snake_case			,  sampling_rate=self.feature_extractor.sampling_rate			,  return_tensors="pt"						)
  							return processed
  
  
  
  
  
  
  	def 			_a(self							:   int			,  snake_case							:   Union[str, Any]						)   -> List[Any]:
  							_lowercase					:  Optional[int]    			=      self.model(**snake_case						)
  							return model_outputs
  
  
  
  
  
  
  	def 			_a(self							:   str			,  snake_case							:   Union[str, Any]			,  snake_case							:   Any=5						)   -> str:
  							_lowercase					:  Any    			=      model_outputs.logits[0].softmax(-1						)
  							_lowercase			,						_lowercase					:  Union[str, Any]    			=      probs.topk(snake_case						)
  
  							_lowercase					:  Optional[Any]    			=      scores.tolist()
  							_lowercase					:  int    			=      ids.tolist()
  
  							_lowercase					:  Union[str, Any]    			=      [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case			,  snake_case						)]
  
  							return labels
  
  
  
  
  
style_context: 
  import os
  
  
  
  def 		UpperCamelCase    (					)      -> Optional[int]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Tuple    			=      0
  						_lowercase					:  List[Any]    			=      0
  						with open(os.path.dirname(_UpperCAmelCase     ) + "/p022_names.txt"     ) as file:
  												_lowercase					:  Any    			=      str(file.readlines()[0]     )
  												_lowercase					:  int    			=      name.replace("\""   , ""     ).split(","     )
  
  						name.sort()
  						for i in range(len(_UpperCAmelCase     )     ):
  												for j in name[i]:
  																		temp_sum += ord(_UpperCAmelCase     ) - ord("A"     ) + 1
  												total_sum += (i + 1) * temp_sum
  												_lowercase					:  Tuple    			=      0
  						return total_sum
  
  
  if __name__ == "__main__":
  							print(solution())
  
  
  
  
  