code: 
  """simple docstring"""
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  __magic_name__		       =				logging.get_logger(__name__)
  
  
  class    _lowerCAmelCase						(						lowerCamelCase						):
  							lowercase_     :     List[Any]       =							['''input_ids''', '''attention_mask''']
  
  
  
  							def __init__(      self ,						a_="[PAD]" ,						a_="[BOS]" ,						a_="[EOS]" ,						a_="[MASK]" ,						a_="[CLS]" ,						a_="[SEP]" ,						a_=2048 ,						**a_ ,						)							->							None:
  								_UpperCAmelCase =						AddedToken(a_ ,						lstrip=a_ ,						rstrip=a_   ) if isinstance(a_ ,						a_   ) else pad_token
  								_UpperCAmelCase =						AddedToken(a_ ,						lstrip=a_ ,						rstrip=a_   ) if isinstance(a_ ,						a_   ) else bos_token
  								_UpperCAmelCase =						AddedToken(a_ ,						lstrip=a_ ,						rstrip=a_   ) if isinstance(a_ ,						a_   ) else eos_token
  								_UpperCAmelCase =						AddedToken(a_ ,						lstrip=a_ ,						rstrip=a_   ) if isinstance(a_ ,						a_   ) else mask_token
  								_UpperCAmelCase =						AddedToken(a_ ,						lstrip=a_ ,						rstrip=a_   ) if isinstance(a_ ,						a_   ) else cls_token
  								_UpperCAmelCase =						AddedToken(a_ ,						lstrip=a_ ,						rstrip=a_   ) if isinstance(a_ ,						a_   ) else sep_token
  
  								super().__init__(
  								    pad_token=a_ ,						bos_token=a_ ,						eos_token=a_ ,						mask_token=a_ ,						cls_token=a_ ,						sep_token=a_ ,						model_max_length=a_ ,						**a_ ,						)
  
  								_UpperCAmelCase =						2**8  # utf is 8 bits
  
  								# define special tokens dict
  								_UpperCAmelCase =						{
  								    self.pad_token: 0,
  								    self.bos_token: 1,
  								    self.eos_token: 2,
  								    self.mask_token: 3,
  								    self.cls_token: 4,
  								    self.sep_token: 5,
  								}
  								_UpperCAmelCase =						len(self.special_tokens_encoder   )
  								_UpperCAmelCase =						{v: k for k, v in self.special_tokens_encoder.items()}
  
  
  
  							def       _a   (      self   )							->							Dict[str, int]:
  								_UpperCAmelCase =						self.special_tokens_encoder.copy()
  								vocab.update(self.added_tokens_encoder   )
  								for i in range(self._utf_vocab_size   ):
  									_UpperCAmelCase =						chr(a_   )
  									_UpperCAmelCase =						i + len(self.special_tokens_encoder   )
  								return vocab
  
  
  
  							@property
  							def       _a   (      self   )							->							Union[str, Any]:
  								return self._utf_vocab_size + self._num_special_tokens
  
  
  
  							def       _a   (      self ,						a_ ,						a_ = None ,						a_ = False   )							->							List[int]:
  								if already_has_special_tokens:
  									return super().get_special_tokens_mask(
  									    token_ids_a=a_ ,						token_ids_a=a_ ,						already_has_special_tokens=a_   )
  
  								# normal case: some special tokens
  								if token_ids_a is None:
  									return [1] + [0] * len(a_   ) + [1]
  								return [1] + ([0] * len(a_   )) + [1] + ([0] * len(a_   )) + [1]
  
  
  
  							def       _a   (      self ,						a_ ,						a_ = None   )							->							List[int]:
  								if token_ids_a is None:
  									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  								else:
  									return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
  
  
  
  							def       _a   (      self ,						a_   )							->							List[str]:
  								_UpperCAmelCase =						[chr(a_   ) for i in text.encode("utf-8"   )]
  								return tokens
  
  
  
  							def       _a   (      self ,						a_   )							->							Union[str, Any]:
  								if token in self.special_tokens_encoder:
  									_UpperCAmelCase =						self.special_tokens_encoder[token]
  								elif token in self.added_tokens_encoder:
  									_UpperCAmelCase =						self.added_tokens_encoder[token]
  								elif len(a_   ) != 1:
  									_UpperCAmelCase =						self.unk_token_id
  								else:
  									_UpperCAmelCase =						ord(a_   ) + self._num_special_tokens
  								return token_id
  
  
  
  							def       _a   (      self ,						a_   )							->							Union[str, Any]:
  								if index in self.special_tokens_decoder:
  									_UpperCAmelCase =						self.special_tokens_decoder[index]
  								elif index in self.added_tokens_decoder:
  									_UpperCAmelCase =						self.added_tokens_decoder[index]
  								else:
  									_UpperCAmelCase =						chr(index - self._num_special_tokens   )
  								return token
  
  
  
  							def       _a   (      self ,						a_   )							->							Union[str, Any]:
  								_UpperCAmelCase =						B""
  								for token in tokens:
  									if token in self.special_tokens_decoder:
  										_UpperCAmelCase =						self.special_tokens_decoder[token].encode("utf-8"   )
  									elif token in self.added_tokens_decoder:
  										_UpperCAmelCase =						self.special_tokens_decoder[token].encode("utf-8"   )
  									elif token in self.special_tokens_encoder:
  										_UpperCAmelCase =						token.encode("utf-8"   )
  									elif token in self.added_tokens_encoder:
  										_UpperCAmelCase =						token.encode("utf-8"   )
  									else:
  										_UpperCAmelCase =						bytes([ord(a_   )]   )
  									bstring += tok_string
  								_UpperCAmelCase =						bstring.decode("utf-8" ,						errors="replace"   )
  								return string
  
  
  
  							def       _a   (      self ,						a_ ,						a_ = None   )							->							Tuple[str]:
  								return ()
  
  
  
  
  
style_context: 
  """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
  
  
  
  
  