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
  
  __snake_case							      =						logging.get_logger(__name__)
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			bytes  ,						SCREAMING_SNAKE_CASE__						:			int ):
      UpperCamelCase :Any										=     F'''{sampling_rate}'''
      UpperCamelCase :Dict										=     '''1'''
      UpperCamelCase :int										=     '''f32le'''
      UpperCamelCase :int										=     [
          '''ffmpeg''',
          '''-i''',
          '''pipe:0''',
          '''-ac''',
          ac,
          '''-ar''',
          ar,
          '''-f''',
          format_for_conversion,
          '''-hide_banner''',
          '''-loglevel''',
          '''quiet''',
          '''pipe:1''',
      ]
  
      try:
          UpperCamelCase :Optional[int]										=     subprocess.Popen(SCREAMING_SNAKE_CASE__  ,						stdin=subprocess.PIPE  ,						stdout=subprocess.PIPE )
      except FileNotFoundError:
          raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' )
      UpperCamelCase :int										=     ffmpeg_process.communicate(SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :int										=     output_stream[0]
  
      UpperCamelCase :Dict										=     np.frombuffer(SCREAMING_SNAKE_CASE__  ,						np.floataa )
      if audio.shape[0] == 0:
          raise ValueError('''Malformed soundfile''' )
      return audio
  
  
  
  
  
  
  
  @add_end_docstrings(lowercase				)
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        def __init__(       self					,       *SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)       ->   Optional[Any]:
            # Default, might be overriden by the model.config.
            UpperCamelCase :Any										=     5
            super().__init__(*SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
  
            if self.framework != "pt":
                raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''				)
  
            self.check_model_type(SCREAMING_SNAKE_CASE_				)
        def __call__(       self					,       SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_					,       )       ->   int:
            return super().__call__(SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_=None					,       **SCREAMING_SNAKE_CASE_				)       ->   List[str]:
            # No parameters on this pipeline right now
            UpperCamelCase :Optional[int]										=     {}
            if top_k is not None:
                if top_k > self.model.config.num_labels:
                    UpperCamelCase :List[str]										=     self.model.config.num_labels
                UpperCamelCase :Dict										=     top_k
            return {}, {}, postprocess_params
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   Tuple:
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_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
                    UpperCamelCase :List[str]										=     requests.get(SCREAMING_SNAKE_CASE_				).content
                else:
                    with open(SCREAMING_SNAKE_CASE_					,       '''rb'''				) as f:
                        UpperCamelCase :Any										=     f.read()
  
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :Dict										=     ffmpeg_read(SCREAMING_SNAKE_CASE_					,       self.feature_extractor.sampling_rate				)
  
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_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'''				)
  
                UpperCamelCase :Union[str, Any]										=     inputs.pop('''raw'''					,       SCREAMING_SNAKE_CASE_				)
                if _inputs is None:
                    # Remove path which will not be used from `datasets`.
                    inputs.pop('''path'''					,       SCREAMING_SNAKE_CASE_				)
                    UpperCamelCase :int										=     inputs.pop('''array'''					,       SCREAMING_SNAKE_CASE_				)
                UpperCamelCase :Tuple										=     inputs.pop('''sampling_rate'''				)
                UpperCamelCase :Tuple										=     _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 :int										=     F.resample(
                        torch.from_numpy(SCREAMING_SNAKE_CASE_				)					,       SCREAMING_SNAKE_CASE_					,       self.feature_extractor.sampling_rate				).numpy()
  
            if not isinstance(SCREAMING_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'''				)
  
            UpperCamelCase :Union[str, Any]										=     self.feature_extractor(
                SCREAMING_SNAKE_CASE_					,       sampling_rate=self.feature_extractor.sampling_rate					,       return_tensors='''pt'''				)
            return processed
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   List[Any]:
            UpperCamelCase :List[Any]										=     self.model(**SCREAMING_SNAKE_CASE_				)
            return model_outputs
  
  
  
  
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_=5				)       ->   Optional[int]:
            UpperCamelCase :List[str]										=     model_outputs.logits[0].softmax(-1				)
            UpperCamelCase							,     UpperCamelCase :str										=     probs.topk(SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :List[Any]										=     scores.tolist()
            UpperCamelCase :Union[str, Any]										=     ids.tolist()
  
            UpperCamelCase :List[str]										=     [{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)]
  
            return labels
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from collections.abc import Callable
  from math import log
  from os import name, system
  from random import gauss, seed
  from typing import TypeVar
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			float  ,						SCREAMING_SNAKE_CASE__						:			float  ,						SCREAMING_SNAKE_CASE__						:			int ):
      seed(1 )
      return [gauss(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ) for _ in range(SCREAMING_SNAKE_CASE__ )]
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			list ):
      return [k for k in range(SCREAMING_SNAKE_CASE__ ) for _ in range(instance_count[k] )]
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			list ):
      return sum(SCREAMING_SNAKE_CASE__ ) / instance_count
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			int ):
      return instance_count / total_count
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			int ):
      UpperCamelCase :Optional[Any]										=     []  # An empty list to store all squared differences
      # iterate over number of elements in items
      for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
          # for loop iterates over number of elements in inner layer of items
          for j in range(len(items[i] ) ):
              # appending squared differences to 'squared_diff' list
              squared_diff.append((items[i][j] - means[i]) ** 2 )
  
      # one divided by (the number of all instances - number of classes) multiplied by
      # sum of all squared differences
      UpperCamelCase :Tuple										=     len(SCREAMING_SNAKE_CASE__ )  # Number of classes in dataset
      return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE__ )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			float  ,						SCREAMING_SNAKE_CASE__						:			list ):
      UpperCamelCase :Dict										=     []
      # for loop iterates over number of elements in list
      for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
          # for loop iterates over number of inner items of each element
          for j in range(len(x_items[i] ) ):
              UpperCamelCase :List[Any]										=     []  # to store all discriminant values of each item as a list
              # for loop iterates over number of classes we have in our dataset
              for k in range(len(SCREAMING_SNAKE_CASE__ ) ):
                  # appending values of discriminants for each class to 'temp' list
                  temp.append(
                      x_items[i][j] * (means[k] / variance)
                      - (means[k] ** 2 / (2 * variance))
                      + log(probabilities[k] ) )
              # appending discriminant values of each item to 'results' list
              results.append(SCREAMING_SNAKE_CASE__ )
  
      return [result.index(max(SCREAMING_SNAKE_CASE__ ) ) for result in results]
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			list  ,						SCREAMING_SNAKE_CASE__						:			list ):
      UpperCamelCase :List[Any]										=     sum(1 for i, j in zip(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ) if i == j )
      # percentage of accuracy equals to number of correct predictions divided by number
      # of all data and multiplied by 100
      return (correct / len(SCREAMING_SNAKE_CASE__ )) * 100
  
  
  __snake_case							      =						TypeVar("""num""")
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Callable[[object], num]  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Callable[[num], bool] = lambda SCREAMING_SNAKE_CASE__ : True  ,						SCREAMING_SNAKE_CASE__						:			str | None = None  ,						):
      while True:
          try:
              UpperCamelCase :List[Any]										=     input_type(input(SCREAMING_SNAKE_CASE__ ).strip() or default )
              if condition(SCREAMING_SNAKE_CASE__ ):
                  return user_input
              else:
                  print(F'''{user_input}: {err_msg}''' )
                  continue
          except ValueError:
              print(
                  F'''{user_input}: Incorrect input type, expected {input_type.__name__!r}''' )
  
  
  def 						_A     (       ):
      while True:
          print(''' Linear Discriminant Analysis '''.center(50  ,						'''*''' ) )
          print('''*''' * 50  ,						'''\n''' )
          print('''First of all we should specify the number of classes that''' )
          print('''we want to generate as training dataset''' )
          # Trying to get number of classes
          UpperCamelCase :List[Any]										=     valid_input(
              input_type=SCREAMING_SNAKE_CASE__  ,						condition=lambda SCREAMING_SNAKE_CASE__ : x > 0  ,						input_msg='''Enter the number of classes (Data Groupings): '''  ,						err_msg='''Number of classes should be positive!'''  ,						)
  
          print('''-''' * 100 )
  
          # Trying to get the value of standard deviation
          UpperCamelCase :Any										=     valid_input(
              input_type=SCREAMING_SNAKE_CASE__  ,						condition=lambda SCREAMING_SNAKE_CASE__ : x >= 0  ,						input_msg=(
                  '''Enter the value of standard deviation'''
                  '''(Default value is 1.0 for all classes): '''
              )  ,						err_msg='''Standard deviation should not be negative!'''  ,						default='''1.0'''  ,						)
  
          print('''-''' * 100 )
  
          # Trying to get number of instances in classes and theirs means to generate
          # dataset
          UpperCamelCase :Tuple										=     []  # An empty list to store instance counts of classes in dataset
          for i in range(SCREAMING_SNAKE_CASE__ ):
              UpperCamelCase :List[Any]										=     valid_input(
                  input_type=SCREAMING_SNAKE_CASE__  ,						condition=lambda SCREAMING_SNAKE_CASE__ : x > 0  ,						input_msg=(F'''Enter The number of instances for class_{i+1}: ''')  ,						err_msg='''Number of instances should be positive!'''  ,						)
              counts.append(SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # An empty list to store values of user-entered means of classes
          UpperCamelCase :Union[str, Any]										=     []
          for a in range(SCREAMING_SNAKE_CASE__ ):
              UpperCamelCase :str										=     valid_input(
                  input_type=SCREAMING_SNAKE_CASE__  ,						input_msg=(F'''Enter the value of mean for class_{a+1}: ''')  ,						err_msg='''This is an invalid value.'''  ,						)
              user_means.append(SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          print('''Standard deviation: '''  ,						SCREAMING_SNAKE_CASE__ )
          # print out the number of instances in classes in separated line
          for i, count in enumerate(SCREAMING_SNAKE_CASE__  ,						1 ):
              print(F'''Number of instances in class_{i} is: {count}''' )
          print('''-''' * 100 )
  
          # print out mean values of classes separated line
          for i, user_mean in enumerate(SCREAMING_SNAKE_CASE__  ,						1 ):
              print(F'''Mean of class_{i} is: {user_mean}''' )
          print('''-''' * 100 )
  
          # Generating training dataset drawn from gaussian distribution
          UpperCamelCase :Tuple										=     [
              gaussian_distribution(user_means[j]  ,						SCREAMING_SNAKE_CASE__  ,						counts[j] )
              for j in range(SCREAMING_SNAKE_CASE__ )
          ]
          print('''Generated Normal Distribution: \n'''  ,						SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # Generating Ys to detecting corresponding classes
          UpperCamelCase :int										=     y_generator(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
          print('''Generated Corresponding Ys: \n'''  ,						SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # Calculating the value of actual mean for each class
          UpperCamelCase :Tuple										=     [calculate_mean(counts[k]  ,						x[k] ) for k in range(SCREAMING_SNAKE_CASE__ )]
          # for loop iterates over number of elements in 'actual_means' list and print
          # out them in separated line
          for i, actual_mean in enumerate(SCREAMING_SNAKE_CASE__  ,						1 ):
              print(F'''Actual(Real) mean of class_{i} is: {actual_mean}''' )
          print('''-''' * 100 )
  
          # Calculating the value of probabilities for each class
          UpperCamelCase :Dict										=     [
              calculate_probabilities(counts[i]  ,						sum(SCREAMING_SNAKE_CASE__ ) ) for i in range(SCREAMING_SNAKE_CASE__ )
          ]
  
          # for loop iterates over number of elements in 'probabilities' list and print
          # out them in separated line
          for i, probability in enumerate(SCREAMING_SNAKE_CASE__  ,						1 ):
              print(F'''Probability of class_{i} is: {probability}''' )
          print('''-''' * 100 )
  
          # Calculating the values of variance for each class
          UpperCamelCase :int										=     calculate_variance(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						sum(SCREAMING_SNAKE_CASE__ ) )
          print('''Variance: '''  ,						SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # Predicting Y values
          # storing predicted Y values in 'pre_indexes' variable
          UpperCamelCase :Any										=     predict_y_values(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )
          print('''-''' * 100 )
  
          # Calculating Accuracy of the model
          print(F'''Accuracy: {accuracy(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ )}''' )
          print('''-''' * 100 )
          print(''' DONE '''.center(100  ,						'''+''' ) )
  
          if input('''Press any key to restart or \'q\' for quit: ''' ).strip().lower() == "q":
              print('''\n''' + '''GoodBye!'''.center(100  ,						'''-''' ) + '''\n''' )
              break
          system('''cls''' if name == '''nt''' else '''clear''' )  # noqa: S605
  
  
  if __name__ == "__main__":
   main()
  
  
  
  
  
  