






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













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()





