






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					(       UpperCamelCase_   :			float      ,				UpperCamelCase_   :			float      ,				UpperCamelCase_   :			int				)	->				list:
						"""simple docstring"""







						seed(1				)
						return [gauss(UpperCamelCase_      ,				UpperCamelCase_				) for _ in range(UpperCamelCase_				)]


def     _a					(       UpperCamelCase_   :			int      ,				UpperCamelCase_   :			list				)	->				list:
						"""simple docstring"""







						return [k for k in range(UpperCamelCase_				) for _ in range(instance_count[k]				)]


def     _a					(       UpperCamelCase_   :			int      ,				UpperCamelCase_   :			list				)	->				float:
						"""simple docstring"""







						return sum(UpperCamelCase_				) / instance_count


def     _a					(       UpperCamelCase_   :			int      ,				UpperCamelCase_   :			int				)	->				float:
						"""simple docstring"""







						return instance_count / total_count


def     _a					(       UpperCamelCase_   :			list      ,				UpperCamelCase_   :			list      ,				UpperCamelCase_   :			int				)	->				float:
						"""simple docstring"""







						lowerCAmelCase__	 =  []  # An empty list to store all squared differences
						# iterate over number of elements in items
						for i in range(len(UpperCamelCase_				)				):
												# 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
						lowerCAmelCase__	 =  len(UpperCamelCase_				)  # Number of classes in dataset
						return 1 / (total_count - n_classes) * sum(UpperCamelCase_				)


def     _a					(       UpperCamelCase_   :			list      ,				UpperCamelCase_   :			list      ,				UpperCamelCase_   :			float      ,				UpperCamelCase_   :			list				)	->				list:
						"""simple docstring"""







						lowerCAmelCase__	 =  []
						# for loop iterates over number of elements in list
						for i in range(len(UpperCamelCase_				)				):
												# for loop iterates over number of inner items of each element
												for j in range(len(x_items[i]				)				):
																		lowerCAmelCase__	 =  []  # 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(UpperCamelCase_				)				):
																								# 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(UpperCamelCase_				)

						return [result.index(max(UpperCamelCase_				)				) for result in results]


def     _a					(       UpperCamelCase_   :			list      ,				UpperCamelCase_   :			list				)	->				float:
						"""simple docstring"""







						lowerCAmelCase__	 =  sum(1 for i, j in zip(UpperCamelCase_      ,				UpperCamelCase_				) 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(UpperCamelCase_				)) * 100


a_							=      TypeVar('''num''')


def     _a					(       UpperCamelCase_   :			Callable[[object], num]      ,				UpperCamelCase_   :			str      ,				UpperCamelCase_   :			str      ,				UpperCamelCase_   :			Callable[[num], bool] = lambda UpperCamelCase_				: True      ,				UpperCamelCase_   :			str | None = None      ,				)	->				num:
						"""simple docstring"""







						while True:
												try:
																		lowerCAmelCase__	 =  input_type(input(UpperCamelCase_				).strip() or default				)
																		if condition(UpperCamelCase_				):
																								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					(       )	->				str:
						"""simple docstring"""







						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
												lowerCAmelCase__	 =  valid_input(
												    input_type=UpperCamelCase_      ,				condition=lambda UpperCamelCase_				: 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
												lowerCAmelCase__	 =  valid_input(
												    input_type=UpperCamelCase_      ,				condition=lambda UpperCamelCase_				: 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
												lowerCAmelCase__	 =  []  # An empty list to store instance counts of classes in dataset
												for i in range(UpperCamelCase_				):
																		lowerCAmelCase__	 =  valid_input(
																		    input_type=UpperCamelCase_      ,				condition=lambda UpperCamelCase_				: x > 0      ,				input_msg=(F"Enter The number of instances for class_{i+1}: ")      ,				err_msg="Number of instances should be positive!"      ,				)
																		counts.append(UpperCamelCase_				)
												print("-" * 100				)

												# An empty list to store values of user-entered means of classes
												lowerCAmelCase__	 =  []
												for a in range(UpperCamelCase_				):
																		lowerCAmelCase__	 =  valid_input(
																		    input_type=UpperCamelCase_      ,				input_msg=(F"Enter the value of mean for class_{a+1}: ")      ,				err_msg="This is an invalid value."      ,				)
																		user_means.append(UpperCamelCase_				)
												print("-" * 100				)

												print("Standard deviation: "      ,				UpperCamelCase_				)
												# print out the number of instances in classes in separated line
												for i, count in enumerate(UpperCamelCase_      ,				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(UpperCamelCase_      ,				1				):
																		print(F"Mean of class_{i} is: {user_mean}"				)
												print("-" * 100				)

												# Generating training dataset drawn from gaussian distribution
												lowerCAmelCase__	 =  [
												    gaussian_distribution(user_means[j]      ,				UpperCamelCase_      ,				counts[j]				)
												    for j in range(UpperCamelCase_				)
												]
												print("Generated Normal Distribution: \n"      ,				UpperCamelCase_				)
												print("-" * 100				)

												# Generating Ys to detecting corresponding classes
												lowerCAmelCase__	 =  y_generator(UpperCamelCase_      ,				UpperCamelCase_				)
												print("Generated Corresponding Ys: \n"      ,				UpperCamelCase_				)
												print("-" * 100				)

												# Calculating the value of actual mean for each class
												lowerCAmelCase__	 =  [calculate_mean(counts[k]      ,				x[k]				) for k in range(UpperCamelCase_				)]
												# for loop iterates over number of elements in 'actual_means' list and print
												# out them in separated line
												for i, actual_mean in enumerate(UpperCamelCase_      ,				1				):
																		print(F"Actual(Real) mean of class_{i} is: {actual_mean}"				)
												print("-" * 100				)

												# Calculating the value of probabilities for each class
												lowerCAmelCase__	 =  [
												    calculate_probabilities(counts[i]      ,				sum(UpperCamelCase_				)				) for i in range(UpperCamelCase_				)
												]

												# for loop iterates over number of elements in 'probabilities' list and print
												# out them in separated line
												for i, probability in enumerate(UpperCamelCase_      ,				1				):
																		print(F"Probability of class_{i} is: {probability}"				)
												print("-" * 100				)

												# Calculating the values of variance for each class
												lowerCAmelCase__	 =  calculate_variance(UpperCamelCase_      ,				UpperCamelCase_      ,				sum(UpperCamelCase_				)				)
												print("Variance: "      ,				UpperCamelCase_				)
												print("-" * 100				)

												# Predicting Y values
												# storing predicted Y values in 'pre_indexes' variable
												lowerCAmelCase__	 =  predict_y_values(UpperCamelCase_      ,				UpperCamelCase_      ,				UpperCamelCase_      ,				UpperCamelCase_				)
												print("-" * 100				)

												# Calculating Accuracy of the model
												print(F"Accuracy: {accuracy(UpperCamelCase_      ,				UpperCamelCase_				)}"				)
												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()














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

a_							=      logging.get_logger(__name__)


def     _a					(       UpperCamelCase_   :			bytes      ,				UpperCamelCase_   :			int				)	->				np.array:
						"""simple docstring"""







						lowerCAmelCase__	 =  F"{sampling_rate}"
						lowerCAmelCase__	 =  "1"
						lowerCAmelCase__	 =  "f32le"
						lowerCAmelCase__	 =  [
						    "ffmpeg",
						    "-i",
						    "pipe:0",
						    "-ac",
						    ac,
						    "-ar",
						    ar,
						    "-f",
						    format_for_conversion,
						    "-hide_banner",
						    "-loglevel",
						    "quiet",
						    "pipe:1",
						]

						try:
												lowerCAmelCase__	 =  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"				)
						lowerCAmelCase__	 =  ffmpeg_process.communicate(UpperCamelCase_				)
						lowerCAmelCase__	 =  output_stream[0]

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





@add_end_docstrings(_UpperCAmelCase		)
class        lowercase__       (     _UpperCAmelCase		):

	def __init__(							self						,      *__UpperCAmelCase						,      **__UpperCAmelCase    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  5
							super().__init__(*__UpperCAmelCase						,      **__UpperCAmelCase    )

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

							self.check_model_type(__UpperCAmelCase    )

	def __call__(							self						,      __UpperCAmelCase						,      **__UpperCAmelCase						,      )->   List[str]:







							'''simple docstring'''





							return super().__call__(__UpperCAmelCase						,      **__UpperCAmelCase    )

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase=None						,      **__UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  {}
							if top_k is not None:
													if top_k > self.model.config.num_labels:
																			lowerCAmelCase__	 =  self.model.config.num_labels
													lowerCAmelCase__	 =  top_k
							return {}, {}, postprocess_params

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   str:







							'''simple docstring'''





							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													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
																			lowerCAmelCase__	 =  requests.get(__UpperCAmelCase    ).content
													else:
																			with open(__UpperCAmelCase						,      "rb"    ) as f:
																									lowerCAmelCase__	 =  f.read()

							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													lowerCAmelCase__	 =  ffmpeg_read(__UpperCAmelCase						,      self.feature_extractor.sampling_rate    )

							if isinstance(__UpperCAmelCase						,      __UpperCAmelCase    ):
													# 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"    )

													lowerCAmelCase__	 =  inputs.pop("raw"						,      __UpperCAmelCase    )
													if _inputs is None:
																			# Remove path which will not be used from `datasets`.
																			inputs.pop("path"						,      __UpperCAmelCase    )
																			lowerCAmelCase__	 =  inputs.pop("array"						,      __UpperCAmelCase    )
													lowerCAmelCase__	 =  inputs.pop("sampling_rate"    )
													lowerCAmelCase__	 =  _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`."    )

																			lowerCAmelCase__	 =  F.resample(
																			    torch.from_numpy(__UpperCAmelCase    )						,      __UpperCAmelCase						,      self.feature_extractor.sampling_rate    ).numpy()

							if not isinstance(__UpperCAmelCase						,      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"    )

							lowerCAmelCase__	 =  self.feature_extractor(
							    __UpperCAmelCase						,      sampling_rate=self.feature_extractor.sampling_rate						,      return_tensors="pt"    )
							return processed

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Tuple:







							'''simple docstring'''





							lowerCAmelCase__	 =  self.model(**__UpperCAmelCase    )
							return model_outputs


	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase=5    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  model_outputs.logits[0].softmax(-1    )
							lowerCAmelCase__ ,							lowerCAmelCase__	 =  probs.topk(__UpperCAmelCase    )

							lowerCAmelCase__	 =  scores.tolist()
							lowerCAmelCase__	 =  ids.tolist()

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

							return labels






