






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A:   List[Any]						=					{
    "auto_factory": ["get_values"],
    "configuration_auto": ["ALL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CONFIG_MAPPING", "MODEL_NAMES_MAPPING", "AutoConfig"],
    "feature_extraction_auto": ["FEATURE_EXTRACTOR_MAPPING", "AutoFeatureExtractor"],
    "image_processing_auto": ["IMAGE_PROCESSOR_MAPPING", "AutoImageProcessor"],
    "processing_auto": ["PROCESSOR_MAPPING", "AutoProcessor"],
    "tokenization_auto": ["TOKENIZER_MAPPING", "AutoTokenizer"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:   List[Any]						=					[
						    "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
						    "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING",
						    "MODEL_FOR_AUDIO_XVECTOR_MAPPING",
						    "MODEL_FOR_BACKBONE_MAPPING",
						    "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING",
						    "MODEL_FOR_CAUSAL_LM_MAPPING",
						    "MODEL_FOR_CTC_MAPPING",
						    "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
						    "MODEL_FOR_DEPTH_ESTIMATION_MAPPING",
						    "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
						    "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING",
						    "MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING",
						    "MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
						    "MODEL_FOR_MASKED_LM_MAPPING",
						    "MODEL_FOR_MASK_GENERATION_MAPPING",
						    "MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
						    "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
						    "MODEL_FOR_OBJECT_DETECTION_MAPPING",
						    "MODEL_FOR_PRETRAINING_MAPPING",
						    "MODEL_FOR_QUESTION_ANSWERING_MAPPING",
						    "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
						    "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
						    "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
						    "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
						    "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
						    "MODEL_FOR_TEXT_ENCODING_MAPPING",
						    "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
						    "MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING",
						    "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING",
						    "MODEL_FOR_VISION_2_SEQ_MAPPING",
						    "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING",
						    "MODEL_MAPPING",
						    "MODEL_WITH_LM_HEAD_MAPPING",
						    "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
						    "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING",
						    "AutoModel",
						    "AutoBackbone",
						    "AutoModelForAudioClassification",
						    "AutoModelForAudioFrameClassification",
						    "AutoModelForAudioXVector",
						    "AutoModelForCausalLM",
						    "AutoModelForCTC",
						    "AutoModelForDepthEstimation",
						    "AutoModelForImageClassification",
						    "AutoModelForImageSegmentation",
						    "AutoModelForInstanceSegmentation",
						    "AutoModelForMaskGeneration",
						    "AutoModelForTextEncoding",
						    "AutoModelForMaskedImageModeling",
						    "AutoModelForMaskedLM",
						    "AutoModelForMultipleChoice",
						    "AutoModelForNextSentencePrediction",
						    "AutoModelForObjectDetection",
						    "AutoModelForPreTraining",
						    "AutoModelForQuestionAnswering",
						    "AutoModelForSemanticSegmentation",
						    "AutoModelForSeq2SeqLM",
						    "AutoModelForSequenceClassification",
						    "AutoModelForSpeechSeq2Seq",
						    "AutoModelForTableQuestionAnswering",
						    "AutoModelForTokenClassification",
						    "AutoModelForUniversalSegmentation",
						    "AutoModelForVideoClassification",
						    "AutoModelForVision2Seq",
						    "AutoModelForVisualQuestionAnswering",
						    "AutoModelForDocumentQuestionAnswering",
						    "AutoModelWithLMHead",
						    "AutoModelForZeroShotImageClassification",
						    "AutoModelForZeroShotObjectDetection",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:   List[Any]						=					[
						    "TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
						    "TF_MODEL_FOR_CAUSAL_LM_MAPPING",
						    "TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
						    "TF_MODEL_FOR_MASK_GENERATION_MAPPING",
						    "TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING",
						    "TF_MODEL_FOR_MASKED_LM_MAPPING",
						    "TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
						    "TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
						    "TF_MODEL_FOR_PRETRAINING_MAPPING",
						    "TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
						    "TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
						    "TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
						    "TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
						    "TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
						    "TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
						    "TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING",
						    "TF_MODEL_FOR_TEXT_ENCODING_MAPPING",
						    "TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
						    "TF_MODEL_FOR_VISION_2_SEQ_MAPPING",
						    "TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING",
						    "TF_MODEL_MAPPING",
						    "TF_MODEL_WITH_LM_HEAD_MAPPING",
						    "TFAutoModel",
						    "TFAutoModelForAudioClassification",
						    "TFAutoModelForCausalLM",
						    "TFAutoModelForImageClassification",
						    "TFAutoModelForMaskedImageModeling",
						    "TFAutoModelForMaskedLM",
						    "TFAutoModelForMaskGeneration",
						    "TFAutoModelForMultipleChoice",
						    "TFAutoModelForNextSentencePrediction",
						    "TFAutoModelForPreTraining",
						    "TFAutoModelForDocumentQuestionAnswering",
						    "TFAutoModelForQuestionAnswering",
						    "TFAutoModelForSemanticSegmentation",
						    "TFAutoModelForSeq2SeqLM",
						    "TFAutoModelForSequenceClassification",
						    "TFAutoModelForSpeechSeq2Seq",
						    "TFAutoModelForTableQuestionAnswering",
						    "TFAutoModelForTextEncoding",
						    "TFAutoModelForTokenClassification",
						    "TFAutoModelForVision2Seq",
						    "TFAutoModelForZeroShotImageClassification",
						    "TFAutoModelWithLMHead",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:   int						=					[
						    "FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
						    "FLAX_MODEL_FOR_CAUSAL_LM_MAPPING",
						    "FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING",
						    "FLAX_MODEL_FOR_MASKED_LM_MAPPING",
						    "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
						    "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
						    "FLAX_MODEL_FOR_PRETRAINING_MAPPING",
						    "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
						    "FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
						    "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
						    "FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING",
						    "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
						    "FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING",
						    "FLAX_MODEL_MAPPING",
						    "FlaxAutoModel",
						    "FlaxAutoModelForCausalLM",
						    "FlaxAutoModelForImageClassification",
						    "FlaxAutoModelForMaskedLM",
						    "FlaxAutoModelForMultipleChoice",
						    "FlaxAutoModelForNextSentencePrediction",
						    "FlaxAutoModelForPreTraining",
						    "FlaxAutoModelForQuestionAnswering",
						    "FlaxAutoModelForSeq2SeqLM",
						    "FlaxAutoModelForSequenceClassification",
						    "FlaxAutoModelForSpeechSeq2Seq",
						    "FlaxAutoModelForTokenClassification",
						    "FlaxAutoModelForVision2Seq",
						]


if TYPE_CHECKING:
						from .auto_factory import get_values
						from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
						from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
						from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
						from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
						from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_auto import (
												    MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
												    MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
												    MODEL_FOR_AUDIO_XVECTOR_MAPPING,
												    MODEL_FOR_BACKBONE_MAPPING,
												    MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
												    MODEL_FOR_CAUSAL_LM_MAPPING,
												    MODEL_FOR_CTC_MAPPING,
												    MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
												    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
												    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
												    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
												    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
												    MODEL_FOR_MASK_GENERATION_MAPPING,
												    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
												    MODEL_FOR_MASKED_LM_MAPPING,
												    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
												    MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
												    MODEL_FOR_OBJECT_DETECTION_MAPPING,
												    MODEL_FOR_PRETRAINING_MAPPING,
												    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
												    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
												    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
												    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
												    MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
												    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
												    MODEL_FOR_TEXT_ENCODING_MAPPING,
												    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
												    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
												    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
												    MODEL_FOR_VISION_2_SEQ_MAPPING,
												    MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
												    MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
												    MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
												    MODEL_MAPPING,
												    MODEL_WITH_LM_HEAD_MAPPING,
												    AutoBackbone,
												    AutoModel,
												    AutoModelForAudioClassification,
												    AutoModelForAudioFrameClassification,
												    AutoModelForAudioXVector,
												    AutoModelForCausalLM,
												    AutoModelForCTC,
												    AutoModelForDepthEstimation,
												    AutoModelForDocumentQuestionAnswering,
												    AutoModelForImageClassification,
												    AutoModelForImageSegmentation,
												    AutoModelForInstanceSegmentation,
												    AutoModelForMaskedImageModeling,
												    AutoModelForMaskedLM,
												    AutoModelForMaskGeneration,
												    AutoModelForMultipleChoice,
												    AutoModelForNextSentencePrediction,
												    AutoModelForObjectDetection,
												    AutoModelForPreTraining,
												    AutoModelForQuestionAnswering,
												    AutoModelForSemanticSegmentation,
												    AutoModelForSeqaSeqLM,
												    AutoModelForSequenceClassification,
												    AutoModelForSpeechSeqaSeq,
												    AutoModelForTableQuestionAnswering,
												    AutoModelForTextEncoding,
												    AutoModelForTokenClassification,
												    AutoModelForUniversalSegmentation,
												    AutoModelForVideoClassification,
												    AutoModelForVisionaSeq,
												    AutoModelForVisualQuestionAnswering,
												    AutoModelForZeroShotImageClassification,
												    AutoModelForZeroShotObjectDetection,
												    AutoModelWithLMHead,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_auto import (
												    TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
												    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
												    TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
												    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
												    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
												    TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
												    TF_MODEL_FOR_MASKED_LM_MAPPING,
												    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
												    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
												    TF_MODEL_FOR_PRETRAINING_MAPPING,
												    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
												    TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
												    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
												    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
												    TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
												    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
												    TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
												    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
												    TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
												    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
												    TF_MODEL_MAPPING,
												    TF_MODEL_WITH_LM_HEAD_MAPPING,
												    TFAutoModel,
												    TFAutoModelForAudioClassification,
												    TFAutoModelForCausalLM,
												    TFAutoModelForDocumentQuestionAnswering,
												    TFAutoModelForImageClassification,
												    TFAutoModelForMaskedImageModeling,
												    TFAutoModelForMaskedLM,
												    TFAutoModelForMaskGeneration,
												    TFAutoModelForMultipleChoice,
												    TFAutoModelForNextSentencePrediction,
												    TFAutoModelForPreTraining,
												    TFAutoModelForQuestionAnswering,
												    TFAutoModelForSemanticSegmentation,
												    TFAutoModelForSeqaSeqLM,
												    TFAutoModelForSequenceClassification,
												    TFAutoModelForSpeechSeqaSeq,
												    TFAutoModelForTableQuestionAnswering,
												    TFAutoModelForTextEncoding,
												    TFAutoModelForTokenClassification,
												    TFAutoModelForVisionaSeq,
												    TFAutoModelForZeroShotImageClassification,
												    TFAutoModelWithLMHead,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_auto import (
												    FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
												    FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
												    FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
												    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
												    FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
												    FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
												    FLAX_MODEL_FOR_PRETRAINING_MAPPING,
												    FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
												    FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
												    FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
												    FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
												    FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
												    FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
												    FLAX_MODEL_MAPPING,
												    FlaxAutoModel,
												    FlaxAutoModelForCausalLM,
												    FlaxAutoModelForImageClassification,
												    FlaxAutoModelForMaskedLM,
												    FlaxAutoModelForMultipleChoice,
												    FlaxAutoModelForNextSentencePrediction,
												    FlaxAutoModelForPreTraining,
												    FlaxAutoModelForQuestionAnswering,
												    FlaxAutoModelForSeqaSeqLM,
												    FlaxAutoModelForSequenceClassification,
												    FlaxAutoModelForSpeechSeqaSeq,
												    FlaxAutoModelForTokenClassification,
												    FlaxAutoModelForVisionaSeq,
												)

else:
						import sys

						A:   Any						=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








'''simple docstring'''

from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar






def 						_UpperCAmelCase     (	a		:						float    ,      a		:						float    ,      a		:						int				)      ->      list:





						"""simple docstring"""
						seed(1				)
						return [gauss(a    ,      a				) for _ in range(a				)]






def 						_UpperCAmelCase     (	a		:						int    ,      a		:						list				)      ->      list:





						"""simple docstring"""
						return [k for k in range(a				) for _ in range(instance_count[k]				)]






def 						_UpperCAmelCase     (	a		:						int    ,      a		:						list				)      ->      float:





						"""simple docstring"""
						return sum(a				) / instance_count






def 						_UpperCAmelCase     (	a		:						int    ,      a		:						int				)      ->      float:





						"""simple docstring"""
						return instance_count / total_count






def 						_UpperCAmelCase     (	a		:						list    ,      a		:						list    ,      a		:						int				)      ->      float:





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






def 						_UpperCAmelCase     (	a		:						list    ,      a		:						list    ,      a		:						float    ,      a		:						list				)      ->      list:





						"""simple docstring"""
						lowercase_   : str   	=      []
						# for loop iterates over number of elements in list
						for i in range(len(a				)				):
												# for loop iterates over number of inner items of each element
												for j in range(len(x_items[i]				)				):
																		lowercase_   : 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(a				)				):
																								# 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(a				)

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






def 						_UpperCAmelCase     (	a		:						list    ,      a		:						list				)      ->      float:





						"""simple docstring"""
						lowercase_   : Optional[Any]   	=      sum(1 for i, j in zip(a    ,      a				) 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(a				)) * 1_0_0


A:   Union[str, Any]						=					TypeVar("num")






def 						_UpperCAmelCase     (	a		:						Callable[[object], num]    ,      a		:						str    ,      a		:						str    ,      a		:						Callable[[num], bool] = lambda a				: True    ,      a		:						str | None = None    ,      )      ->      num:





						"""simple docstring"""
						while True:
												try:
																		lowercase_   : int   	=      input_type(input(a				).strip() or default				)
																		if condition(a				):
																								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 						_UpperCAmelCase     (	)      ->      List[str]:





						"""simple docstring"""
						while True:
												print(' Linear Discriminant Analysis '.center(5_0    ,      '*'				)				)
												print('*' * 5_0    ,      '\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
												lowercase_   : Dict   	=      valid_input(
												    input_type=a    ,      condition=lambda a				: x > 0    ,      input_msg='Enter the number of classes (Data Groupings): '    ,      err_msg='Number of classes should be positive!'    ,      )

												print('-' * 1_0_0				)

												# Trying to get the value of standard deviation
												lowercase_   : List[Any]   	=      valid_input(
												    input_type=a    ,      condition=lambda a				: 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('-' * 1_0_0				)

												# Trying to get number of instances in classes and theirs means to generate
												# dataset
												lowercase_   : Any   	=      []  # An empty list to store instance counts of classes in dataset
												for i in range(a				):
																		lowercase_   : Union[str, Any]   	=      valid_input(
																		    input_type=a    ,      condition=lambda a				: x > 0    ,      input_msg=(f"Enter The number of instances for class_{i+1}: ")    ,      err_msg='Number of instances should be positive!'    ,      )
																		counts.append(a				)
												print('-' * 1_0_0				)

												# An empty list to store values of user-entered means of classes
												lowercase_   : int   	=      []
												for a in range(a				):
																		lowercase_   : Union[str, Any]   	=      valid_input(
																		    input_type=a    ,      input_msg=(f"Enter the value of mean for class_{a+1}: ")    ,      err_msg='This is an invalid value.'    ,      )
																		user_means.append(a				)
												print('-' * 1_0_0				)

												print('Standard deviation: '    ,      a				)
												# print out the number of instances in classes in separated line
												for i, count in enumerate(a    ,      1				):
																		print(f"Number of instances in class_{i} is: {count}"				)
												print('-' * 1_0_0				)

												# print out mean values of classes separated line
												for i, user_mean in enumerate(a    ,      1				):
																		print(f"Mean of class_{i} is: {user_mean}"				)
												print('-' * 1_0_0				)

												# Generating training dataset drawn from gaussian distribution
												lowercase_   : Optional[int]   	=      [
												    gaussian_distribution(user_means[j]    ,      a    ,      counts[j]				)
												    for j in range(a				)
												]
												print('Generated Normal Distribution: \n'    ,      a				)
												print('-' * 1_0_0				)

												# Generating Ys to detecting corresponding classes
												lowercase_   : Optional[int]   	=      y_generator(a    ,      a				)
												print('Generated Corresponding Ys: \n'    ,      a				)
												print('-' * 1_0_0				)

												# Calculating the value of actual mean for each class
												lowercase_   : str   	=      [calculate_mean(counts[k]    ,      x[k]				) for k in range(a				)]
												# for loop iterates over number of elements in 'actual_means' list and print
												# out them in separated line
												for i, actual_mean in enumerate(a    ,      1				):
																		print(f"Actual(Real) mean of class_{i} is: {actual_mean}"				)
												print('-' * 1_0_0				)

												# Calculating the value of probabilities for each class
												lowercase_   : Union[str, Any]   	=      [
												    calculate_probabilities(counts[i]    ,      sum(a				)				) for i in range(a				)
												]

												# for loop iterates over number of elements in 'probabilities' list and print
												# out them in separated line
												for i, probability in enumerate(a    ,      1				):
																		print(f"Probability of class_{i} is: {probability}"				)
												print('-' * 1_0_0				)

												# Calculating the values of variance for each class
												lowercase_   : List[str]   	=      calculate_variance(a    ,      a    ,      sum(a				)				)
												print('Variance: '    ,      a				)
												print('-' * 1_0_0				)

												# Predicting Y values
												# storing predicted Y values in 'pre_indexes' variable
												lowercase_   : Tuple   	=      predict_y_values(a    ,      a    ,      a    ,      a				)
												print('-' * 1_0_0				)

												# Calculating Accuracy of the model
												print(f"Accuracy: {accuracy(a    ,      a				)}"				)
												print('-' * 1_0_0				)
												print(' DONE '.center(1_0_0    ,      '+'				)				)

												if input('Press any key to restart or \'q\' for quit: '				).strip().lower() == "q":
																		print('\n' + 'GoodBye!'.center(1_0_0    ,      '-'				) + '\n'				)
																		break
												system('cls' if name == 'nt' else 'clear'				)  # noqa: S605


if __name__ == "__main__":
						main()
