






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFXLMForMultipleChoice,
     TFXLMForQuestionAnsweringSimple,
     TFXLMForSequenceClassification,
     TFXLMForTokenClassification,
     TFXLMModel,
     TFXLMWithLMHeadModel,
     XLMConfig,
 )







class     UpperCAmelCase_		:



      """simple docstring"""
      def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       )       ->   str:
          UpperCamelCase :Tuple										=     parent
          UpperCamelCase :Dict										=     13
          UpperCamelCase :List[str]										=     7
          UpperCamelCase :Tuple										=     True
          UpperCamelCase :str										=     True
          UpperCamelCase :List[Any]										=     True
          UpperCamelCase :Tuple										=     True
          UpperCamelCase :List[str]										=     True
          UpperCamelCase :Optional[int]										=     False
          UpperCamelCase :List[Any]										=     False
          UpperCamelCase :int										=     False
          UpperCamelCase :List[str]										=     2
          UpperCamelCase :Dict										=     99
          UpperCamelCase :List[Any]										=     0
          UpperCamelCase :Tuple										=     32
          UpperCamelCase :List[Any]										=     2
          UpperCamelCase :Tuple										=     4
          UpperCamelCase :List[Any]										=     0.1
          UpperCamelCase :str										=     0.1
          UpperCamelCase :Tuple										=     512
          UpperCamelCase :Dict										=     16
          UpperCamelCase :Tuple										=     2
          UpperCamelCase :Optional[Any]										=     0.02
          UpperCamelCase :int										=     3
          UpperCamelCase :int										=     4
          UpperCamelCase :Any										=     '''last'''
          UpperCamelCase :Dict										=     True
          UpperCamelCase :Dict										=     None
          UpperCamelCase :str										=     0
      def 					UpperCAmelCase		(       self				)       ->   Dict:
          UpperCamelCase :Union[str, Any]										=     ids_tensor([self.batch_size, self.seq_length]					,       self.vocab_size				)
          UpperCamelCase :Any										=     random_attention_mask([self.batch_size, self.seq_length]					,       dtype=tf.floataa				)

          UpperCamelCase :List[str]										=     None
          if self.use_input_lengths:
              UpperCamelCase :Optional[int]										=     (
                  ids_tensor([self.batch_size]					,       vocab_size=2				) + self.seq_length - 2
              )  # small variation of seq_length

          UpperCamelCase :Union[str, Any]										=     None
          if self.use_token_type_ids:
              UpperCamelCase :Dict										=     ids_tensor([self.batch_size, self.seq_length]					,       self.n_langs				)

          UpperCamelCase :Union[str, Any]										=     None
          UpperCamelCase :Optional[int]										=     None
          UpperCamelCase :Dict										=     None
          if self.use_labels:
              UpperCamelCase :Any										=     ids_tensor([self.batch_size]					,       self.type_sequence_label_size				)
              UpperCamelCase :Optional[int]										=     ids_tensor([self.batch_size, self.seq_length]					,       self.num_labels				)
              UpperCamelCase :Union[str, Any]										=     ids_tensor([self.batch_size]					,       2					,       dtype=tf.floataa				)
              UpperCamelCase :List[Any]										=     ids_tensor([self.batch_size]					,       self.num_choices				)

          UpperCamelCase :Tuple										=     XLMConfig(
              vocab_size=self.vocab_size					,       n_special=self.n_special					,       emb_dim=self.hidden_size					,       n_layers=self.num_hidden_layers					,       n_heads=self.num_attention_heads					,       dropout=self.hidden_dropout_prob					,       attention_dropout=self.attention_probs_dropout_prob					,       gelu_activation=self.gelu_activation					,       sinusoidal_embeddings=self.sinusoidal_embeddings					,       asm=self.asm					,       causal=self.causal					,       n_langs=self.n_langs					,       max_position_embeddings=self.max_position_embeddings					,       initializer_range=self.initializer_range					,       summary_type=self.summary_type					,       use_proj=self.use_proj					,       bos_token_id=self.bos_token_id					,       )

          return (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          )
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   Union[str, Any]:
          UpperCamelCase :Union[str, Any]										=     TFXLMModel(config=SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :Dict										=     {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
          UpperCamelCase :Optional[Any]										=     model(SCREAMING_SNAKE_CASE_				)

          UpperCamelCase :Optional[int]										=     [input_ids, input_mask]
          UpperCamelCase :str										=     model(SCREAMING_SNAKE_CASE_				)
          self.parent.assertEqual(result.last_hidden_state.shape					,       (self.batch_size, self.seq_length, self.hidden_size)				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   List[Any]:
          UpperCamelCase :Any										=     TFXLMWithLMHeadModel(SCREAMING_SNAKE_CASE_				)

          UpperCamelCase :Optional[int]										=     {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
          UpperCamelCase :Dict										=     model(SCREAMING_SNAKE_CASE_				)

          UpperCamelCase :Dict										=     outputs

          self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.seq_length, self.vocab_size)				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   Tuple:
          UpperCamelCase :Optional[int]										=     TFXLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE_				)

          UpperCamelCase :Optional[Any]										=     {'''input_ids''': input_ids, '''lengths''': input_lengths}

          UpperCamelCase :str										=     model(SCREAMING_SNAKE_CASE_				)

          self.parent.assertEqual(result.start_logits.shape					,       (self.batch_size, self.seq_length)				)
          self.parent.assertEqual(result.end_logits.shape					,       (self.batch_size, self.seq_length)				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   Optional[Any]:
          UpperCamelCase :Tuple										=     TFXLMForSequenceClassification(SCREAMING_SNAKE_CASE_				)

          UpperCamelCase :List[Any]										=     {'''input_ids''': input_ids, '''lengths''': input_lengths}

          UpperCamelCase :Optional[Any]										=     model(SCREAMING_SNAKE_CASE_				)

          self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.type_sequence_label_size)				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   List[str]:
          UpperCamelCase :Union[str, Any]										=     self.num_labels
          UpperCamelCase :Any										=     TFXLMForTokenClassification(config=SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :int										=     {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
          UpperCamelCase :int										=     model(SCREAMING_SNAKE_CASE_				)
          self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.seq_length, self.num_labels)				)
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   str:
          UpperCamelCase :Optional[Any]										=     self.num_choices
          UpperCamelCase :int										=     TFXLMForMultipleChoice(config=SCREAMING_SNAKE_CASE_				)
          UpperCamelCase :Tuple										=     tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_					,       1				)					,       (1, self.num_choices, 1)				)
          UpperCamelCase :int										=     tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_					,       1				)					,       (1, self.num_choices, 1)				)
          UpperCamelCase :Union[str, Any]										=     tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_					,       1				)					,       (1, self.num_choices, 1)				)
          UpperCamelCase :List[str]										=     {
              '''input_ids''': multiple_choice_inputs_ids,
              '''attention_mask''': multiple_choice_input_mask,
              '''token_type_ids''': multiple_choice_token_type_ids,
          }
          UpperCamelCase :Any										=     model(SCREAMING_SNAKE_CASE_				)
          self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.num_choices)				)




      def 					UpperCAmelCase		(       self				)       ->   Optional[int]:
          UpperCamelCase :Dict										=     self.prepare_config_and_inputs()
          (
              (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     (
              UpperCamelCase
          )							,     
          ) :Optional[int]										=     config_and_inputs
          UpperCamelCase :Dict										=     {
              '''input_ids''': input_ids,
              '''token_type_ids''': token_type_ids,
              '''langs''': token_type_ids,
              '''lengths''': input_lengths,
          }
          return config, inputs_dict







@require_tf
class     UpperCAmelCase_		(      lowercase,			lowercase,			unittest.TestCase				):



      """simple docstring"""
      UpperCamelCase_       :      int     =(
          (
              TFXLMModel,
              TFXLMWithLMHeadModel,
              TFXLMForSequenceClassification,
              TFXLMForQuestionAnsweringSimple,
              TFXLMForTokenClassification,
              TFXLMForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      UpperCamelCase_       :      Dict     =(
          (TFXLMWithLMHeadModel,) if is_tf_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      UpperCamelCase_       :      List[Any]     =(
          {
              'feature-extraction': TFXLMModel,
              'fill-mask': TFXLMWithLMHeadModel,
              'question-answering': TFXLMForQuestionAnsweringSimple,
              'text-classification': TFXLMForSequenceClassification,
              'text-generation': TFXLMWithLMHeadModel,
              'token-classification': TFXLMForTokenClassification,
              'zero-shot': TFXLMForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      UpperCamelCase_       :      List[str]     =False
      UpperCamelCase_       :      Union[str, Any]     =False
      def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Dict:
          if (
              pipeline_test_casse_name == "QAPipelineTests"
              and tokenizer_name is not None
              and not tokenizer_name.endswith('''Fast'''				)
          ):
              # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
              # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
              # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
              return True

          return False
      def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
          UpperCamelCase :Union[str, Any]										=     TFXLMModelTester(self				)
          UpperCamelCase :int										=     ConfigTester(self					,       config_class=SCREAMING_SNAKE_CASE_					,       emb_dim=37				)
      def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
          self.config_tester.run_common_tests()
      def 					UpperCAmelCase		(       self				)       ->   Any:
          UpperCamelCase :List[str]										=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   Any:
          UpperCamelCase :Tuple										=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   List[Any]:
          UpperCamelCase :int										=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   Union[str, Any]:
          UpperCamelCase :Union[str, Any]										=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   List[Any]:
          UpperCamelCase :Tuple										=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_token_classification(*SCREAMING_SNAKE_CASE_				)
      def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
          UpperCamelCase :Tuple										=     self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE_				)




      @slow
      def 					UpperCAmelCase		(       self				)       ->   Optional[Any]:
          for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase :List[str]										=     TFXLMModel.from_pretrained(SCREAMING_SNAKE_CASE_				)
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)







@require_tf
class     UpperCAmelCase_		(      unittest.TestCase				):



      """simple docstring"""
      @slow
      def 					UpperCAmelCase		(       self				)       ->   int:
          UpperCamelCase :Tuple										=     TFXLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''				)
          UpperCamelCase :Optional[int]										=     tf.convert_to_tensor([[14, 447]]					,       dtype=tf.intaa				)  # the president
          UpperCamelCase :Optional[int]										=     [
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
              14,
              447,
          ]  # the president the president the president the president the president the president the president the president the president the president
          # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
          UpperCamelCase :List[str]										=     model.generate(SCREAMING_SNAKE_CASE_					,       do_sample=SCREAMING_SNAKE_CASE_				)
          self.assertListEqual(output_ids[0].numpy().tolist()					,       SCREAMING_SNAKE_CASE_				)













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()





