code: 
  
  
  
  
  
  
  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_				)
  
  
  
  
  
  
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()
  
  
  
  
  
  