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







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					list:
       seed(1       )
       return [gauss(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       ) for _ in range(SCREAMING_SNAKE_CASE__       )]







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					list:
       return [k for k in range(SCREAMING_SNAKE_CASE__       ) for _ in range(instance_count[k]       )]







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					float:
       return sum(SCREAMING_SNAKE_CASE__       ) / instance_count







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					float:
       return instance_count / total_count







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					float:
       lowercase							:  Optional[int] 			=   []  # 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
       lowercase							:  Optional[Any] 			=   len(SCREAMING_SNAKE_CASE__       )  # Number of classes in dataset
       return 1 / (total_count - n_classes) * sum(SCREAMING_SNAKE_CASE__       )







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					list:
       lowercase							:  Tuple 			=   []
       # 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]       )       ):
                     lowercase							:  int 			=   []  # 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 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					float:
       lowercase							:  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


lowercase						:      List[str]							=							TypeVar("""num""")







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__ = lambda SCREAMING_SNAKE_CASE__       : True      ,			SCREAMING_SNAKE_CASE__ = None      ,			)				->					num:
       while True:
              try:
                     lowercase							:  Tuple 			=   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 		_snake_case(						)				->					Tuple:
       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
              lowercase							:  Tuple 			=   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
              lowercase							:  int 			=   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
              lowercase							:  Optional[int] 			=   []  # An empty list to store instance counts of classes in dataset
              for i in range(SCREAMING_SNAKE_CASE__       ):
                     lowercase							:  List[str] 			=   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
              lowercase							:  List[str] 			=   []
              for a in range(SCREAMING_SNAKE_CASE__       ):
                     lowercase							:  Optional[int] 			=   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
              lowercase							:  Dict 			=   [
                  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
              lowercase							:  List[str] 			=   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
              lowercase							:  List[Any] 			=   [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
              lowercase							:  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
              lowercase							:  Dict 			=   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
              lowercase							:  Tuple 			=   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()


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
  import torch

  from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer

@require_torch
class __snake_case	(   unittest.TestCase  ):
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  int 			=   XLMProphetNetForConditionalGeneration.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""   )
           model.to(snake_case   )

           # encoder-decoder outputs
           lowercase							:  List[str] 			=   torch.tensor([[17, 96208, 103471, 2]]   ).to(snake_case   )
           lowercase							:  List[str] 			=   torch.tensor(
               [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]   ).to(snake_case   )
           lowercase							:  str 			=   model(
               input_ids=snake_case	,attention_mask=snake_case	,encoder_outputs=snake_case	,decoder_input_ids=snake_case   )
           lowercase							:  Tuple 			=   output[0]
           lowercase							:  int 			=   torch.Size((1, 14, 250012)   )
           self.assertEqual(output_predited_logis.shape	,snake_case   )
           lowercase							:  Union[str, Any] 			=   torch.tensor(
               [[[-6.3_986, -8.2_391, 12.5_189], [-6.3_289, -8.0_864, 12.6_211], [-6.2_418, -8.0_445, 12.7_968]]]   ).to(snake_case   )
           self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]	,snake_case	,atol=1e-4   )   )

           # encoder outputs
           lowercase							:  Tuple 			=   model.prophetnet.encoder(snake_case   )[0]
           lowercase							:  Any 			=   torch.tensor(
               [[[-1.4_260, -0.7_628, 0.8_453], [-1.4_719, -0.1_391, 0.7_807], [-1.7_678, 0.0_114, 0.4_646]]]   ).to(snake_case   )
           lowercase							:  Optional[Any] 			=   torch.Size((1, 4, 1024)   )
           self.assertEqual(encoder_outputs.shape	,snake_case   )
           self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3]	,snake_case	,atol=1e-4   )   )

           # decoder outputs
           lowercase							:  Tuple 			=   model.prophetnet.decoder(
               snake_case	,encoder_hidden_states=snake_case	,)
           lowercase							:  Optional[int] 			=   decoder_outputs[1].view(1	,model.config.ngram	,14	,-1   )
           lowercase							:  Optional[int] 			=   model.lm_head(snake_case   )
           lowercase							:  List[Any] 			=   predicting_streams_logits[:, 0]
           self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3]	,snake_case	,atol=1e-4   )   )
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Optional[Any] 			=   XLMProphetNetForConditionalGeneration.from_pretrained(
               """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""   )
           model.to(snake_case   )

           lowercase							:  Optional[int] 			=   torch.tensor([[17, 96208, 103471, 2]]   ).to(snake_case   )
           lowercase							:  Dict 			=   torch.tensor(
               [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]   ).to(snake_case   )
           lowercase							:  Any 			=   model(
               input_ids=snake_case	,attention_mask=snake_case	,encoder_outputs=snake_case	,decoder_input_ids=snake_case   )
           lowercase							:  Optional[int] 			=   output[0]
           lowercase							:  Dict 			=   torch.Size((1, 14, 250012)   )
           self.assertEqual(output_predited_logis.shape	,snake_case   )
           # compare the actual values for a slice.
           lowercase							:  int 			=   torch.tensor(
               [[[-9.2_253, -9.7_173, -6.3_529], [-7.6_701, -9.0_145, -1.9_382], [-8.0_195, -7.0_004, -0.1_523]]]   ).to(snake_case   )

           self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3]	,snake_case	,atol=1e-4   )   )
    @slow
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







           lowercase							:  Any 			=   XLMProphetNetForConditionalGeneration.from_pretrained(
               """microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""   )
           model.to(snake_case   )
           lowercase							:  Optional[int] 			=   512

           lowercase							:  List[Any] 			=   XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased-xglue-ntg"""   )

           lowercase							:  int 			=   (
               """Microsoft Corporation intends to officially end free support for the Windows 7 operating system after"""
               """ January 14, 2020, according to the official portal of the organization. From that day, users of this"""
               """ system will not be able to receive security updates, which could make their computers vulnerable to"""
               """ cyber attacks."""
           )
           lowercase							:  List[str] 			=   (
               """орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7"""
               """ после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи"""
               """ этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми"""
               """ к кибератакам."""
           )
           lowercase							:  str 			=   (
               """根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。"""
           )

           lowercase							:  Tuple 			=   tokenizer(
               [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE]	,padding=snake_case	,max_length=255	,return_tensors="""pt"""   ).input_ids
           lowercase							:  Union[str, Any] 			=   input_ids.to(snake_case   )

           lowercase							:  Dict 			=   model.generate(
               snake_case	,num_beams=10	,length_penalty=1.0	,no_repeat_ngram_size=3	,early_stopping=snake_case   )
           lowercase							:  Any 			=   [tokenizer.decode(snake_case	,skip_special_tokens=snake_case   ) for g in summary_ids]
           lowercase							:  Union[str, Any] 			=   """Microsoft to end Windows 7 free support after January 14, 2020"""
           lowercase							:  Tuple 			=   """Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года"""
           lowercase							:  Dict 			=   """微软打算终止对Windows 7操作系统的免费支持"""
           self.assertListEqual(
               [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH]	,snake_case	,)

           lowercase							:  int 			=   model.generate(
               snake_case	,num_beams=1	,length_penalty=1.0	,no_repeat_ngram_size=3	,early_stopping=snake_case   )
           lowercase							:  List[Any] 			=   [
               tokenizer.convert_ids_to_tokens(snake_case	,skip_special_tokens=snake_case   ) for g in summary_ids_beama
           ]
           lowercase							:  List[str] 			=   """▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7""".split(""" """   )
           lowercase							:  Optional[Any] 			=   """▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года""".split(
               """ """   )
           lowercase							:  Optional[Any] 			=   """微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持""".split(""" """   )
           self.assertListEqual(
               [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK]	,snake_case	,)
