import unittest

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


if is_torch_available():
				import torch

				from transformers import CamembertModel





@require_torch
@require_sentencepiece
@require_tokenizers
class 					__snake_case    (     unittest.TestCase      ):




					@slow
					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      int   				= CamembertModel.from_pretrained('camembert-base'						)
									model.to(__UpperCamelCase						)

									snake_case__   :      Optional[Any]   				= torch.tensor(
									    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]       ,			device=__UpperCamelCase       ,			dtype=torch.long       ,			)  # J'aime le camembert !
									with torch.no_grad():
													snake_case__   :      Dict   				= model(__UpperCamelCase						)['''last_hidden_state''']
									snake_case__   :      Union[str, Any]   				= torch.Size((1, 10, 768)						)
									self.assertEqual(output.shape       ,			__UpperCamelCase						)
									# compare the actual values for a slice.
									snake_case__   :      Union[str, Any]   				= torch.tensor(
									    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]       ,			device=__UpperCamelCase       ,			dtype=torch.float       ,			)
									# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
									# camembert.eval()
									# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

									self.assertTrue(torch.allclose(output[:, :3, :3]       ,			__UpperCamelCase       ,			atol=1E-4						)						)



'''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 (					__UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float ,   __UpperCAmelCase  :					int			)							->	list:
  seed(1			)
  return [gauss(__UpperCAmelCase ,   __UpperCAmelCase			) for _ in range(__UpperCAmelCase			)]







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					list			)							->	list:
  return [k for k in range(__UpperCAmelCase			) for _ in range(instance_count[k]			)]







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					list			)							->	float:
  return sum(__UpperCAmelCase			) / instance_count







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int			)							->	float:
  return instance_count / total_count







def 				__UpperCamelCase (					__UpperCAmelCase  :					list ,   __UpperCAmelCase  :					list ,   __UpperCAmelCase  :					int			)							->	float:
  lowercase				:							List[Any]									=   []  # An empty list to store all squared differences
  # iterate over number of elements in items
  for i in range(len(__UpperCAmelCase			)			):
    # for loop iterates over number of elements in inner layer of items
    for j in range(len(items[i]			)			):
      # appending squared differences to 'squared_diff' list
      squared_diff.append((items[i][j] - means[i]) ** 2			)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
  lowercase				:							List[str]									=   len(__UpperCAmelCase			)  # Number of classes in dataset
  return 1 / (total_count - n_classes) * sum(__UpperCAmelCase			)







def 				__UpperCamelCase (					__UpperCAmelCase  :					list ,   __UpperCAmelCase  :					list ,   __UpperCAmelCase  :					float ,   __UpperCAmelCase  :					list			)							->	list:
  lowercase				:							Union[str, Any]									=   []
  # for loop iterates over number of elements in list
  for i in range(len(__UpperCAmelCase			)			):
    # for loop iterates over number of inner items of each element
    for j in range(len(x_items[i]			)			):
      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(__UpperCAmelCase			)			):
        # appending values of discriminants for each class to 'temp' list
        temp.append(
            x_items[i][j] * (means[k] / variance)
            - (means[k] ** 2 / (2 * variance))
            + log(probabilities[k]			)			)
      # appending discriminant values of each item to 'results' list
      results.append(__UpperCAmelCase			)

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







def 				__UpperCamelCase (					__UpperCAmelCase  :					list ,   __UpperCAmelCase  :					list			)							->	float:
  lowercase				:							List[str]									=   sum(1 for i, j in zip(__UpperCAmelCase ,   __UpperCAmelCase			) if i == j			)
  # percentage of accuracy equals to number of correct predictions divided by number
  # of all data and multiplied by 100
  return (correct / len(__UpperCAmelCase			)) * 100


_A		:      Dict						   =TypeVar('''num''')







def 				__UpperCamelCase (					__UpperCAmelCase  :					Callable[[object], num] ,   __UpperCAmelCase  :					str ,   __UpperCAmelCase  :					str ,   __UpperCAmelCase  :					Callable[[num], bool] = lambda __UpperCAmelCase			: True ,   __UpperCAmelCase  :					str | None = None ,   )							->	num:
  while True:
    try:
      lowercase				:							int									=   input_type(input(__UpperCAmelCase			).strip() or default			)
      if condition(__UpperCAmelCase			):
        return user_input
      else:
        print(f"""{user_input}: {err_msg}"""			)
        continue
    except ValueError:
      print(
          f"""{user_input}: Incorrect input type, expected {input_type.__name__!r}"""			)







def 				__UpperCamelCase (					)							->	Optional[int]:
  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				:							List[str]									=   valid_input(
        input_type=__UpperCAmelCase ,   condition=lambda __UpperCAmelCase			: x > 0 ,   input_msg='''Enter the number of classes (Data Groupings): ''' ,   err_msg='''Number of classes should be positive!''' ,   )

    print('''-''' * 100			)

    # Trying to get the value of standard deviation
    lowercase				:							Dict									=   valid_input(
        input_type=__UpperCAmelCase ,   condition=lambda __UpperCAmelCase			: x >= 0 ,   input_msg=(
            '''Enter the value of standard deviation'''
            '''(Default value is 1.0 for all classes): '''
        ) ,   err_msg='''Standard deviation should not be negative!''' ,   default='''1.0''' ,   )

    print('''-''' * 100			)

    # Trying to get number of instances in classes and theirs means to generate
    # dataset
    lowercase				:							str									=   []  # An empty list to store instance counts of classes in dataset
    for i in range(__UpperCAmelCase			):
      lowercase				:							List[Any]									=   valid_input(
          input_type=__UpperCAmelCase ,   condition=lambda __UpperCAmelCase			: x > 0 ,   input_msg=(f"""Enter The number of instances for class_{i+1}: """) ,   err_msg='''Number of instances should be positive!''' ,   )
      counts.append(__UpperCAmelCase			)
    print('''-''' * 100			)

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

    print('''Standard deviation: ''' ,   __UpperCAmelCase			)
    # print out the number of instances in classes in separated line
    for i, count in enumerate(__UpperCAmelCase ,   1			):
      print(f"""Number of instances in class_{i} is: {count}"""			)
    print('''-''' * 100			)

    # print out mean values of classes separated line
    for i, user_mean in enumerate(__UpperCAmelCase ,   1			):
      print(f"""Mean of class_{i} is: {user_mean}"""			)
    print('''-''' * 100			)

    # Generating training dataset drawn from gaussian distribution
    lowercase				:							Union[str, Any]									=   [
        gaussian_distribution(user_means[j] ,   __UpperCAmelCase ,   counts[j]			)
        for j in range(__UpperCAmelCase			)
    ]
    print('''Generated Normal Distribution: \n''' ,   __UpperCAmelCase			)
    print('''-''' * 100			)

    # Generating Ys to detecting corresponding classes
    lowercase				:							List[str]									=   y_generator(__UpperCAmelCase ,   __UpperCAmelCase			)
    print('''Generated Corresponding Ys: \n''' ,   __UpperCAmelCase			)
    print('''-''' * 100			)

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

    # Calculating the value of probabilities for each class
    lowercase				:							Optional[int]									=   [
        calculate_probabilities(counts[i] ,   sum(__UpperCAmelCase			)			) for i in range(__UpperCAmelCase			)
    ]

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

    # Calculating the values of variance for each class
    lowercase				:							Tuple									=   calculate_variance(__UpperCAmelCase ,   __UpperCAmelCase ,   sum(__UpperCAmelCase			)			)
    print('''Variance: ''' ,   __UpperCAmelCase			)
    print('''-''' * 100			)

    # Predicting Y values
    # storing predicted Y values in 'pre_indexes' variable
    lowercase				:							Any									=   predict_y_values(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
    print('''-''' * 100			)

    # Calculating Accuracy of the model
    print(f"""Accuracy: {accuracy(__UpperCAmelCase ,   __UpperCAmelCase			)}"""			)
    print('''-''' * 100			)
    print(''' DONE '''.center(100 ,   '''+'''			)			)

    if input('''Press any key to restart or \'q\' for quit: '''			).strip().lower() == "q":
      print('''\n''' + '''GoodBye!'''.center(100 ,   '''-'''			) + '''\n'''			)
      break
    system('''cls''' if name == '''nt''' else '''clear'''			)  # noqa: S605


if __name__ == "__main__":
 main()