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



def 		lowercase_     (  A__					,						A__					,						A__						)				->				list:
    """simple docstring"""

    seed(1						)
    return [gauss(A__					,						A__						) for _ in range(A__						)]



def 		lowercase_     (  A__					,						A__						)				->				list:
    """simple docstring"""

    return [k for k in range(A__						) for _ in range(instance_count[k]						)]



def 		lowercase_     (  A__					,						A__						)				->				float:
    """simple docstring"""

    return sum(A__						) / instance_count



def 		lowercase_     (  A__					,						A__						)				->				float:
    """simple docstring"""

    return instance_count / total_count



def 		lowercase_     (  A__					,						A__					,						A__						)				->				float:
    """simple docstring"""

    snake_case						=   []  # 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
    snake_case						=   len(A__						)  # Number of classes in dataset
    return 1 / (total_count - n_classes) * sum(A__						)



def 		lowercase_     (  A__					,						A__					,						A__					,						A__						)				->				list:
    """simple docstring"""

    snake_case						=   []
    # 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]						)						):
            snake_case						=   []  # 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 		lowercase_     (  A__					,						A__						)				->				float:
    """simple docstring"""

    snake_case						=   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__						)) * 100


_A       =					TypeVar("num")



def 		lowercase_     (  A__					,						A__					,						A__					,						A__ = lambda A__						: True					,						A__ = None					,						)				->				num:
    """simple docstring"""

    while True:
        try:
            snake_case						=   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 		lowercase_     (  )				->				int:
    """simple docstring"""

    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
        snake_case						=   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("-" * 100						)

        # Trying to get the value of standard deviation
        snake_case						=   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("-" * 100						)

        # Trying to get number of instances in classes and theirs means to generate
        # dataset
        snake_case						=   []  # An empty list to store instance counts of classes in dataset
        for i in range(A__						):
            snake_case						=   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("-" * 100						)

        # An empty list to store values of user-entered means of classes
        snake_case						=   []
        for a in range(A__						):
            snake_case						=   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("-" * 100						)

        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("-" * 100						)

        # 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("-" * 100						)

        # Generating training dataset drawn from gaussian distribution
        snake_case						=   [
            gaussian_distribution(user_means[j]					,						A__					,						counts[j]						)
            for j in range(A__						)
        ]
        print("Generated Normal Distribution: \n"					,						A__						)
        print("-" * 100						)

        # Generating Ys to detecting corresponding classes
        snake_case						=   y_generator(A__					,						A__						)
        print("Generated Corresponding Ys: \n"					,						A__						)
        print("-" * 100						)

        # Calculating the value of actual mean for each class
        snake_case						=   [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("-" * 100						)

        # Calculating the value of probabilities for each class
        snake_case						=   [
            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("-" * 100						)

        # Calculating the values of variance for each class
        snake_case						=   calculate_variance(A__					,						A__					,						sum(A__						)						)
        print("Variance: "					,						A__						)
        print("-" * 100						)

        # Predicting Y values
        # storing predicted Y values in 'pre_indexes' variable
        snake_case						=   predict_y_values(A__					,						A__					,						A__					,						A__						)
        print("-" * 100						)

        # Calculating Accuracy of the model
        print(F'Accuracy: {accuracy(A__					,						A__						)}'						)
        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()



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A       =					logging.get_logger(__name__)

_A       =					{
    "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				List[Any] 	=						"cpmant"





     def __init__(self :       List[Any]							,       _A :       int = 3_0_7_2_0							,       _A :       int = 4_0_9_6							,       _A :       int = 3_2							,       _A :       int = 1_2_8							,       _A :       int = 1_0_2_4_0							,       _A :       int = 4_8							,       _A :       int = 0.0							,       _A :       int = 5_1_2							,       _A :       int = 2_0_4_8							,       _A :       int = 1E-6							,       _A :       float = 1.0							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       bool = True							,       **_A :       Dict							,       )		->		Dict:
         super().__init__(**_A  )
         snake_case						=   prompt_types
         snake_case						=   prompt_length
         snake_case						=   segment_types
         snake_case						=   hidden_size
         snake_case						=   num_attention_heads
         snake_case						=   dim_head
         snake_case						=   dim_ff
         snake_case						=   num_hidden_layers
         snake_case						=   position_bias_num_buckets
         snake_case						=   position_bias_max_distance
         snake_case						=   dropout_p
         snake_case						=   eps
         snake_case						=   use_cache
         snake_case						=   vocab_size
         snake_case						=   init_std

