'''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			(  _a						:						List[Any]					,  _a						:						Dict					,  _a						:						List[Any]      ):




    """simple docstring"""



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







def 			_UpperCamelCase			(  _a						:						int					,  _a						:						List[str]      ):




    """simple docstring"""



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







def 			_UpperCamelCase			(  _a						:						Any					,  _a						:						int      ):




    """simple docstring"""



    return sum(A__      ) / instance_count







def 			_UpperCamelCase			(  _a						:						Union[str, Any]					,  _a						:						int      ):




    """simple docstring"""



    return instance_count / total_count







def 			_UpperCamelCase			(  _a						:						str					,  _a						:						Optional[int]					,  _a						:						Optional[int]      ):




    """simple docstring"""



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







def 			_UpperCamelCase			(  _a						:						List[str]					,  _a						:						int					,  _a						:						Optional[int]					,  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						int		     =	[]
    # 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]      )      ):
            __UpperCamelCase						:						Dict		     =	[]  # 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 			_UpperCamelCase			(  _a						:						List[Any]					,  _a						:						Any      ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[Any]		     =	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__      )) * 1_0_0


a=	TypeVar('''num''')







def 			_UpperCamelCase			(  _a						:						Optional[Any]					,  _a						:						Any					,  _a						:						List[Any]					,  _a						:						Dict = lambda _a      : True					,  _a						:						Union[str, Any] = None					,  ):




    """simple docstring"""



    while True:
        try:
            __UpperCamelCase						:						List[str]		     =	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 			_UpperCamelCase			(  ):




    """simple docstring"""



    while True:
        print(' Linear Discriminant Analysis '.center(5_0					,  '*'      )      )
        print('*' * 5_0					,  '\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						:						Optional[Any]		     =	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('-' * 1_0_0      )

        # Trying to get the value of standard deviation
        __UpperCamelCase						:						List[str]		     =	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('-' * 1_0_0      )

        # Trying to get number of instances in classes and theirs means to generate
        # dataset
        __UpperCamelCase						:						Union[str, Any]		     =	[]  # An empty list to store instance counts of classes in dataset
        for i in range(A__      ):
            __UpperCamelCase						:						Dict		     =	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('-' * 1_0_0      )

        # An empty list to store values of user-entered means of classes
        __UpperCamelCase						:						Dict		     =	[]
        for a in range(A__      ):
            __UpperCamelCase						:						str		     =	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('-' * 1_0_0      )

        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('-' * 1_0_0      )

        # 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('-' * 1_0_0      )

        # Generating training dataset drawn from gaussian distribution
        __UpperCamelCase						:						Union[str, Any]		     =	[
            gaussian_distribution(user_means[j]					,  A__					,  counts[j]      )
            for j in range(A__      )
        ]
        print('Generated Normal Distribution: \n'					,  A__      )
        print('-' * 1_0_0      )

        # Generating Ys to detecting corresponding classes
        __UpperCamelCase						:						Dict		     =	y_generator(A__					,  A__      )
        print('Generated Corresponding Ys: \n'					,  A__      )
        print('-' * 1_0_0      )

        # Calculating the value of actual mean for each class
        __UpperCamelCase						:						Any		     =	[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('-' * 1_0_0      )

        # Calculating the value of probabilities for each class
        __UpperCamelCase						:						List[str]		     =	[
            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('-' * 1_0_0      )

        # Calculating the values of variance for each class
        __UpperCamelCase						:						List[str]		     =	calculate_variance(A__					,  A__					,  sum(A__      )      )
        print('Variance: '					,  A__      )
        print('-' * 1_0_0      )

        # Predicting Y values
        # storing predicted Y values in 'pre_indexes' variable
        __UpperCamelCase						:						Tuple		     =	predict_y_values(A__					,  A__					,  A__					,  A__      )
        print('-' * 1_0_0      )

        # Calculating Accuracy of the model
        print(f"""Accuracy: {accuracy(A__					,  A__      )}"""      )
        print('-' * 1_0_0      )
        print(' DONE '.center(1_0_0					,  '+'      )      )

        if input('Press any key to restart or \'q\' for quit: '      ).strip().lower() == "q":
            print('\n' + 'GoodBye!'.center(1_0_0					,  '-'      ) + '\n'      )
            break
        system('cls' if name == 'nt' else 'clear'      )  # noqa: S605


if __name__ == "__main__":
  main()





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


_A       =					logging.getLogger(__name__)







@dataclass
class 	lowerCamelCase						:
     UpperCAmelCase__							:				str 	=						field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}    )
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}    )
     UpperCAmelCase__							:				bool 	=						field(default=A_				,					metadata={"help": "Set this flag to use fast tokenization."}    )
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     UpperCAmelCase__							:				Optional[str] 	=						field(
         default=A_				,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}				,					)



def 		lowercase_     (  )				->				int:
    """simple docstring"""

    snake_case						=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)

    if len(sys.argv						) == 2 and sys.argv[1].endswith(".json"						):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        snake_case	,				snake_case	,				snake_case						=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
    else:
        snake_case	,				snake_case	,				snake_case						=   parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir						)
        and os.listdir(training_args.output_dir						)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
            " --overwrite_output_dir to overcome."						)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"					,						datefmt="%m/%d/%Y %H:%M:%S"					,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,						)
    logger.warning(
        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"					,						training_args.local_rank					,						training_args.device					,						training_args.n_gpu					,						bool(training_args.local_rank != -1						)					,						training_args.fpaa					,						)
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank						):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info("Training/evaluation parameters %s"					,						A__						)

    # Prepare Question-Answering task
    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    snake_case						=   AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path					,						cache_dir=model_args.cache_dir					,						)
    snake_case						=   AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,						cache_dir=model_args.cache_dir					,						use_fast=A__					,						)
    snake_case						=   AutoModelForQuestionAnswering.from_pretrained(
        model_args.model_name_or_path					,						from_tf=bool(".ckpt" in model_args.model_name_or_path						)					,						config=A__					,						cache_dir=model_args.cache_dir					,						)

    # Get datasets
    snake_case						=   hasattr(model.config					,						"lang2id"						)
    snake_case						=   (
        SquadDataset(
            A__					,						tokenizer=A__					,						is_language_sensitive=A__					,						cache_dir=model_args.cache_dir						)
        if training_args.do_train
        else None
    )
    snake_case						=   (
        SquadDataset(
            A__					,						tokenizer=A__					,						mode="dev"					,						is_language_sensitive=A__					,						cache_dir=model_args.cache_dir					,						)
        if training_args.do_eval
        else None
    )

    # Data collator
    snake_case						=   DataCollatorWithPadding(A__					,						pad_to_multiple_of=8						) if training_args.fpaa else None

    # Initialize our Trainer
    snake_case						=   Trainer(
        model=A__					,						args=A__					,						train_dataset=A__					,						eval_dataset=A__					,						data_collator=A__					,						)

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_master():
            tokenizer.save_pretrained(training_args.output_dir						)



def 		lowercase_     (  A__						)				->				Optional[Any]:
    """simple docstring"""

    main()


if __name__ == "__main__":
   main()

