





import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser


def 				SCREAMING_SNAKE_CASE__    (     __a=None ,			__a=None ):
	return field(default_factory=lambda: default ,			metadata=__a )
@dataclass
class SCREAMING_SNAKE_CASE_    :
						__magic_name__:      str			   =							field(
						    metadata={"help": "The csv file to plot."}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={"help": "Disable logarithmic scale when plotting"}							,					)
						__magic_name__:      bool			   =							field(
						    default=snake_case_							,					metadata={
						        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
						    }							,					)
						__magic_name__:      Optional[str]			   =							field(
						    default=snake_case_							,					metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}							,					)
						__magic_name__:      Optional[List[str]]			   =							list_field(
						    default=snake_case_							,					metadata={"help": "List of model names that are used instead of the ones in the csv file."}       )


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	try:
		int(__a )
		return True
	except ValueError:
		return False


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	try:
		float(__a )
		return True
	except ValueError:
		return False
class SCREAMING_SNAKE_CASE_    :






						def __init__(   self   :   Dict		, _A   :   List[Any]						)				->     str:







							"""simple docstring"""


							snake_case_		:      int           =						args
							snake_case_		:      List[Any]           =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

							with open(self.args.csv_file		, newline=''						) as csv_file:
								snake_case_		:      Union[str, Any]           =						csv.DictReader(_A						)
								for row in reader:
									snake_case_		:      List[str]           =						row['model']
									self.result_dict[model_name]["bsz"].append(int(row['batch_size']						)						)
									self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']						)						)
									if can_convert_to_int(row['result']						):
										# value is not None
										snake_case_		:      Dict           =						int(row['result']						)
									elif can_convert_to_float(row['result']						):
										# value is not None
										snake_case_		:      Optional[int]           =						float(row['result']						)






						def UpperCAmelCase_					(   self   :   Any						)				->     Optional[Any]:







							"""simple docstring"""


							snake_case_    ,snake_case_		:      Any           =						plt.subplots()
							snake_case_		:      Tuple           =						'Time usage' if self.args.is_time else 'Memory usage'
							snake_case_		:      Tuple           =						title_str + ' for training' if self.args.is_train else title_str + ' for inference'

							if not self.args.no_log_scale:
								# set logarithm scales
								ax.set_xscale('log'						)
								ax.set_yscale('log'						)

							for axis in [ax.xaxis, ax.yaxis]:
								axis.set_major_formatter(ScalarFormatter()						)

							for model_name_idx, model_name in enumerate(self.result_dict.keys()						):
								snake_case_		:      Tuple           =						sorted(set(self.result_dict[model_name]['bsz']						)						)
								snake_case_		:      Dict           =						sorted(set(self.result_dict[model_name]['seq_len']						)						)
								snake_case_		:      Optional[int]           =						self.result_dict[model_name]['result']

								((snake_case_)    ,(snake_case_))		:      Optional[int]           =						(
								    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
								)

								snake_case_		:      List[str]           =						(
								    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
								)

								for inner_loop_value in inner_loop_array:
									if self.args.plot_along_batch:
										snake_case_		:      int           =						np.asarray(
										    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		, dtype=_A		, )
									else:
										snake_case_		:      str           =						np.asarray(
										    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		, dtype=np.floataa		, )

									((snake_case_)    ,(snake_case_))		:      Union[str, Any]           =						(
									    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
									)

									snake_case_		:      Tuple           =						np.asarray(_A		, _A						)[: len(_A						)]
									plt.scatter(
									    _A		, _A		, label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""						)
									plt.plot(_A		, _A		, '--'						)

								title_str += F""" {label_model_name} vs."""

							snake_case_		:      Optional[int]           =						title_str[:-4]
							snake_case_		:      Tuple           =						'Time in s' if self.args.is_time else 'Memory in MB'

							# plot
							plt.title(_A						)
							plt.xlabel(_A						)
							plt.ylabel(_A						)
							plt.legend()

							if self.args.figure_png_file is not None:
								plt.savefig(self.args.figure_png_file						)
							else:
								plt.show()


def 				SCREAMING_SNAKE_CASE__    (     ):
	snake_case_		:      Dict           =						HfArgumentParser(__a )
	snake_case_		:      List[str]           =						parser.parse_args_into_dataclasses()[0]
	snake_case_		:      int           =						Plot(args=__a )
	plot.plot()


if __name__ == "__main__":
						main()









'''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		:						float    ,      a		:						float    ,      a		:						int				)      ->      list:





						"""simple docstring"""
						seed(1				)
						return [gauss(a    ,      a				) for _ in range(a				)]






def 						_UpperCAmelCase     (	a		:						int    ,      a		:						list				)      ->      list:





						"""simple docstring"""
						return [k for k in range(a				) for _ in range(instance_count[k]				)]






def 						_UpperCAmelCase     (	a		:						int    ,      a		:						list				)      ->      float:





						"""simple docstring"""
						return sum(a				) / instance_count






def 						_UpperCAmelCase     (	a		:						int    ,      a		:						int				)      ->      float:





						"""simple docstring"""
						return instance_count / total_count






def 						_UpperCAmelCase     (	a		:						list    ,      a		:						list    ,      a		:						int				)      ->      float:





						"""simple docstring"""
						lowercase_   : str   	=      []  # 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
						lowercase_   : Tuple   	=      len(a				)  # Number of classes in dataset
						return 1 / (total_count - n_classes) * sum(a				)






def 						_UpperCAmelCase     (	a		:						list    ,      a		:						list    ,      a		:						float    ,      a		:						list				)      ->      list:





						"""simple docstring"""
						lowercase_   : str   	=      []
						# 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]				)				):
																		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(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    ,      a		:						list				)      ->      float:





						"""simple docstring"""
						lowercase_   : 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:   Union[str, Any]						=					TypeVar("num")






def 						_UpperCAmelCase     (	a		:						Callable[[object], num]    ,      a		:						str    ,      a		:						str    ,      a		:						Callable[[num], bool] = lambda a				: True    ,      a		:						str | None = None    ,      )      ->      num:





						"""simple docstring"""
						while True:
												try:
																		lowercase_   : int   	=      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     (	)      ->      List[str]:





						"""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
												lowercase_   : Dict   	=      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
												lowercase_   : List[Any]   	=      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
												lowercase_   : Any   	=      []  # An empty list to store instance counts of classes in dataset
												for i in range(a				):
																		lowercase_   : Union[str, Any]   	=      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
												lowercase_   : int   	=      []
												for a in range(a				):
																		lowercase_   : Union[str, Any]   	=      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
												lowercase_   : Optional[int]   	=      [
												    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
												lowercase_   : Optional[int]   	=      y_generator(a    ,      a				)
												print('Generated Corresponding Ys: \n'    ,      a				)
												print('-' * 1_0_0				)

												# Calculating the value of actual mean for each class
												lowercase_   : str   	=      [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
												lowercase_   : Union[str, Any]   	=      [
												    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
												lowercase_   : 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
												lowercase_   : 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()
