
import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator




def 						A_			(				snake_case				: Union[str, Any]			)   ->     Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      fname.split(os.path.sep			)[-1]
						return re.search(r'''^(.*)_\d+\.jpg$'''			,   snake_case			).groups()[0]






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_     ):

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=None  ,						SCREAMING_SNAKE_CASE_=None      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      file_names
													__UpperCamelCase  	=      image_transform
													__UpperCamelCase  	=      label_to_id



							def __len__(	self      )-> Any:





													'''simple docstring'''






													return len(self.file_names      )



							def __getitem__(	self  ,						SCREAMING_SNAKE_CASE_      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      self.file_names[idx]
													__UpperCamelCase  	=      PIL.Image.open(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      raw_image.convert('''RGB'''      )
													if self.image_transform is not None:
																			__UpperCamelCase  	=      self.image_transform(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      extract_label(SCREAMING_SNAKE_CASE_      )
													if self.label_to_id is not None:
																			__UpperCamelCase  	=      self.label_to_id[label]
													return {"image": image, "label": label}





def 						A_			(				snake_case				: List[str]			,   snake_case				: Dict			)   ->     int:



						'''simple docstring'''

						__UpperCamelCase  	=      Accelerator(cpu=args.cpu			,   mixed_precision=args.mixed_precision			)

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__UpperCamelCase  	=      config['''lr''']
						__UpperCamelCase  	=      int(config['''num_epochs''']			)
						__UpperCamelCase  	=      int(config['''seed''']			)
						__UpperCamelCase  	=      int(config['''batch_size''']			)
						__UpperCamelCase  	=      config['''image_size''']
						if not isinstance(snake_case			,   (list, tuple)			):
												__UpperCamelCase  	=      (image_size, image_size)

						# Grab all the image filenames
						__UpperCamelCase  	=      [os.path.join(args.data_dir			,   snake_case			) for fname in os.listdir(args.data_dir			) if fname.endswith('''.jpg'''			)]

						# Build the label correspondences
						__UpperCamelCase  	=      [extract_label(snake_case			) for fname in file_names]
						__UpperCamelCase  	=      list(set(snake_case			)			)
						id_to_label.sort()
						__UpperCamelCase  	=      {lbl: i for i, lbl in enumerate(snake_case			)}

						# Set the seed before splitting the data.
						np.random.seed(snake_case			)
						torch.manual_seed(snake_case			)
						torch.cuda.manual_seed_all(snake_case			)

						# Split our filenames between train and validation
						__UpperCamelCase  	=      np.random.permutation(len(snake_case			)			)
						__UpperCamelCase  	=      int(0.8 * len(snake_case			)			)
						__UpperCamelCase  	=      random_perm[:cut]
						__UpperCamelCase  	=      random_perm[cut:]

						# For training we use a simple RandomResizedCrop
						__UpperCamelCase  	=      Compose([RandomResizedCrop(snake_case			,   scale=(0.5, 1.0)			), ToTensor()]			)
						__UpperCamelCase  	=      PetsDataset(
						    [file_names[i] for i in train_split]			,   image_transform=snake_case			,   label_to_id=snake_case			)

						# For evaluation, we use a deterministic Resize
						__UpperCamelCase  	=      Compose([Resize(snake_case			), ToTensor()]			)
						__UpperCamelCase  	=      PetsDataset([file_names[i] for i in eval_split]			,   image_transform=snake_case			,   label_to_id=snake_case			)

						# Instantiate dataloaders.
						__UpperCamelCase  	=      DataLoader(snake_case			,   shuffle=snake_case			,   batch_size=snake_case			,   num_workers=4			)
						__UpperCamelCase  	=      DataLoader(snake_case			,   shuffle=snake_case			,   batch_size=snake_case			,   num_workers=4			)

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__UpperCamelCase  	=      create_model('''resnet50d'''			,   pretrained=snake_case			,   num_classes=len(snake_case			)			)

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						__UpperCamelCase  	=      model.to(accelerator.device			)

						# Freezing the base model
						for param in model.parameters():
												__UpperCamelCase  	=      False
						for param in model.get_classifier().parameters():
												__UpperCamelCase  	=      True

						# We normalize the batches of images to be a bit faster.
						__UpperCamelCase  	=      torch.tensor(model.default_cfg['''mean''']			)[None, :, None, None].to(accelerator.device			)
						__UpperCamelCase  	=      torch.tensor(model.default_cfg['''std''']			)[None, :, None, None].to(accelerator.device			)

						# Instantiate optimizer
						__UpperCamelCase  	=      torch.optim.Adam(params=model.parameters()			,   lr=lr / 25			)

						# Instantiate learning rate scheduler
						__UpperCamelCase  	=      OneCycleLR(optimizer=snake_case			,   max_lr=snake_case			,   epochs=snake_case			,   steps_per_epoch=len(snake_case			)			)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase  	=      accelerator.prepare(
						    snake_case			,   snake_case			,   snake_case			,   snake_case			,   snake_case			)

						# Now we train the model
						for epoch in range(snake_case			):
												model.train()
												for step, batch in enumerate(snake_case			):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		__UpperCamelCase  	=      {k: v.to(accelerator.device			) for k, v in batch.items()}
																		__UpperCamelCase  	=      (batch['''image'''] - mean) / std
																		__UpperCamelCase  	=      model(snake_case			)
																		__UpperCamelCase  	=      torch.nn.functional.cross_entropy(snake_case			,   batch['''label''']			)
																		accelerator.backward(snake_case			)
																		optimizer.step()
																		lr_scheduler.step()
																		optimizer.zero_grad()

												model.eval()
												__UpperCamelCase  	=      0
												__UpperCamelCase  	=      0
												for _, batch in enumerate(snake_case			):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		__UpperCamelCase  	=      {k: v.to(accelerator.device			) for k, v in batch.items()}
																		__UpperCamelCase  	=      (batch['''image'''] - mean) / std
																		with torch.no_grad():
																								__UpperCamelCase  	=      model(snake_case			)
																		__UpperCamelCase  	=      outputs.argmax(dim=-1			)
																		__UpperCamelCase	,				__UpperCamelCase  	=      accelerator.gather_for_metrics((predictions, batch['''label'''])			)
																		__UpperCamelCase  	=      predictions == references
																		num_elems += accurate_preds.shape[0]
																		accurate += accurate_preds.long().sum()

												__UpperCamelCase  	=      accurate.item() / num_elems
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}: {100 * eval_metric:.2f}"			)




def 						A_			(				)   ->     Any:



						'''simple docstring'''

						__UpperCamelCase  	=      argparse.ArgumentParser(description='''Simple example of training script.'''			)
						parser.add_argument('''--data_dir'''			,   required=snake_case			,   help='''The data folder on disk.'''			)
						parser.add_argument(
						    '''--mixed_precision'''			,   type=snake_case			,   default=snake_case			,   choices=['''no''', '''fp16''', '''bf16''', '''fp8''']			,   help='''Whether to use mixed precision. Choose'''
						    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
						    '''and an Nvidia Ampere GPU.'''			,   )
						parser.add_argument(
						    '''--checkpointing_steps'''			,   type=snake_case			,   default=snake_case			,   help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''			,   )
						parser.add_argument('''--cpu'''			,   action='''store_true'''			,   help='''If passed, will train on the CPU.'''			)
						__UpperCamelCase  	=      parser.parse_args()
						__UpperCamelCase  	=      {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
						training_function(snake_case			,   snake_case			)


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()
