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 					__lowerCAmelCase			(  _A					):



							"""simple docstring"""


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


class _lowercase					(       _UpperCAmelCase						):





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase=None     ,   UpperCAmelCase=None  ):







														'''simple docstring'''






														_lowercase				=	file_names
														_lowercase				=	image_transform
														_lowercase				=	label_to_id



							def __len__(    self  ):







														'''simple docstring'''






														return len(self.file_names  )



							def __getitem__(    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






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






def 					__lowerCAmelCase			(  _A ,_A					):



							"""simple docstring"""


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

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

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

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

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

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

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

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

							# Instantiate dataloaders.
							_lowercase				=	DataLoader(_A ,shuffle=_A ,batch_size=_A ,num_workers=4					)
							_lowercase				=	DataLoader(_A ,shuffle=_A ,batch_size=_A ,num_workers=4					)

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

							# 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).
							_lowercase				=	model.to(accelerator.device					)

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

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

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

							# Instantiate learning rate scheduler
							_lowercase				=	OneCycleLR(optimizer=_A ,max_lr=_A ,epochs=_A ,steps_per_epoch=len(_A					)					)

							# 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.
							_lowercase				,       _lowercase				,       _lowercase				,       _lowercase				,       _lowercase				=	accelerator.prepare(
							    _A ,_A ,_A ,_A ,_A					)

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

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

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


def 					__lowerCAmelCase			(  ):



							"""simple docstring"""


							_lowercase				=	argparse.ArgumentParser(description="""Simple example of training script."""					)
							parser.add_argument("""--data_dir""" ,required=_A ,help="""The data folder on disk."""					)
							parser.add_argument(
							    """--mixed_precision""" ,type=_A ,default=_A ,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=_A ,default=_A ,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."""					)
							_lowercase				=	parser.parse_args()
							_lowercase				=	{"""lr""": 3E-2, """num_epochs""": 3, """seed""": 42, """batch_size""": 64, """image_size""": 224}
							training_function(_A ,_A					)


if __name__ == "__main__":
				main()



import random


class _lowercase					:





							"""simple docstring"""




							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase = 0.01     ,   UpperCAmelCase = 1000     ,   UpperCAmelCase = -1     ,   ):







														'''simple docstring'''






														_lowercase				=	sample
														if len(self.sample  ) == 0:
																					raise ValueError("""Sample data can not be empty"""  )
														_lowercase				=	target
														if len(self.target  ) == 0:
																					raise ValueError("""Target data can not be empty"""  )
														if len(self.sample  ) != len(self.target  ):
																					raise ValueError("""Sample data and Target data do not have matching lengths"""  )
														_lowercase				=	learning_rate
														_lowercase				=	epoch_number
														_lowercase				=	bias
														_lowercase				=	len(UpperCAmelCase  )
														_lowercase				=	len(sample[0]  )  # number of columns in dataset
														_lowercase				=	[]



							def     _UpperCAmelCase       (    self  ):







														'''simple docstring'''






														for sample in self.sample:
																					sample.insert(0     ,   self.bias  )

														for _ in range(self.col_sample  ):
																					self.weight.append(random.random()  )

														self.weight.insert(0     ,   self.bias  )

														_lowercase				=	0

														while True:
																					_lowercase				=	False
																					for i in range(self.number_sample  ):
																												_lowercase				=	0
																												for j in range(self.col_sample + 1  ):
																																			_lowercase				=	u + self.weight[j] * self.sample[i][j]
																												_lowercase				=	self.sign(UpperCAmelCase  )
																												if y != self.target[i]:
																																			for j in range(self.col_sample + 1  ):
																																										_lowercase				=	(
																																										    self.weight[j]
																																										    + self.learning_rate
																																										    * (self.target[i] - y)
																																										    * self.sample[i][j]
																																										)
																																			_lowercase				=	True
            # print('Epoch: \n',epoch_count)
																					_lowercase				=	epoch_count + 1
																					# if you want control the epoch or just by error
																					if not has_misclassified:
																												print(("""\nEpoch:\n""", epoch_count)  )
																												print("""------------------------\n"""  )
																												# if epoch_count > self.epoch_number or not error:
																												break



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






														if len(self.sample  ) == 0:
																					raise ValueError("""Sample data can not be empty"""  )
														sample.insert(0     ,   self.bias  )
														_lowercase				=	0
														for i in range(self.col_sample + 1  ):
																					_lowercase				=	u + self.weight[i] * sample[i]

														_lowercase				=	self.sign(UpperCAmelCase  )

														if y == -1:
																					print(("""Sample: """, sample)  )
																					print("""classification: P1"""  )
														else:
																					print(("""Sample: """, sample)  )
																					print("""classification: P2"""  )



							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase  ):







														'''simple docstring'''






														return 1 if u >= 0 else -1






A_:      Optional[int]        =			[
    [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
    [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
    [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
    [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
    [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
    [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
    [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
    [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
    [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
    [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
    [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
    [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
    [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
    [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
    [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
    [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
    [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
    [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
    [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
    [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
    [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
    [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
    [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
    [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
    [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
    [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
    [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
    [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
    [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
    [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
]

A_:      Optional[int]        =			[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				A_:      str        =			Perceptron(
				    sample=samples, target=target, learning_rate=0.0_1, epoch_number=1000, bias=-1
				)
				network.training()
				print('Finished training perceptron')
				print('Enter values to predict or q to exit')
				while True:
								A_:      list        =			[]
								for i in range(len(samples[0])):
												A_:      Dict        =			input('value: ').strip()
												if user_input == "q":
																break
												A_:      Union[str, Any]        =			float(user_input)
												sample.insert(i, observation)
								network.sort(sample)


