






'''simple docstring'''

import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
A:   Optional[int]						=					False
A:   Dict						=					Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
A:   Optional[int]						=					datasets.Features(
    OrderedDict(
        {
            "attr_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "attr_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "boxes": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="float32"),
            "img_id": datasets.Value("int32"),
            "obj_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "obj_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "roi_features": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_0_4_8), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)






class 				__magic_name__  :







	"""simple docstring"""



	def __init__(				self	,   _lowercase=sys.argv[1:]							)    ->	List[Any]:
							lowercase_   : int   	=      None
							lowercase_   : List[str]   	=      None
							lowercase_   : Tuple   	=      None
							lowercase_   : Dict   	=      1
							lowercase_	,   lowercase_   : List[str]   	=      getopt.getopt(_lowercase	,   'i:o:b:s'	,   ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']							)
							for opt, arg in opts:
													if opt in ("-i", "--inputdir"):
																			lowercase_   : Union[str, Any]   	=      arg
													elif opt in ("-o", "--outfile"):
																			lowercase_   : Dict   	=      arg
													elif opt in ("-b", "--batch_size"):
																			lowercase_   : str   	=      int(_lowercase							)
													elif opt in ("-s", "--subset_list"):
																			lowercase_   : List[str]   	=      arg

							assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
							assert outputfile is not None and not os.path.isfile(_lowercase							), f"{outputfile}"
							if subset_list is not None:
													with open(os.path.realpath(_lowercase							)							) as f:
																			lowercase_   : str   	=      {self._vqa_file_split()[0] for x in tryload(_lowercase							)}
							else:
													lowercase_   : Union[str, Any]   	=      None

							lowercase_   : Optional[int]   	=      CONFIG
							if torch.cuda.is_available():
													lowercase_   : Dict   	=      'cuda'
							lowercase_   : Any   	=      os.path.realpath(_lowercase							)
							lowercase_   : Optional[Any]   	=      os.path.realpath(_lowercase							)
							lowercase_   : str   	=      Preprocess(self.config							)
							lowercase_   : Union[str, Any]   	=      GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'	,   config=self.config							)
							lowercase_   : Optional[Any]   	=      batch_size if batch_size != 0 else 1
							lowercase_   : List[Any]   	=      DEFAULT_SCHEMA
	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[Any]:
							lowercase_   : Tuple   	=      int(file.split('.'							)[0].split('_'							)[-1]							)
							lowercase_   : str   	=      os.path.join(self.inputdir	,   _lowercase							)
							return (img_id, filepath)
	@property
	def 							lowerCamelCase__		(				self							)    ->	int:
							lowercase_   : Optional[int]   	=      []
							for i, file in enumerate(os.listdir(self.inputdir							)							):
													if self.subset_list is not None and i not in self.subset_list:
																			continue
													batch.append(self._vqa_file_split(_lowercase							)							)
													if len(_lowercase							) == self.batch:
																			lowercase_   : int   	=      batch
																			lowercase_   : Optional[int]   	=      []
																			yield list(map(_lowercase	,   zip(*_lowercase							)							)							)

							for i in range(1							):
													yield list(map(_lowercase	,   zip(*_lowercase							)							)							)







	def __call__(				self							)    ->	Optional[int]:
							# make writer
							if not TEST:
													lowercase_   : Union[str, Any]   	=      datasets.ArrowWriter(features=self.schema	,   path=self.outputfile							)
							# do file generator
							for i, (img_ids, filepaths) in enumerate(self.file_generator							):
													lowercase_	,   lowercase_	,   lowercase_   : Optional[Any]   	=      self.preprocess(_lowercase							)
													lowercase_   : Any   	=      self.model(
													    _lowercase	,   _lowercase	,   scales_yx=_lowercase	,   padding='max_detections'	,   max_detections=self.config.MAX_DETECTIONS	,   pad_value=0	,   return_tensors='np'	,   location='cpu'	,   )
													lowercase_   : List[str]   	=      output_dict.pop('normalized_boxes'							)
													if not TEST:
																			lowercase_   : Optional[Any]   	=      np.array(_lowercase							)
																			lowercase_   : Dict   	=      self.schema.encode_batch(_lowercase							)
																			writer.write_batch(_lowercase							)
													if TEST:
																			break
													# finalizer the writer
							if not TEST:
													lowercase_	,   lowercase_   : List[str]   	=      writer.finalize()
													print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"							)






def 						_UpperCAmelCase     (	a		:						Dict				)      ->      Union[str, Any]:





						"""simple docstring"""
						try:
												lowercase_   : Optional[Any]   	=      json.load(a				)
												try:
																		lowercase_   : str   	=      list(data.keys()				)
												except Exception:
																		lowercase_   : Optional[int]   	=      [d['img_id'] for d in data]
						except Exception:
												try:
																		lowercase_   : Any   	=      eval(stream.read()				)
												except Exception:
																		lowercase_   : Dict   	=      stream.read().split('\n'				)
						return data


if __name__ == "__main__":
						A:   int						=					Extract(sys.argv[1:])
						extract()
						if not TEST:
												A:   Optional[int]						=					datasets.Dataset.from_file(extract.outputfile)
												# wala!
												# print(np.array(dataset[0:2]["roi_features"]).shape)








'''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()
