





'''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					: str      =    False
_a					: Optional[Any]      =    Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
_a					: 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     _UpperCAmelCase							:
		def __init__(  self,__SCREAMING_SNAKE_CASE=sys.argv[1:]			):



					'''simple docstring'''

					__lowerCAmelCase											=				None
					__lowerCAmelCase											=				None
					__lowerCAmelCase											=				None
					__lowerCAmelCase											=				1
					__lowerCAmelCase						,       __lowerCAmelCase											=				getopt.getopt(__SCREAMING_SNAKE_CASE,"""i:o:b:s""",["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]			)
					for opt, arg in opts:
								if opt in ("-i", "--inputdir"):
											__lowerCAmelCase											=				arg
								elif opt in ("-o", "--outfile"):
											__lowerCAmelCase											=				arg
								elif opt in ("-b", "--batch_size"):
											__lowerCAmelCase											=				int(__SCREAMING_SNAKE_CASE			)
								elif opt in ("-s", "--subset_list"):
											__lowerCAmelCase											=				arg

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

					__lowerCAmelCase											=				CONFIG
					if torch.cuda.is_available():
								__lowerCAmelCase											=				"""cuda"""
					__lowerCAmelCase											=				os.path.realpath(__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				os.path.realpath(__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				Preprocess(self.config			)
					__lowerCAmelCase											=				GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned""",config=self.config			)
					__lowerCAmelCase											=				batch_size if batch_size != 0 else 1
					__lowerCAmelCase											=				DEFAULT_SCHEMA
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				int(file.split("""."""			)[0].split("""_"""			)[-1]			)
					__lowerCAmelCase											=				os.path.join(self.inputdir,__SCREAMING_SNAKE_CASE			)
					return (img_id, filepath)
		@property
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				[]
					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(__SCREAMING_SNAKE_CASE			)			)
								if len(__SCREAMING_SNAKE_CASE			) == self.batch:
											__lowerCAmelCase											=				batch
											__lowerCAmelCase											=				[]
											yield list(map(__SCREAMING_SNAKE_CASE,zip(*__SCREAMING_SNAKE_CASE			)			)			)

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







		def __call__(  self			):



					'''simple docstring'''

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





def    _lowerCAmelCase						(						lowercase	)					->					str:
			try:
						__lowerCAmelCase											=				json.load(lowercase	)
						try:
									__lowerCAmelCase											=				list(data.keys()	)
						except Exception:
									__lowerCAmelCase											=				[d["""img_id"""] for d in data]
			except Exception:
						try:
									__lowerCAmelCase											=				eval(stream.read()	)
						except Exception:
									__lowerCAmelCase											=				stream.read().split("""\n"""	)
			return data


if __name__ == "__main__":
	_a					: Union[str, Any]      =    Extract(sys.argv[1:])
	extract()
	if not TEST:
		_a					: Tuple      =    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    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					list:
			seed(1	)
			return [gauss(lowercase					,	lowercase	) for _ in range(lowercase	)]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					list:
			return [k for k in range(lowercase	) for _ in range(instance_count[k]	)]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return sum(lowercase	) / instance_count





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			return instance_count / total_count





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				[]  # An empty list to store all squared differences
			# iterate over number of elements in items
			for i in range(len(lowercase	)	):
						# 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
			__lowerCAmelCase											=				len(lowercase	)  # Number of classes in dataset
			return 1 / (total_count - n_classes) * sum(lowercase	)





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase	)					->					list:
			__lowerCAmelCase											=				[]
			# for loop iterates over number of elements in list
			for i in range(len(lowercase	)	):
						# for loop iterates over number of inner items of each element
						for j in range(len(x_items[i]	)	):
									__lowerCAmelCase											=				[]  # 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(lowercase	)	):
												# 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(lowercase	)

			return [result.index(max(lowercase	)	) for result in results]





def    _lowerCAmelCase						(						lowercase					,	lowercase	)					->					float:
			__lowerCAmelCase											=				sum(1 for i, j in zip(lowercase					,	lowercase	) 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(lowercase	)) * 100


_a					: Optional[int]      =    TypeVar("""num""")





def    _lowerCAmelCase						(						lowercase					,	lowercase					,	lowercase					,	lowercase = lambda lowercase	: True					,	lowercase = None					,	)					->					num:
			while True:
						try:
									__lowerCAmelCase											=				input_type(input(lowercase	).strip() or default	)
									if condition(lowercase	):
												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    _lowerCAmelCase						(						)					->					List[str]:
			while True:
						print(""" Linear Discriminant Analysis """.center(50					,	"""*"""	)	)
						print("""*""" * 50					,	"""\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
						__lowerCAmelCase											=				valid_input(
						    input_type=lowercase					,	condition=lambda lowercase	: x > 0					,	input_msg="""Enter the number of classes (Data Groupings): """					,	err_msg="""Number of classes should be positive!"""					,	)

						print("""-""" * 100	)

						# Trying to get the value of standard deviation
						__lowerCAmelCase											=				valid_input(
						    input_type=lowercase					,	condition=lambda lowercase	: 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("""-""" * 100	)

						# Trying to get number of instances in classes and theirs means to generate
						# dataset
						__lowerCAmelCase											=				[]  # An empty list to store instance counts of classes in dataset
						for i in range(lowercase	):
									__lowerCAmelCase											=				valid_input(
									    input_type=lowercase					,	condition=lambda lowercase	: x > 0					,	input_msg=(f'Enter The number of instances for class_{i+1}: ')					,	err_msg="""Number of instances should be positive!"""					,	)
									counts.append(lowercase	)
						print("""-""" * 100	)

						# An empty list to store values of user-entered means of classes
						__lowerCAmelCase											=				[]
						for a in range(lowercase	):
									__lowerCAmelCase											=				valid_input(
									    input_type=lowercase					,	input_msg=(f'Enter the value of mean for class_{a+1}: ')					,	err_msg="""This is an invalid value."""					,	)
									user_means.append(lowercase	)
						print("""-""" * 100	)

						print("""Standard deviation: """					,	lowercase	)
						# print out the number of instances in classes in separated line
						for i, count in enumerate(lowercase					,	1	):
									print(f'Number of instances in class_{i} is: {count}'	)
						print("""-""" * 100	)

						# print out mean values of classes separated line
						for i, user_mean in enumerate(lowercase					,	1	):
									print(f'Mean of class_{i} is: {user_mean}'	)
						print("""-""" * 100	)

						# Generating training dataset drawn from gaussian distribution
						__lowerCAmelCase											=				[
						    gaussian_distribution(user_means[j]					,	lowercase					,	counts[j]	)
						    for j in range(lowercase	)
						]
						print("""Generated Normal Distribution: \n"""					,	lowercase	)
						print("""-""" * 100	)

						# Generating Ys to detecting corresponding classes
						__lowerCAmelCase											=				y_generator(lowercase					,	lowercase	)
						print("""Generated Corresponding Ys: \n"""					,	lowercase	)
						print("""-""" * 100	)

						# Calculating the value of actual mean for each class
						__lowerCAmelCase											=				[calculate_mean(counts[k]					,	x[k]	) for k in range(lowercase	)]
						# for loop iterates over number of elements in 'actual_means' list and print
						# out them in separated line
						for i, actual_mean in enumerate(lowercase					,	1	):
									print(f'Actual(Real) mean of class_{i} is: {actual_mean}'	)
						print("""-""" * 100	)

						# Calculating the value of probabilities for each class
						__lowerCAmelCase											=				[
						    calculate_probabilities(counts[i]					,	sum(lowercase	)	) for i in range(lowercase	)
						]

						# for loop iterates over number of elements in 'probabilities' list and print
						# out them in separated line
						for i, probability in enumerate(lowercase					,	1	):
									print(f'Probability of class_{i} is: {probability}'	)
						print("""-""" * 100	)

						# Calculating the values of variance for each class
						__lowerCAmelCase											=				calculate_variance(lowercase					,	lowercase					,	sum(lowercase	)	)
						print("""Variance: """					,	lowercase	)
						print("""-""" * 100	)

						# Predicting Y values
						# storing predicted Y values in 'pre_indexes' variable
						__lowerCAmelCase											=				predict_y_values(lowercase					,	lowercase					,	lowercase					,	lowercase	)
						print("""-""" * 100	)

						# Calculating Accuracy of the model
						print(f'Accuracy: {accuracy(lowercase					,	lowercase	)}'	)
						print("""-""" * 100	)
						print(""" DONE """.center(100					,	"""+"""	)	)

						if input("""Press any key to restart or 'q' for quit: """	).strip().lower() == "q":
									print("""\n""" + """GoodBye!""".center(100					,	"""-"""	) + """\n"""	)
									break
						system("""cls""" if name == """nt""" else """clear"""	)  # noqa: S605


if __name__ == "__main__":
	main()

