




import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
		# Initialising the CNN
		# (Sequential- Building the model layer by layer)
		__UpperCamelCase   :			str          = models.Sequential()

		# Step 1 - Convolution
		# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
		# (3,3) is the kernel size (filter matrix)
		classifier.add(
		    layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
		)

		# Step 2 - Pooling
		classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

		# Adding a second convolutional layer
		classifier.add(layers.ConvaD(32, (3, 3), activation="relu"))
		classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

		# Step 3 - Flattening
		classifier.add(layers.Flatten())

		# Step 4 - Full connection
		classifier.add(layers.Dense(units=128, activation="relu"))
		classifier.add(layers.Dense(units=1, activation="sigmoid"))

		# Compiling the CNN
		classifier.compile(
		    optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
		)

		# Part 2 - Fitting the CNN to the images

		# Load Trained model weights

		# from keras.models import load_model
		# regressor=load_model('cnn.h5')

		__UpperCamelCase   :			Optional[Any]          = tf.keras.preprocessing.image.ImageDataGenerator(
		    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
		)

		__UpperCamelCase   :			Union[str, Any]          = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

		__UpperCamelCase   :			Optional[int]          = train_datagen.flow_from_directory(
		    "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
		)

		__UpperCamelCase   :			Optional[int]          = test_datagen.flow_from_directory(
		    "dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
		)

		classifier.fit_generator(
		    training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
		)

		classifier.save("cnn.h5")

		# Part 3 - Making new predictions

		__UpperCamelCase   :			Union[str, Any]          = tf.keras.preprocessing.image.load_img(
		    "dataset/single_prediction/image.png", target_size=(64, 64)
		)
		__UpperCamelCase   :			Dict          = tf.keras.preprocessing.image.img_to_array(test_image)
		__UpperCamelCase   :			Tuple          = np.expand_dims(test_image, axis=0)
		__UpperCamelCase   :			int          = classifier.predict(test_image)
		# training_set.class_indices
		if result[0][0] == 0:
				__UpperCamelCase   :			Union[str, Any]          = "Normal"
		if result[0][0] == 1:
				__UpperCamelCase   :			Optional[Any]          = "Abnormality detected"







def 						__A						(      ) ->			list[list[int]]:
	return [list(range(1000 - i				,     -1000 - i				,     -1       )       ) for i in range(1000       )]


__UpperCamelCase   :			str          = generate_large_matrix()
__UpperCamelCase   :			Tuple          = (
    [[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
    [[3, 2], [1, 0]],
    [[7, 7, 6]],
    [[7, 7, 6], [-1, -2, -3]],
    grid,
)





def 						__A						(      __lowerCamelCase       ) ->			None:
	assert all(row == sorted(__lowerCamelCase				,     reverse=__lowerCamelCase       ) for row in grid       )
	assert all(list(__lowerCamelCase       ) == sorted(__lowerCamelCase				,     reverse=__lowerCamelCase       ) for col in zip(*__lowerCamelCase       )       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	a									=				0
	a									=				len(__lowerCamelCase       ) - 1

	# Edge cases such as no values or all numbers are negative.
	if not array or array[0] < 0:
		return 0

	while right + 1 > left:
		a									=				(left + right) // 2
		a									=				array[mid]

		# Num must be negative and the index must be greater than or equal to 0.
		if num < 0 and array[mid - 1] >= 0:
			return mid

		if num >= 0:
			a									=				mid + 1
		else:
			a									=				mid - 1
    # No negative numbers so return the last index of the array + 1 which is the length.
	return len(__lowerCamelCase       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	a									=				0
	a									=				len(grid[0]       )

	for i in range(len(__lowerCamelCase       )       ):
		a									=				find_negative_index(grid[i][:bound]       )
		total += bound
	return (len(__lowerCamelCase       ) * len(grid[0]       )) - total





def 						__A						(      __lowerCamelCase       ) ->			int:
	return len([number for row in grid for number in row if number < 0]       )





def 						__A						(      __lowerCamelCase       ) ->			int:
	a									=				0
	for row in grid:
		for i, number in enumerate(__lowerCamelCase       ):
			if number < 0:
				total += len(__lowerCamelCase       ) - i
				break
	return total





def 						__A						(      ) ->			None:
	from timeit import timeit

	print("""Running benchmarks"""       )
	a									=				(
	    """from __main__ import count_negatives_binary_search, """
	    """count_negatives_brute_force, count_negatives_brute_force_with_break, grid"""
	)
	for func in (
	    "count_negatives_binary_search",  # took 0.7727 seconds
	    "count_negatives_brute_force_with_break",  # took 4.6505 seconds
	    "count_negatives_brute_force",  # took 12.8160 seconds
	):
		a									=				timeit(f'{func}(grid=grid)'				,     setup=__lowerCamelCase				,     number=500       )
		print(f'{func}() took {time:0.4f} seconds'       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()

