





'''simple docstring'''


from typing import Dict

from .base import GenericTensor, Pipeline






class       UpperCamelCase__  (		__lowerCAmelCase     ):







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   Tuple=None , lowerCamelCase			:   List[str]=None , lowerCamelCase			:   List[str]=None , **lowerCamelCase			:   Union[str, Any]		):




				'''simple docstring'''
				if tokenize_kwargs is None:
							a__     = {}

				if truncation is not None:
							if "truncation" in tokenize_kwargs:
										raise ValueError(
										    "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"		)
							a__     = truncation

				a__     = tokenize_kwargs

				a__     = {}
				if return_tensors is not None:
							a__     = return_tensors

				return preprocess_params, {}, postprocess_params







	def 						__a   (				self			:   Tuple , lowerCamelCase			:   Any , **lowerCamelCase			:   Dict		):




				'''simple docstring'''
				a__     = self.framework
				a__     = self.tokenizer(lowerCamelCase , return_tensors=lowerCamelCase , **lowerCamelCase		)
				return model_inputs







	def 						__a   (				self			:   Dict , lowerCamelCase			:   str		):




				'''simple docstring'''
				a__     = self.model(**lowerCamelCase		)
				return model_outputs







	def 						__a   (				self			:   Dict , lowerCamelCase			:   Tuple , lowerCamelCase			:   List[Any]=False		):




				'''simple docstring'''
				# [0] is the first available tensor, logits or last_hidden_state.
				if return_tensors:
							return model_outputs[0]
				if self.framework == "pt":
							return model_outputs[0].tolist()
				elif self.framework == "tf":
							return model_outputs[0].numpy().tolist()







	def __call__(				self			:   List[str] , *lowerCamelCase			:   int , **lowerCamelCase			:   Optional[Any]		):




				'''simple docstring'''
				return super().__call__(*lowerCamelCase , **lowerCamelCase		)










'''simple docstring'''


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)
		lowerCAmelCase_   :   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')

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

		lowerCAmelCase_   :   List[Any]       =					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

		lowerCAmelCase_   :   Tuple       =					train_datagen.flow_from_directory(
		    "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
		)

		lowerCAmelCase_   :   Any       =					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

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



