


"""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)
				__SCREAMING_SNAKE_CASE							:    int		=    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')

				__SCREAMING_SNAKE_CASE							:    List[str]		=    tf.keras.preprocessing.image.ImageDataGenerator(
				    rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
				)

				__SCREAMING_SNAKE_CASE							:    Any		=    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

				__SCREAMING_SNAKE_CASE							:    Dict		=    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

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







"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    [
				    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BioGptForCausalLM',
				    'BioGptForTokenClassification',
				    'BioGptForSequenceClassification',
				    'BioGptModel',
				    'BioGptPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
				from .tokenization_biogpt import BioGptTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_biogpt import (
								    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BioGptForCausalLM,
								    BioGptForSequenceClassification,
								    BioGptForTokenClassification,
								    BioGptModel,
								    BioGptPreTrainedModel,
								)


else:
				import sys

				__SCREAMING_SNAKE_CASE							:    Dict		=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



