



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

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

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

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




from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE           =  {
    """configuration_altclip""": [
        """ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AltCLIPConfig""",
        """AltCLIPTextConfig""",
        """AltCLIPVisionConfig""",
    ],
    """processing_altclip""": ["""AltCLIPProcessor"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE           =  [
							    """ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AltCLIPPreTrainedModel""",
							    """AltCLIPModel""",
							    """AltCLIPTextModel""",
							    """AltCLIPVisionModel""",
							]


if TYPE_CHECKING:
							from .configuration_altclip import (
							    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    AltCLIPConfig,
							    AltCLIPTextConfig,
							    AltCLIPVisionConfig,
							)
							from .processing_altclip import AltCLIPProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_altclip import (
														    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AltCLIPModel,
														    AltCLIPPreTrainedModel,
														    AltCLIPTextModel,
														    AltCLIPVisionModel,
														)


else:
							import sys

							__SCREAMING_SNAKE_CASE           =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)